From 50659d8181256e6380c0110b086c6adf7867261c Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Fri, 14 Jun 2024 17:50:48 +0800 Subject: [PATCH 001/118] feat: add data agent in the agentUniverse. --- agentuniverse_data/__init__.py | 7 + agentuniverse_data/config/__init__.py | 7 + agentuniverse_data/config/config.toml | 30 ++++ agentuniverse_data/config/log_config.toml | 16 ++ agentuniverse_data/dispatch/__init__.py | 7 + agentuniverse_data/dispatch/data/__init__.py | 7 + .../dispatch/data/demo_jsonl.jsonl | 2 + agentuniverse_data/dispatch/dispatch.py | 34 ++++ agentuniverse_data/dispatch/dispatch.yaml | 9 ++ agentuniverse_data/dispatch/llm/__init__.py | 7 + agentuniverse_data/dispatch/llm/openai_llm.py | 52 ++++++ .../dispatch/llm/openai_llm.yaml | 9 ++ agentuniverse_data/flow/__init__.py | 7 + agentuniverse_data/flow/auto_event.yaml | 40 +++++ agentuniverse_data/flow/dataflow.py | 85 ++++++++++ agentuniverse_data/flow/dataset_build.yaml | 68 ++++++++ agentuniverse_data/flow/eval_report.yaml | 25 +++ agentuniverse_data/flow/instruct_select.yaml | 47 ++++++ agentuniverse_data/flow/model_deploy.yaml | 21 +++ agentuniverse_data/flow/train_dump.yaml | 42 +++++ agentuniverse_data/node/__init__.py | 7 + agentuniverse_data/node/base/__init__.py | 7 + .../node/base/data_node_base.py | 17 ++ .../node/base/eval_node_base.py | 19 +++ .../node/base/model_node_base.py | 35 ++++ agentuniverse_data/node/base/node_base.py | 85 ++++++++++ agentuniverse_data/node/enum/__init__.py | 7 + agentuniverse_data/node/enum/enum.py | 39 +++++ agentuniverse_data/util/__init__.py | 7 + agentuniverse_data/util/fileio/__init__.py | 7 + .../util/fileio/node_msg_jsonl.py | 150 ++++++++++++++++++ 31 files changed, 902 insertions(+) create mode 100644 agentuniverse_data/__init__.py create mode 100644 agentuniverse_data/config/__init__.py create mode 100644 agentuniverse_data/config/config.toml create mode 100644 agentuniverse_data/config/log_config.toml create mode 100644 agentuniverse_data/dispatch/__init__.py create mode 100644 agentuniverse_data/dispatch/data/__init__.py create mode 100644 agentuniverse_data/dispatch/data/demo_jsonl.jsonl create mode 100644 agentuniverse_data/dispatch/dispatch.py create mode 100644 agentuniverse_data/dispatch/dispatch.yaml create mode 100644 agentuniverse_data/dispatch/llm/__init__.py create mode 100644 agentuniverse_data/dispatch/llm/openai_llm.py create mode 100644 agentuniverse_data/dispatch/llm/openai_llm.yaml create mode 100644 agentuniverse_data/flow/__init__.py create mode 100644 agentuniverse_data/flow/auto_event.yaml create mode 100644 agentuniverse_data/flow/dataflow.py create mode 100644 agentuniverse_data/flow/dataset_build.yaml create mode 100644 agentuniverse_data/flow/eval_report.yaml create mode 100644 agentuniverse_data/flow/instruct_select.yaml create mode 100644 agentuniverse_data/flow/model_deploy.yaml create mode 100644 agentuniverse_data/flow/train_dump.yaml create mode 100644 agentuniverse_data/node/__init__.py create mode 100644 agentuniverse_data/node/base/__init__.py create mode 100644 agentuniverse_data/node/base/data_node_base.py create mode 100644 agentuniverse_data/node/base/eval_node_base.py create mode 100644 agentuniverse_data/node/base/model_node_base.py create mode 100644 agentuniverse_data/node/base/node_base.py create mode 100644 agentuniverse_data/node/enum/__init__.py create mode 100644 agentuniverse_data/node/enum/enum.py create mode 100644 agentuniverse_data/util/__init__.py create mode 100644 agentuniverse_data/util/fileio/__init__.py create mode 100644 agentuniverse_data/util/fileio/node_msg_jsonl.py diff --git a/agentuniverse_data/__init__.py b/agentuniverse_data/__init__.py new file mode 100644 index 00000000..ebd94327 --- /dev/null +++ b/agentuniverse_data/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/11 20:46 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/config/__init__.py b/agentuniverse_data/config/__init__.py new file mode 100644 index 00000000..d9346c95 --- /dev/null +++ b/agentuniverse_data/config/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 16:41 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/config/config.toml b/agentuniverse_data/config/config.toml new file mode 100644 index 00000000..b2040774 --- /dev/null +++ b/agentuniverse_data/config/config.toml @@ -0,0 +1,30 @@ +[BASE_INFO] +# The app name will be applied to all processes including agent service integration. +appname = 'agentuniverse_data' + +[CORE_PACKAGE] +# Perform a full component scan and registration for all the paths under this list. +default = ['agentuniverse_data'] +# Scan and register agent components for all paths under this list, with priority over the default. +agent = [''] +# Scan and register knowledge components for all paths under this list, with priority over the default. +knowledge = [''] +# Scan and register llm components for all paths under this list, with priority over the default. +llm = ['agentuniverse_data.dispatch.llm'] +# Scan and register planner components for all paths under this list, with priority over the default. +planner = [''] +# Scan and register tool components for all paths under this list, with priority over the default. +tool = [''] +# Scan and register memory components for all paths under this list, with priority over the default. +memory = [''] +# Scan and register service components for all paths under this list, with priority over the default. +service = [''] +# Scan and register prompt components for all paths under this list, with priority over the default. +prompt = [''] + + +[SUB_CONFIG_PATH] +# Log config file path, an absolute path or a relative path based on the dir where the current config file is located. +log_config_path = './log_config.toml' +# Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore. +#custom_key_path = './custom_key.toml' diff --git a/agentuniverse_data/config/log_config.toml b/agentuniverse_data/config/log_config.toml new file mode 100644 index 00000000..dbeae3dc --- /dev/null +++ b/agentuniverse_data/config/log_config.toml @@ -0,0 +1,16 @@ +[LOG_CONFIG] +[LOG_CONFIG.BASIC_CONFIG] +# Loguru log level. +log_level = "INFO" +# Output path of the log file. If value is empty, agentuniverse will create a subdir under your workdir to save logs. +log_path = "./dispatch" +# Specifies the log rotation policy, controlling when a new log file is created. It can be a time period +# (e.g., "1 week"), a file size (e.g., "100 MB"), or a function returning True when rotation should occur. +log_rotation = "10MB" +# Specifies the duration to keep old log files. It can be a time span (e.g., "30 days") or a function to filter the +# files to be retained. Files outside this policy are purged. +log_retention = "3 days" + +[LOG_CONFIG.EXTEND_MODULE] +# Whether use aliyun simple log server. If value is True, you should fill the ALIYUN_SLS_CONFIG below. +sls_log = "False" diff --git a/agentuniverse_data/dispatch/__init__.py b/agentuniverse_data/dispatch/__init__.py new file mode 100644 index 00000000..58db2189 --- /dev/null +++ b/agentuniverse_data/dispatch/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/11 20:48 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/dispatch/data/__init__.py b/agentuniverse_data/dispatch/data/__init__.py new file mode 100644 index 00000000..7b61ca3e --- /dev/null +++ b/agentuniverse_data/dispatch/data/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/11 20:49 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/dispatch/data/demo_jsonl.jsonl b/agentuniverse_data/dispatch/data/demo_jsonl.jsonl new file mode 100644 index 00000000..6986502f --- /dev/null +++ b/agentuniverse_data/dispatch/data/demo_jsonl.jsonl @@ -0,0 +1,2 @@ +{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} +{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} \ No newline at end of file diff --git a/agentuniverse_data/dispatch/dispatch.py b/agentuniverse_data/dispatch/dispatch.py new file mode 100644 index 00000000..bf1d2332 --- /dev/null +++ b/agentuniverse_data/dispatch/dispatch.py @@ -0,0 +1,34 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/11 20:52 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: dispatch.py +import yaml + +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.base.agentuniverse import AgentUniverse + +from agentuniverse_data.flow.dataflow import Dataflow + +AgentUniverse().start(config_path='../../config/config.toml') + + +def load_dataflows_from_yaml(conf_file): + with open(conf_file, 'r') as file: + config = yaml.safe_load(file) + dataflows = [] + yaml_list = config.get('dataflows') + for item in yaml_list: + dataflows.append(Dataflow(item)) + return dataflows + + +def dispatch(conf_file='dispatch.yaml'): + dataflows = load_dataflows_from_yaml(conf_file) + try: + for dataflow in dataflows: + dataflow.execute() + except Exception as e: + LOGGER.error(f"An error occurred: {e}") diff --git a/agentuniverse_data/dispatch/dispatch.yaml b/agentuniverse_data/dispatch/dispatch.yaml new file mode 100644 index 00000000..7102d37f --- /dev/null +++ b/agentuniverse_data/dispatch/dispatch.yaml @@ -0,0 +1,9 @@ +name: 'main_dispatch' +description: 'dispatch with multi-dataflows which will execute one after another' +dataflows: + - ../flow/auto_event.yaml + - ../flow/dataset_build.yaml + - ../flow/train_dump.yaml + - ../flow/eval_report.yaml + - ../flow/model_deploy.yaml + - ../flow/instruct_select.yaml \ No newline at end of file diff --git a/agentuniverse_data/dispatch/llm/__init__.py b/agentuniverse_data/dispatch/llm/__init__.py new file mode 100644 index 00000000..d2ec5ef6 --- /dev/null +++ b/agentuniverse_data/dispatch/llm/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:18 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/dispatch/llm/openai_llm.py b/agentuniverse_data/dispatch/llm/openai_llm.py new file mode 100644 index 00000000..4f5c4aa8 --- /dev/null +++ b/agentuniverse_data/dispatch/llm/openai_llm.py @@ -0,0 +1,52 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:19 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: demo_llm.py +import asyncio + +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.llm.openai_llm import OpenAILLM + + +class DemoOpenAILLM(OpenAILLM): + + def batch_call(self, prompts: list[str]): + return asyncio.run(self.async_batch_call(prompts)) + + async def async_batch_call(self, prompts: list[str]): + tasks = [] + prompt_len = len(prompts) + for i in range(0, prompt_len): + messages = [{"role": "user", "content": prompts[i]}] + tasks.append(self.acall(messages=messages, timeout=200)) + + task = asyncio.create_task(self.show_progress(len(prompts), asyncio.get_running_loop())) + outputs = await asyncio.gather(*tasks, return_exceptions=True) + task.cancel() + + results = [] + for i, output in enumerate(outputs): + if isinstance(output, Exception): + LOGGER.warn(f'>>>except[async_llm_call]:{output}') + results.append(None) + else: + results.append(output.text) + + self.print_progress(len(results), prompt_len) + + return results + + def print_progress(self, completed: int, task_count: int): + progress = (completed / task_count) * 100 + LOGGER.info(f"\r>>>llm progress: {completed}/{task_count} = {progress:.2f}%") + + async def show_progress(self, task_count, loop): + completed = 0 + while completed < task_count: + completed_tasks = [t for t in asyncio.all_tasks(loop=loop) if t.done()] + completed = len(completed_tasks) + self.print_progress(completed, task_count) + await asyncio.sleep(10) diff --git a/agentuniverse_data/dispatch/llm/openai_llm.yaml b/agentuniverse_data/dispatch/llm/openai_llm.yaml new file mode 100644 index 00000000..42af35f9 --- /dev/null +++ b/agentuniverse_data/dispatch/llm/openai_llm.yaml @@ -0,0 +1,9 @@ +name: 'openai_llm' +description: 'demo openai' +model_name: 'gpt-4o' +max_tokens: 1000 +max_retries: 2 +metadata: + type: 'LLM' + module: 'agentuniverse_data.dispatch.llm.openai_llm' + class: 'DemoOpenAILLM' \ No newline at end of file diff --git a/agentuniverse_data/flow/__init__.py b/agentuniverse_data/flow/__init__.py new file mode 100644 index 00000000..582b80f7 --- /dev/null +++ b/agentuniverse_data/flow/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 16:58 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/flow/auto_event.yaml b/agentuniverse_data/flow/auto_event.yaml new file mode 100644 index 00000000..df7b1889 --- /dev/null +++ b/agentuniverse_data/flow/auto_event.yaml @@ -0,0 +1,40 @@ +name: 'auto_event_flow' +description: "event collect agent via perceiver planner executor" +nodes: + - module: 'agentuniverse_data.node.data.event.perceiver' + # data perceiver in event agent which could perceive tables in jsonl .etc + class: 'PerceiverNode' + param_in_jsonl: + param_out_jsonl: 'auto_event_flow.perceiver.param.out' + datasets_in_jsonl: + dataset_out_jsonl: 'auto_event_flow.perceiver.dataset.out' + node_param: + # supported event_db: jsonl + event_db: jsonl + # if jsonl, please input jsonl file prefix as event_uri and make sure jsonl file in path data/; then event_sql is useless + event_uri: demo_jsonl + # perceive limited lines + event_sql: + prompt_col: prompt + answer_col: answer + + - module: 'agentuniverse_data.node.data.event.planner' + # data planner in event agent which could plan python code to extract with reflections + class: 'PlannerNode' + param_in_jsonl: 'auto_event_flow.perceiver.param.out' + param_out_jsonl: 'auto_event_flow.planner.plan.out' + datasets_in_jsonl: + - 'auto_event_flow.perceiver.dataset.out' + dataset_out_jsonl: + node_param: + verify_lines: 1 + + - module: 'agentuniverse_data.node.data.event.executor' + # data executor in event agent which execute python code from planner + class: 'ExecutorNode' + param_in_jsonl: 'auto_event_flow.planner.plan.out' + param_out_jsonl: + datasets_in_jsonl: + dataset_out_jsonl: 'auto_event_flow.executor.out' + + diff --git a/agentuniverse_data/flow/dataflow.py b/agentuniverse_data/flow/dataflow.py new file mode 100644 index 00000000..51581ac5 --- /dev/null +++ b/agentuniverse_data/flow/dataflow.py @@ -0,0 +1,85 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:05 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: dataflow.py +import importlib +import sys +import traceback +from typing import List, Optional + +from pydantic import BaseModel + +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.base.config.configer import Configer +from agentuniverse_data.node.base.node_base import NodeBase + + +class Dataflow(BaseModel): + """The Dataflow class, which is used to define the class of data flow.""" + + _flow_name: str = None + _flow_desc: str = None + _node_sequence_list: List[NodeBase] = None + + _configer: Optional[Configer] = None + + def __init__(self, conf_path: str = None): + super().__init__() + if conf_path: + self._configer = Configer(conf_path) + + def _flow_preprocess(self) -> None: + self._configer.load() + self._flow_name = self._configer.get('name') + self._flow_desc = self._configer.get('description') + + nodes = self._configer.get('nodes') + if not nodes: + LOGGER.error('no nodes in yaml conf') + return + + self._node_sequence_list = [] + for i in range(0, len(nodes)): + node_obj = nodes[i] + module_str = node_obj.get('module') + class_str = node_obj.get('class') + param_in_jsonl_str = node_obj.get('param_in_jsonl') + param_out_json_str = node_obj.get('param_out_jsonl') + datasets_in_jsonl_list = node_obj.get('datasets_in_jsonl') + dataset_out_jsonl_str = node_obj.get('dataset_out_jsonl') + node_param_json = node_obj.get('node_param') + + module = importlib.import_module(module_str) + clz = getattr(module, class_str) + node: NodeBase = clz() + node.set_param_in_jsonl(param_in_jsonl_str) + node.set_param_out_jsonl(param_out_json_str) + node.set_datasets_in_jsonl(datasets_in_jsonl_list) + node.set_dataset_out_jsonl(dataset_out_jsonl_str) + node.set_node_param_json(node_param_json) + + self._node_sequence_list.append(node) + + return + + def _flow_process(self) -> None: + if self._node_sequence_list: + for i in range(0, len(self._node_sequence_list)): + self._node_sequence_list[i].execute() + + def _flow_postprocess(self) -> None: + pass + + def execute(self) -> None: + try: + self._flow_preprocess() + self._flow_process() + self._flow_postprocess() + except Exception as e: + # logging later + LOGGER.warn(e) + LOGGER.warn(traceback.format_exc()) + LOGGER.warn(traceback.extract_tb(sys.exc_info())) diff --git a/agentuniverse_data/flow/dataset_build.yaml b/agentuniverse_data/flow/dataset_build.yaml new file mode 100644 index 00000000..e76ca635 --- /dev/null +++ b/agentuniverse_data/flow/dataset_build.yaml @@ -0,0 +1,68 @@ +name: 'dataset_build_flow' +description: "train dataset build agent flow" +nodes: + - module: 'agentuniverse_data.node.data.prompt.input.seed' + # query seed from LLM + class: 'SeedNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + dataset_out_jsonl: 'dataset_build_flow.seed.out' + node_param: + seeds_num: 10 + seed_gen_requirement: '金融领域' + + - module: 'agentuniverse_data.node.data.prompt.input.rewrite' + # rewrite query from LLM + class: 'RewriteNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.seed.out' + dataset_out_jsonl: 'dataset_build_flow.rewrite.out' + + - module: 'agentuniverse_data.node.data.prompt.input.extend' + # extend query from LLM + class: 'ExtendNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.rewrite.out' + dataset_out_jsonl: 'dataset_build_flow.extend.out' + node_param: + extend_times: 2 + + - module: 'agentuniverse_data.node.data.prompt.input.dedupe' + # dedupe queries which improve sft result + class: 'DedupeNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.extend.out' + dataset_out_jsonl: 'dataset_build_flow.dedupe.out' + node_param: + diversify_hamming_threshold: 18 + freq_top_percent: 1.0 + freq_least_count: 100 + + - module: 'agentuniverse_data.node.data.answer.answer' + # build answers from the prompt via LLM as training data + class: 'AnswerNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.dedupe.out' + dataset_out_jsonl: 'dataset_build_flow.answer.out' + + - module: 'agentuniverse_data.node.data.answer.filter' + # filter training data to improve data quality + class: 'FilterNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.answer.out' + dataset_out_jsonl: 'dataset_build_flow.filter.out' + node_param: + dimscore_threshold: 60 + avgscore_threshold: 75 + diff --git a/agentuniverse_data/flow/eval_report.yaml b/agentuniverse_data/flow/eval_report.yaml new file mode 100644 index 00000000..d7c9449d --- /dev/null +++ b/agentuniverse_data/flow/eval_report.yaml @@ -0,0 +1,25 @@ +name: 'eval_report_flow' +description: "eval for nodes and summarize report" +nodes: + - module: 'agentuniverse_data.node.eval.eval' + # eval dump result from LLM infer + class: 'EvalNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'none_flow.aci_dump.out' + dataset_out_jsonl: 'eval_report_flow.eval.out' + node_param: + eval_lines: 100 + + - module: 'agentuniverse_data.node.eval.report' + # compare multi-eval result as a report + class: 'ReportNode' + param_in_jsonl: + param_out_jsonl: + # multiple datasets + datasets_in_jsonl: + - 'eval_report_flow.eval.out' + - 'none_flow.eval.out' + dataset_out_jsonl: 'eval_report_flow.report.out' + diff --git a/agentuniverse_data/flow/instruct_select.yaml b/agentuniverse_data/flow/instruct_select.yaml new file mode 100644 index 00000000..db2ad1a5 --- /dev/null +++ b/agentuniverse_data/flow/instruct_select.yaml @@ -0,0 +1,47 @@ +name: 'instruct_select_flow' +description: "extend instruct from sample and choose the best via pre-deploy model" +nodes: + - module: 'agentuniverse_data.node.data.prompt.instruct.seed' + # produce multiple instruct seed candidates + class: 'SeedNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + dataset_out_jsonl: 'instruct_select_flow.seed.out' + node_param: + extend_times: 10 + + - module: 'agentuniverse_data.node.data.prompt.instruct.prompt_gen' + # produce prompt from instruct and query + class: 'PromptGenNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + # instruct jsonl + - 'instruct_select_flow.seed.out' + # input jsonl + - 'dataset_build_flow.dedupe.out' + dataset_out_jsonl: 'instruct_select_flow.prompt_gen.out' + node_param: + instruct_num: 10 + + - module: '' + # dump answer from pre deployed LLM infer via prompt as input + class: 'DumpNode' + param_in_jsonl: 'train_dump_flow.pre_deploy.out' + param_out_jsonl: + datasets_in_jsonl: + - 'instruct_select_flow.prompt_gen.out' + dataset_out_jsonl: 'instruct_select_flow.dump.out' + + - module: 'agentuniverse_data.node.eval.eval' + # eval dump result through 7 dimensions + class: 'EvalNode' + param_in_jsonl: + param_out_jsonl: + datasets_in_jsonl: + - 'instruct_select_flow.dump.out' + dataset_out_jsonl: 'instruct_select_flow.eval.out' + node_param: + eval_lines: 100 + diff --git a/agentuniverse_data/flow/model_deploy.yaml b/agentuniverse_data/flow/model_deploy.yaml new file mode 100644 index 00000000..9cc35645 --- /dev/null +++ b/agentuniverse_data/flow/model_deploy.yaml @@ -0,0 +1,21 @@ +name: 'model_deploy_flow' +description: "deploy with artifact" +nodes: + - module: '' + # deploy LLM from artifact + class: 'DeployNode' + param_in_jsonl: 'train_dump_flow.train.out' + param_out_jsonl: 'train_dump_flow.deploy.out' + datasets_in_jsonl: + dataset_out_jsonl: + + - module: '' + # dump answer from deploy via prompt + class: 'DumpNode' + param_in_jsonl: 'train_dump_flow.deploy.out' + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.filter.out' + dataset_out_jsonl: 'train_dump_flow.dump.deploy.out' +train_dump: + yaml: \ No newline at end of file diff --git a/agentuniverse_data/flow/train_dump.yaml b/agentuniverse_data/flow/train_dump.yaml new file mode 100644 index 00000000..d90311f1 --- /dev/null +++ b/agentuniverse_data/flow/train_dump.yaml @@ -0,0 +1,42 @@ +name: 'train_dump_flow' +description: "train then dump instructions from dataset input" +nodes: + - module: '' + # upload sft dataset to training platform + class: 'UploadNode' + param_in_jsonl: + param_out_jsonl: 'train_dump_flow.upload.out' + datasets_in_jsonl: + - 'none_flow.filter.out' + dataset_out_jsonl: + node_param: + datagraph_token_name: 'dataflow token' + dataset_name: 'dataagent_dataset' + dataset_desc: 'dataagent dataset common' + + - module: '' + # train sft model via sfs dataset + class: 'TrainNode' + param_in_jsonl: 'train_dump_flow.upload.out' + param_out_jsonl: 'train_dump_flow.aci_train.out' + datasets_in_jsonl: + dataset_out_jsonl: + + - module: '' + # pre deployed LLM infer service via artifact from train stage via platform with deploy func + class: 'PreDeployNode' + param_in_jsonl: 'train_dump_flow.aci_train.out' + param_out_jsonl: 'train_dump_flow.aci_pre_deploy.out' + datasets_in_jsonl: + dataset_out_jsonl: + + - module: '' + # dump answers from LLM inference service via prompts from platform + class: 'DumpNode' + param_in_jsonl: 'train_dump_flow.aci_pre_deploy.out' + param_out_jsonl: + datasets_in_jsonl: + - 'dataset_build_flow.filter.out' + dataset_out_jsonl: 'train_dump_flow.aci_dump.out' + node_param: + dump_lines: 100 \ No newline at end of file diff --git a/agentuniverse_data/node/__init__.py b/agentuniverse_data/node/__init__.py new file mode 100644 index 00000000..63761024 --- /dev/null +++ b/agentuniverse_data/node/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:06 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/base/__init__.py b/agentuniverse_data/node/base/__init__.py new file mode 100644 index 00000000..63761024 --- /dev/null +++ b/agentuniverse_data/node/base/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:06 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/base/data_node_base.py b/agentuniverse_data/node/base/data_node_base.py new file mode 100644 index 00000000..1f54912a --- /dev/null +++ b/agentuniverse_data/node/base/data_node_base.py @@ -0,0 +1,17 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:06 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: data_node.py +from agentuniverse_data.node.base.node_base import NodeBase + + +class DataNodeBase(NodeBase): + """The DataBase class, which is used to define the base class of data node.""" + _batch_line_size: int = 10 + _batch_prompt_size: int = 40 + + def _node_process(self) -> None: + pass diff --git a/agentuniverse_data/node/base/eval_node_base.py b/agentuniverse_data/node/base/eval_node_base.py new file mode 100644 index 00000000..5084edf0 --- /dev/null +++ b/agentuniverse_data/node/base/eval_node_base.py @@ -0,0 +1,19 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:31 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: eval_node_base.py +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.node_base import NodeBase + + +class EvalNodeBase(NodeBase): + """The EvalNodeBase class, which is used to define the base class of eval node.""" + + def __init__(self): + super().__init__(node_type=NodeEnum.EVAL) + + def _node_process(self) -> None: + pass diff --git a/agentuniverse_data/node/base/model_node_base.py b/agentuniverse_data/node/base/model_node_base.py new file mode 100644 index 00000000..7caacccd --- /dev/null +++ b/agentuniverse_data/node/base/model_node_base.py @@ -0,0 +1,35 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:32 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: model_node_base.py +import json + +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.node_base import NodeBase + + +class ModelNodeBase(NodeBase): + """The ModelNodeBase class, which is used to define the base class of model node.""" + + # a json line in jsonl for msg between nodes + _param_in_json_obj: json = None + _platform_mode_is_pro: bool = False + + def __init__(self): + super().__init__(node_type=NodeEnum.MODEL) + + def _node_preprocess(self) -> None: + super()._node_preprocess() + if self._param_in_handler: + self._param_in_json_obj = self._param_in_handler.read_json_obj() + + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + super()._node_postprocess() + if self._param_in_json_obj and self._param_out_handler: + self._param_out_handler.write_json_obj(self._param_in_json_obj) diff --git a/agentuniverse_data/node/base/node_base.py b/agentuniverse_data/node/base/node_base.py new file mode 100644 index 00000000..7f68e7f8 --- /dev/null +++ b/agentuniverse_data/node/base/node_base.py @@ -0,0 +1,85 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:06 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: node_base.py +from abc import abstractmethod +import json +from typing import Any, List, Dict, Optional + +from pydantic import BaseModel + +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileWriter, JsonFileReader + + +class NodeBase(BaseModel): + """The NodeBase class, which is used to define the base class of node.""" + + node_type: NodeEnum = None + _is_flow_start_node: bool = False + + # jsonl message between nodes + param_in_jsonl: str = None + datasets_in_jsonl: List[str] = None + param_out_jsonl: str = None + dataset_out_jsonl: str = None + + node_param_json: Dict[str, Any] = None + + _param_in_handler: Any = None + _param_out_handler: Any = None + _dataset_in_handler: Any = None + _dataset_out_handler: Any = None + + def set_flow_start_node(self) -> None: + self._is_flow_start_node = True + + def set_param_in_jsonl(self, param_in_jsonl: str) -> None: + self.param_in_jsonl = param_in_jsonl + + def set_param_out_jsonl(self, param_out_jsonl: str) -> None: + self.param_out_jsonl = param_out_jsonl + + def set_datasets_in_jsonl(self, datasets_in_jsonl: list[str]) -> None: + self.datasets_in_jsonl = datasets_in_jsonl + + def set_dataset_out_jsonl(self, dataset_out_jsonl: str) -> None: + self.dataset_out_jsonl = dataset_out_jsonl + + def set_node_param_json(self, node_param_json: json) -> None: + if node_param_json: + self.node_param_json = node_param_json + + def _node_preprocess(self) -> None: + if self.param_in_jsonl: + self._param_in_handler = JsonFileReader(self.param_in_jsonl) + # init the first dataset as default + if self.datasets_in_jsonl and len(self.datasets_in_jsonl) > 0: + self._dataset_in_handler = JsonFileReader(self.datasets_in_jsonl[0]) + if self.param_out_jsonl: + self._param_out_handler = JsonFileWriter(self.param_out_jsonl) + if self.dataset_out_jsonl: + self._dataset_out_handler = JsonFileWriter(self.dataset_out_jsonl) + + @abstractmethod + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + pass + + def execute(self) -> None: + + self._node_preprocess() + self._node_process() + self._node_postprocess() + + def _get_node_param(self, key: str, default=None) -> Optional[any]: + if self.node_param_json: + if key in self.node_param_json: + return self.node_param_json.get(key) + + return default diff --git a/agentuniverse_data/node/enum/__init__.py b/agentuniverse_data/node/enum/__init__.py new file mode 100644 index 00000000..8138e72d --- /dev/null +++ b/agentuniverse_data/node/enum/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:07 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/enum/enum.py b/agentuniverse_data/node/enum/enum.py new file mode 100644 index 00000000..556a1e28 --- /dev/null +++ b/agentuniverse_data/node/enum/enum.py @@ -0,0 +1,39 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:07 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: enum.py +from enum import Enum + + +class NodeEnum(Enum): + """The enumeration of the supported nodes.""" + + # DATA + PROMPT = "PROMPT" + PROMPT_ANSWER = "PROMPT_ANSWER" + + # MODEL + MODEL = "MODEL" + + # CRITIC + EVAL = "EVAL" + QUALITY = "QUALITY" + + @staticmethod + def to_value_list(): + """Return the value list of the enumeration.""" + return [item.value for item in NodeEnum] + + +class InOutEnum(Enum): + """The enumeration of the supported inputs and outputs.""" + + JSONL = "JSONL" + + @staticmethod + def to_value_list(): + """Return the value list of the enumeration.""" + return [item.value for item in InOutEnum] diff --git a/agentuniverse_data/util/__init__.py b/agentuniverse_data/util/__init__.py new file mode 100644 index 00000000..cc148e69 --- /dev/null +++ b/agentuniverse_data/util/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:12 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/util/fileio/__init__.py b/agentuniverse_data/util/fileio/__init__.py new file mode 100644 index 00000000..cc148e69 --- /dev/null +++ b/agentuniverse_data/util/fileio/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:12 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/util/fileio/node_msg_jsonl.py b/agentuniverse_data/util/fileio/node_msg_jsonl.py new file mode 100644 index 00000000..94cbb43b --- /dev/null +++ b/agentuniverse_data/util/fileio/node_msg_jsonl.py @@ -0,0 +1,150 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:14 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: node_msg_jsonl.py +import json +import os +import sys + +from agentuniverse.base.util.logging.logging_util import LOGGER + +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + +DATA_DIR = 'data/' +os.makedirs(DATA_DIR, exist_ok=True) + + +class JsonFileOps(object): + def __init__(self): + return + + @classmethod + def is_file_exist(cls, input_file_name, extension='jsonl'): + infile = DATA_DIR + input_file_name + '.' + extension + return os.path.exists(infile) + + @classmethod + def rm_file_if_exist(cls, input_file_name, extension='jsonl'): + infile = DATA_DIR + input_file_name + '.' + extension + if os.path.exists(infile): + os.remove(infile) + return + + +class JsonFileWriter(object): + def __init__(self, output_file_name: str, extension='jsonl'): + self.outfile_path = DATA_DIR + output_file_name + '.' + extension + # create directory if not exist + directory = os.path.dirname(self.outfile_path) + if not os.path.exists(directory): + os.makedirs(directory) + + self.outfile_handler = open(self.outfile_path, 'w', encoding='utf-8') + + def write_json_obj(self, json_obj: dict): + try: + # Confirm that it's a json string and then write + # json.loads(json_obj) + json_line = json.dumps(json_obj, ensure_ascii=False) + self.outfile_handler.write(json_line.strip() + '\n') + self.outfile_handler.flush() + except Exception as e: + LOGGER.warn(f"except[write_json_obj]>>>{e}:{json_obj}") + + return + + def write_json_obj_list(self, json_obj_list: list): + for i in range(0, len(json_obj_list)): + self.write_json_obj(json_obj_list[i]) + return + + def write_json_prompt(self, prompt: str): + json_obj = {"prompt": prompt} + self.write_json_obj(json_obj) + + def write_json_prompt_list(self, prompt_list: list): + for i in range(0, len(prompt_list)): + self.write_json_prompt(prompt_list[i]) + + def write_json_prompt_answer(self, prompt: str, answer: str): + json_obj = {"prompt": prompt, "answer": answer} + self.write_json_obj(json_obj) + + def write_json_prompt_answer_list(self, prompt_answer_list: list): + for i in range(0, len(prompt_answer_list)): + self.write_json_prompt_answer(prompt_answer_list[i][0], prompt_answer_list[i][1]) + + +class JsonFileReader(object): + def __init__(self, input_file_name: str, extension='jsonl'): + self.infile_handler = None + self.filename = None + if JsonFileOps.is_file_exist(input_file_name, extension): + self.filename = input_file_name + '.' + extension + self.infile_handler = open(DATA_DIR + self.filename, 'r', encoding='utf-8') + + def read_json_obj(self): + if not self.infile_handler: + raise Exception(f"None json file to read: {self.filename}") + return None + + json_line = self.infile_handler.readline() + if json_line: + try: + json_obj = json.loads(json_line.strip()) + return json_obj + except Exception as e: + LOGGER.warn(f"except[read_json_line]>>>{e}:{json_line}") + return json.loads('{}') + else: + return None + + def read_json_obj_list(self): + obj_list = [] + + while True: + obj = self.read_json_obj() + if obj is None: + break + obj_list.append(obj) + + return obj_list + + def read_json_prompt(self): + json_obj = self.read_json_obj() + if json_obj is not None: + return json_obj.get('prompt') + else: + return None + + def read_json_prompt_list(self): + prompts = [] + while True: + prompt = self.read_json_prompt() + if prompt is None: + break + prompts.append(prompt) + + return prompts + + def read_json_prompt_answer(self): + json_obj = self.read_json_obj() + if json_obj is not None: + prompt = json_obj.get('prompt') if 'prompt' in json_obj else None + answer = json_obj.get('answer') if 'answer' in json_obj else None + return prompt, answer + else: + return None, None + + def read_json_prompt_answer_list(self): + prompt_answer_list = [] + while True: + prompt, answer = self.read_json_prompt_answer() + if prompt is None: + break + prompt_answer_list.append((prompt, answer)) + + return prompt_answer_list From b14417e36f1e450c8d033dd1c89b4e4173acae48 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 14 Jun 2024 18:33:14 +0800 Subject: [PATCH 002/118] docs: update readme --- README.md | 2 ++ README_jp.md | 2 ++ README_zh.md | 2 ++ 3 files changed, 6 insertions(+) diff --git a/README.md b/README.md index ee6656b2..00e50d9f 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,9 @@ For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md). ## Use Cases [Legal Consultation Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) + [Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) + [Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) ## Guidebook diff --git a/README_jp.md b/README_jp.md index ec284e38..269ccec2 100644 --- a/README_jp.md +++ b/README_jp.md @@ -45,7 +45,9 @@ pip install agentUniverse ## 使用ケース [法律相談Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) + [Pythonコード生成と実行Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) + [多回多Agentによるディスカッショングループ](./docs/guidebook/en/6_2_1_Discussion_Group.md) ## ガイドブック diff --git a/README_zh.md b/README_zh.md index d35db732..b9043bf0 100644 --- a/README_zh.md +++ b/README_zh.md @@ -49,7 +49,9 @@ pip install agentUniverse ## 使用案例 [法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) + [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) + [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) ## 用户指南 From 2fab455fa0576fbc039e72a4ed4d97e1ff6cd077 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 12:58:56 +0800 Subject: [PATCH 003/118] add tool doc --- ...20\347\232\204\345\267\245\345\205\267.md" | 140 ++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 "docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" new file mode 100644 index 00000000..4fb390b2 --- /dev/null +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -0,0 +1,140 @@ +# 支持的工具 + +当前au的 sample工程当中，集成了以下工具： + +## 1. 搜索工具 + +### 1.1 谷歌搜索 +[工具地址](../../../sample_standard_app/app/core/tool/google_search_tool.yaml) +详细配置信息： + +```yaml +name: 'google_search_tool' +description: | + 该工具可以用来进行谷歌搜索，工具的输入是你想搜索的内容。 + 工具输入示例： + 示例1: 你想要搜索上海的天气时，工具的输入应该是：上海今天的天气 + 示例2: 你想要搜索日本的天气时，工具的输入应该是：日本的天气 +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.google_search_tool' + class: 'GoogleSearchTool' +``` +为了使用该api,你必须申请(https://serper.dev)BING_SUBSCRIPTION_KEY,并配置在环境变量当中。 +配置方法： +1. 通过python代码配置 +必须配置：SERPER_API_KEY +```python +import os +os.environ['SERPER_API_KEY'] = 'xxxx' +``` +2. 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +SERPER_API_KEY="xxxx" +``` + + +### 1.2 Bing(必应)搜索 +当前集成了Bing官方的搜索 +[工具地址](../../../sample_standard_app/app/core/tool/bing_search_tool.yaml) +工具配置： +```yaml +name: 'bing_search_tool' +description: 'demo bing search tool' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.bing_search_tool' + class: 'BingSearchTool' +``` +为了使用该api,你必须申请BING_SUBSCRIPTION_KEY,并配置在环境变量当中。 +配置方法： +1. 通过python代码配置 +必须配置：BING_SUBSCRIPTION_KEY +```python +import os +os.environ['BING_SUBSCRIPTION_KEY'] = 'xxxx' +``` +2. 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +BING_SUBSCRIPTION_KEY="xxxx" +``` + + + +### 1.3 Search API +支持多种搜索工具，例如： +- [百度搜索](../../../sample_standard_app/app/core/tool/search_api_baidu_tool.yaml) +- [Bing搜索](../../../sample_standard_app/app/core/tool/search_api_bing_tool.yaml) +其他搜索引擎还包括: 谷歌搜索、亚马逊搜索、YouTube搜索等,详细信息可以参考:https://www.searchapi.io/ +工具配置： +```yaml +name: 'search_api_baidu_tool' +description: '百度(必应)搜索工具，输入为一个要搜索内容的字符串，例如：input=黄金价格是多少' +tool_type: 'api' +input_keys: ['input'] +engine: 'baidu' +search_type: 'json' +search_params: + num: 10 +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.search_api_tool' + class: 'SearchAPITool' +``` +参数说明: +search_type: 代表你期望返回的搜索结果的格式，json代表你期望为json格式，common代表你期望为string字符串格式。 +search_params: 代表你需要额外的传递给搜索引擎的参数，比如在百度搜索中，num代表了返回的搜索结果条数，详细参数需要参考https://www.searchapi.io/ 。 +engine: 你期望使用的搜索引擎，baidu、google、bing、amazon、youtube... +为了使用该api,你必须从[官网](https://www.searchapi.io/)申请SEARCH_API_KEY,并配置在环境变量当中。 +配置方法： +1. 通过python代码配置 +必须配置：SEARCH_API_KEY +```python +import os +os.environ['SEARCHAPI_API_KEY'] = 'xxxxxx' +``` +2. 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +SEARCHAPI_API_KEY="xxxxxx" +``` + + +## 2. 代码工具 + +### 2.1 PythonRepl +[工具地址](../../../sample_standard_app/app/core/tool/python_repl_tool.yaml) +该工具可以执行一段Python代码,工具的配置信息: +```yaml +name: 'python_runner' +description: '使用该工具可以执行python代码，可以在pycharm中直接运行的代码.工具的输入必须时一段有效的python代码. 如何你想要查看工具的执行结果, 必须在python代码中使用print(...)打印你想查看的内容。 + 工具输入示例： + 你想要计算1+3等于多少时，工具的输入应该是： + ```py + print(1+3) + ``` + 你想要获取百度页面的信息时，工具的输入应该是: + ```py + import requests + resp=requests.get("https://www.baidu.com") + print(resp.content) + ```' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.python_repl' + class: 'PythonREPLTool' +``` + +该工具可以直接使用，无需任何key，但是为了系统安全，请不要在生产环境使用该工具 + + + + From f450c79a77957e0377ebc176abe4e58254319006 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 13:16:46 +0800 Subject: [PATCH 004/118] add en doc --- docs/guidebook/en/2_2_3_Integrated_Tools.md | 138 ++++++++++++++++++ ...20\347\232\204\345\267\245\345\205\267.md" | 2 +- 2 files changed, 139 insertions(+), 1 deletion(-) create mode 100644 docs/guidebook/en/2_2_3_Integrated_Tools.md diff --git a/docs/guidebook/en/2_2_3_Integrated_Tools.md b/docs/guidebook/en/2_2_3_Integrated_Tools.md new file mode 100644 index 00000000..6f7e977e --- /dev/null +++ b/docs/guidebook/en/2_2_3_Integrated_Tools.md @@ -0,0 +1,138 @@ +# Integrated Tools + +In the current au's sample project, the following tools are integrated. + +## 1. Search Tools + +### 1.1 Google Search +[工具地址](../../../sample_standard_app/app/core/tool/google_search_tool.yaml) +Detailed Configuration Information: + +```yaml +name: 'google_search_tool' +description: | + This tool can be used to perform Google searches. The tool's input is the content you want to search for. + Example inputs for the tool: + Example 1: If you want to search for the weather in Shanghai, the tool's input should be: "Shanghai weather today" + Example 2: If you want to search for the weather in Japan, the tool's input should be: "Japan weather" +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.google_search_tool' + class: 'GoogleSearchTool' +``` +To use this API, you must apply for a BING_SUBSCRIPTION_KEY at https://serper.dev and configure it in your environment variables. +Configuration method: +1. Configure via Python code You must configure: SERPER_API_KEY +```python +import os +os.environ['SERPER_API_KEY'] = 'xxxx' +``` +2. Configure via configuration file In the custom_key.toml file located in the config directory of the project, add the configuration: +```toml +SERPER_API_KEY="xxxx" +``` + + +### 1.2 Bing Search +Currently, it integrates with the official Bing search. +[工具地址](../../../sample_standard_app/app/core/tool/bing_search_tool.yaml) +Tool configuration: +```yaml +name: 'bing_search_tool' +description: 'demo bing search tool' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.bing_search_tool' + class: 'BingSearchTool' +``` +To use this API, you must apply for BING_SUBSCRIPTION_KEY and configure it in environment variables. +Configuration method: +1. Configure through Python code +Mandatory configuration: BING_SUBSCRIPTION_KEY +```python +import os +os.environ['BING_SUBSCRIPTION_KEY'] = 'xxxx' +``` +2. Configure through configuration file +In custom_key.toml under config directory of the project, add configuration: +```toml +BING_SUBSCRIPTION_KEY="xxxx" +``` + + + +### 1.3 Search API +Supports multiple search tools, such as: +- [Baidu search](../../../sample_standard_app/app/core/tool/search_api_baidu_tool.yaml) +- [Bing search](../../../sample_standard_app/app/core/tool/search_api_bing_tool.yaml) +Other search engines also include: Google search, Amazon search, YouTube search, etc. For more information, please refer to: https://www.searchapi.io/ +Tool configuration: +```yaml +name: 'search_api_baidu_tool' +description: 'Baidu (Bing) search tool, input is a string of content to be searched, e.g.: input="What is the price of gold?"' +tool_type: 'api' +input_keys: ['input'] +engine: 'baidu' +search_type: 'json' +search_params: + num: 10 +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.search_api_tool' + class: 'SearchAPITool' +``` +Parameter description: + +search_type: Represents the format of the expected search results, where json represents the expectation for JSON format and common represents the expectation for string string format. +search_params: Represents additional parameters that need to be passed to the search engine, such as in Baidu search, num represents the number of returned search results, detailed parameters need to be referenced at [https://www.searchapi.io/]. +engine: The search engine you expect to use, including baidu, google, bing, amazon, youtube, ... To use this API, you must apply for SEARCH_API_KEY from the official website ([https://www.searchapi.io/]) and configure it in environment variables. +Configuration method: +You must configure：SEARCHAPI_API_KEY +1. Configure via Python code : +```python +import os +os.environ['SEARCHAPI_API_KEY'] = 'xxxxxx' +``` +2. Configure through configuration file +Add configuration in custom_key.toml under the config directory of the project: +```toml +SEARCHAPI_API_KEY="xxxxxx" +``` + + +## 2. 代码工具 + +### 2.1 PythonRepl +[Tool address](../../../sample_standard_app/app/core/tool/python_repl_tool.yaml) +This tool can execute a piece of Python code, the configuration information of the tool: +```yaml +name: 'python_runner' +description: 'The tool can execute Python code, which can be directly run in PyCharm. The input to the tool must be valid Python code. If you want to view the execution result of the tool, you must use print(...) to print the content you want to view in the Python code. + Example of tool input: + When you want to calculate what 1 + 3 equals, the input to the tool should be: + ```py + print(1+3) + ``` + When you want to get information about the Baidu page, the input to the tool should be: + ```py + import requests + resp=requests.get("https://www.baidu.com") + print(resp.content) + ```' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.python_repl' + class: 'PythonREPLTool' +``` + +This tool can be used directly without any key, but for system security, please do not use this tool in production environments. + + + + diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index 4fb390b2..12057d5d 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -1,4 +1,4 @@ -# 支持的工具 +# 集成的工具 当前au的 sample工程当中，集成了以下工具： From 81c3a759e57e8c9e76ecb2152cf05cfca04c9849 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 14:47:52 +0800 Subject: [PATCH 005/118] =?UTF-8?q?=E6=9A=82=E5=AD=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../agent/action/knowledge/knowledge.py | 37 +++++++++++++++++++ agentuniverse/agent/agent.py | 36 ++++++++++++++++++ .../app/test/test_rag_agent.py | 10 +++-- 3 files changed, 79 insertions(+), 4 deletions(-) diff --git a/agentuniverse/agent/action/knowledge/knowledge.py b/agentuniverse/agent/action/knowledge/knowledge.py index f8e631a7..f8bc3bb7 100644 --- a/agentuniverse/agent/action/knowledge/knowledge.py +++ b/agentuniverse/agent/action/knowledge/knowledge.py @@ -7,6 +7,9 @@ # @FileName: knowledge.py from typing import Optional, Dict, List, Any +from langchain_core.utils.json import parse_json_markdown +from langchain.tools import Tool as LangchainTool + from agentuniverse.agent.action.knowledge.reader.reader import Reader from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query @@ -77,3 +80,37 @@ def initialize_by_component_configer(self, component_configer: KnowledgeConfiger if component_configer.ext_info: self.ext_info = component_configer.ext_info return self + + def langchain_query(self, query: str) -> str: + """Query the knowledge using LangChain. + + Query documents from the store and return the results. + """ + parse_query = parse_json_markdown(query) + query = Query(**parse_query) + knowledge = self.store.query(query) + res = ['This is Query Result'] + for doc in knowledge: + res.append(doc.text) + return "\n=========================================\n".join(res) + + def as_langchain_tool(self) -> Any: + """Convert the Knowledge object to a LangChain tool. + + Returns: + Any: the LangChain tool object + """ + args_description = """ + This is a knowledge base tool, which stores the content you may need. To use this tool, you need to give a json string with the following format: + ```json + { + "query_str": "", + "top_k": , + } + ``` + """ + return LangchainTool( + name=self.name, + description=self.description + args_description, + func=self.langchain_query, + ) diff --git a/agentuniverse/agent/agent.py b/agentuniverse/agent/agent.py index 2f4099fc..40f34354 100644 --- a/agentuniverse/agent/agent.py +++ b/agentuniverse/agent/agent.py @@ -6,10 +6,13 @@ # @Email : lc299034@antgroup.com # @FileName: agent.py """The definition of agent paradigm.""" +import json from abc import abstractmethod from datetime import datetime from typing import Optional +from langchain_core.utils.json import parse_json_markdown + from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.output_object import OutputObject @@ -21,6 +24,7 @@ import ApplicationConfigManager from agentuniverse.base.config.component_configer.configers.agent_configer \ import AgentConfiger +from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse.llm.llm import LLM @@ -156,3 +160,35 @@ def initialize_by_component_configer(self, component_configer: AgentConfiger) -> plan=plan, memory=memory, action=action) self.agent_model = agent_model return self + + def langchain_run(self, input: str, callbacks=None, **kwargs): + """Run the agent model using LangChain.""" + try: + parse_result = parse_json_markdown(input) + except Exception as e: + LOGGER.error(f"langchain run parse_json_markdown error,input(parse_result) error({str(e)})") + return "Error , Your Action Input is not a valid JSON string" + output_object = self.run(**parse_result, callbacks=callbacks, **kwargs) + result_dict = {} + for key in self.output_keys(): + result_dict[key] = output_object.get_data(key) + return result_dict + + def as_langchain_tool(self): + """Convert to LangChain tool.""" + from langchain.agents.tools import Tool + format_dict = {} + for key in self.input_keys(): + format_dict.setdefault(key, "input val") + format_str = json.dumps(format_dict) + + args_description = f""" + to use this tool,your input must be a json string,must contain all keys of {self.input_keys()}, + and the value of the key must be a json string,the format of the json string is as follows: + ```{format_str}``` + """ + return Tool( + name=self.agent_model.info.get("name"), + func=self.langchain_run, + description=self.agent_model.info.get("description") + args_description + ) diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 06c3d9b8..2b7178d5 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -24,10 +24,12 @@ def setUp(self) -> None: def test_rag_agent(self): """Test demo rag agent.""" instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') - output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') - res_info = f"\nRag agent execution result is :\n" - res_info += output_object.get_data('output') - print(res_info) + # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') + # res_info = f"\nRag agent execution result is :\n" + # res_info += output_object.get_data('output') + # print(res_info) + tool = instance.as_langchain_tool() + print(tool.description) if __name__ == '__main__': From c1f8bfb7ca2705d6aaac98212b8a6e78e29e9ee7 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 16:06:47 +0800 Subject: [PATCH 006/118] add knowlege --- .../agent/action/knowledge/knowledge.py | 4 ++-- .../plan/planner/react_planner/react_planner.py | 12 ++++++++++++ sample_standard_app/app/test/test_rag_agent.py | 17 ++++++++++------- 3 files changed, 24 insertions(+), 9 deletions(-) diff --git a/agentuniverse/agent/action/knowledge/knowledge.py b/agentuniverse/agent/action/knowledge/knowledge.py index f8bc3bb7..d6fc4920 100644 --- a/agentuniverse/agent/action/knowledge/knowledge.py +++ b/agentuniverse/agent/action/knowledge/knowledge.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: knowledge.py -from typing import Optional, Dict, List, Any +from typing import Optional, Dict, List from langchain_core.utils.json import parse_json_markdown from langchain.tools import Tool as LangchainTool @@ -94,7 +94,7 @@ def langchain_query(self, query: str) -> str: res.append(doc.text) return "\n=========================================\n".join(res) - def as_langchain_tool(self) -> Any: + def as_langchain_tool(self) -> LangchainTool: """Convert the Knowledge object to a LangChain tool. Returns: diff --git a/agentuniverse/agent/plan/planner/react_planner/react_planner.py b/agentuniverse/agent/plan/planner/react_planner/react_planner.py index ef1f76f9..77e2b0f2 100644 --- a/agentuniverse/agent/plan/planner/react_planner/react_planner.py +++ b/agentuniverse/agent/plan/planner/react_planner/react_planner.py @@ -11,8 +11,11 @@ from langchain.tools import Tool as LangchainTool from langchain_core.runnables import RunnableConfig +from agentuniverse.agent.action.knowledge.knowledge import Knowledge +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.action.tool.tool import Tool from agentuniverse.agent.action.tool.tool_manager import ToolManager +from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.memory.chat_memory import ChatMemory @@ -78,6 +81,15 @@ def acquire_tools(action) -> list[LangchainTool]: for tool_name in tool_names: tool: Tool = ToolManager().get_instance_obj(tool_name) lc_tools.append(tool.as_langchain()) + knowledge: list = action.get('knowledge') or list() + for knowledge_name in knowledge: + knowledge_tool: Knowledge = KnowledgeManager().get_instance_obj(knowledge_name) + lc_tools.append(knowledge_tool.as_langchain_tool()) + + agents: list = action.get('agent') or list() + for agent_name in agents: + agent_tool: Agent = AgentModel.load_by_name(agent_name) + lc_tools.append(agent_tool.as_langchain_tool()) return lc_tools def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt: diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 2b7178d5..1d97de49 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -7,6 +7,8 @@ # @FileName: test_rag_agent.py import unittest +from agentuniverse.agent.action.knowledge.knowledge import Knowledge +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.output_object import OutputObject @@ -23,13 +25,14 @@ def setUp(self) -> None: def test_rag_agent(self): """Test demo rag agent.""" - instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') - # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') - # res_info = f"\nRag agent execution result is :\n" - # res_info += output_object.get_data('output') - # print(res_info) - tool = instance.as_langchain_tool() - print(tool.description) + # instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') + # # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') + # # res_info = f"\nRag agent execution result is :\n" + # # res_info += output_object.get_data('output') + # # print(res_info) + # tool = instance.as_langchain_tool() + knowledge: Knowledge = KnowledgeManager().get_instance_obj('demo_knowledge') + print(knowledge.as_langchain_tool().description) if __name__ == '__main__': From 76f2621345a790fc259c3fe597d2f299a3db6dc1 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 16:07:50 +0800 Subject: [PATCH 007/118] fix output --- sample_standard_app/app/test/test_rag_agent.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 1d97de49..3c678aa1 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -23,16 +23,13 @@ class RagAgentTest(unittest.TestCase): def setUp(self) -> None: AgentUniverse().start(config_path='../../config/config.toml') - def test_rag_agent(self): + def test_rag_fgent(self): """Test demo rag agent.""" - # instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') - # # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') - # # res_info = f"\nRag agent execution result is :\n" - # # res_info += output_object.get_data('output') - # # print(res_info) - # tool = instance.as_langchain_tool() - knowledge: Knowledge = KnowledgeManager().get_instance_obj('demo_knowledge') - print(knowledge.as_langchain_tool().description) + instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') + output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') + res_info = f"\nRag agent execution result is :\n" + res_info += output_object.get_data('output') + print(res_info) if __name__ == '__main__': From 521295a33d1901a7f70bfdd7238d400bd1635e41 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 16:08:42 +0800 Subject: [PATCH 008/118] remove unused import --- sample_standard_app/app/test/test_rag_agent.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 3c678aa1..14ba678f 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -7,8 +7,6 @@ # @FileName: test_rag_agent.py import unittest -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.output_object import OutputObject From b7dbe1066ffc52e6ce47998dfc8fa9821e08e723 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Mon, 17 Jun 2024 19:16:45 +0800 Subject: [PATCH 009/118] feat: add data agent in the agentUniverse. --- agentuniverse_data/config/config.toml | 4 +- agentuniverse_data/flow/auto_event.yaml | 2 + agentuniverse_data/flow/dataflow.py | 7 +- agentuniverse_data/flow/dataset_build.yaml | 9 + agentuniverse_data/flow/eval_report.yaml | 2 + agentuniverse_data/flow/instruct_select.yaml | 6 + .../{dispatch => }/llm/__init__.py | 0 .../{dispatch => }/llm/openai_llm.py | 0 .../{dispatch => }/llm/openai_llm.yaml | 0 agentuniverse_data/node/base/node_base.py | 11 + agentuniverse_data/node/data/__init__.py | 7 + .../node/data/answer/__init__.py | 7 + agentuniverse_data/node/data/answer/answer.py | 40 ++++ agentuniverse_data/node/data/answer/filter.py | 123 ++++++++++ agentuniverse_data/node/data/base/__init__.py | 7 + .../node/data/base/prompt_answer_base.py | 32 +++ .../node/data/base/prompt_base.py | 32 +++ .../node/data/event/__init__.py | 7 + .../node/data/event/executor.py | 90 ++++++++ .../node/data/event/perceiver.py | 60 +++++ agentuniverse_data/node/data/event/planner.py | 216 ++++++++++++++++++ .../node/data/prompt/__init__.py | 7 + .../node/data/prompt/input/__init__.py | 7 + .../node/data/prompt/input/dedupe.py | 66 ++++++ .../node/data/prompt/input/extend.py | 57 +++++ .../node/data/prompt/input/rewrite.py | 45 ++++ .../node/data/prompt/input/seed.py | 65 ++++++ .../node/data/prompt/instruct/__init__.py | 7 + .../node/data/prompt/instruct/prompt_gen.py | 49 ++++ .../node/data/prompt/instruct/seed.py | 48 ++++ agentuniverse_data/node/eval/__init__.py | 7 + agentuniverse_data/node/eval/eval.py | 117 ++++++++++ agentuniverse_data/node/eval/report.py | 66 ++++++ agentuniverse_data/node/model/__init__.py | 7 + .../node/model/base/__init__.py | 7 + .../node/model/base/deploy_base.py | 21 ++ .../node/model/base/dump_base.py | 29 +++ .../node/model/base/pre_deploy_base.py | 21 ++ .../node/model/base/train_base.py | 23 ++ .../node/model/base/upload_base.py | 29 +++ agentuniverse_data/prompt/__init__.py | 7 + .../prompt/dataset_extend_node_cn.yaml | 23 ++ .../prompt/dataset_filter_node_cn.yaml | 39 ++++ .../prompt/dataset_input_seed_node_cn.yaml | 26 +++ .../prompt/dataset_rewrite_node_cn.yaml | 26 +++ agentuniverse_data/prompt/eval_node_cn.yaml | 32 +++ .../prompt/event_planner_node_cn.yaml | 37 +++ .../prompt/instruct_prompt_gen_cn.yaml | 7 + .../prompt/instruct_seed_node_cn.yaml | 30 +++ 49 files changed, 1564 insertions(+), 3 deletions(-) rename agentuniverse_data/{dispatch => }/llm/__init__.py (100%) rename agentuniverse_data/{dispatch => }/llm/openai_llm.py (100%) rename agentuniverse_data/{dispatch => }/llm/openai_llm.yaml (100%) create mode 100644 agentuniverse_data/node/data/__init__.py create mode 100644 agentuniverse_data/node/data/answer/__init__.py create mode 100644 agentuniverse_data/node/data/answer/answer.py create mode 100644 agentuniverse_data/node/data/answer/filter.py create mode 100644 agentuniverse_data/node/data/base/__init__.py create mode 100644 agentuniverse_data/node/data/base/prompt_answer_base.py create mode 100644 agentuniverse_data/node/data/base/prompt_base.py create mode 100644 agentuniverse_data/node/data/event/__init__.py create mode 100644 agentuniverse_data/node/data/event/executor.py create mode 100644 agentuniverse_data/node/data/event/perceiver.py create mode 100644 agentuniverse_data/node/data/event/planner.py create mode 100644 agentuniverse_data/node/data/prompt/__init__.py create mode 100644 agentuniverse_data/node/data/prompt/input/__init__.py create mode 100644 agentuniverse_data/node/data/prompt/input/dedupe.py create mode 100644 agentuniverse_data/node/data/prompt/input/extend.py create mode 100644 agentuniverse_data/node/data/prompt/input/rewrite.py create mode 100644 agentuniverse_data/node/data/prompt/input/seed.py create mode 100644 agentuniverse_data/node/data/prompt/instruct/__init__.py create mode 100644 agentuniverse_data/node/data/prompt/instruct/prompt_gen.py create mode 100644 agentuniverse_data/node/data/prompt/instruct/seed.py create mode 100644 agentuniverse_data/node/eval/__init__.py create mode 100644 agentuniverse_data/node/eval/eval.py create mode 100644 agentuniverse_data/node/eval/report.py create mode 100644 agentuniverse_data/node/model/__init__.py create mode 100644 agentuniverse_data/node/model/base/__init__.py create mode 100644 agentuniverse_data/node/model/base/deploy_base.py create mode 100644 agentuniverse_data/node/model/base/dump_base.py create mode 100644 agentuniverse_data/node/model/base/pre_deploy_base.py create mode 100644 agentuniverse_data/node/model/base/train_base.py create mode 100644 agentuniverse_data/node/model/base/upload_base.py create mode 100644 agentuniverse_data/prompt/__init__.py create mode 100644 agentuniverse_data/prompt/dataset_extend_node_cn.yaml create mode 100644 agentuniverse_data/prompt/dataset_filter_node_cn.yaml create mode 100644 agentuniverse_data/prompt/dataset_input_seed_node_cn.yaml create mode 100644 agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml create mode 100644 agentuniverse_data/prompt/eval_node_cn.yaml create mode 100644 agentuniverse_data/prompt/event_planner_node_cn.yaml create mode 100644 agentuniverse_data/prompt/instruct_prompt_gen_cn.yaml create mode 100644 agentuniverse_data/prompt/instruct_seed_node_cn.yaml diff --git a/agentuniverse_data/config/config.toml b/agentuniverse_data/config/config.toml index b2040774..0b4543ee 100644 --- a/agentuniverse_data/config/config.toml +++ b/agentuniverse_data/config/config.toml @@ -10,7 +10,7 @@ agent = [''] # Scan and register knowledge components for all paths under this list, with priority over the default. knowledge = [''] # Scan and register llm components for all paths under this list, with priority over the default. -llm = ['agentuniverse_data.dispatch.llm'] +llm = ['agentuniverse_data.llm'] # Scan and register planner components for all paths under this list, with priority over the default. planner = [''] # Scan and register tool components for all paths under this list, with priority over the default. @@ -20,7 +20,7 @@ memory = [''] # Scan and register service components for all paths under this list, with priority over the default. service = [''] # Scan and register prompt components for all paths under this list, with priority over the default. -prompt = [''] +prompt = ['agentuniverse_data.prompt'] [SUB_CONFIG_PATH] diff --git a/agentuniverse_data/flow/auto_event.yaml b/agentuniverse_data/flow/auto_event.yaml index df7b1889..1dcbdb5a 100644 --- a/agentuniverse_data/flow/auto_event.yaml +++ b/agentuniverse_data/flow/auto_event.yaml @@ -21,6 +21,8 @@ nodes: - module: 'agentuniverse_data.node.data.event.planner' # data planner in event agent which could plan python code to extract with reflections class: 'PlannerNode' + llm: 'openai_llm' + prompt_version: 'event_planner_node.cn' param_in_jsonl: 'auto_event_flow.perceiver.param.out' param_out_jsonl: 'auto_event_flow.planner.plan.out' datasets_in_jsonl: diff --git a/agentuniverse_data/flow/dataflow.py b/agentuniverse_data/flow/dataflow.py index 51581ac5..422383b1 100644 --- a/agentuniverse_data/flow/dataflow.py +++ b/agentuniverse_data/flow/dataflow.py @@ -23,7 +23,7 @@ class Dataflow(BaseModel): _flow_name: str = None _flow_desc: str = None _node_sequence_list: List[NodeBase] = None - + _llm_name: str = None _configer: Optional[Configer] = None def __init__(self, conf_path: str = None): @@ -52,6 +52,9 @@ def _flow_preprocess(self) -> None: dataset_out_jsonl_str = node_obj.get('dataset_out_jsonl') node_param_json = node_obj.get('node_param') + node_llm = node_obj.get('llm') + node_prompt_version = node_obj.get('prompt_version') + module = importlib.import_module(module_str) clz = getattr(module, class_str) node: NodeBase = clz() @@ -60,6 +63,8 @@ def _flow_preprocess(self) -> None: node.set_datasets_in_jsonl(datasets_in_jsonl_list) node.set_dataset_out_jsonl(dataset_out_jsonl_str) node.set_node_param_json(node_param_json) + node.set_llm(node_llm) + node.set_prompt_version(node_prompt_version) self._node_sequence_list.append(node) diff --git a/agentuniverse_data/flow/dataset_build.yaml b/agentuniverse_data/flow/dataset_build.yaml index e76ca635..a1c95b20 100644 --- a/agentuniverse_data/flow/dataset_build.yaml +++ b/agentuniverse_data/flow/dataset_build.yaml @@ -4,6 +4,8 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.input.seed' # query seed from LLM class: 'SeedNode' + llm: 'openai_llm' + prompt_version: 'dataset_input_seed_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -15,6 +17,8 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.input.rewrite' # rewrite query from LLM class: 'RewriteNode' + llm: 'openai_llm' + prompt_version: 'dataset_rewrite_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -24,6 +28,8 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.input.extend' # extend query from LLM class: 'ExtendNode' + llm: 'openai_llm' + prompt_version: 'dataset_extend_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -48,6 +54,7 @@ nodes: - module: 'agentuniverse_data.node.data.answer.answer' # build answers from the prompt via LLM as training data class: 'AnswerNode' + llm: 'openai_llm' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -57,6 +64,8 @@ nodes: - module: 'agentuniverse_data.node.data.answer.filter' # filter training data to improve data quality class: 'FilterNode' + llm: 'openai_llm' + prompt_version: 'dataset_filter_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: diff --git a/agentuniverse_data/flow/eval_report.yaml b/agentuniverse_data/flow/eval_report.yaml index d7c9449d..9eb31458 100644 --- a/agentuniverse_data/flow/eval_report.yaml +++ b/agentuniverse_data/flow/eval_report.yaml @@ -4,6 +4,8 @@ nodes: - module: 'agentuniverse_data.node.eval.eval' # eval dump result from LLM infer class: 'EvalNode' + llm: 'openai_llm' + prompt_version: 'eval_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: diff --git a/agentuniverse_data/flow/instruct_select.yaml b/agentuniverse_data/flow/instruct_select.yaml index db2ad1a5..8b30effc 100644 --- a/agentuniverse_data/flow/instruct_select.yaml +++ b/agentuniverse_data/flow/instruct_select.yaml @@ -4,6 +4,8 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.instruct.seed' # produce multiple instruct seed candidates class: 'SeedNode' + llm: 'openai_llm' + prompt_version: 'instruct_seed_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -14,6 +16,8 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.instruct.prompt_gen' # produce prompt from instruct and query class: 'PromptGenNode' + llm: 'openai_llm' + prompt_version: 'instruct_prompt_gen_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -37,6 +41,8 @@ nodes: - module: 'agentuniverse_data.node.eval.eval' # eval dump result through 7 dimensions class: 'EvalNode' + llm: 'openai_llm' + prompt_version: 'eval_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: diff --git a/agentuniverse_data/dispatch/llm/__init__.py b/agentuniverse_data/llm/__init__.py similarity index 100% rename from agentuniverse_data/dispatch/llm/__init__.py rename to agentuniverse_data/llm/__init__.py diff --git a/agentuniverse_data/dispatch/llm/openai_llm.py b/agentuniverse_data/llm/openai_llm.py similarity index 100% rename from agentuniverse_data/dispatch/llm/openai_llm.py rename to agentuniverse_data/llm/openai_llm.py diff --git a/agentuniverse_data/dispatch/llm/openai_llm.yaml b/agentuniverse_data/llm/openai_llm.yaml similarity index 100% rename from agentuniverse_data/dispatch/llm/openai_llm.yaml rename to agentuniverse_data/llm/openai_llm.yaml diff --git a/agentuniverse_data/node/base/node_base.py b/agentuniverse_data/node/base/node_base.py index 7f68e7f8..7a77f6d3 100644 --- a/agentuniverse_data/node/base/node_base.py +++ b/agentuniverse_data/node/base/node_base.py @@ -27,6 +27,9 @@ class NodeBase(BaseModel): param_out_jsonl: str = None dataset_out_jsonl: str = None + llm: str = None + prompt_version: str = None + node_param_json: Dict[str, Any] = None _param_in_handler: Any = None @@ -53,6 +56,14 @@ def set_node_param_json(self, node_param_json: json) -> None: if node_param_json: self.node_param_json = node_param_json + def set_llm(self, llm: str) -> None: + if llm: + self.llm = llm + + def set_prompt_version(self, prompt_version: str) -> None: + if prompt_version: + self.prompt_version = prompt_version + def _node_preprocess(self) -> None: if self.param_in_jsonl: self._param_in_handler = JsonFileReader(self.param_in_jsonl) diff --git a/agentuniverse_data/node/data/__init__.py b/agentuniverse_data/node/data/__init__.py new file mode 100644 index 00000000..fdd5faaa --- /dev/null +++ b/agentuniverse_data/node/data/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:18 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/answer/__init__.py b/agentuniverse_data/node/data/answer/__init__.py new file mode 100644 index 00000000..fdd5faaa --- /dev/null +++ b/agentuniverse_data/node/data/answer/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:18 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/answer/answer.py b/agentuniverse_data/node/data/answer/answer.py new file mode 100644 index 00000000..55f05447 --- /dev/null +++ b/agentuniverse_data/node/data/answer/answer.py @@ -0,0 +1,40 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- +# @Time : 2024/6/16 19:19 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: answer.py +from agentuniverse_data.node.data.base.prompt_answer_base import PromptAnswerBase +from agentuniverse.llm.llm_manager import LLMManager + + +class AnswerNode(PromptAnswerBase): + """The RewriteNode class, which is used to define the class of rewrite node.""" + + def _node_process(self) -> None: + if not self._prompt_answer_list or len(self._prompt_answer_list) == 0: + return + + total_prompt_num = 0 + prompts = [] + prompt_list_num = len(self._prompt_answer_list) + prompt_answer_list = self._prompt_answer_list + self._prompt_answer_list = [] + for i in range(0, prompt_list_num): + prompt = prompt_answer_list[i][0] + prompts.append(prompt) + total_prompt_num = total_prompt_num + 1 + llm = LLMManager().get_instance_obj(self.llm) + if total_prompt_num % self._batch_prompt_size == 0: + res = llm.batch_call(prompts) + for prompt, answer in zip(prompts, res): + if res is not None: + self._prompt_answer_list.append((prompt, answer)) + prompts = [] + else: + if total_prompt_num == prompt_list_num and len(prompts) > 0: + res = llm.batch_call(prompts) + for prompt, answer in zip(prompts, res): + if res is not None: + self._prompt_answer_list.append((prompt, answer)) + return diff --git a/agentuniverse_data/node/data/answer/filter.py b/agentuniverse_data/node/data/answer/filter.py new file mode 100644 index 00000000..c9a2fed8 --- /dev/null +++ b/agentuniverse_data/node/data/answer/filter.py @@ -0,0 +1,123 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 14:46 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: filter.py +import json + +from langchain.output_parsers.json import parse_json_markdown + +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.data.base.prompt_answer_base import PromptAnswerBase +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader, JsonFileWriter + + +class FilterNode(PromptAnswerBase): + """The FilterNode class, which is used to define the class of filter node.""" + + dimscore_threshold: int = 70 + avgscore_threshold: int = 70 + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + self.dimscore_threshold = self._get_node_param('dimscore_threshold') + self.avgscore_threshold = self._get_node_param('avgscore_threshold') + + def _node_process(self) -> None: + if not self._prompt_answer_list or len(self._prompt_answer_list) == 0: + return + + self.__quality_eval() + self.__filter_items() + + def __quality_eval(self) -> None: + prompts = [] + total_lines = 0 + jfw_quality = JsonFileWriter(self.dataset_out_jsonl + '.eval') + + list_len = len(self._prompt_answer_list) + for i in range(0, list_len): + do_req = False + prompt = self._prompt_answer_list[i][0] + answer = self._prompt_answer_list[i][1] + total_lines += 1 + + if not prompt or not answer: + continue + + prompts.append(self.generate_prompt(prompt, answer)) + + if total_lines % self._batch_prompt_size == 0: + do_req = True + elif i + 1 == list_len and len(prompts) > 0: + do_req = True + + if do_req: + llm = LLMManager().get_instance_obj(self.llm) + res = llm.batch_call(prompts) + + start_line_num = total_lines - len(prompts) + for res_idx in range(0, len(res)): + try: + if res[res_idx] and res[res_idx] != '': + json_obj = parse_json_markdown(str(res[res_idx])) + json_obj['line'] = start_line_num + res_idx + 1 + jfw_quality.write_json_obj(json_obj) + except json.JSONDecodeError as e: + LOGGER.warn(f'except[__quality_eval]>>>{e}:{res[res_idx]}') + prompts = [] + + def __filter_items(self) -> None: + jfr_quality = JsonFileReader(self.dataset_out_jsonl + '.eval') + jfw_pos = JsonFileWriter(self.dataset_out_jsonl + '.pos') + jfw_neg = JsonFileWriter(self.dataset_out_jsonl + '.neg') + + prompt_answer_list = self._prompt_answer_list + self._prompt_answer_list = [] + + while True: + json_obj = jfr_quality.read_json_obj() + if json_obj: + dimensions = json_obj.get('dimensions') + + score_sum = 0.0 + is_pos = True + for i in range(0, len(dimensions)): + score = float(dimensions[i].get('score')) + score_sum = score_sum + score + if score < self.dimscore_threshold: + is_pos = False + + avg_score = score_sum / len(dimensions) + if avg_score < self.avgscore_threshold: + is_pos = False + + if is_pos: + jfw_pos.write_json_obj(json_obj) + if 'line' in json_obj: + line = json_obj['line'] + if line > 0: + self._prompt_answer_list.append(prompt_answer_list[line - 1]) + else: + jfw_neg.write_json_obj(json_obj) + + else: + break + + def generate_prompt(self, prompt_str: str, answer_str: str) -> str: + if len(prompt_str) > 2000: + prompt_str = prompt_str[0:2000] + if len(answer_str) > 5000: + answer = answer_str[0:5000] + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + + prompt = version_prompt.prompt_template.replace("", prompt_str) + prompt = prompt.replace("", answer_str) + + return prompt diff --git a/agentuniverse_data/node/data/base/__init__.py b/agentuniverse_data/node/data/base/__init__.py new file mode 100644 index 00000000..01448f22 --- /dev/null +++ b/agentuniverse_data/node/data/base/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:19 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/base/prompt_answer_base.py b/agentuniverse_data/node/data/base/prompt_answer_base.py new file mode 100644 index 00000000..24a0b8da --- /dev/null +++ b/agentuniverse_data/node/data/base/prompt_answer_base.py @@ -0,0 +1,32 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:20 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: prompt_answer_base.py +from typing import List, Tuple + +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.data_node_base import DataNodeBase + + +class PromptAnswerBase(DataNodeBase): + """The PromptAnswerNodeBase class, which is used to define the base class of prompt answer node.""" + + _prompt_answer_list: List[Tuple[str, str]] = None + + def __init__(self, *args, **kwargs): + super().__init__(node_type=NodeEnum.PROMPT_ANSWER) + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + if self._dataset_in_handler: + self._prompt_answer_list = self._dataset_in_handler.read_json_prompt_answer_list() + + def _node_postprocess(self) -> None: + super()._node_postprocess() + + if self._prompt_answer_list: + self._dataset_out_handler.write_json_prompt_answer_list(self._prompt_answer_list) diff --git a/agentuniverse_data/node/data/base/prompt_base.py b/agentuniverse_data/node/data/base/prompt_base.py new file mode 100644 index 00000000..ceac9db5 --- /dev/null +++ b/agentuniverse_data/node/data/base/prompt_base.py @@ -0,0 +1,32 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:19 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: prompt_base.py +from typing import List + +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.data_node_base import DataNodeBase + + +class PromptBase(DataNodeBase): + """The PromptNodeBase class, which is used to define the base class of prompt node.""" + + _prompt_list: List[str] = None + + def __init__(self, *args, **kwargs): + super().__init__(node_type=NodeEnum.PROMPT) + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + if self._dataset_in_handler: + self._prompt_list = self._dataset_in_handler.read_json_prompt_list() + + def _node_postprocess(self) -> None: + super()._node_postprocess() + + if self._prompt_list and self._dataset_out_handler: + self._dataset_out_handler.write_json_prompt_list(self._prompt_list) diff --git a/agentuniverse_data/node/data/event/__init__.py b/agentuniverse_data/node/data/event/__init__.py new file mode 100644 index 00000000..e2b69ee8 --- /dev/null +++ b/agentuniverse_data/node/data/event/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:21 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/event/executor.py b/agentuniverse_data/node/data/event/executor.py new file mode 100644 index 00000000..c063cb98 --- /dev/null +++ b/agentuniverse_data/node/data/event/executor.py @@ -0,0 +1,90 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:22 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: executor.py +from typing import List, Tuple, Dict + +import pandas as pd + +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.data_node_base import DataNodeBase +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader + + +class ExecutorNode(DataNodeBase): + """The ExecutorNode class, which is used to define the class of executor node.""" + + event_db: str = None + event_uri: str = None + event_sql: str = None + prompt_col: str = None + answer_col: str = None + + _data_frame: pd.DataFrame = None + _plan_dict: Dict = None + _input_data_jsonlist: List[Dict] = None + _dump_prompt_answer_list: List[Tuple[str, str]] = [] + + def __init__(self, *args, **kwargs): + super().__init__(node_type=NodeEnum.PROMPT_ANSWER) + + def _node_preprocess(self) -> None: + super()._node_preprocess() + if self._param_in_handler: + self._plan_dict = self._param_in_handler.read_json_obj() + + self.event_db = self._plan_dict.get('event_db') + self.event_uri = self._plan_dict.get('event_uri') + self.event_sql = self._plan_dict.get('event_sql') + self.prompt_col = self._plan_dict.get('prompt_col') + self.answer_col = self._plan_dict.get('answer_col') + + def _node_process(self) -> None: + if self.event_db: + if self.event_db == 'jsonl': + self._input_data_jsonlist = JsonFileReader(self.event_uri).read_json_obj_list() + self.__dump_from_plan() + + def __dump_from_plan(self): + prompt_plan_code = self._plan_dict.get('prompt_plan').get('plan_code') + answer_plan_code = self._plan_dict.get('answer_plan').get('plan_code') + prompt_input_var = self._plan_dict.get('prompt_plan').get('input_var') + answer_input_var = self._plan_dict.get('answer_plan').get('input_var') + prompt_output_var = self._plan_dict.get('prompt_plan').get('output_var') + answer_output_var = self._plan_dict.get('answer_plan').get('output_var') + + for i in range(0, len(self._input_data_jsonlist)): + prompt_related_str = self._input_data_jsonlist[i].get(self.prompt_col) + answer_related_str = self._input_data_jsonlist[i].get(self.answer_col) + + prompt_namespace = { + prompt_input_var: prompt_related_str, + prompt_output_var: '' + } + answer_namespace = { + answer_input_var: answer_related_str, + answer_output_var: '' + } + + try: + exec(prompt_plan_code, globals(), prompt_namespace) + exec(answer_plan_code, globals(), answer_namespace) + except Exception as e: + LOGGER.warn( + f'exception from exec>>>{e}:\nprompt_plan_code:{prompt_plan_code}\nanswer_plan_code:{answer_plan_code}') + break + + prompt = prompt_namespace.get(prompt_output_var) + answer = answer_namespace.get(answer_output_var) + self._dump_prompt_answer_list.append((prompt, answer)) + + if self._dataset_out_handler: + self._dataset_out_handler.write_json_prompt_answer_list(self._dump_prompt_answer_list) + self._dump_prompt_answer_list.clear() + + def _node_postprocess(self) -> None: + super()._node_postprocess() diff --git a/agentuniverse_data/node/data/event/perceiver.py b/agentuniverse_data/node/data/event/perceiver.py new file mode 100644 index 00000000..89b5dcce --- /dev/null +++ b/agentuniverse_data/node/data/event/perceiver.py @@ -0,0 +1,60 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:25 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: perceiver.py +from typing import List, Dict + +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.data_node_base import DataNodeBase +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader + + +class PerceiverNode(DataNodeBase): + """The PerceiverNode class, which is used to define the class of perceiver node.""" + + event_db: str = None + event_uri: str = None + event_sql: str = None + prompt_col: str = None + answer_col: str = None + + _input_data_jsonlist: List[Dict] = None + + def __init__(self, *args, **kwargs): + super().__init__(node_type=NodeEnum.PROMPT_ANSWER) + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + self.event_db = self._get_node_param('event_db') + self.event_uri = self._get_node_param('event_uri') + self.event_sql = self._get_node_param('event_sql') + self.prompt_col = self._get_node_param('prompt_col') + self.answer_col = self._get_node_param('answer_col') + + def _node_process(self) -> None: + if self.event_db: + if self.event_db == 'jsonl': + self._input_data_jsonlist = JsonFileReader(self.event_uri).read_json_obj_list() + + def _node_postprocess(self) -> None: + super()._node_postprocess() + if self._input_data_jsonlist is not None and self._dataset_out_handler: + for i in range(0, len(self._input_data_jsonlist)): + prompt_related_str = self._input_data_jsonlist[i].get(self.prompt_col) + answer_related_str = self._input_data_jsonlist[i].get(self.answer_col) + + self._dataset_out_handler.write_json_prompt_answer(prompt_related_str, answer_related_str) + + if self._param_out_handler: + param_obj = { + 'event_db': self.event_db, + 'event_uri': self.event_uri, + 'event_sql': self.event_sql, + 'prompt_col': self.prompt_col, + 'answer_col': self.answer_col + } + self._param_out_handler.write_json_obj(param_obj) diff --git a/agentuniverse_data/node/data/event/planner.py b/agentuniverse_data/node/data/event/planner.py new file mode 100644 index 00000000..d943c2ec --- /dev/null +++ b/agentuniverse_data/node/data/event/planner.py @@ -0,0 +1,216 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/16 19:26 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: planner.py +import json +from typing import List, Dict + +from langchain.output_parsers.json import parse_json_markdown + +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader +from agentuniverse_data.node.enum.enum import NodeEnum +from agentuniverse_data.node.base.data_node_base import DataNodeBase + + +class PlannerNode(DataNodeBase): + """The PlannerNode class, which is used to define the class of planner node.""" + + _perceived_prompt_list: List[str] = [] + _perceived_answer_list: List[str] = [] + _perceived_list_size: int = 0 + + _new_plan_dict: Dict = {} + _last_plan_dict: Dict = None + _verify_lines: int = None + + def __init__(self, *args, **kwargs): + super().__init__(node_type=NodeEnum.PROMPT_ANSWER) + + def _node_preprocess(self) -> None: + super()._node_preprocess() + self._verify_lines = int(self._get_node_param('verify_lines')) + if self._dataset_in_handler: + perceived_list = self._dataset_in_handler.read_json_obj_list() + # one for plan , at least one for verification + if not perceived_list or len(perceived_list) <= 1: + raise Exception('perceived json list does not provide at least 2 samples!') + + for i in range(0, len(perceived_list)): + json_obj = perceived_list[i] + self._perceived_prompt_list.append(json_obj['prompt']) + self._perceived_answer_list.append(json_obj['answer']) + self._perceived_list_size += 1 + + if self.param_out_jsonl: + jr = JsonFileReader(self.param_out_jsonl) + self._last_plan_dict = jr.read_json_obj() + if self._param_in_handler: + self._new_plan_dict.update(self._param_in_handler.read_json_obj()) + + self._new_plan_dict['prompt_plan'] = { + 'input_var': 'input_str', + 'output_var': 'prompt', + 'plan_code': None + } + self._new_plan_dict['answer_plan'] = { + 'input_var': 'input_str', + 'output_var': 'answer', + 'plan_code': None + } + + def _node_process(self) -> None: + # check whether last plan works or not + if self.__check_last_plan(): + self._new_plan_dict = self._last_plan_dict + return + + prompt_plan_code = self.__generate_plan_and_verify(self._new_plan_dict.get('prompt_plan'), + self._perceived_prompt_list) + answer_plan_code = self.__generate_plan_and_verify(self._new_plan_dict.get('answer_plan'), + self._perceived_answer_list) + if prompt_plan_code and answer_plan_code: + self._new_plan_dict['prompt_plan']['plan_code'] = prompt_plan_code + self._new_plan_dict['answer_plan']['plan_code'] = answer_plan_code + + def __check_last_plan(self) -> bool: + if not self._last_plan_dict: + return False + + last_plan = self._last_plan_dict.get('prompt_plan') + last_plan_code = last_plan.get('plan_code') + last_output_var = last_plan.get('output_var') + dict_obj = self.execute_from_plan(self._perceived_prompt_list, 0, 1, last_plan_code, last_output_var) + if dict_obj.get('reflection'): + return False + + last_plan = self._last_plan_dict.get('answer_plan') + last_plan_code = last_plan.get('plan_code') + last_output_var = last_plan.get('output_var') + dict_obj = self.execute_from_plan(self._perceived_answer_list, 0, 1, last_plan_code, last_output_var) + if dict_obj.get('reflection'): + return False + + return True + + def __generate_plan_and_verify(self, plan_list: str, data_list: list) -> str: + + output_var = plan_list.get('output_var') + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + + prompt_template = getattr(version_prompt, 'data_event_plan', '').replace('', output_var) + prompt_reflection_template = prompt_template.replace('', data_list[0]) + prompt = prompt_reflection_template.replace('', 'None') + + llm_retry = 10 + reflection_retry = 10 + last_reflection = None + while True: + if llm_retry <= 0 or reflection_retry <= 0: + return None + + LOGGER.debug(prompt) + llm = LLMManager().get_instance_obj(self.llm) + res = llm.batch_call([prompt]) + LOGGER.debug(res) + + try: + res_obj = parse_json_markdown(res[0].strip()) + LOGGER.debug(res_obj) + except json.decoder.JSONDecodeError as e: + reflection = 'exception: ' + str(e) + '\ncode: json.loads' + if reflection == last_reflection: + reflection = 'None' + prompt = prompt_reflection_template.replace('', reflection) + last_reflection = reflection + LOGGER.warn(e) + continue + + plan_code = res_obj["plan_code"].strip() + LOGGER.debug(f'plan_code>>>{plan_code}') + + reflection_retry -= 1 + start_idx = 1 + verify_lines = 10 if not self._verify_lines else self._verify_lines + if verify_lines > self._perceived_list_size - start_idx: + verify_lines = self._perceived_list_size - start_idx + + dict_obj = self.execute_from_plan(data_list, start_idx, verify_lines, plan_code, output_var) + reflection = dict_obj.get('reflection') + LOGGER.debug(f'reflection:{reflection}') + + if reflection: + if reflection == last_reflection: + reflection = 'None' + prompt = prompt_reflection_template.replace('', reflection) + last_reflection = reflection + else: + return plan_code + + return None + + def _node_postprocess(self) -> None: + super()._node_postprocess() + + if self._param_out_handler: + self._param_out_handler.write_json_obj(self._new_plan_dict) + + def check_data_reflection(self, data_str: str) -> str: + if not data_str or data_str == '': + return 'Output cannot be empty' + + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + + prompt = getattr(version_prompt, 'exec_result_verification', '').replace('', data_str) + LOGGER.debug(prompt) + llm = LLMManager().get_instance_obj(self.llm) + res = llm.batch_call([prompt]) + LOGGER.debug(res[0]) + json_obj = parse_json_markdown(res[0]) + if json_obj.get('success'): + return None + else: + return json_obj.get('reflection') + + def execute_from_plan(self, data_list: list, start_idx: int, execute_lines: int, plan_code: str, + output_var: str) -> Dict: + reflection = None + output_list = [] + for i in range(start_idx, start_idx + execute_lines): + namespace = { + 'input_str': data_list[i], + output_var: '', + } + + try: + exec(plan_code, globals(), namespace) + except Exception as e: + LOGGER.warn(f'except from exec>>>{e}') + reflection = 'exec Exception:' + str(e) + '\ncode:' + plan_code + break + + output = namespace.get(output_var) + LOGGER.debug(f'after exec >>>{output}') + + if output == '': + reflection = "The output is empty" + break + if not isinstance(output, str): + reflection = 'The type of output must be a string，the current output type is: ' + str( + type(output)) + ', the original code is as follows: ' + plan_code + break + + result_str = output_var + ':' + output + reflection = self.check_data_reflection(result_str) + if reflection: + break + else: + output_list.append(output) + + return {'reflection': reflection, 'output_list': output_list} diff --git a/agentuniverse_data/node/data/prompt/__init__.py b/agentuniverse_data/node/data/prompt/__init__.py new file mode 100644 index 00000000..3cddf9e6 --- /dev/null +++ b/agentuniverse_data/node/data/prompt/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 14:54 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/prompt/input/__init__.py b/agentuniverse_data/node/data/prompt/input/__init__.py new file mode 100644 index 00000000..3cddf9e6 --- /dev/null +++ b/agentuniverse_data/node/data/prompt/input/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 14:54 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/prompt/input/dedupe.py b/agentuniverse_data/node/data/prompt/input/dedupe.py new file mode 100644 index 00000000..ecb041fd --- /dev/null +++ b/agentuniverse_data/node/data/prompt/input/dedupe.py @@ -0,0 +1,66 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 17:17 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: dedupe.py +from collections import Counter + +from simhash import Simhash +from agentuniverse_data.node.data.base.prompt_base import PromptBase + + +class DedupeNode(PromptBase): + """The DedupeNode class, which is used to define the class of dedupe node.""" + + diversify_hamming_threshold: int = 18 + freq_top_percent: float = 1.0 + freq_least_count: int = 100 + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + self.diversify_hamming_threshold = self._get_node_param('diversify_hamming_threshold') + self.freq_top_percent = self._get_node_param('freq_top_percent') + self.freq_least_count = self._get_node_param('freq_least_count') + + def _node_process(self) -> None: + if not self._prompt_list or len(self._prompt_list) == 0: + return + + # calculate the simhash value for each document. + simhashes = [(doc, Simhash(doc)) for doc in self._prompt_list] + + unique_documents = [] + replaced_documents = [] + for doc1, sh1 in simhashes: + if all(sh1.distance(sh2) > self.diversify_hamming_threshold for _, sh2 in unique_documents): + unique_documents.append((doc1, sh1)) + dis = sh1.distance(unique_documents[0][1]) + least_hamming = self.diversify_hamming_threshold + least_hamming_doc = None + for i in range(0, len(unique_documents)): + sh2 = unique_documents[i][1] + hamming_dist = sh1.distance(sh2) + if hamming_dist < least_hamming: + least_hamming = hamming_dist + least_hamming_doc = unique_documents[i][0] + replaced_documents.append(least_hamming_doc) + + freq_counter = Counter(replaced_documents) + sorted_docs = sorted(freq_counter.items(), key=lambda x: x[1], reverse=True) + + orig_len = len(sorted_docs) + freq_top_num = int(orig_len * self.freq_top_percent) + if freq_top_num < self.freq_least_count: + freq_top_num = self.freq_least_count + if self.freq_least_count > orig_len: + freq_top_num = orig_len + + freq_top_docs = sorted_docs[:freq_top_num] + + self._prompt_list = [] + for doc, freq in freq_top_docs: + if doc is not None: + self._prompt_list.append(doc) diff --git a/agentuniverse_data/node/data/prompt/input/extend.py b/agentuniverse_data/node/data/prompt/input/extend.py new file mode 100644 index 00000000..d1a28671 --- /dev/null +++ b/agentuniverse_data/node/data/prompt/input/extend.py @@ -0,0 +1,57 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 17:30 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: extend.py +import json +import math + +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse.base.util.logging.logging_util import LOGGER + + +class ExtendNode(PromptBase): + """The ExtendNode class, which is used to define the class of extend node.""" + + extend_times: int = 4 + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + self.extend_times = self._get_node_param('extend_times') + + def _node_process(self) -> None: + if not self._prompt_list or len(self._prompt_list) == 0: + return + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + prompt_with_extend_times = version_prompt.prompt_template.replace('', str(self.extend_times)) + + input_list = '' + input_len = len(self._prompt_list) + inputs = self._prompt_list + prompts = [] + batch_size = math.ceil(20 / self.extend_times) + for i in range(0, input_len): + input_list = input_list + inputs[i] + '\n' + if (i + 1) % batch_size == 0 or i == input_len - 1: + prompts.append(prompt_with_extend_times.replace('', input_list)) + input_list = '' + + llm = LLMManager().get_instance_obj(self.llm) + responses = llm.batch_call(prompts) + + for i in range(0, len(responses)): + try: + if responses[i] != '' and responses[i] is not None: + data = json.loads(responses[i]) + if 'extend_inputs' in data: + extend_inputs = data['extend_inputs'] + self._prompt_list.extend(extend_inputs) + except Exception as e: + LOGGER.warn(f'except[]>>>{e}:{responses[i]}') + continue diff --git a/agentuniverse_data/node/data/prompt/input/rewrite.py b/agentuniverse_data/node/data/prompt/input/rewrite.py new file mode 100644 index 00000000..75920ef4 --- /dev/null +++ b/agentuniverse_data/node/data/prompt/input/rewrite.py @@ -0,0 +1,45 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 17:34 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: rewrite.py +import json + +from langchain.output_parsers.json import parse_json_markdown + +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.data.base.prompt_base import PromptBase + + +class RewriteNode(PromptBase): + """The RewriteNode class, which is used to define the class of rewrite node.""" + + def _node_process(self) -> None: + if not self._prompt_list or len(self._prompt_list) == 0: + return + + prompts = [] + inputs = self._prompt_list + inputs_all = '' + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + for i in range(0, len(inputs)): + inputs_all = inputs_all + inputs[i] + '\n' + if (i + 1) % self._batch_prompt_size == 0 or i == len(inputs) - 1: + prompts.append(version_prompt.prompt_template.replace('', inputs_all)) + inputs_all = '' + + llm = LLMManager().get_instance_obj(self.llm) + responses = llm.batch_call(prompts) + + self._prompt_list = [] + for i in range(0, len(responses)): + try: + rewrite_input = parse_json_markdown(responses[i])['rewrite_inputs'] + self._prompt_list.extend(rewrite_input) + except json.JSONDecodeError as e: + # rewrite_inputs_all.extend(inputs) + continue diff --git a/agentuniverse_data/node/data/prompt/input/seed.py b/agentuniverse_data/node/data/prompt/input/seed.py new file mode 100644 index 00000000..923d8942 --- /dev/null +++ b/agentuniverse_data/node/data/prompt/input/seed.py @@ -0,0 +1,65 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 17:45 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: seed.py +import json + +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse.base.util.logging.logging_util import LOGGER + + +class SeedNode(PromptBase): + """The SeedNode class, which is used to define the class of seed node.""" + + seeds_num: int = 100 + seed_gen_requirement: str = "金融领域" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + if 'seeds_num' in kwargs: + self.seeds_num = kwargs['seeds_num'] + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + seeds_num = self._get_node_param('seeds_num') + if seeds_num: + self.seeds_num = seeds_num + seed_gen_requirement = self._get_node_param('seed_gen_requirement') + if seed_gen_requirement: + self.seed_gen_requirement = seed_gen_requirement + + def _node_process(self) -> None: + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + prompt_with_seed_requirement = version_prompt.prompt_template.replace('', + self.seed_gen_requirement) + prompts = [] + + for i in range(0, self.seeds_num): + if (i + 1) % self._batch_line_size == 0: + prompt = prompt_with_seed_requirement.replace('', str(self._batch_line_size)) + prompts.append(prompt) + elif i == self.seeds_num - 1: + left_num = (i + 1) % self._batch_line_size + prompt = prompt_with_seed_requirement.replace('', str(left_num)) + prompts.append(prompt) + + llm = LLMManager().get_instance_obj(self.llm) + responses = llm.batch_call(prompts) + + self._prompt_list = [] + for i in range(0, len(responses)): + try: + seeds = json.loads(responses[i])['seeds'] + self._prompt_list.extend(seeds) + except TypeError as e: + continue + except json.decoder.JSONDecodeError as e: + LOGGER.warn(f'except>>>{e}:{responses}') + continue diff --git a/agentuniverse_data/node/data/prompt/instruct/__init__.py b/agentuniverse_data/node/data/prompt/instruct/__init__.py new file mode 100644 index 00000000..3cddf9e6 --- /dev/null +++ b/agentuniverse_data/node/data/prompt/instruct/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 14:54 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py b/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py new file mode 100644 index 00000000..495a21ab --- /dev/null +++ b/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py @@ -0,0 +1,49 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 14:57 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: prompt_gen.py +from typing import List + +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader + + +class PromptGenNode(PromptBase): + """The PromptGenNode class, which is used to define the class of prompt generate node.""" + + _instruct_list: List[str] = None + _input_sample: str = None + + _instruct_num: int = None + + def _node_preprocess(self) -> None: + if not self.datasets_in_jsonl or len(self.datasets_in_jsonl) != 2: + raise Exception(f"Node param {self.datasets_in_jsonl} should contain 2 elements:1.instruct 2.input") + + instruct_in_handler = JsonFileReader(self.datasets_in_jsonl[0]) + input_in_handler = JsonFileReader(self.datasets_in_jsonl[1]) + + if instruct_in_handler: + self._instruct_list = instruct_in_handler.read_json_prompt_list() + if input_in_handler: + self._input_sample = input_in_handler.read_json_prompt() + + self._instruct_num = self._get_node_param('instruct_num') + + def _node_process(self) -> None: + + self._prompt_list = [] + for instruct_idx in range(0, min(self._instruct_num, len(self._instruct_list))): + prompt = self.generate_prompt_from_instruct_and_input(self._instruct_list[instruct_idx], self._input_sample) + self._prompt_list.append(prompt) + + def generate_prompt_from_instruct_and_input(self, instruct='', input=''): + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + prompt = version_prompt.prompt_template.replace("", instruct) + prompt = prompt.replace("", input) + return prompt.strip() diff --git a/agentuniverse_data/node/data/prompt/instruct/seed.py b/agentuniverse_data/node/data/prompt/instruct/seed.py new file mode 100644 index 00000000..8467bc3d --- /dev/null +++ b/agentuniverse_data/node/data/prompt/instruct/seed.py @@ -0,0 +1,48 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 15:02 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: seed.py +from langchain.output_parsers.json import parse_json_markdown + +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse.base.util.logging.logging_util import LOGGER + +_DEFAULT_INSTRUCT_FOR_PROMPT = """ +你是一个极致严谨的金融专家，请尽量找出问题对应的金融解读框架，并基于解读框架一步一步结构化详细回答如下问题: +""" + + +class SeedNode(PromptBase): + """The SeedNode class, which is used to define the class of extend node.""" + + extend_times: int = 20 + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + self.extend_times = self._get_node_param('extend_times') + + def _node_process(self) -> None: + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + prompt_with_extend_times = version_prompt.replace('', + str(self.extend_times)) + + prompts = [prompt_with_extend_times.replace('', _DEFAULT_INSTRUCT_FOR_PROMPT)] + llm = LLMManager().get_instance_obj(self.llm) + responses = llm.batch_call(prompts) + self._prompt_list = [] + if len(responses) == 1: + try: + if responses[0] != '' and responses[0] is not None: + data = parse_json_markdown(responses[0]) + if 'instructs' in data: + extend_instructs = data['instructs'] + self._prompt_list.extend(extend_instructs) + except Exception as e: + LOGGER.warn(f'except[]>>>{e}:{responses[0]}') diff --git a/agentuniverse_data/node/eval/__init__.py b/agentuniverse_data/node/eval/__init__.py new file mode 100644 index 00000000..f52c0757 --- /dev/null +++ b/agentuniverse_data/node/eval/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 18:00 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/eval/eval.py b/agentuniverse_data/node/eval/eval.py new file mode 100644 index 00000000..b0bc3597 --- /dev/null +++ b/agentuniverse_data/node/eval/eval.py @@ -0,0 +1,117 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 18:07 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: eval.py +from typing import List, Tuple + +from langchain.output_parsers.json import parse_json_markdown + +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse_data.node.base.eval_node_base import EvalNodeBase +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.base.util.logging.logging_util import LOGGER + +_EVAL_DIM_FACTUALITY_NAME = "事实性" +_EVAL_DIM_FACTUALITY_REQUIREMENT = """ +1.不符合准入条件 +数据错误，不符合事实 + +2.门槛值 +个别数据不准确，但不影响整体事实性 + +3.加分项 +解读内容中所有引用数据真实可信，数据来源官方可信 +""" + + +def get_eval_dims(): + eval_dims = [(_EVAL_DIM_FACTUALITY_NAME, _EVAL_DIM_FACTUALITY_REQUIREMENT)] + return eval_dims + + +class EvalNode(EvalNodeBase): + """The EvalNode class, which is used to define the class of eval node.""" + + _prompt_answer_list: List[Tuple[str, str]] = None + _eval_dims_json_list: List[str] = None + _eval_lines: int = 100 + + def set_eval_lines(self, eval_lines: int) -> None: + if eval_lines > 0: + self._eval_lines = eval_lines + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + self._eval_lines = self._get_node_param('eval_lines') + if self._dataset_in_handler: + self._prompt_answer_list = self._dataset_in_handler.read_json_prompt_answer_list() + + def _node_postprocess(self) -> None: + super()._node_postprocess() + + if self._dataset_out_handler and self._eval_dims_json_list: + self._dataset_out_handler.write_json_obj_list(self._eval_dims_json_list) + + def _node_process(self) -> None: + if not self._prompt_answer_list or len(self._prompt_answer_list) == 0: + return + + eval_dims = get_eval_dims() + + line_num = 0 + self._eval_dims_json_list = [] + for i in range(0, len(self._prompt_answer_list)): + prompt = self._prompt_answer_list[i][0] + answer = self._prompt_answer_list[i][1] + + if prompt is None: + break + + line_num += 1 + if line_num > self._eval_lines: + break + + if len(prompt) > 2000: + prompt = prompt[0:2000] + if len(answer) > 5000: + answer = answer[0:5000] + + version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) + + eval_prompt_temp = version_prompt.prompt_template.replace('', prompt) + eval_prompt_temp = eval_prompt_temp.replace('', answer) + eval_prompts = [] + + for i in range(0, len(eval_dims)): + eval_dim_name = eval_dims[i][0] + eval_dim_requirement = eval_dims[i][1] + eval_prompt = eval_prompt_temp.replace('', eval_dim_name) + eval_prompt = eval_prompt.replace('', eval_dim_requirement) + eval_prompts.append(eval_prompt) + + llm = LLMManager().get_instance_obj(self.llm) + res = llm.batch_call(eval_prompts) + + dim_score_json = {'line': line_num} + dimensions = [] + avg_score = 0.0 + for j in range(0, len(res)): + try: + if res[j] != '' and res[j] is not None: + data = parse_json_markdown(res[j]) + avg_score += data['score'] + dimensions.append(data) + except Exception as e: + LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>>{e}:{res[j]}') + continue + if len(res) > 0: + avg_score = avg_score / len(res) + dim_score_json['avg_score'] = avg_score + dim_score_json['dimensions'] = dimensions + + self._eval_dims_json_list.append(dim_score_json) diff --git a/agentuniverse_data/node/eval/report.py b/agentuniverse_data/node/eval/report.py new file mode 100644 index 00000000..8cb00e14 --- /dev/null +++ b/agentuniverse_data/node/eval/report.py @@ -0,0 +1,66 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 18:02 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: report.py +from typing import List + +from agentuniverse_data.node.base.eval_node_base import EvalNodeBase +from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader + + +class ReportNode(EvalNodeBase): + """The ReportNode class, which is used to define the class of report node.""" + + _eval_report_json_list: List[str] = None + + def _node_preprocess(self) -> None: + if not self.datasets_in_jsonl or len(self.datasets_in_jsonl) == 0: + raise Exception(f"No input datasets: {self.datasets_in_jsonl}") + + def _node_postprocess(self) -> None: + super()._node_postprocess() + + if self._dataset_out_handler and self._eval_report_json_list: + self._dataset_out_handler.write_json_obj_list(self._eval_report_json_list) + + def _node_process(self) -> None: + self._eval_report_json_list = [] + for i in range(0, len(self.datasets_in_jsonl)): + jfr = JsonFileReader(self.datasets_in_jsonl[i]) + line_objs = jfr.read_json_obj_list() + if line_objs is None: + break + + line_num = 0 + total_avg_score = 0.0 + dim_avg_score = {} + for j in range(0, len(line_objs)): + json_obj = line_objs[j] + line_num += 1 + total_avg_score += json_obj['avg_score'] + dimensions = json_obj['dimensions'] + + dim_num = len(dimensions) + for k in range(0, dim_num): + name = dimensions[k]['name'] + score = dimensions[k]['score'] + if name in dim_avg_score: + dim_avg_score[name] += float(score) + else: + dim_avg_score[name] = float(score) + + for key in dim_avg_score: + dim_total_score = dim_avg_score[key] + dim_avg_score[key] = dim_total_score / line_num + + total_avg_score = total_avg_score / line_num + + report_line_obj = { + 'input_file': self.datasets_in_jsonl[i], + 'total_avg_score': total_avg_score, + 'dim_avg_score': dim_avg_score + } + self._eval_report_json_list.append(report_line_obj) diff --git a/agentuniverse_data/node/model/__init__.py b/agentuniverse_data/node/model/__init__.py new file mode 100644 index 00000000..cdbd34bd --- /dev/null +++ b/agentuniverse_data/node/model/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:53 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/model/base/__init__.py b/agentuniverse_data/node/model/base/__init__.py new file mode 100644 index 00000000..cdbd34bd --- /dev/null +++ b/agentuniverse_data/node/model/base/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:53 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/node/model/base/deploy_base.py b/agentuniverse_data/node/model/base/deploy_base.py new file mode 100644 index 00000000..9a871d6a --- /dev/null +++ b/agentuniverse_data/node/model/base/deploy_base.py @@ -0,0 +1,21 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:53 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: deploy_base.py +from agentuniverse_data.node.base.model_node_base import ModelNodeBase + + +class DeployBase(ModelNodeBase): + """The DeployBase class, which is used to define the base class of deploy node.""" + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + super()._node_postprocess() diff --git a/agentuniverse_data/node/model/base/dump_base.py b/agentuniverse_data/node/model/base/dump_base.py new file mode 100644 index 00000000..adb59b48 --- /dev/null +++ b/agentuniverse_data/node/model/base/dump_base.py @@ -0,0 +1,29 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:54 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: dump_base.py +from typing import List, Tuple + +from agentuniverse_data.node.base.model_node_base import ModelNodeBase + + +class DumpBase(ModelNodeBase): + """The DumpBase class, which is used to define the base class of dump node.""" + + _prompt_answer_out_list: List[Tuple[str, str]] = None + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + self._param_in_json_obj = None + super()._node_postprocess() + + if self._prompt_answer_out_list and self._dataset_out_handler: + self._dataset_out_handler.write_json_prompt_answer_list(self._prompt_answer_out_list) diff --git a/agentuniverse_data/node/model/base/pre_deploy_base.py b/agentuniverse_data/node/model/base/pre_deploy_base.py new file mode 100644 index 00000000..bca5712b --- /dev/null +++ b/agentuniverse_data/node/model/base/pre_deploy_base.py @@ -0,0 +1,21 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:54 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: pre_deploy_base.py +from agentuniverse_data.node.base.model_node_base import ModelNodeBase + + +class PreDeployBase(ModelNodeBase): + """The PreDeployBase class, which is used to define the base class of pre-deploy node.""" + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + super()._node_postprocess() diff --git a/agentuniverse_data/node/model/base/train_base.py b/agentuniverse_data/node/model/base/train_base.py new file mode 100644 index 00000000..b2339510 --- /dev/null +++ b/agentuniverse_data/node/model/base/train_base.py @@ -0,0 +1,23 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:54 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: train_base.py +from agentuniverse_data.node.base.model_node_base import ModelNodeBase + + +class TrainBase(ModelNodeBase): + """The TrainBase class, which is used to define the base class of train node.""" + + train_out_artifact: str = None + + def _node_preprocess(self) -> None: + super()._node_preprocess() + + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + super()._node_postprocess() diff --git a/agentuniverse_data/node/model/base/upload_base.py b/agentuniverse_data/node/model/base/upload_base.py new file mode 100644 index 00000000..e916bd3d --- /dev/null +++ b/agentuniverse_data/node/model/base/upload_base.py @@ -0,0 +1,29 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/14 17:59 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: upload_base.py +from agentuniverse_data.node.base.model_node_base import ModelNodeBase + + +class UploadBase(ModelNodeBase): + """The UploadBase class, which is used to define the base class of upload node.""" + + dataset_jsonl_filename: str = None + + def __init__(self, **kwargs): + super().__init__() + self.set_flow_start_node() + + def _node_preprocess(self) -> None: + super()._node_preprocess() + if self.datasets_in_jsonl and len(self.datasets_in_jsonl) > 0: + self.dataset_jsonl_filename = self.datasets_in_jsonl[0] + '.jsonl' + + def _node_process(self) -> None: + pass + + def _node_postprocess(self) -> None: + super()._node_postprocess() diff --git a/agentuniverse_data/prompt/__init__.py b/agentuniverse_data/prompt/__init__.py new file mode 100644 index 00000000..68b2996a --- /dev/null +++ b/agentuniverse_data/prompt/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 10:45 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/prompt/dataset_extend_node_cn.yaml b/agentuniverse_data/prompt/dataset_extend_node_cn.yaml new file mode 100644 index 00000000..845ffd6f --- /dev/null +++ b/agentuniverse_data/prompt/dataset_extend_node_cn.yaml @@ -0,0 +1,23 @@ +introduction: 你是一个极致严谨的金融专家，对于输入的字符串列表，请基于以下要求扩展，用来提交给大模型从而能获取高质量结果，请确保输出字符串只包含扩展后的内容且每一行是一条，如不能有“扩展后的问题:”之类的表述。 +instruction: | + 扩展要求： + 1.请确保扩展出来的输出字符串和输入字符串语义上完全不一样 + 2.请确保扩展出来的输出字符串和输入字符串大部分字符不相同 + 3.请确保扩展出来的输出字符串是对原输入有多样性大幅提升 + 4.请确保扩展出来的输出字符串和原输入对比是完全不同的金融话题 + 5.扩展倍数：以输入作为参考，输出尽量扩展条数为输入的倍 + + + 输入列表： + + + 请确保输出包含且只包含如下的json格式: + { + "extend_inputs": + [ + "xxx" + ] + } +metadata: + type: 'PROMPT' + version: 'dataset_extend_node.cn' diff --git a/agentuniverse_data/prompt/dataset_filter_node_cn.yaml b/agentuniverse_data/prompt/dataset_filter_node_cn.yaml new file mode 100644 index 00000000..15cf1ef9 --- /dev/null +++ b/agentuniverse_data/prompt/dataset_filter_node_cn.yaml @@ -0,0 +1,39 @@ +introduction: 你是一个极致严谨的金融专家，需要帮助按照如下要求评估answer在各维度的质量打分。 +instruction: | + 各维度质量打分请分别以0~100分来评估并确保严格要求不轻易给出高分： + 1.不符合要求的打0～20分 + 2.基本符合要求的打20～40分 + 3.比较优秀的打40～60分 + 4.特别突出的打60～80分 + 5.极致卓越的打80～100分 + + prompt: + + + answer: + + + 质量打分维度描述: + + 1.事实性: + 文本中如果有声明、数据和引用都应是可查证的、基于可靠数据源和事实的。 + 避免这些信息是推测性的语言，确保所有信息都有确凿的证据支持。 + + 请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: + { + "dimensions": [ + { + "name":质量打分维度, + "score":分数, + "negative substr":扣分最多的子串 + }, + { + "name":质量打分维度, + "score":分数, + "negative substr":扣分最多的子串 + } + ] + } +metadata: + type: 'PROMPT' + version: 'dataset_filter_node.cn' diff --git a/agentuniverse_data/prompt/dataset_input_seed_node_cn.yaml b/agentuniverse_data/prompt/dataset_input_seed_node_cn.yaml new file mode 100644 index 00000000..5ba9d53d --- /dev/null +++ b/agentuniverse_data/prompt/dataset_input_seed_node_cn.yaml @@ -0,0 +1,26 @@ +introduction: 你是一个极致严谨的金融专家，请尽量确保生成条金融问题用来提交给大模型从而能获取更多高质量的问题，请确保输出字符串是中文的且只包含生成后的问题，如不能有“生成的问题：”之类的表述。 +instruction: | + 输出表达要求： + 1.同义词替换：用同义词替换原始查询中的某些词汇，以扩大搜索结果的覆盖范围。 + 2.标准化处理：将查询中的词汇标准化，比如将复数形式转换为单数形式，或者使用基本形式的词替换其它形式的词。 + 3.去噪声词：从查询中去除不重要的词汇，如介词和连接词，以突出关键信息。 + 4.查询扩展：在原始查询基础上添加额外的词汇或短语，以提高查询的详细度或覆盖面。 + 5.重排和重构：根据特定的上下文或目标，对查询中的词语进行重新排序或重构，以改善其表达的清晰度或逻辑性。 + 6.意图识别：识别用户查询的潜在意图，然后根据这一意图进行相应的改写。 + 7.表达形式：尽可能的丰富指令的表达方式和长度，既有非常简洁的表达也有略长的表达 + 8.格式处理：规范化格式，提出不必要的标点、空格、回车、乱码等 + 9.结构形式：请保持相似的角色和指令结构或者框架，使的大模型能遵从相关回答结构 + + 一句话描述生成的种子： + + + 请确保输出包含且只包含如下的json格式: + { + "seeds": + [ + "xxx" + ] + } +metadata: + type: 'PROMPT' + version: 'dataset_input_seed_node.cn' diff --git a/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml b/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml new file mode 100644 index 00000000..59b36364 --- /dev/null +++ b/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml @@ -0,0 +1,26 @@ +introduction: 你是一个极致严谨的金融专家，对于输入的一列字符串，请基于以下要求改写，用来提交给大模型从而能获取高质量结果，请确保输出每一行字符串与输入每一行字符串一一对应且只包含改写后的内容，如不能有“改写后的问题：”之类的表述。 +instruction: | + 改写要求： + 1.同义词替换：用同义词替换原始查询中的某些词汇，以扩大搜索结果的覆盖范围。 + 2.标准化处理：将查询中的词汇标准化，比如将复数形式转换为单数形式，或者使用基本形式的词替换其它形式的词。 + 3.去噪声词：从查询中去除不重要的词汇，如介词和连接词，以突出关键信息。 + 4.查询扩展：在原始查询基础上添加额外的词汇或短语，以提高查询的详细度或覆盖面。 + 5.重排和重构：根据特定的上下文或目标，对查询中的词语进行重新排序或重构，以改善其表达的清晰度或逻辑性。 + 6.意图识别：识别用户查询的潜在意图，然后根据这一意图进行相应的改写。 + 7.表达形式：尽可能的丰富指令的表达方式和长度，既有非常简洁的表达也有略长的表达 + 8.格式处理：规范化格式，提出不必要的标点、空格、回车、乱码等 + 9.结构形式：请保持相似的角色和指令结构或者框架，使的大模型能遵从相关回答结构 + + 输入字符串： + + + 请确保输出包含且只包含如下的json格式: + { + "rewrite_inputs": + [ + "xxx" + ] + } +metadata: + type: 'PROMPT' + version: 'dataset_rewrite_node.cn' diff --git a/agentuniverse_data/prompt/eval_node_cn.yaml b/agentuniverse_data/prompt/eval_node_cn.yaml new file mode 100644 index 00000000..f508a948 --- /dev/null +++ b/agentuniverse_data/prompt/eval_node_cn.yaml @@ -0,0 +1,32 @@ +introduction: 你是一个极致严谨的金融专家，需要帮助按照如下要求评估answer在各维度的质量打分。 +instruction: | + 打分要求，请精确到小数点后1位： + 1.不符合准入条件（0.0分） + 2.门槛值（0.0～3.0分） + 3.加分项（0.0～2.0分） + + prompt: + + + answer: + + + 质量打分维度包括: + + + + 质量打分维度名称: + + + 质量打分维度要求: + + + 请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: + { + "name":质量打分维度名称, + "score":分数, + "negative substr":扣分最多的子串 + } +metadata: + type: 'PROMPT' + version: 'eval_node.cn' diff --git a/agentuniverse_data/prompt/event_planner_node_cn.yaml b/agentuniverse_data/prompt/event_planner_node_cn.yaml new file mode 100644 index 00000000..0e30f999 --- /dev/null +++ b/agentuniverse_data/prompt/event_planner_node_cn.yaml @@ -0,0 +1,37 @@ +data_event_plan: | + 你是一个极致严谨的金融指令工程专家，我们认为大模型输入input为prompt输出output为answer, prompt包括指令、上下文和用户输入， answer是针对prompt输入后LLM的回答 + 你需要针对输入str，规划对应的plan从而基于plan的代码输出满足要求的str + + 如下是输入： + input_str: + + + ### + 请严格遵循如下输出要求： + 1.请特别注意输入字符串是否是json编码的字符串并做相应的处理, 如果不是请不要解码避免引起不必要的麻烦 + 2.请确保代码输出的所有变量都是人可阅读的str类型, 如果不是请解析并提取到输出，同时尽量不要遗漏输入中的可阅读内容 + 3.请确保提供最简洁安全的代码片段, 请不要打印到屏幕 + 4.请参考之前的反馈: + + ### + 请确保你的回答包含且仅包含如下json格式的内容并严格遵循json规范: + { + "plan_code": 输入变量为input_str,输出变量为的python exec可执行的代码片段 + } +exec_result_verification: | + 你是一个极致严谨的数据质量专家,如下输入字符串每一行都是一条数据,你的工作是一行一行来确认是不是符合如下要求: + 1.人可以读取理解的文本 + 2.输入字段是完全解码的 + + 输入: + + + ### + 请确保你的回答包含且仅包含如下json格式的内容: + { + "success": 如果输入每行符合上面要求则为true否则为false, + "reflection": 不符合上面要求的原因和改进建议 + } +metadata: + type: 'PROMPT' + version: 'event_planner_node.cn' diff --git a/agentuniverse_data/prompt/instruct_prompt_gen_cn.yaml b/agentuniverse_data/prompt/instruct_prompt_gen_cn.yaml new file mode 100644 index 00000000..c2f72358 --- /dev/null +++ b/agentuniverse_data/prompt/instruct_prompt_gen_cn.yaml @@ -0,0 +1,7 @@ +instruction: | + + + +metadata: + type: 'PROMPT' + version: 'instruct_prompt_gen_node.cn' diff --git a/agentuniverse_data/prompt/instruct_seed_node_cn.yaml b/agentuniverse_data/prompt/instruct_seed_node_cn.yaml new file mode 100644 index 00000000..fa1d81dd --- /dev/null +++ b/agentuniverse_data/prompt/instruct_seed_node_cn.yaml @@ -0,0 +1,30 @@ +introduction: 你是一个极致严谨的金融专家。 +instruction: | + 请参考输入尽量按照扩展要求确保生成金融指令用来提交给大模型从而能获取更多高质量的问题，请确保输出字符串是中文的且只包含生成后的指令，如不能有“生成的指令：”“输出：”之类的表述。 + 同时请确保指令中没有具体的金融领域对象，只包含对输出的要求不包含输入的金融问题本身，角色的描述也可以扩展同类表述。 + + 指令输出要求： + 1.清晰表述：指令中使用清晰、明确的描述，避免模糊的词语，例如可以加上数字，具体的风格 + 2.指令位置：把指令放在prompt开头，并用三个引号或者三个井号等特殊的内容将待处理的内容和指令分开 + 3.输出格式：为了得到稳定的输出，指令中请尽量指定好输出格式 + 4.角色扮演：指令中请包含角色扮演技巧，以及针对角色扮演的专业要求 + 5.用户角色：指令中告诉模型用户是什么角色，从而得到的答案会令你感到惊艳 + 6.语义差异：请确保扩展出来的输出字符串和输入字符串语义上完全不一样 + 7.多样性：请确保扩展出来的输出字符串是对原输入有多样性大幅提升 + 8.字符串：请确保扩展出来的输出字符串和原输入对比是完全不同的金融话题 + 9.扩展倍数：以输入作为参考，输出尽量扩展条数为输入的倍 + + + 指令输入参考： + + + 请确保输出包含且只包含如下的json格式: + { + "instructs": + [ + "xxx" + ] + } +metadata: + type: 'PROMPT' + version: 'instruct_seed_node.cn' From fb2b4871fd5329325bf5ffdb5bd8d4fa1081e267 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Mon, 17 Jun 2024 21:14:32 +0800 Subject: [PATCH 010/118] docs: update README for new version --- README.md | 106 ++++++++++++------ README_zh.md | 98 +++++++++++----- docs/guidebook/en/1_Core_Features.md | 12 ++ docs/guidebook/en/1_Why_Use_agentUniverse.md | 10 ++ .../zh/1_1_\347\256\200\344\273\213.md" | 99 +++++++++++++--- ...0\351\200\211\346\213\251agentUniverse.md" | 10 ++ ...70\345\277\203\347\211\271\346\200\247.md" | 12 ++ 7 files changed, 270 insertions(+), 77 deletions(-) create mode 100644 docs/guidebook/en/1_Core_Features.md create mode 100644 docs/guidebook/en/1_Why_Use_agentUniverse.md create mode 100644 "docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" create mode 100644 "docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" diff --git a/README.md b/README.md index 00e50d9f..d247c332 100644 --- a/README.md +++ b/README.md @@ -10,59 +10,101 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語 ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** -## Overview -agentUniverse is a framework for developing applications powered by multi-agent base on large language model. It provides all the essential components for building a single agent, and a multi-agent collaboration mechanism which serves as a pattern factory that allowing developers to buid and customize multi-agent collaboration patterns. With this framework, developers can easily construct multi-agent applications, and share the pattern practices from different technical and business fields. +## What is agentUniverse? -The framework will come with serveral pre-install multi-agent collaboration patterns which have been proven effective in real business scenarios, and will continue to be enriched in the future. Patterns that are currently about to be released include: +**agentUniverse is a multi-agent framework based on large language models.** agentUniverse provides you with the flexible and easily extensible capability to build single agents. At its core, agentUniverse features a rich set of multi-agent collaboration mode components (which can be viewed as a Collaboration Mode Factory, or Pattern Factory). These components allow agents to maximize their effectiveness by specializing in different domains to solve problems. agentUniverse also focuses on the integration of domain expertise, helping you seamlessly incorporate domain knowledge into the work of your agents.🎉🎉🎉 -- PEER pattern： -This pattern utilizes four distinct agent roles: Plan, Execute, Express, and Review, to achieve a multi-step breakdown and sequential execution of a complex task. It also performs autonomous iteration based on evaluative feedback which enhancing performance in reasoning and analytical tasks. +**🌈🌈🌈agentUniverse helps developers and enterprises to easily build powerful collaborative agents that perform at an expert level in their respective domains.** +![](docs/guidebook/_picture/agent_universe_framework_resize.jpg) -- DOE pattern： -This pattern consists of three agents: Data-fining agent, which is designed to solve data-intensive and high-computational-precision task; Opinion-inject agent, which combines the data results from first agent and the expert opinions which are pre-collected and structured; the third agent, Express agent generates the final result base on given document type and language style. - -More patterns are coming soon... +We encourage you to practice and share different domain Patterns within the community. The framework comes pre-loaded with several multi-agent collaboration mode components that have been validated in real-world industries and will continue to expand in the future. The components that will be available soon include: -![](docs/guidebook/_picture/agent_universe_framework_resize.jpg) +* PEER Mode Component: This pattern uses agents with different responsibilities—Plan, Execute, Express, and Review—to break down complex problems into manageable steps, execute the steps in sequence, and iteratively improve based on feedback, enhancing the performance of reasoning and analysis tasks. Typical use cases: Event interpretation, industry analysis. +* DOE Mode Component: This pattern employs three agents—Data-fining, Opinion-inject, and Express—to improve the effectiveness of tasks that are data-intensive, require high computational precision, and incorporate expert opinions. Typical use cases: Financial report generation. -## agentUniverseSample Project -[agentUniverse Sample Project](sample_standard_app/README.md) +More patterns are coming soon... -## Quick Installation +**************************************** +## Table of Contents +* [Quick Start](#Quick Start) +* [Cases and Example Projects](#Cases and Example Projects) +* [More](#More) + * [Why Use agentUniverse](#Why Use agentUniverse) + * [Core Features](#Core Features) + * [User Guide](#User Guide) + * [API Reference](#API Reference) + * [Support](#Support) + * [Acknowledgements](#Acknowledgements) +**************************************** +## Quick Start Using pip: ```shell pip install agentUniverse ``` -## Quick Start We will show you how to: -* Prepare the environment and application project + +* Prepare the environment and application projects * Build a simple agent -* Use pattern components to complete multi-agent collaboration -* Test and optimize the performance of the agent -* Quickly serve the agent -For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md). +* Use mode components for multi-agent collaboration +* Test and tune the execution effectiveness of an agent +* Quickly deploy an agent as a service -## Use Cases +For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_Start.md). + +**************************************** +## Cases and Example Projects +### 🌟 Use Cases [Legal Consultation Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) [Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) [Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) -## Guidebook -For more detailed information, please refer to the [Guidebook](docs/guidebook/en/0_index.md). +### 🌟 Example Projects +[agentUniverse Example Projects](sample_standard_app) -## API Reference -[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) +### 🌟 Product Cases Built with agentUniverse +['Zhi Xiao Zhu' AI Assistant for Financial Professionals](https://zhu.alipay.com/) +**************************************** +## More +### Why Use agentUniverse +💡 [Why Use agentUniverse?](./docs/guidebook/en/1_Why_Use_agentUniverse.md) -## More Ways to Contact Us -* github: https://github.com/alipay/agentUniverse -* gitee: https://gitee.com/agentUniverse/agentUniverse -* gitcode: https://gitcode.com/agentUniverse -* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ -* WeChat Official Account: agentUniverse智多星 -* DingTalk Group: +### Core Features + +* **Rich Multi-Agent Collaboration Modes:** Provides industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express). It also supports user-defined patterns for new modes, enabling organic collaboration among multiple agents. +* **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents. +* **Seamless Integration of Domain Expertise:** Offers capabilities for domain-specific prompts, knowledge construction, and management, and supports domain-level SOP orchestration and embedding, aligning agents to the expert level in their fields. + +💡 For more specific details, see the [Core Features of agentUniverse](./docs/guidebook/en/1_Core_Features.md). + +### User Guide +💡 For more detailed information, please read the [User Guide](./docs/guidebook/en/0_index.md). + +### API Reference +💡 Please read the [API Reference](https://agentuniverse.readthedocs.io/en/latest/). + +### Support +#### Submit Questions via GitHub Issues +😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days. + +#### Contact Us via Discord +😊 Join our [Discord Channel](https://discord.gg/VfhEvJzQ) to interact with us. + +#### Contact Us via DingTalk +😊 Join our DingTalk support group to get in touch with us. ![](./docs/guidebook/_picture/dingtalk_util20250429.png) + +#### Contact Us via Administrator Email +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### WeChat Official Account + +😊 Official Account ID: **agentUniverse智多星** + +You can get more related articles and information in our WeChat Official Account. + +## Acknowledgements +This project is partially built on excellent open-source projects such as langchain, pydantic, gunicorn, flask, SQLAlchemy, chromadb, etc. (The detailed dependency list can be found in pyproject.toml). We would like to extend special thanks to the related projects and contributors. 🙏🙏🙏 diff --git a/README_zh.md b/README_zh.md index b9043bf0..a8636102 100644 --- a/README_zh.md +++ b/README_zh.md @@ -10,34 +10,38 @@ ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** -## 项目介绍 +## agentUniverse是什么？ -本框架是一个大模型多智能体框架。核心提供了多智能体协作编排组件，其相当于一个模式工厂（pattern factory），允许开发者对多智能体协作模式进行开发定制，同时附带了搭建单一智能体的全部关键组件。开发者可以基于本框架轻松构建多智能体应用，并通过社区对不同领域的pattern实践进行交流共享。 +**agentUniverse 是一个基于大型语言模型的多智能体框架。** agentUniverse为您提供灵活易拓展的单智能体构建能力；agentUniverse核心拥有丰富的多智能体协同模式组件（可视为一个协同模式工厂Pattern Factory），它能让智能体们各司其职在解决不同领域问题时发挥最大的能力；同时agentUniverse专注于领域经验的融合，帮助您轻松将领域经验融入到智能体的工作中。🎉🎉🎉 -框架将预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： - -- PEER 模式组件： -该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 - - -- DOE 模式组件： -该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 - -更多模式组件持续推出中... +**🌈🌈🌈agentUniverse帮助开发者、企业轻松构建出领域专家级别的强大智能体协同为您工作。** ![](docs/guidebook/_picture/agent_universe_framework_resize.jpg) +我们期待您通过社区对不同领域的Pattern进行实践与交流共享，框架也预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： +* PEER 模式组件： 该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 +* DOE 模式组件： 该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 -## agentUniverse 示例项目 -[agentUniverse 示例项目](sample_standard_app/README_zh.md) +更多模式组件持续推出中... -## 快速安装 +**************************************** +## 目录 +* [快速开始](#快速开始) +* [案例与样例工程](#案例与样例工程) +* [更多](#更多) + * [为什么使用agentUniverse](#为什么使用agentUniverse) + * [核心特性](#核心特性) + * [用户指南](#用户指南) + * [API参考](#API参考) + * [支持](#支持) + * [鸣谢](#鸣谢) +**************************************** +## 快速开始 使用pip： ```shell pip install agentUniverse ``` -## 快速开始 我们将向您展示如何： * 进行环境与应用工程准备 * 构建一个简单的agent @@ -46,26 +50,60 @@ pip install agentUniverse * 对agent进行快速服务化 详情请阅读[快速开始](docs/guidebook/zh/1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。 - -## 使用案例 +**************************************** +## 案例与样例工程 +### 🌟 使用案例 [法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) -## 用户指南 -更多详细信息，请参阅[指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 +### 🌟 示例项目 +[agentUniverse 示例项目](sample_standard_app) + +### 🌟 使用aU构建的产品案例 +[支小助 金融从业专家AI助手](https://zhu.alipay.com/) +**************************************** +## 更多 +### 为什么使用agentUniverse +💡 [为什么使用agentUniverse?](./docs/guidebook/zh/1_为什么选择agentUniverse.md) -## API 参考 -[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) +### 核心特性 -## 更多方式联系我们 -* github: https://github.com/alipay/agentUniverse -* gitee: https://gitee.com/agentUniverse/agentUniverse -* gitcode: https://gitcode.com/agentUniverse -* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ -* 微信公众号: agentUniverse智多星 -* 钉钉答疑群: +* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作； + +* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体； + +* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； + +💡 更多特定见[agentUniverse核心特性](docs/guidebook/zh/1_核心特性.md)部分。 + +### 用户指南 +💡 更多详细信息，请阅读[用户指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 + +### API参考 +💡 请阅读[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) 。 + +### 支持 +#### 通过github issue提交疑问 +😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 + +#### 通过Discord联系我们 +😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。 + +#### 通过钉钉群联系我们 +😊 加入我们的钉钉答疑群与我们联系。 ![](./docs/guidebook/_picture/dingtalk_util20250429.png) + +#### 通过管理员Email联系我们 +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### 微信公众号 + +😊 公众号ID：**agentUniverse智多星** + +更多相关的文章与资讯你可以在微信公众号中获取。 + +## 鸣谢 +本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏 diff --git a/docs/guidebook/en/1_Core_Features.md b/docs/guidebook/en/1_Core_Features.md new file mode 100644 index 00000000..92d7f23e --- /dev/null +++ b/docs/guidebook/en/1_Core_Features.md @@ -0,0 +1,12 @@ +# Core Features +* **Agent-Oriented Design:** The framework is designed with agents in mind, customizing agents through the perception, planning, action, and memory modules. +* **Rich Multi-Agent Collaboration Modes:** Offers industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express), and supports user-defined orchestration of new modes, enabling organic collaboration among multiple agents. +* **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents. +* **Seamless Integration of Domain Expertise:** Provides capabilities for domain-specific prompts, knowledge construction and management, and supports domain-level SOP orchestration and embedding to align agents to expert levels in their fields. +* **Multi-LLM and Extensible:** Built-in support for multiple models such as the OpenAI series, Qwen series, Baichuan series, Kimi series, etc., while offering standard extensions to integrate any third-party model services and privately deployed models. +* **Multi-Data Source and Extensible:** Supports various data sources, whether traditional ones like SQLite, MySQL, Oracle, or vector databases like ChromaDB, Milvu. It also provides standard extension methods to integrate any third-party data services and other databases. +* **Convenient Ecosystem Integration:** The framework integrates mature and practically tested large model application ecosystem capabilities, shielding most of the underlying configurations and implementations. This includes LLM ecosystem components like LangChain, llama_index, as well as technical components like Grpc and SLS. +* **Observable and Feedback Capable:** With the monitor component, all service interactions and model interactions within agentUniverse can be recorded and observed. Combined with the framework's automatic agent evaluation capabilities, you can easily grasp the performance gains from agent or model iterations and feed them back into service iterations and model training. +* **Enterprise Private Component Extensions:** The framework offers a private technical component extension and loading mechanism, allowing internal components like self-developed RPC protocol components, messaging components, and logging components to be integrated via configuration. +* **One-Click Service Deployment:** Supports one-click startup of a web server through the capabilities of Flask and Gunicorn, making it easy to integrate agent capabilities into any existing system. +* **Standard Container Delivery:** Provides a standard Docker image and offers recommended integration solutions based on Docker and Kubernetes (K8S) capabilities. \ No newline at end of file diff --git a/docs/guidebook/en/1_Why_Use_agentUniverse.md b/docs/guidebook/en/1_Why_Use_agentUniverse.md new file mode 100644 index 00000000..3c91c45f --- /dev/null +++ b/docs/guidebook/en/1_Why_Use_agentUniverse.md @@ -0,0 +1,10 @@ +# Why Use agentUniverse +With agentUniverse, you can easily build powerful collaborative agents that perform at an expert level in their respective domains. + +agentUniverse provides all the essential components for building single agents. All visible components are extensible and customizable, allowing them to seamlessly connect with large model ecosystem components and middleware, helping you build powerful single agents. + +agentUniverse features a rich set of multi-agent collaboration capabilities. Different collaboration modes are specialized in solving domain-specific problems, with multi-agents playing different roles and working together towards a common goal. This greatly enhances the agents' performance in completing tasks. + +agentUniverse focuses on the construction and management of domain prompts, domain knowledge, and domain SOPs. Through these capabilities, domain expertise is infused into the collaborative work of all agents, ensuring that agents have expert-level proficiency in specific domains. + +In addition, agentUniverse already has numerous industry-grade products and practices in the financial sector. We will share and open up collaboration modes, domain expertise, and solutions that have been validated in the financial industry as case studies. \ No newline at end of file diff --git "a/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" "b/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" index d0d25ee7..9047edce 100644 --- "a/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" +++ "b/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" @@ -1,25 +1,94 @@ -# agentUniverse项目简介 -## 项目介绍 -本框架是一个大模型多智能体框架。核心提供了多智能体协作编排组件，其相当于一个模式工厂（pattern factory），允许开发者对多智能体协作模式进行开发定制，同时附带了搭建单一智能体的全部关键组件。开发者可以基于本框架轻松构建多智能体应用，并通过社区对不同领域的pattern实践进行交流共享。 +## agentUniverse是什么？ -框架将预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： +**agentUniverse 是一个基于大型语言模型的多智能体框架。** agentUniverse为您提供灵活易拓展的单智能体构建能力；agentUniverse核心拥有丰富的多智能体协同模式组件（可视为一个协同模式工厂Pattern Factory），它能让智能体们各司其职在解决不同领域问题时发挥最大的能力；同时agentUniverse专注于领域经验的融合，帮助您轻松将领域经验融入到智能体的工作中。🎉🎉🎉 -- PEER 模式组件： -该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 +**🌈🌈🌈agentUniverse帮助开发者、企业轻松构建出领域专家级别的强大智能体协同为您工作。** +![](../_picture/agent_universe_framework_resize.jpg) -- DOE 模式组件： -该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 +我们期待您通过社区对不同领域的Pattern进行实践与交流共享，框架也预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： +* PEER 模式组件： 该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 +* DOE 模式组件： 该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 +**************************************** +## 目录 +* [快速开始](#快速开始) +* [案例与样例工程](#案例与样例工程) +* [更多](#更多) + * [为什么使用agentUniverse](#为什么使用agentUniverse) + * [核心特性](#核心特性) + * [用户指南](#用户指南) + * [API参考](#API参考) + * [支持](#支持) + * [鸣谢](#鸣谢) +**************************************** +## 快速开始 +使用pip： +```shell +pip install agentUniverse +``` -更多模式组件持续推出中... +我们将向您展示如何： +* 进行环境与应用工程准备 +* 构建一个简单的agent +* 使用模式组件完成多agent协同 +* 对agent执行效果进行测试调优 +* 对agent进行快速服务化 -![](../_picture/agent_universe_framework_resize.jpg) +详情请阅读[快速开始](1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。 +**************************************** +## 案例与样例工程 +### 🌟 使用案例 +[法律咨询Agent](7_1_1_法律咨询案例.md) + +[Python代码生成与执行Agent](7_1_1_Python自动执行案例.md) + +[基于多轮多Agent的讨论小组](6_2_1_讨论组.md) + +### 🌟 示例项目 +[agentUniverse 示例项目](../../../sample_standard_app) + +### 🌟 使用aU构建的产品案例 +[支小助 金融从业专家AI助手](https://zhu.alipay.com/) +**************************************** +## 更多 +### 为什么使用agentUniverse +💡[为什么使用agentUniverse?](1_为什么选择agentUniverse.md) + +### 核心特性 + +* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作； + +* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体； + +* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； + +💡更多特定见[agentUniverse核心特性](1_核心特性.md)部分。 + +### 用户指南 +💡更多详细信息，请阅读[用户指南](0_%E7%9B%AE%E5%BD%95.md) 。 + +### API参考 +💡请阅读[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) 。 + +### 支持 +#### 通过github issue提交疑问 +😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 + +#### 通过Discord联系我们 +😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。 + +#### 通过钉钉群联系我们 +😊 加入我们的钉钉答疑群与我们联系。 +![](../_picture/dingtalk_util20250429.png) + +#### 通过管理员Email联系我们 +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### 微信公众号 -除了丰富的协作模式外agentUniverse还包含如下主要特性： +😊 公众号ID：**agentUniverse智多星** -* **快速简单的研发体验**： 通过配置化与简单的接口实现，您可以基于本框架快速完成单智能体搭建、多智能体协同过程定义与服务化； -* **丰富的组件与扩展定制**： 框架提供了大量常见的领域组件（LLM、知识、工具、记忆、协同模式等组件）与技术组件（DB、RPC、Message等组件）的默认实现；提供了基于所有组件类型的扩展标准，您可以对任何部分进行定制并用来强化您的agent能力； -* **prompt友好与管理**：框架内置了一整套prompt管理机制，您可以对于prompt进行多版本管理、切换与专家经验领域prompt化。 +更多相关的文章与资讯你可以在微信公众号中获取。 ## 鸣谢 -本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等开源项目（详细依赖列表可见requirements.txt）实现，在此特别感谢相关项目与关联方。 \ No newline at end of file +本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏 \ No newline at end of file diff --git "a/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" "b/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" new file mode 100644 index 00000000..e5f0e4d7 --- /dev/null +++ "b/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" @@ -0,0 +1,10 @@ +# 为什么选择agentUniverse +通过agentUniverse您可以轻松构建出领域专家级别的强大智能体协同为您工作。 + +agentUniverse提供了搭建单智能体的全部关键组件，所有可见的组件都是可扩展与定制的，它们可以非常轻松的连接大模型生态组件与中间件，帮助您构建能强大的单智能体。 + +agentUniverse拥有丰富的多智能体协同能力，不同的协同模式专精于解决专属场景下的领域问题，多智能体将扮演不同的角色将各司其职围绕着任务目标协同工作，这将大大提升智能体们完成任务的表现； + +agentUniverse专注于领域prompt、领域知识、领域SOP构建管理的能力，通过这些能力将领域经验注入到所有智能体的协同工作中去，保证智能体在特定领域拥有对齐人工专家的能力； + +此外agentUniverse在金融领域已经有非常多的产业级产品与实践，我们会将金融领域经过产业验证的协同模式、领域经验、解决方案作为案例分享开放。 \ No newline at end of file diff --git "a/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" "b/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" new file mode 100644 index 00000000..3899fd53 --- /dev/null +++ "b/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" @@ -0,0 +1,12 @@ +# 核心特性 +* **面向智能体设计：** 框架所有设计均从智能体领域出发，通过感知、规划、行动、记忆模块定制智能体； +* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作； +* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体； +* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； +* **面向多LLM且可扩展：** 内置OpenAi系列、qwen系列、Baichuan系列、Kimi系列等数种模型LLM接入方式，同时提供模型扩展标准，支持任意第三方模型服务与私有部署模型接入； +* **面向多数据源且可扩展：** 框架支持多种数据源，无论是SQLite, MySQL, Oracle等这一类传统数据源、还是ChromaDB、Milvu这一类向量数据库，同时提供数据源扩展标准，支持任意第三方数据服务与其他数据库自定义接入； +* **便捷的生态融合能力：** 框架将当前比较成熟先进、经得起实际考验的大模型应用生态能力融合，屏蔽了大部分底层配置与实现，无论是LangChain、llama_index这类LLM生态组件，还是如Grpc、SLS这一类技术组件。 +* **效果可观测与可反馈：** 通过monitor组件所有agentUniverse的服务交互、模型交互均可被记录观测，结合框架提供的智能体自动评测能力，您可以轻松的掌握智能体或模型迭代带来的效果增益，并反馈至服务迭代与模型训练中。 +* **面向企业私有组件扩展：** 框架提供私有技术组件扩展加载机制，通过配置可以将企业内部如自研RPC协议组件、消息组件、日志组件接入； +* **支持一键服务化：** 框架通过Flask+Gunicore的能力支持一键启动WebServer，通过服务化能力您可以将agent的能力融合至任何现有的系统中去； +* **面向标准容器交付：** 框架提供了标准的Docker镜像，同时基于Docker与K8S能力给出推荐集成方案。 \ No newline at end of file From b95060b524c2f446f19cdf56e3e773b4cfe93fa9 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Mon, 17 Jun 2024 21:21:12 +0800 Subject: [PATCH 011/118] docs: update readme for new version. --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index d247c332..53f8e807 100644 --- a/README.md +++ b/README.md @@ -27,13 +27,13 @@ More patterns are coming soon... **************************************** ## Table of Contents -* [Quick Start](#Quick Start) -* [Cases and Example Projects](#Cases and Example Projects) +* [Quick Start](#Quick-Start) +* [Cases and Example Projects](#Cases-and-Example-Projects) * [More](#More) - * [Why Use agentUniverse](#Why Use agentUniverse) - * [Core Features](#Core Features) - * [User Guide](#User Guide) - * [API Reference](#API Reference) + * [Why Use agentUniverse](#Why-Use-agentUniverse) + * [Core Features](#Core-Features) + * [User Guide](#User-Guide) + * [API Reference](#API-Reference) * [Support](#Support) * [Acknowledgements](#Acknowledgements) **************************************** From c97964061b47b5ebff14726657980f853d90622b Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Mon, 17 Jun 2024 22:19:07 +0800 Subject: [PATCH 012/118] docs: update Discord code --- README.md | 6 +----- README_jp.md | 4 +--- README_zh.md | 2 +- 3 files changed, 3 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 53f8e807..4be46069 100644 --- a/README.md +++ b/README.md @@ -91,11 +91,7 @@ For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_St 😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days. #### Contact Us via Discord -😊 Join our [Discord Channel](https://discord.gg/VfhEvJzQ) to interact with us. - -#### Contact Us via DingTalk -😊 Join our DingTalk support group to get in touch with us. -![](./docs/guidebook/_picture/dingtalk_util20250429.png) +😊 Join our [Discord Channel](https://discord.gg/DHFcdkWAhn) to interact with us. #### Contact Us via Administrator Email 😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) diff --git a/README_jp.md b/README_jp.md index 269ccec2..a5e92862 100644 --- a/README_jp.md +++ b/README_jp.md @@ -61,7 +61,5 @@ pip install agentUniverse * gitee: https://gitee.com/agentUniverse/agentUniverse * gitcode: https://gitcode.com/agentUniverse * Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ +* Discord: https://discord.gg/DHFcdkWAhn * WeChat公式アカウント: agentUniverse智多星 -* DingTalkグループ: -![](./docs/guidebook/_picture/dingtalk_util20250429.png) diff --git a/README_zh.md b/README_zh.md index a8636102..2c4967e1 100644 --- a/README_zh.md +++ b/README_zh.md @@ -90,7 +90,7 @@ pip install agentUniverse 😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 #### 通过Discord联系我们 -😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。 +😊 加入我们的 [Discord频道](https://discord.gg/DHFcdkWAhn) 与我们进行交流。 #### 通过钉钉群联系我们 😊 加入我们的钉钉答疑群与我们联系。 From b39cf9c5805a271e7290139b483ed44ccdb4db08 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Tue, 18 Jun 2024 14:02:24 +0800 Subject: [PATCH 013/118] docs: update README --- README.md | 3 ++- README_jp.md | 1 + README_zh.md | 1 + 3 files changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ee6656b2..92d45c9e 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,8 @@ For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md). ## Use Cases [Legal Consultation Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) [Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) -[Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) +[Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) +[Financial Event Analysis Based on PEER Multi-Agent Mode](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) ## Guidebook For more detailed information, please refer to the [Guidebook](docs/guidebook/en/0_index.md). diff --git a/README_jp.md b/README_jp.md index ec284e38..957e2f4e 100644 --- a/README_jp.md +++ b/README_jp.md @@ -47,6 +47,7 @@ pip install agentUniverse [法律相談Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) [Pythonコード生成と実行Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) [多回多Agentによるディスカッショングループ](./docs/guidebook/en/6_2_1_Discussion_Group.md) +[PEERマルチAgentモードに基づいた金融イベント分析](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) ## ガイドブック 詳細情報については、[ガイドブック](docs/guidebook/en/0_index.md)を参照してください。 diff --git a/README_zh.md b/README_zh.md index d35db732..490395a2 100644 --- a/README_zh.md +++ b/README_zh.md @@ -51,6 +51,7 @@ pip install agentUniverse [法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) +[基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md)] ## 用户指南 更多详细信息，请参阅[指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 From 9012d183cf5bd520ed313207f0eb56015e8bc928 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 18 Jun 2024 14:28:37 +0800 Subject: [PATCH 014/118] feat: add data agent in the agentUniverse. --- agentuniverse_data/config/config.toml | 14 ++--- agentuniverse_data/config/log_config.toml | 2 +- agentuniverse_data/dispatch/dispatch.yaml | 9 --- .../{dispatch => guide}/__init__.py | 0 .../data => guide/dispatch}/__init__.py | 2 +- .../dispatch/data/demo_jsonl.jsonl | 0 .../{ => guide}/dispatch/dispatch.py | 4 ++ .../guide/dispatch/dispatch.yaml | 4 ++ agentuniverse_data/llm/openai_llm.py | 52 ----------------- agentuniverse_data/llm/openai_llm.yaml | 4 +- .../node/base/data_node_base.py | 2 +- agentuniverse_data/node/data/answer/answer.py | 7 +-- agentuniverse_data/node/data/answer/filter.py | 5 +- agentuniverse_data/node/data/event/planner.py | 8 +-- .../node/data/prompt/input/extend.py | 5 +- .../node/data/prompt/input/rewrite.py | 5 +- .../node/data/prompt/input/seed.py | 5 +- .../node/data/prompt/instruct/seed.py | 5 +- agentuniverse_data/node/eval/eval.py | 5 +- .../prompt/dataset_filter_node_cn.yaml | 44 +++++++++++++- agentuniverse_data/util/llm/__init__.py | 7 +++ agentuniverse_data/util/llm/llm_call.py | 57 +++++++++++++++++++ sample_standard_app/pyproject.toml | 2 +- 23 files changed, 146 insertions(+), 102 deletions(-) delete mode 100644 agentuniverse_data/dispatch/dispatch.yaml rename agentuniverse_data/{dispatch => guide}/__init__.py (100%) rename agentuniverse_data/{dispatch/data => guide/dispatch}/__init__.py (83%) rename agentuniverse_data/{ => guide}/dispatch/data/demo_jsonl.jsonl (100%) rename agentuniverse_data/{ => guide}/dispatch/dispatch.py (94%) create mode 100644 agentuniverse_data/guide/dispatch/dispatch.yaml delete mode 100644 agentuniverse_data/llm/openai_llm.py create mode 100644 agentuniverse_data/util/llm/__init__.py create mode 100644 agentuniverse_data/util/llm/llm_call.py diff --git a/agentuniverse_data/config/config.toml b/agentuniverse_data/config/config.toml index 0b4543ee..1be5c7e0 100644 --- a/agentuniverse_data/config/config.toml +++ b/agentuniverse_data/config/config.toml @@ -4,21 +4,21 @@ appname = 'agentuniverse_data' [CORE_PACKAGE] # Perform a full component scan and registration for all the paths under this list. -default = ['agentuniverse_data'] +default = [] # Scan and register agent components for all paths under this list, with priority over the default. -agent = [''] +agent = [] # Scan and register knowledge components for all paths under this list, with priority over the default. -knowledge = [''] +knowledge = [] # Scan and register llm components for all paths under this list, with priority over the default. llm = ['agentuniverse_data.llm'] # Scan and register planner components for all paths under this list, with priority over the default. -planner = [''] +planner = [] # Scan and register tool components for all paths under this list, with priority over the default. -tool = [''] +tool = [] # Scan and register memory components for all paths under this list, with priority over the default. -memory = [''] +memory = [] # Scan and register service components for all paths under this list, with priority over the default. -service = [''] +service = [] # Scan and register prompt components for all paths under this list, with priority over the default. prompt = ['agentuniverse_data.prompt'] diff --git a/agentuniverse_data/config/log_config.toml b/agentuniverse_data/config/log_config.toml index dbeae3dc..9a621c09 100644 --- a/agentuniverse_data/config/log_config.toml +++ b/agentuniverse_data/config/log_config.toml @@ -3,7 +3,7 @@ # Loguru log level. log_level = "INFO" # Output path of the log file. If value is empty, agentuniverse will create a subdir under your workdir to save logs. -log_path = "./dispatch" +log_path = "./log" # Specifies the log rotation policy, controlling when a new log file is created. It can be a time period # (e.g., "1 week"), a file size (e.g., "100 MB"), or a function returning True when rotation should occur. log_rotation = "10MB" diff --git a/agentuniverse_data/dispatch/dispatch.yaml b/agentuniverse_data/dispatch/dispatch.yaml deleted file mode 100644 index 7102d37f..00000000 --- a/agentuniverse_data/dispatch/dispatch.yaml +++ /dev/null @@ -1,9 +0,0 @@ -name: 'main_dispatch' -description: 'dispatch with multi-dataflows which will execute one after another' -dataflows: - - ../flow/auto_event.yaml - - ../flow/dataset_build.yaml - - ../flow/train_dump.yaml - - ../flow/eval_report.yaml - - ../flow/model_deploy.yaml - - ../flow/instruct_select.yaml \ No newline at end of file diff --git a/agentuniverse_data/dispatch/__init__.py b/agentuniverse_data/guide/__init__.py similarity index 100% rename from agentuniverse_data/dispatch/__init__.py rename to agentuniverse_data/guide/__init__.py diff --git a/agentuniverse_data/dispatch/data/__init__.py b/agentuniverse_data/guide/dispatch/__init__.py similarity index 83% rename from agentuniverse_data/dispatch/data/__init__.py rename to agentuniverse_data/guide/dispatch/__init__.py index 7b61ca3e..cedbf3c1 100644 --- a/agentuniverse_data/dispatch/data/__init__.py +++ b/agentuniverse_data/guide/dispatch/__init__.py @@ -1,7 +1,7 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- -# @Time : 2024/6/11 20:49 +# @Time : 2024/6/17 19:54 # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: __init__.py.py diff --git a/agentuniverse_data/dispatch/data/demo_jsonl.jsonl b/agentuniverse_data/guide/dispatch/data/demo_jsonl.jsonl similarity index 100% rename from agentuniverse_data/dispatch/data/demo_jsonl.jsonl rename to agentuniverse_data/guide/dispatch/data/demo_jsonl.jsonl diff --git a/agentuniverse_data/dispatch/dispatch.py b/agentuniverse_data/guide/dispatch/dispatch.py similarity index 94% rename from agentuniverse_data/dispatch/dispatch.py rename to agentuniverse_data/guide/dispatch/dispatch.py index bf1d2332..de8214b7 100644 --- a/agentuniverse_data/dispatch/dispatch.py +++ b/agentuniverse_data/guide/dispatch/dispatch.py @@ -32,3 +32,7 @@ def dispatch(conf_file='dispatch.yaml'): dataflow.execute() except Exception as e: LOGGER.error(f"An error occurred: {e}") + + +if __name__ == '__main__': + dispatch('dispatch.yaml') diff --git a/agentuniverse_data/guide/dispatch/dispatch.yaml b/agentuniverse_data/guide/dispatch/dispatch.yaml new file mode 100644 index 00000000..a08c81c5 --- /dev/null +++ b/agentuniverse_data/guide/dispatch/dispatch.yaml @@ -0,0 +1,4 @@ +name: 'main_dispatch' +description: 'dispatch with multi-dataflows which will execute one after another' +dataflows: + - ../../flow/dataset_build.yaml diff --git a/agentuniverse_data/llm/openai_llm.py b/agentuniverse_data/llm/openai_llm.py deleted file mode 100644 index 4f5c4aa8..00000000 --- a/agentuniverse_data/llm/openai_llm.py +++ /dev/null @@ -1,52 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:19 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: demo_llm.py -import asyncio - -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.llm.openai_llm import OpenAILLM - - -class DemoOpenAILLM(OpenAILLM): - - def batch_call(self, prompts: list[str]): - return asyncio.run(self.async_batch_call(prompts)) - - async def async_batch_call(self, prompts: list[str]): - tasks = [] - prompt_len = len(prompts) - for i in range(0, prompt_len): - messages = [{"role": "user", "content": prompts[i]}] - tasks.append(self.acall(messages=messages, timeout=200)) - - task = asyncio.create_task(self.show_progress(len(prompts), asyncio.get_running_loop())) - outputs = await asyncio.gather(*tasks, return_exceptions=True) - task.cancel() - - results = [] - for i, output in enumerate(outputs): - if isinstance(output, Exception): - LOGGER.warn(f'>>>except[async_llm_call]:{output}') - results.append(None) - else: - results.append(output.text) - - self.print_progress(len(results), prompt_len) - - return results - - def print_progress(self, completed: int, task_count: int): - progress = (completed / task_count) * 100 - LOGGER.info(f"\r>>>llm progress: {completed}/{task_count} = {progress:.2f}%") - - async def show_progress(self, task_count, loop): - completed = 0 - while completed < task_count: - completed_tasks = [t for t in asyncio.all_tasks(loop=loop) if t.done()] - completed = len(completed_tasks) - self.print_progress(completed, task_count) - await asyncio.sleep(10) diff --git a/agentuniverse_data/llm/openai_llm.yaml b/agentuniverse_data/llm/openai_llm.yaml index 42af35f9..838da47b 100644 --- a/agentuniverse_data/llm/openai_llm.yaml +++ b/agentuniverse_data/llm/openai_llm.yaml @@ -5,5 +5,5 @@ max_tokens: 1000 max_retries: 2 metadata: type: 'LLM' - module: 'agentuniverse_data.dispatch.llm.openai_llm' - class: 'DemoOpenAILLM' \ No newline at end of file + module: 'agentuniverse.llm.default.default_openai_llm' + class: 'DefaultOpenAILLM' \ No newline at end of file diff --git a/agentuniverse_data/node/base/data_node_base.py b/agentuniverse_data/node/base/data_node_base.py index 1f54912a..cbe203e0 100644 --- a/agentuniverse_data/node/base/data_node_base.py +++ b/agentuniverse_data/node/base/data_node_base.py @@ -11,7 +11,7 @@ class DataNodeBase(NodeBase): """The DataBase class, which is used to define the base class of data node.""" _batch_line_size: int = 10 - _batch_prompt_size: int = 40 + _batch_prompt_size: int = 10 def _node_process(self) -> None: pass diff --git a/agentuniverse_data/node/data/answer/answer.py b/agentuniverse_data/node/data/answer/answer.py index 55f05447..c4605a5a 100644 --- a/agentuniverse_data/node/data/answer/answer.py +++ b/agentuniverse_data/node/data/answer/answer.py @@ -5,7 +5,7 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: answer.py from agentuniverse_data.node.data.base.prompt_answer_base import PromptAnswerBase -from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse_data.util.llm.llm_call import batch_call class AnswerNode(PromptAnswerBase): @@ -24,16 +24,15 @@ def _node_process(self) -> None: prompt = prompt_answer_list[i][0] prompts.append(prompt) total_prompt_num = total_prompt_num + 1 - llm = LLMManager().get_instance_obj(self.llm) if total_prompt_num % self._batch_prompt_size == 0: - res = llm.batch_call(prompts) + res = batch_call(prompts, self.llm) for prompt, answer in zip(prompts, res): if res is not None: self._prompt_answer_list.append((prompt, answer)) prompts = [] else: if total_prompt_num == prompt_list_num and len(prompts) > 0: - res = llm.batch_call(prompts) + res = batch_call(prompts, self.llm) for prompt, answer in zip(prompts, res): if res is not None: self._prompt_answer_list.append((prompt, answer)) diff --git a/agentuniverse_data/node/data/answer/filter.py b/agentuniverse_data/node/data/answer/filter.py index c9a2fed8..02d9f0ce 100644 --- a/agentuniverse_data/node/data/answer/filter.py +++ b/agentuniverse_data/node/data/answer/filter.py @@ -10,11 +10,11 @@ from langchain.output_parsers.json import parse_json_markdown from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_answer_base import PromptAnswerBase from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader, JsonFileWriter +from agentuniverse_data.util.llm.llm_call import batch_call class FilterNode(PromptAnswerBase): @@ -59,8 +59,7 @@ def __quality_eval(self) -> None: do_req = True if do_req: - llm = LLMManager().get_instance_obj(self.llm) - res = llm.batch_call(prompts) + res = batch_call(prompts, self.llm) start_line_num = total_lines - len(prompts) for res_idx in range(0, len(res)): diff --git a/agentuniverse_data/node/data/event/planner.py b/agentuniverse_data/node/data/event/planner.py index d943c2ec..42b8e766 100644 --- a/agentuniverse_data/node/data/event/planner.py +++ b/agentuniverse_data/node/data/event/planner.py @@ -11,12 +11,12 @@ from langchain.output_parsers.json import parse_json_markdown from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader from agentuniverse_data.node.enum.enum import NodeEnum from agentuniverse_data.node.base.data_node_base import DataNodeBase +from agentuniverse_data.util.llm.llm_call import batch_call class PlannerNode(DataNodeBase): @@ -116,8 +116,7 @@ def __generate_plan_and_verify(self, plan_list: str, data_list: list) -> str: return None LOGGER.debug(prompt) - llm = LLMManager().get_instance_obj(self.llm) - res = llm.batch_call([prompt]) + res = batch_call([prompt], self.llm) LOGGER.debug(res) try: @@ -169,8 +168,7 @@ def check_data_reflection(self, data_str: str) -> str: prompt = getattr(version_prompt, 'exec_result_verification', '').replace('', data_str) LOGGER.debug(prompt) - llm = LLMManager().get_instance_obj(self.llm) - res = llm.batch_call([prompt]) + res = batch_call([prompt], self.llm) LOGGER.debug(res[0]) json_obj = parse_json_markdown(res[0]) if json_obj.get('success'): diff --git a/agentuniverse_data/node/data/prompt/input/extend.py b/agentuniverse_data/node/data/prompt/input/extend.py index d1a28671..ce90f585 100644 --- a/agentuniverse_data/node/data/prompt/input/extend.py +++ b/agentuniverse_data/node/data/prompt/input/extend.py @@ -8,11 +8,11 @@ import json import math -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_base import PromptBase from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse_data.util.llm.llm_call import batch_call class ExtendNode(PromptBase): @@ -42,8 +42,7 @@ def _node_process(self) -> None: prompts.append(prompt_with_extend_times.replace('', input_list)) input_list = '' - llm = LLMManager().get_instance_obj(self.llm) - responses = llm.batch_call(prompts) + responses = batch_call(prompts, self.llm) for i in range(0, len(responses)): try: diff --git a/agentuniverse_data/node/data/prompt/input/rewrite.py b/agentuniverse_data/node/data/prompt/input/rewrite.py index 75920ef4..d8588c6e 100644 --- a/agentuniverse_data/node/data/prompt/input/rewrite.py +++ b/agentuniverse_data/node/data/prompt/input/rewrite.py @@ -9,10 +9,10 @@ from langchain.output_parsers.json import parse_json_markdown -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse_data.util.llm.llm_call import batch_call class RewriteNode(PromptBase): @@ -32,8 +32,7 @@ def _node_process(self) -> None: prompts.append(version_prompt.prompt_template.replace('', inputs_all)) inputs_all = '' - llm = LLMManager().get_instance_obj(self.llm) - responses = llm.batch_call(prompts) + responses = batch_call(prompts, self.llm) self._prompt_list = [] for i in range(0, len(responses)): diff --git a/agentuniverse_data/node/data/prompt/input/seed.py b/agentuniverse_data/node/data/prompt/input/seed.py index 923d8942..c212605e 100644 --- a/agentuniverse_data/node/data/prompt/input/seed.py +++ b/agentuniverse_data/node/data/prompt/input/seed.py @@ -7,11 +7,11 @@ # @FileName: seed.py import json -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_base import PromptBase from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse_data.util.llm.llm_call import batch_call class SeedNode(PromptBase): @@ -50,8 +50,7 @@ def _node_process(self) -> None: prompt = prompt_with_seed_requirement.replace('', str(left_num)) prompts.append(prompt) - llm = LLMManager().get_instance_obj(self.llm) - responses = llm.batch_call(prompts) + responses = batch_call(prompts, self.llm) self._prompt_list = [] for i in range(0, len(responses)): diff --git a/agentuniverse_data/node/data/prompt/instruct/seed.py b/agentuniverse_data/node/data/prompt/instruct/seed.py index 8467bc3d..3c4d2114 100644 --- a/agentuniverse_data/node/data/prompt/instruct/seed.py +++ b/agentuniverse_data/node/data/prompt/instruct/seed.py @@ -7,11 +7,11 @@ # @FileName: seed.py from langchain.output_parsers.json import parse_json_markdown -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_base import PromptBase from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse_data.util.llm.llm_call import batch_call _DEFAULT_INSTRUCT_FOR_PROMPT = """ 你是一个极致严谨的金融专家，请尽量找出问题对应的金融解读框架，并基于解读框架一步一步结构化详细回答如下问题: @@ -34,8 +34,7 @@ def _node_process(self) -> None: str(self.extend_times)) prompts = [prompt_with_extend_times.replace('', _DEFAULT_INSTRUCT_FOR_PROMPT)] - llm = LLMManager().get_instance_obj(self.llm) - responses = llm.batch_call(prompts) + responses = batch_call(prompts, self.llm) self._prompt_list = [] if len(responses) == 1: try: diff --git a/agentuniverse_data/node/eval/eval.py b/agentuniverse_data/node/eval/eval.py index b0bc3597..766ad252 100644 --- a/agentuniverse_data/node/eval/eval.py +++ b/agentuniverse_data/node/eval/eval.py @@ -12,8 +12,8 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.base.eval_node_base import EvalNodeBase -from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse_data.util.llm.llm_call import batch_call _EVAL_DIM_FACTUALITY_NAME = "事实性" _EVAL_DIM_FACTUALITY_REQUIREMENT = """ @@ -94,8 +94,7 @@ def _node_process(self) -> None: eval_prompt = eval_prompt.replace('', eval_dim_requirement) eval_prompts.append(eval_prompt) - llm = LLMManager().get_instance_obj(self.llm) - res = llm.batch_call(eval_prompts) + res = batch_call(eval_prompts, self.llm) dim_score_json = {'line': line_num} dimensions = [] diff --git a/agentuniverse_data/prompt/dataset_filter_node_cn.yaml b/agentuniverse_data/prompt/dataset_filter_node_cn.yaml index 15cf1ef9..b7f22c65 100644 --- a/agentuniverse_data/prompt/dataset_filter_node_cn.yaml +++ b/agentuniverse_data/prompt/dataset_filter_node_cn.yaml @@ -15,10 +15,52 @@ instruction: | 质量打分维度描述: - 1.事实性: + 1.合规性: + 文本内容应遵守所有相关的法律法规、行业准则和公司政策。 + 必须遵循金融监管机构的要求，包括但不限于披露、报告和交易规则。 + 应保证文本中的任何建议或信息不违反利益冲突和道德行为准则。 + + 2.安全性: + 保护文本中包含的敏感信息，包括个人信息和财务数据，确保其不会被非授权人员访问。 + 应实施数据加密、访问控制和其他安全措施以防止数据泄露或未授权使用。 + + 3.事实性: 文本中如果有声明、数据和引用都应是可查证的、基于可靠数据源和事实的。 避免这些信息是推测性的语言，确保所有信息都有确凿的证据支持。 + 4.结构性: + 文本应具有清晰、逻辑性强的结构，包括合适的标题、子标题和段落。 + 信息的呈现应有助于理解，遵循从总体到细节的组织方式。 + + 5.合理性: + 呈现的分析或论点应基于合理的假设和逻辑推理。 + 结论应当合理，不偏离数据和证据所能支持的范围。 + + 6.严谨性: + 文本应遵循高标准的专业准则，精确使用术语，严谨处理数据。 + 所提供的分析和结论应深入、准确，无逻辑漏洞。 + + 7.重复性: + 文本内容在反复使用或引用时应保持一致，能够被同样的方式理解和解释。 + 术语定义和使用标准应一致，确保在不同场合和时间的解读不发生变化。 + + 8.时效性: + 文本内容应反映最新的信息和数据，及时更新以匹配当前的金融市场和环境。 + 如有数据和信息必须标明对应的时点，确保读者明白其相关性和有效性。 + + 9.全面性: + 文本应全面覆盖主题，考虑到所有相关方面和潜在影响。 + 应包含足够的背景信息、上下文和可能的影响分析。 + + 10.相关性: + 评估answer相对于prompt的相关性 + a.不符合准入条件：答非所问，不相关 + b.门槛值：能够知道到用户在问什么，并且所答即所问，包括： + 【有明确意图时】识别出用户的问题并对问题有回复，且回复的内容有相关性；有主意图的时候，回复的内容要与主意图有相关 + 【无明确意图时】遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 + c.加分项 + 能够get到用户在问什么后：对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应;回应的内容整体相关性都很高;回复的问题全面且易懂 + 请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: { "dimensions": [ diff --git a/agentuniverse_data/util/llm/__init__.py b/agentuniverse_data/util/llm/__init__.py new file mode 100644 index 00000000..ae852a5c --- /dev/null +++ b/agentuniverse_data/util/llm/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 20:47 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/util/llm/llm_call.py b/agentuniverse_data/util/llm/llm_call.py new file mode 100644 index 00000000..0dfc1858 --- /dev/null +++ b/agentuniverse_data/util/llm/llm_call.py @@ -0,0 +1,57 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/17 20:47 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: llm_call.py +import asyncio + +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.llm.llm import LLM +from agentuniverse.llm.llm_manager import LLMManager + + +def batch_call(prompts: list[str], llm_name: str): + return asyncio.run(async_batch_call(prompts, llm_name)) + + +async def async_batch_call(prompts: list[str], llm_name: str): + tasks = [] + llm: LLM = LLMManager().get_instance_obj(llm_name) + if llm is None: + raise Exception('LLM not found for agentuniverse data.') + prompt_len = len(prompts) + for i in range(0, prompt_len): + messages = [{"role": "user", "content": prompts[i]}] + tasks.append(llm.acall(messages=messages, timeout=200)) + + task = asyncio.create_task(show_progress(len(prompts), asyncio.get_running_loop())) + outputs = await asyncio.gather(*tasks, return_exceptions=True) + task.cancel() + + results = [] + for i, output in enumerate(outputs): + if isinstance(output, Exception): + LOGGER.warn(f'>>>except[async_llm_call]:{output}') + results.append(None) + else: + results.append(output.text) + + print_progress(len(results), prompt_len) + + return results + + +def print_progress(completed: int, task_count: int): + progress = (completed / task_count) * 100 + LOGGER.info(f"\r>>>llm progress: {completed}/{task_count} = {progress:.2f}%") + + +async def show_progress(task_count, loop): + completed = 0 + while completed < task_count: + completed_tasks = [t for t in asyncio.all_tasks(loop=loop) if t.done()] + completed = len(completed_tasks) + print_progress(completed, task_count) + await asyncio.sleep(10) diff --git a/sample_standard_app/pyproject.toml b/sample_standard_app/pyproject.toml index 7b763d8d..eab8e02b 100644 --- a/sample_standard_app/pyproject.toml +++ b/sample_standard_app/pyproject.toml @@ -11,7 +11,7 @@ packages = [ [tool.poetry.dependencies] python = "^3.10" -agentUniverse = "^0.0.5" +agentUniverse = "^0.0.9" [tool.poetry.group.dev.dependencies] pytest = "^7.2.0" From 36ba3e855731ee24e493208c18dbd9d317f6edbe Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 18 Jun 2024 15:56:13 +0800 Subject: [PATCH 015/118] feat: add data agent in the agentUniverse. --- agentuniverse_data/flow/eval_report.yaml | 4 +- agentuniverse_data/flow/instruct_select.yaml | 40 ++--- agentuniverse_data/flow/train_dump.yaml | 2 +- .../guide/dispatch/dispatch.yaml | 2 +- .../node/data/prompt/instruct/prompt_gen.py | 3 + .../node/data/prompt/instruct/seed.py | 4 +- agentuniverse_data/node/eval/eval.py | 18 +-- agentuniverse_data/node/eval/report.py | 1 + agentuniverse_data/util/constant/__init__.py | 7 + .../util/constant/eval_node_dimensions.py | 150 ++++++++++++++++++ 10 files changed, 188 insertions(+), 43 deletions(-) create mode 100644 agentuniverse_data/util/constant/__init__.py create mode 100644 agentuniverse_data/util/constant/eval_node_dimensions.py diff --git a/agentuniverse_data/flow/eval_report.yaml b/agentuniverse_data/flow/eval_report.yaml index 9eb31458..0895a139 100644 --- a/agentuniverse_data/flow/eval_report.yaml +++ b/agentuniverse_data/flow/eval_report.yaml @@ -9,7 +9,8 @@ nodes: param_in_jsonl: param_out_jsonl: datasets_in_jsonl: - - 'none_flow.aci_dump.out' +# - 'train_dump_flow.aci_dump.out' + - 'dataset_build_flow.filter.out' dataset_out_jsonl: 'eval_report_flow.eval.out' node_param: eval_lines: 100 @@ -22,6 +23,5 @@ nodes: # multiple datasets datasets_in_jsonl: - 'eval_report_flow.eval.out' - - 'none_flow.eval.out' dataset_out_jsonl: 'eval_report_flow.report.out' diff --git a/agentuniverse_data/flow/instruct_select.yaml b/agentuniverse_data/flow/instruct_select.yaml index 8b30effc..b8de36ef 100644 --- a/agentuniverse_data/flow/instruct_select.yaml +++ b/agentuniverse_data/flow/instruct_select.yaml @@ -29,25 +29,25 @@ nodes: node_param: instruct_num: 10 - - module: '' - # dump answer from pre deployed LLM infer via prompt as input - class: 'DumpNode' - param_in_jsonl: 'train_dump_flow.pre_deploy.out' - param_out_jsonl: - datasets_in_jsonl: - - 'instruct_select_flow.prompt_gen.out' - dataset_out_jsonl: 'instruct_select_flow.dump.out' +# - module: '' +# # dump answer from pre deployed LLM infer via prompt as input +# class: 'DumpNode' +# param_in_jsonl: 'train_dump_flow.pre_deploy.out' +# param_out_jsonl: +# datasets_in_jsonl: +# - 'instruct_select_flow.prompt_gen.out' +# dataset_out_jsonl: 'instruct_select_flow.dump.out' - - module: 'agentuniverse_data.node.eval.eval' - # eval dump result through 7 dimensions - class: 'EvalNode' - llm: 'openai_llm' - prompt_version: 'eval_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - - 'instruct_select_flow.dump.out' - dataset_out_jsonl: 'instruct_select_flow.eval.out' - node_param: - eval_lines: 100 +# - module: 'agentuniverse_data.node.eval.eval' +# # eval dump result through 7 dimensions +# class: 'EvalNode' +# llm: 'openai_llm' +# prompt_version: 'eval_node.cn' +# param_in_jsonl: +# param_out_jsonl: +# datasets_in_jsonl: +# - 'instruct_select_flow.dump.out' +# dataset_out_jsonl: 'instruct_select_flow.eval.out' +# node_param: +# eval_lines: 100 diff --git a/agentuniverse_data/flow/train_dump.yaml b/agentuniverse_data/flow/train_dump.yaml index d90311f1..21d847fe 100644 --- a/agentuniverse_data/flow/train_dump.yaml +++ b/agentuniverse_data/flow/train_dump.yaml @@ -7,7 +7,7 @@ nodes: param_in_jsonl: param_out_jsonl: 'train_dump_flow.upload.out' datasets_in_jsonl: - - 'none_flow.filter.out' + - 'dataset_build_flow.filter.out' dataset_out_jsonl: node_param: datagraph_token_name: 'dataflow token' diff --git a/agentuniverse_data/guide/dispatch/dispatch.yaml b/agentuniverse_data/guide/dispatch/dispatch.yaml index a08c81c5..6dbcb0fc 100644 --- a/agentuniverse_data/guide/dispatch/dispatch.yaml +++ b/agentuniverse_data/guide/dispatch/dispatch.yaml @@ -1,4 +1,4 @@ name: 'main_dispatch' description: 'dispatch with multi-dataflows which will execute one after another' dataflows: - - ../../flow/dataset_build.yaml + - ../../flow/instruct_select.yaml diff --git a/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py b/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py index 495a21ab..e14043e9 100644 --- a/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py +++ b/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py @@ -22,6 +22,9 @@ class PromptGenNode(PromptBase): _instruct_num: int = None def _node_preprocess(self) -> None: + + super()._node_preprocess() + if not self.datasets_in_jsonl or len(self.datasets_in_jsonl) != 2: raise Exception(f"Node param {self.datasets_in_jsonl} should contain 2 elements:1.instruct 2.input") diff --git a/agentuniverse_data/node/data/prompt/instruct/seed.py b/agentuniverse_data/node/data/prompt/instruct/seed.py index 3c4d2114..c198d844 100644 --- a/agentuniverse_data/node/data/prompt/instruct/seed.py +++ b/agentuniverse_data/node/data/prompt/instruct/seed.py @@ -30,8 +30,8 @@ def _node_preprocess(self) -> None: def _node_process(self) -> None: version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - prompt_with_extend_times = version_prompt.replace('', - str(self.extend_times)) + prompt_with_extend_times = version_prompt.prompt_template.replace('', + str(self.extend_times)) prompts = [prompt_with_extend_times.replace('', _DEFAULT_INSTRUCT_FOR_PROMPT)] responses = batch_call(prompts, self.llm) diff --git a/agentuniverse_data/node/eval/eval.py b/agentuniverse_data/node/eval/eval.py index 766ad252..8ba58d36 100644 --- a/agentuniverse_data/node/eval/eval.py +++ b/agentuniverse_data/node/eval/eval.py @@ -13,25 +13,9 @@ from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.base.eval_node_base import EvalNodeBase from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse_data.util.constant.eval_node_dimensions import get_eval_dims from agentuniverse_data.util.llm.llm_call import batch_call -_EVAL_DIM_FACTUALITY_NAME = "事实性" -_EVAL_DIM_FACTUALITY_REQUIREMENT = """ -1.不符合准入条件 -数据错误，不符合事实 - -2.门槛值 -个别数据不准确，但不影响整体事实性 - -3.加分项 -解读内容中所有引用数据真实可信，数据来源官方可信 -""" - - -def get_eval_dims(): - eval_dims = [(_EVAL_DIM_FACTUALITY_NAME, _EVAL_DIM_FACTUALITY_REQUIREMENT)] - return eval_dims - class EvalNode(EvalNodeBase): """The EvalNode class, which is used to define the class of eval node.""" diff --git a/agentuniverse_data/node/eval/report.py b/agentuniverse_data/node/eval/report.py index 8cb00e14..3efbb1d8 100644 --- a/agentuniverse_data/node/eval/report.py +++ b/agentuniverse_data/node/eval/report.py @@ -17,6 +17,7 @@ class ReportNode(EvalNodeBase): _eval_report_json_list: List[str] = None def _node_preprocess(self) -> None: + super()._node_preprocess() if not self.datasets_in_jsonl or len(self.datasets_in_jsonl) == 0: raise Exception(f"No input datasets: {self.datasets_in_jsonl}") diff --git a/agentuniverse_data/util/constant/__init__.py b/agentuniverse_data/util/constant/__init__.py new file mode 100644 index 00000000..34f082dc --- /dev/null +++ b/agentuniverse_data/util/constant/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/18 14:41 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse_data/util/constant/eval_node_dimensions.py b/agentuniverse_data/util/constant/eval_node_dimensions.py new file mode 100644 index 00000000..a3b2312e --- /dev/null +++ b/agentuniverse_data/util/constant/eval_node_dimensions.py @@ -0,0 +1,150 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/18 14:41 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: eval_node_dimensions.py + +_EVAL_DIM_RELATIVE_NAME = "相关性" +_EVAL_DIM_RELATIVE_REQUIREMENT = """ +1.不符合准入条件 +答非所问，不相关 + +2.门槛值 +能够知道到用户在问什么，并且所答即所问，包括： +【有明确意图时】 +识别出用户的问题并对问题有回复，且回复的内容有相关性 +有主意图的时候，回复的内容要与主意图有相关 +【无明确意图时】 +遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 + +3.加分项 +能够get到用户在问什么后： +a、对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应 +b、回应的内容整体相关性都很高 +c、回复的问题全面且易懂 +""" + +_EVAL_DIM_FACTUALITY_NAME = "事实性" +_EVAL_DIM_FACTUALITY_REQUIREMENT = """ +1.不符合准入条件 +数据错误，不符合事实 + +2.门槛值 +个别数据不准确，但不影响整体事实性 + +3.加分项 +解读内容中所有引用数据真实可信，数据来源官方可信 +""" + +_EVAL_DIM_CONSTRUCTIVE_NAME = "结构性" +_EVAL_DIM_CONSTITUTIVE_REQUIREMENT = """ +1.不符合准入条件 +总分结构： +a、总分论点只是抽取了原文中的部分内容，并没有真正的总结归纳 +b、生成模型是总分结构，但是总论点对分论点的总结性小于50% +c、生成模型是总分结构，但是分论点是为了回答query而硬答，与query实际并没有关系。 +d、总结内容论点逻辑冲突； +总结摘要结构： +a、摘要内容是抽取了部分段落，并没有真正的归纳总结 +b、摘要内容，仅仅总结成一段文本，无摘要详述 + +2.门槛值 +总分结构： +a、总分结构，有总结和归纳，分论点有层次或者结构 +b、总分结构，总论点对分论点的总结行大于50% +c、总分结构，分论点之间有逻辑关系（互补、递进），分论点与query之间有相关性 +d、总结内容论点间无冲突； +总结摘要结构： +a、摘要内容有对原文进行归纳总结，不是抽取的原文段落 +b、摘要内容论点有展开叙述 + +3.加分项 +总分结构： +a、总分结构，分论点有归类抽象有条理有逻辑 +b、总分结构，分论点为1234分层结构，有条理 + +摘要结构： +a、摘要内容论点叙述详细充分，有逻辑有条理 +b、总结表达：总结内容话术表达逻辑清晰，表达精炼，字数<= XX字 +""" + +_EVAL_DIM_RATIONALITY_NAME = "合理性" +_EVAL_DIM_RATIONALITY_REQUIREMENT = """ +1.不符合准入条件 +出现专业错误 +（如经济复苏利好股市利空债市、供不应求带来价格上涨、经济下行压力加剧通常会倒逼宏观政策加码等） + +2.门槛值 +符合业内公认的金融逻辑，前后结论不要自相矛盾，不出现明显的专业错误 + +3.加分项 +通篇解读要一以贯之、逻辑自洽，且逻辑推演是有逐层递进的（后文分析以前文结论为依据） +""" + +_EVAL_DIM_TIMELINESS_NAME = "时效性" +_EVAL_DIM_TIMELINESS_REQUIREMENT = """ +1.不符合准入条件 +数据陈旧过时，不符合现状 + +2.门槛值 +次新，符合当下现状 + +3.加分项 +引用官方发布的最新数据，包含清晰的发布日期 +""" + +_EVAL_DIM_COMPREHENSIVE_NAME = "全面性" +_EVAL_DIM_COMPREHENSIVE_REQUIREMENT = """ +3分门槛：从2～3个维度分析，分析类问题的论据较为饱满 +3分～5分看主观感受 +分子维度占分母维度的百分比进行打分： +●信息查询类：暂不涉及 +●标的分析类：暂不涉及 +●宏观分析类： +包含指标与市场预期相比（必填项） 、指标整体对经济\政策的指示意义（必填项）、经济指标同比\环比的趋势分析、指标子项细拆分析（如PMI各个分项）表征含义、对股债资产的影响。尽可能包含更多维度的分析。必填项缺失得零分 +●市场分析类： +尽可能包含以下2-3个维度以上的分析：尽可能从以下多个驱动力维度展开分析，基本面、政策面、情绪面（突发事件）、估值面、资金面，同时驱动力维度的判别要准确 +●政策解读类： +尽可能包含以下2-3个维度以上的分析：宏观政策类：包含政策出台背景（原因）、政策对基本面\流动性\情绪面（风险偏好）的影响、政策对股债资产、相关行业的影响。宏观会议类：包含与历届同类会议的措辞对比、对经济的定调、对货币、财政、房地产、金融、资本市场、防风险等重要政策的定调、整体对股债市场、相关行业的影响 +●大事解读类： +尽可能包含以下2-3个维度以上的分析：行业政策类：包含政策出台背景（原因）、政策对行业基本面\流动性\情绪面（风险偏好）的影响、政策对行业的短期\中长期多空影响。其它非经济\政策类大事对股市的影响：事件背景\原因、事件对A股基本面、政策面、情绪面、估值面、资金面的影响（不需要面面俱到，只需要对驱动力维度的归属准确） +●报告解读类： +尽可能包含以下2-3个维度以上的分析： +财报解读类：分析角度主要包括企业盈利能力、企业成长能力、企业营运水平和企业财务报表质量。企业盈利能力指标包括毛利率、净利率和ROE等。企业成长能力指标包括营业收入增速和净利润增速。企业营运能力分析包括存货周转天数和应收账款周转天数。企业财务报表质量指标包括资产负债率和利息覆盖率。以上指标为常用分析指标，主要供参考，分析解读围绕财务报表即可； +研报解读类：分析师分析一般会从业绩分析、行业分析、市场前景、技术发展、政策、行业事件、估值分析、策略建议等视角，这些视角都可以拆细，通常选取其中主要矛盾和市场核心关注点展开分析； +政府报告解读类：一般包括央行货币执行报告、财政预算报告等，这块解读没有固定框架，主流分析方法主要对政府报告内容引用、概括和提炼精神，能提到报告主要内容多个点即可。 +●策略建议类：暂不涉及 +●泛金融类：暂不涉及 +""" + +_EVAL_DIM_OVERALL_NAME = "整体性" +_EVAL_DIM_OVERALL_REQUIREMENT = """ +1.不符合准入条件 +满足以下任意一点即为0分： +a、逻辑不通顺，或存在语病，或表达冗余内容超长（整体文本长度>XX字） +b、论点&总结重复/论点&论点重复：任意两条论点存在80%以上的重复度 +c、倒装、错别字、语句不通顺、逻辑问题等 +d、存在特殊符号 + +2.门槛值 +a、语句流畅、表达可阅读性强 +b、表达直白（不反复说车轱辘话） +c、论点论据表达无重复 + +3.加分项 +a、答案对用户query有明确回应表达 +b、语言简洁精炼，逻辑清晰 +""" + + +def get_eval_dims(): + eval_dims = [(_EVAL_DIM_RELATIVE_NAME, _EVAL_DIM_RELATIVE_REQUIREMENT), + (_EVAL_DIM_FACTUALITY_NAME, _EVAL_DIM_FACTUALITY_REQUIREMENT), + (_EVAL_DIM_CONSTRUCTIVE_NAME, _EVAL_DIM_CONSTITUTIVE_REQUIREMENT), + (_EVAL_DIM_RATIONALITY_NAME, _EVAL_DIM_RATIONALITY_REQUIREMENT), + (_EVAL_DIM_TIMELINESS_NAME, _EVAL_DIM_TIMELINESS_REQUIREMENT), + (_EVAL_DIM_COMPREHENSIVE_NAME, _EVAL_DIM_COMPREHENSIVE_REQUIREMENT), + (_EVAL_DIM_OVERALL_NAME, _EVAL_DIM_OVERALL_REQUIREMENT)] + return eval_dims From 60b5adff7ed2fc36d3eff4d7ad36350d16199934 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Tue, 18 Jun 2024 16:46:05 +0800 Subject: [PATCH 016/118] docs: update README --- README_jp.md | 1 + README_zh.md | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/README_jp.md b/README_jp.md index 833c9750..f7752ec7 100644 --- a/README_jp.md +++ b/README_jp.md @@ -49,6 +49,7 @@ pip install agentUniverse [Pythonコード生成と実行Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) [多回多Agentによるディスカッショングループ](./docs/guidebook/en/6_2_1_Discussion_Group.md) + [PEERマルチAgentモードに基づいた金融イベント分析](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) ## ガイドブック diff --git a/README_zh.md b/README_zh.md index eecb53d9..ee7c2fda 100644 --- a/README_zh.md +++ b/README_zh.md @@ -58,7 +58,8 @@ pip install agentUniverse [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) -[基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md)] + +[基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md) ### 🌟 示例项目 [agentUniverse 示例项目](sample_standard_app) From 1b19975c21248d429485f880588b5aafb82107a7 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 18 Jun 2024 20:34:51 +0800 Subject: [PATCH 017/118] feat: add data agent in the agentUniverse. --- agentuniverse_data/config/config.toml | 30 ----------------- agentuniverse_data/config/log_config.toml | 16 ---------- agentuniverse_data/guide/__init__.py | 7 ---- agentuniverse_data/llm/openai_llm.yaml | 9 ------ .../node/data/prompt/input/extend.py | 4 ++- .../node/data/prompt/input/seed.py | 4 ++- agentuniverse_data/node/eval/eval.py | 32 +++++++++---------- .../prompt/dataset_rewrite_node_cn.yaml | 3 +- agentuniverse_data/prompt/eval_node_cn.yaml | 26 +++++++++------ .../prompt/instruct_seed_node_cn.yaml | 4 ++- agentuniverse_data/util/llm/llm_call.py | 7 ++-- .../data}/__init__.py | 2 +- .../data}/dispatch/__init__.py | 2 +- .../data}/dispatch/data/demo_jsonl.jsonl | 0 .../data}/dispatch/dispatch.py | 0 .../data}/dispatch/dispatch.yaml | 2 +- .../data/flow}/__init__.py | 2 +- .../data}/flow/auto_event.yaml | 2 +- .../data}/flow/dataset_build.yaml | 12 +++---- .../data}/flow/eval_report.yaml | 2 +- .../data}/flow/instruct_select.yaml | 6 ++-- .../data}/flow/model_deploy.yaml | 0 .../data}/flow/train_dump.yaml | 0 23 files changed, 62 insertions(+), 110 deletions(-) delete mode 100644 agentuniverse_data/config/config.toml delete mode 100644 agentuniverse_data/config/log_config.toml delete mode 100644 agentuniverse_data/guide/__init__.py delete mode 100644 agentuniverse_data/llm/openai_llm.yaml rename {agentuniverse_data/config => sample_standard_app/data}/__init__.py (83%) rename {agentuniverse_data/guide => sample_standard_app/data}/dispatch/__init__.py (83%) rename {agentuniverse_data/guide => sample_standard_app/data}/dispatch/data/demo_jsonl.jsonl (100%) rename {agentuniverse_data/guide => sample_standard_app/data}/dispatch/dispatch.py (100%) rename {agentuniverse_data/guide => sample_standard_app/data}/dispatch/dispatch.yaml (76%) rename {agentuniverse_data/llm => sample_standard_app/data/flow}/__init__.py (83%) rename {agentuniverse_data => sample_standard_app/data}/flow/auto_event.yaml (98%) rename {agentuniverse_data => sample_standard_app/data}/flow/dataset_build.yaml (94%) rename {agentuniverse_data => sample_standard_app/data}/flow/eval_report.yaml (97%) rename {agentuniverse_data => sample_standard_app/data}/flow/instruct_select.yaml (95%) rename {agentuniverse_data => sample_standard_app/data}/flow/model_deploy.yaml (100%) rename {agentuniverse_data => sample_standard_app/data}/flow/train_dump.yaml (100%) diff --git a/agentuniverse_data/config/config.toml b/agentuniverse_data/config/config.toml deleted file mode 100644 index 1be5c7e0..00000000 --- a/agentuniverse_data/config/config.toml +++ /dev/null @@ -1,30 +0,0 @@ -[BASE_INFO] -# The app name will be applied to all processes including agent service integration. -appname = 'agentuniverse_data' - -[CORE_PACKAGE] -# Perform a full component scan and registration for all the paths under this list. -default = [] -# Scan and register agent components for all paths under this list, with priority over the default. -agent = [] -# Scan and register knowledge components for all paths under this list, with priority over the default. -knowledge = [] -# Scan and register llm components for all paths under this list, with priority over the default. -llm = ['agentuniverse_data.llm'] -# Scan and register planner components for all paths under this list, with priority over the default. -planner = [] -# Scan and register tool components for all paths under this list, with priority over the default. -tool = [] -# Scan and register memory components for all paths under this list, with priority over the default. -memory = [] -# Scan and register service components for all paths under this list, with priority over the default. -service = [] -# Scan and register prompt components for all paths under this list, with priority over the default. -prompt = ['agentuniverse_data.prompt'] - - -[SUB_CONFIG_PATH] -# Log config file path, an absolute path or a relative path based on the dir where the current config file is located. -log_config_path = './log_config.toml' -# Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore. -#custom_key_path = './custom_key.toml' diff --git a/agentuniverse_data/config/log_config.toml b/agentuniverse_data/config/log_config.toml deleted file mode 100644 index 9a621c09..00000000 --- a/agentuniverse_data/config/log_config.toml +++ /dev/null @@ -1,16 +0,0 @@ -[LOG_CONFIG] -[LOG_CONFIG.BASIC_CONFIG] -# Loguru log level. -log_level = "INFO" -# Output path of the log file. If value is empty, agentuniverse will create a subdir under your workdir to save logs. -log_path = "./log" -# Specifies the log rotation policy, controlling when a new log file is created. It can be a time period -# (e.g., "1 week"), a file size (e.g., "100 MB"), or a function returning True when rotation should occur. -log_rotation = "10MB" -# Specifies the duration to keep old log files. It can be a time span (e.g., "30 days") or a function to filter the -# files to be retained. Files outside this policy are purged. -log_retention = "3 days" - -[LOG_CONFIG.EXTEND_MODULE] -# Whether use aliyun simple log server. If value is True, you should fill the ALIYUN_SLS_CONFIG below. -sls_log = "False" diff --git a/agentuniverse_data/guide/__init__.py b/agentuniverse_data/guide/__init__.py deleted file mode 100644 index 58db2189..00000000 --- a/agentuniverse_data/guide/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/11 20:48 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_data/llm/openai_llm.yaml b/agentuniverse_data/llm/openai_llm.yaml deleted file mode 100644 index 838da47b..00000000 --- a/agentuniverse_data/llm/openai_llm.yaml +++ /dev/null @@ -1,9 +0,0 @@ -name: 'openai_llm' -description: 'demo openai' -model_name: 'gpt-4o' -max_tokens: 1000 -max_retries: 2 -metadata: - type: 'LLM' - module: 'agentuniverse.llm.default.default_openai_llm' - class: 'DefaultOpenAILLM' \ No newline at end of file diff --git a/agentuniverse_data/node/data/prompt/input/extend.py b/agentuniverse_data/node/data/prompt/input/extend.py index ce90f585..f3979c34 100644 --- a/agentuniverse_data/node/data/prompt/input/extend.py +++ b/agentuniverse_data/node/data/prompt/input/extend.py @@ -8,6 +8,8 @@ import json import math +from langchain.output_parsers.json import parse_json_markdown + from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_base import PromptBase @@ -47,7 +49,7 @@ def _node_process(self) -> None: for i in range(0, len(responses)): try: if responses[i] != '' and responses[i] is not None: - data = json.loads(responses[i]) + data = parse_json_markdown(responses[i]) if 'extend_inputs' in data: extend_inputs = data['extend_inputs'] self._prompt_list.extend(extend_inputs) diff --git a/agentuniverse_data/node/data/prompt/input/seed.py b/agentuniverse_data/node/data/prompt/input/seed.py index c212605e..4ba57071 100644 --- a/agentuniverse_data/node/data/prompt/input/seed.py +++ b/agentuniverse_data/node/data/prompt/input/seed.py @@ -7,6 +7,8 @@ # @FileName: seed.py import json +from langchain.output_parsers.json import parse_json_markdown + from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse_data.node.data.base.prompt_base import PromptBase @@ -55,7 +57,7 @@ def _node_process(self) -> None: self._prompt_list = [] for i in range(0, len(responses)): try: - seeds = json.loads(responses[i])['seeds'] + seeds = parse_json_markdown(responses[i])['seeds'] self._prompt_list.extend(seeds) except TypeError as e: continue diff --git a/agentuniverse_data/node/eval/eval.py b/agentuniverse_data/node/eval/eval.py index 8ba58d36..2953a377 100644 --- a/agentuniverse_data/node/eval/eval.py +++ b/agentuniverse_data/node/eval/eval.py @@ -69,31 +69,31 @@ def _node_process(self) -> None: eval_prompt_temp = version_prompt.prompt_template.replace('', prompt) eval_prompt_temp = eval_prompt_temp.replace('', answer) - eval_prompts = [] + dim_prompts = [] for i in range(0, len(eval_dims)): eval_dim_name = eval_dims[i][0] eval_dim_requirement = eval_dims[i][1] - eval_prompt = eval_prompt_temp.replace('', eval_dim_name) - eval_prompt = eval_prompt.replace('', eval_dim_requirement) - eval_prompts.append(eval_prompt) + dim_prompt = f'dimension name: {eval_dim_name} \n dimension requirement: {eval_dim_requirement}' + dim_prompts.append(dim_prompt) - res = batch_call(eval_prompts, self.llm) + eval_prompt_temp = eval_prompt_temp.replace('', '\n'.join(dim_prompts)) + res = batch_call([eval_prompt_temp], self.llm) dim_score_json = {'line': line_num} dimensions = [] avg_score = 0.0 - for j in range(0, len(res)): - try: - if res[j] != '' and res[j] is not None: - data = parse_json_markdown(res[j]) - avg_score += data['score'] - dimensions.append(data) - except Exception as e: - LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>>{e}:{res[j]}') - continue - if len(res) > 0: - avg_score = avg_score / len(res) + + try: + if res[0] != '' and res[0] is not None: + data = parse_json_markdown(res[0]) + dimensions = data['dimensions'] + avg_score = sum(data['score'] for data in dimensions) + except Exception as e: + LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>>{e}:{res[0]}') + continue + if len(dimensions) > 0: + avg_score = avg_score / len(dimensions) dim_score_json['avg_score'] = avg_score dim_score_json['dimensions'] = dimensions diff --git a/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml b/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml index 59b36364..713ee074 100644 --- a/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml +++ b/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml @@ -1,5 +1,6 @@ introduction: 你是一个极致严谨的金融专家，对于输入的一列字符串，请基于以下要求改写，用来提交给大模型从而能获取高质量结果，请确保输出每一行字符串与输入每一行字符串一一对应且只包含改写后的内容，如不能有“改写后的问题：”之类的表述。 instruction: | + 改写要求： 1.同义词替换：用同义词替换原始查询中的某些词汇，以扩大搜索结果的覆盖范围。 2.标准化处理：将查询中的词汇标准化，比如将复数形式转换为单数形式，或者使用基本形式的词替换其它形式的词。 @@ -11,7 +12,7 @@ instruction: | 8.格式处理：规范化格式，提出不必要的标点、空格、回车、乱码等 9.结构形式：请保持相似的角色和指令结构或者框架，使的大模型能遵从相关回答结构 - 输入字符串： + 输入字符串包括： 请确保输出包含且只包含如下的json格式: diff --git a/agentuniverse_data/prompt/eval_node_cn.yaml b/agentuniverse_data/prompt/eval_node_cn.yaml index f508a948..9824ba32 100644 --- a/agentuniverse_data/prompt/eval_node_cn.yaml +++ b/agentuniverse_data/prompt/eval_node_cn.yaml @@ -11,21 +11,27 @@ instruction: | answer: - 质量打分维度包括: - + 质量打分维度描述: + - 质量打分维度名称: - - 质量打分维度要求: - + 输出格式要求： - 请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: + 请确保输出只包含如下的json格式字段，且每个字段不能为空长度也不超过200，不包含其他内容: { - "name":质量打分维度名称, - "score":分数, - "negative substr":扣分最多的子串 + "dimensions": [ + { + "name":质量打分维度名称, + "score":分数, + "negative substr":扣分最多的子串 + }, + { + "name":质量打分维度, + "score":分数, + "negative substr":扣分最多的子串 + } + ] } metadata: type: 'PROMPT' diff --git a/agentuniverse_data/prompt/instruct_seed_node_cn.yaml b/agentuniverse_data/prompt/instruct_seed_node_cn.yaml index fa1d81dd..f7d0720e 100644 --- a/agentuniverse_data/prompt/instruct_seed_node_cn.yaml +++ b/agentuniverse_data/prompt/instruct_seed_node_cn.yaml @@ -18,7 +18,9 @@ instruction: | 指令输入参考： - 请确保输出包含且只包含如下的json格式: + 指令输出格式要求： + + 请确保输出只包含如下的json格式，不包含其他内容: { "instructs": [ diff --git a/agentuniverse_data/util/llm/llm_call.py b/agentuniverse_data/util/llm/llm_call.py index 0dfc1858..7cea21f8 100644 --- a/agentuniverse_data/util/llm/llm_call.py +++ b/agentuniverse_data/util/llm/llm_call.py @@ -18,11 +18,12 @@ def batch_call(prompts: list[str], llm_name: str): async def async_batch_call(prompts: list[str], llm_name: str): tasks = [] - llm: LLM = LLMManager().get_instance_obj(llm_name) - if llm is None: - raise Exception('LLM not found for agentuniverse data.') + prompt_len = len(prompts) for i in range(0, prompt_len): + llm: LLM = LLMManager().get_instance_obj(component_instance_name=llm_name, new_instance=True) + if llm is None: + raise Exception('LLM not found for agentuniverse data.') messages = [{"role": "user", "content": prompts[i]}] tasks.append(llm.acall(messages=messages, timeout=200)) diff --git a/agentuniverse_data/config/__init__.py b/sample_standard_app/data/__init__.py similarity index 83% rename from agentuniverse_data/config/__init__.py rename to sample_standard_app/data/__init__.py index d9346c95..8f9304d1 100644 --- a/agentuniverse_data/config/__init__.py +++ b/sample_standard_app/data/__init__.py @@ -1,7 +1,7 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- -# @Time : 2024/6/14 16:41 +# @Time : 2024/6/18 16:32 # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: __init__.py.py diff --git a/agentuniverse_data/guide/dispatch/__init__.py b/sample_standard_app/data/dispatch/__init__.py similarity index 83% rename from agentuniverse_data/guide/dispatch/__init__.py rename to sample_standard_app/data/dispatch/__init__.py index cedbf3c1..6d7ca694 100644 --- a/agentuniverse_data/guide/dispatch/__init__.py +++ b/sample_standard_app/data/dispatch/__init__.py @@ -1,7 +1,7 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- -# @Time : 2024/6/17 19:54 +# @Time : 2024/6/18 16:35 # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: __init__.py.py diff --git a/agentuniverse_data/guide/dispatch/data/demo_jsonl.jsonl b/sample_standard_app/data/dispatch/data/demo_jsonl.jsonl similarity index 100% rename from agentuniverse_data/guide/dispatch/data/demo_jsonl.jsonl rename to sample_standard_app/data/dispatch/data/demo_jsonl.jsonl diff --git a/agentuniverse_data/guide/dispatch/dispatch.py b/sample_standard_app/data/dispatch/dispatch.py similarity index 100% rename from agentuniverse_data/guide/dispatch/dispatch.py rename to sample_standard_app/data/dispatch/dispatch.py diff --git a/agentuniverse_data/guide/dispatch/dispatch.yaml b/sample_standard_app/data/dispatch/dispatch.yaml similarity index 76% rename from agentuniverse_data/guide/dispatch/dispatch.yaml rename to sample_standard_app/data/dispatch/dispatch.yaml index 6dbcb0fc..bb6f543d 100644 --- a/agentuniverse_data/guide/dispatch/dispatch.yaml +++ b/sample_standard_app/data/dispatch/dispatch.yaml @@ -1,4 +1,4 @@ name: 'main_dispatch' description: 'dispatch with multi-dataflows which will execute one after another' dataflows: - - ../../flow/instruct_select.yaml + - ../flow/dataset_build.yaml diff --git a/agentuniverse_data/llm/__init__.py b/sample_standard_app/data/flow/__init__.py similarity index 83% rename from agentuniverse_data/llm/__init__.py rename to sample_standard_app/data/flow/__init__.py index d2ec5ef6..8f9304d1 100644 --- a/agentuniverse_data/llm/__init__.py +++ b/sample_standard_app/data/flow/__init__.py @@ -1,7 +1,7 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- -# @Time : 2024/6/14 17:18 +# @Time : 2024/6/18 16:32 # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: __init__.py.py diff --git a/agentuniverse_data/flow/auto_event.yaml b/sample_standard_app/data/flow/auto_event.yaml similarity index 98% rename from agentuniverse_data/flow/auto_event.yaml rename to sample_standard_app/data/flow/auto_event.yaml index 1dcbdb5a..16201770 100644 --- a/agentuniverse_data/flow/auto_event.yaml +++ b/sample_standard_app/data/flow/auto_event.yaml @@ -21,7 +21,7 @@ nodes: - module: 'agentuniverse_data.node.data.event.planner' # data planner in event agent which could plan python code to extract with reflections class: 'PlannerNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'event_planner_node.cn' param_in_jsonl: 'auto_event_flow.perceiver.param.out' param_out_jsonl: 'auto_event_flow.planner.plan.out' diff --git a/agentuniverse_data/flow/dataset_build.yaml b/sample_standard_app/data/flow/dataset_build.yaml similarity index 94% rename from agentuniverse_data/flow/dataset_build.yaml rename to sample_standard_app/data/flow/dataset_build.yaml index a1c95b20..39782dfc 100644 --- a/agentuniverse_data/flow/dataset_build.yaml +++ b/sample_standard_app/data/flow/dataset_build.yaml @@ -4,20 +4,20 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.input.seed' # query seed from LLM class: 'SeedNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'dataset_input_seed_node.cn' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: dataset_out_jsonl: 'dataset_build_flow.seed.out' node_param: - seeds_num: 10 + seeds_num: 5 seed_gen_requirement: '金融领域' - module: 'agentuniverse_data.node.data.prompt.input.rewrite' # rewrite query from LLM class: 'RewriteNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'dataset_rewrite_node.cn' param_in_jsonl: param_out_jsonl: @@ -28,7 +28,7 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.input.extend' # extend query from LLM class: 'ExtendNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'dataset_extend_node.cn' param_in_jsonl: param_out_jsonl: @@ -54,7 +54,7 @@ nodes: - module: 'agentuniverse_data.node.data.answer.answer' # build answers from the prompt via LLM as training data class: 'AnswerNode' - llm: 'openai_llm' + llm: 'qwen_llm' param_in_jsonl: param_out_jsonl: datasets_in_jsonl: @@ -64,7 +64,7 @@ nodes: - module: 'agentuniverse_data.node.data.answer.filter' # filter training data to improve data quality class: 'FilterNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'dataset_filter_node.cn' param_in_jsonl: param_out_jsonl: diff --git a/agentuniverse_data/flow/eval_report.yaml b/sample_standard_app/data/flow/eval_report.yaml similarity index 97% rename from agentuniverse_data/flow/eval_report.yaml rename to sample_standard_app/data/flow/eval_report.yaml index 0895a139..e73d0df4 100644 --- a/agentuniverse_data/flow/eval_report.yaml +++ b/sample_standard_app/data/flow/eval_report.yaml @@ -4,7 +4,7 @@ nodes: - module: 'agentuniverse_data.node.eval.eval' # eval dump result from LLM infer class: 'EvalNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'eval_node.cn' param_in_jsonl: param_out_jsonl: diff --git a/agentuniverse_data/flow/instruct_select.yaml b/sample_standard_app/data/flow/instruct_select.yaml similarity index 95% rename from agentuniverse_data/flow/instruct_select.yaml rename to sample_standard_app/data/flow/instruct_select.yaml index b8de36ef..6de57662 100644 --- a/agentuniverse_data/flow/instruct_select.yaml +++ b/sample_standard_app/data/flow/instruct_select.yaml @@ -4,7 +4,7 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.instruct.seed' # produce multiple instruct seed candidates class: 'SeedNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'instruct_seed_node.cn' param_in_jsonl: param_out_jsonl: @@ -16,7 +16,7 @@ nodes: - module: 'agentuniverse_data.node.data.prompt.instruct.prompt_gen' # produce prompt from instruct and query class: 'PromptGenNode' - llm: 'openai_llm' + llm: 'qwen_llm' prompt_version: 'instruct_prompt_gen_node.cn' param_in_jsonl: param_out_jsonl: @@ -41,7 +41,7 @@ nodes: # - module: 'agentuniverse_data.node.eval.eval' # # eval dump result through 7 dimensions # class: 'EvalNode' -# llm: 'openai_llm' +# llm: 'qwen_llm' # prompt_version: 'eval_node.cn' # param_in_jsonl: # param_out_jsonl: diff --git a/agentuniverse_data/flow/model_deploy.yaml b/sample_standard_app/data/flow/model_deploy.yaml similarity index 100% rename from agentuniverse_data/flow/model_deploy.yaml rename to sample_standard_app/data/flow/model_deploy.yaml diff --git a/agentuniverse_data/flow/train_dump.yaml b/sample_standard_app/data/flow/train_dump.yaml similarity index 100% rename from agentuniverse_data/flow/train_dump.yaml rename to sample_standard_app/data/flow/train_dump.yaml From 73f668590f5c921f7541c9132b5bede77957a8c1 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 19 Jun 2024 10:42:33 +0800 Subject: [PATCH 018/118] feat: add data agent in the agentUniverse. --- agentuniverse_data/node/data/prompt/instruct/seed.py | 6 +----- agentuniverse_data/prompt/instruct_seed_node_cn.yaml | 2 +- sample_standard_app/data/dispatch/data/demo_jsonl.jsonl | 2 +- sample_standard_app/data/dispatch/dispatch.yaml | 2 +- sample_standard_app/data/flow/instruct_select.yaml | 2 +- 5 files changed, 5 insertions(+), 9 deletions(-) diff --git a/agentuniverse_data/node/data/prompt/instruct/seed.py b/agentuniverse_data/node/data/prompt/instruct/seed.py index c198d844..b7ed4a9b 100644 --- a/agentuniverse_data/node/data/prompt/instruct/seed.py +++ b/agentuniverse_data/node/data/prompt/instruct/seed.py @@ -13,10 +13,6 @@ from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse_data.util.llm.llm_call import batch_call -_DEFAULT_INSTRUCT_FOR_PROMPT = """ -你是一个极致严谨的金融专家，请尽量找出问题对应的金融解读框架，并基于解读框架一步一步结构化详细回答如下问题: -""" - class SeedNode(PromptBase): """The SeedNode class, which is used to define the class of extend node.""" @@ -33,7 +29,7 @@ def _node_process(self) -> None: prompt_with_extend_times = version_prompt.prompt_template.replace('', str(self.extend_times)) - prompts = [prompt_with_extend_times.replace('', _DEFAULT_INSTRUCT_FOR_PROMPT)] + prompts = [prompt_with_extend_times] responses = batch_call(prompts, self.llm) self._prompt_list = [] if len(responses) == 1: diff --git a/agentuniverse_data/prompt/instruct_seed_node_cn.yaml b/agentuniverse_data/prompt/instruct_seed_node_cn.yaml index f7d0720e..b75b6040 100644 --- a/agentuniverse_data/prompt/instruct_seed_node_cn.yaml +++ b/agentuniverse_data/prompt/instruct_seed_node_cn.yaml @@ -16,7 +16,7 @@ instruction: | 指令输入参考： - + 你是一个极致严谨的金融专家，请尽量找出问题对应的金融解读框架，并基于解读框架一步一步结构化详细回答如下问题: 指令输出格式要求： diff --git a/sample_standard_app/data/dispatch/data/demo_jsonl.jsonl b/sample_standard_app/data/dispatch/data/demo_jsonl.jsonl index 6986502f..59b1da46 100644 --- a/sample_standard_app/data/dispatch/data/demo_jsonl.jsonl +++ b/sample_standard_app/data/dispatch/data/demo_jsonl.jsonl @@ -1,2 +1,2 @@ {"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} -{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} \ No newline at end of file +{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下英伟达股票大涨的原因", "answer": "英伟达股票大涨的原因可以归纳为以下几个方面：\\n\\n1. **股票拆分**：英伟达宣布的股票拆分计划增加了股票的流动性和吸引力，使得更多投资者能够参与投资，这一举措直接导致股价在宣布后上涨了27%。\\n\\n2. **市场分析乐观**：投资机构KeyBanc Capital Markets将英伟达的目标股价上调至1200美元，显示出市场对英伟达未来业绩的强烈信心，这对股价构成显著正面影响。\\n\\n3. **季度业绩预期**：市场对英伟达即将公布的季度业绩持积极态度，分析师认为业绩表现将超出预期，这种正面预期促使投资者提前买入，推高股价。\\n\\n4. **行业领导地位**：英伟达作为人工智能和高性能计算领域的领导者，其技术优势和市场地位不断巩固，吸引了大量基于长期增长潜力的投资。\\n\\n5. **市场情绪**：英伟达股价创新高不仅提升了自身市值，还激发了市场对于人工智能板块乃至整个股市的热情，带动了全球股市上扬。\\n\\n6. **宏观经济因素**：虽然背景信息未详细提及，但通常股市整体表现、经济环境、政策支持等因素也会影响个股表现，英伟达股价上涨可能也受益于较为有利的宏观经济背景。\\n\\n综上所述，英伟达股价的大涨是多因素共同作用的结果，包括公司自身的战略决策、市场对其业绩的乐观预期、行业地位的强化以及良好的市场情绪等。"} \ No newline at end of file diff --git a/sample_standard_app/data/dispatch/dispatch.yaml b/sample_standard_app/data/dispatch/dispatch.yaml index bb6f543d..7bdb0de4 100644 --- a/sample_standard_app/data/dispatch/dispatch.yaml +++ b/sample_standard_app/data/dispatch/dispatch.yaml @@ -1,4 +1,4 @@ name: 'main_dispatch' description: 'dispatch with multi-dataflows which will execute one after another' dataflows: - - ../flow/dataset_build.yaml + - ../flow/auto_event.yaml diff --git a/sample_standard_app/data/flow/instruct_select.yaml b/sample_standard_app/data/flow/instruct_select.yaml index 6de57662..7fcdfad9 100644 --- a/sample_standard_app/data/flow/instruct_select.yaml +++ b/sample_standard_app/data/flow/instruct_select.yaml @@ -32,7 +32,7 @@ nodes: # - module: '' # # dump answer from pre deployed LLM infer via prompt as input # class: 'DumpNode' -# param_in_jsonl: 'train_dump_flow.pre_deploy.out' +# param_in_jsonl: 'train_dump_flow.aci_pre_deploy.out' # param_out_jsonl: # datasets_in_jsonl: # - 'instruct_select_flow.prompt_gen.out' From 0a97104d4c5a542c814f8cc8068d27fc48e1eefd Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Wed, 19 Jun 2024 15:15:53 +0800 Subject: [PATCH 019/118] docs: update readme docs --- README.md | 2 +- README_zh.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9ebcec95..babf44d5 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_St * **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents. * **Seamless Integration of Domain Expertise:** Offers capabilities for domain-specific prompts, knowledge construction, and management, and supports domain-level SOP orchestration and embedding, aligning agents to the expert level in their fields. -💡 For more specific details, see the [Core Features of agentUniverse](./docs/guidebook/en/1_Core_Features.md). +💡 For more features details, see the [Core Features of agentUniverse](./docs/guidebook/en/1_Core_Features.md). ### User Guide 💡 For more detailed information, please read the [User Guide](./docs/guidebook/en/0_index.md). diff --git a/README_zh.md b/README_zh.md index ee7c2fda..7a144ddf 100644 --- a/README_zh.md +++ b/README_zh.md @@ -79,7 +79,7 @@ pip install agentUniverse * **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； -💡 更多特定见[agentUniverse核心特性](docs/guidebook/zh/1_核心特性.md)部分。 +💡 更多特点见[agentUniverse核心特性](docs/guidebook/zh/1_核心特性.md)部分。 ### 用户指南 💡 更多详细信息，请阅读[用户指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 From 4ff65593214677e89006920a009d6e04eaa07223 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 19 Jun 2024 17:53:42 +0800 Subject: [PATCH 020/118] add deepseek support --- .../planner/react_planner/react_planner.py | 3 +- .../llm/default/deep_seek_openai_style_llm.py | 70 +++++++++++++++++++ .../default/deep_seek_openai_style_llm.yaml | 8 +++ agentuniverse/llm/llm.py | 2 + agentuniverse/llm/openai_style_llm.py | 19 +++++ ...20\347\232\204\345\267\245\345\205\267.md" | 2 +- .../rag_agent_case/demo_multillm_agent.yaml | 6 +- .../agent/rag_agent_case/law_rag_agent.yaml | 2 +- .../react_agent_case/demo_react_agent.yaml | 6 ++ .../core/knowledge/civil_law_knowledge.yaml | 2 +- .../knowledge/criminal_law_knowledge.yaml | 2 +- .../app/core/llm/deep_seek_opneai_llm.yaml | 11 +++ .../app/test/test_deep_seek_llm.py | 56 +++++++++++++++ 13 files changed, 182 insertions(+), 7 deletions(-) create mode 100644 agentuniverse/llm/default/deep_seek_openai_style_llm.py create mode 100644 agentuniverse/llm/default/deep_seek_openai_style_llm.yaml create mode 100644 sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml create mode 100644 sample_standard_app/app/test/test_deep_seek_llm.py diff --git a/agentuniverse/agent/plan/planner/react_planner/react_planner.py b/agentuniverse/agent/plan/planner/react_planner/react_planner.py index 77e2b0f2..b3f20211 100644 --- a/agentuniverse/agent/plan/planner/react_planner/react_planner.py +++ b/agentuniverse/agent/plan/planner/react_planner/react_planner.py @@ -16,6 +16,7 @@ from agentuniverse.agent.action.tool.tool import Tool from agentuniverse.agent.action.tool.tool_manager import ToolManager from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.memory.chat_memory import ChatMemory @@ -88,7 +89,7 @@ def acquire_tools(action) -> list[LangchainTool]: agents: list = action.get('agent') or list() for agent_name in agents: - agent_tool: Agent = AgentModel.load_by_name(agent_name) + agent_tool: Agent = AgentManager().get_instance_obj(agent_name) lc_tools.append(agent_tool.as_langchain_tool()) return lc_tools diff --git a/agentuniverse/llm/default/deep_seek_openai_style_llm.py b/agentuniverse/llm/default/deep_seek_openai_style_llm.py new file mode 100644 index 00000000..485ec685 --- /dev/null +++ b/agentuniverse/llm/default/deep_seek_openai_style_llm.py @@ -0,0 +1,70 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/5/21 17:49 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: claude_llm.py + +from typing import Optional, Any, Union, Iterator, AsyncIterator + +from pydantic import Field + +from agentuniverse.base.annotation.trace import trace_llm +from agentuniverse.base.util.env_util import get_from_env +from agentuniverse.llm.llm_output import LLMOutput +from agentuniverse.llm.openai_style_llm import OpenAIStyleLLM + +DEEpSEEkMAXCONTETNLENGTH = { + "deepseek-chat": 32000, + "deepseek-coder": 32000, + "claude-3-haiku-20240307": 200000, + "claude-2.1": 200000, + "claude-2.0": 100000, + "claude-instant-1.2": 100000 +} + + +class DefaultDeepSeekLLM(OpenAIStyleLLM): + """The agentUniverse default openai llm module. + + LLM parameters, such as name/description/model_name/max_tokens, + are injected into this class by the default_openai_llm.yaml configuration. + """ + + api_key: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_API_KEY")) + organization: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_ORGANIZATION")) + api_base: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_API_BASE")) + proxy: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_PROXY")) + + @trace_llm + def call(self, messages: list, **kwargs: Any) -> Union[LLMOutput, Iterator[LLMOutput]]: + """ The call method of the LLM. + + Users can customize how the model interacts by overriding call method of the LLM class. + + Args: + messages (list): The messages to send to the LLM. + **kwargs: Arbitrary keyword arguments. + """ + return super().call(messages, **kwargs) + + @trace_llm + async def acall(self, messages: list, **kwargs: Any) -> Union[LLMOutput, AsyncIterator[LLMOutput]]: + """ The async call method of the LLM. + + Users can customize how the model interacts by overriding acall method of the LLM class. + + Args: + messages (list): The messages to send to the LLM. + **kwargs: Arbitrary keyword arguments. + """ + return await super().acall(messages, **kwargs) + + def max_context_length(self) -> int: + """Max context length. + + The total length of input tokens and generated tokens is limited by the openai model's context length. + """ + return DEEpSEEkMAXCONTETNLENGTH.get(self.model_name, 4096) + diff --git a/agentuniverse/llm/default/deep_seek_openai_style_llm.yaml b/agentuniverse/llm/default/deep_seek_openai_style_llm.yaml new file mode 100644 index 00000000..3921577a --- /dev/null +++ b/agentuniverse/llm/default/deep_seek_openai_style_llm.yaml @@ -0,0 +1,8 @@ +name: 'default_deepseek_llm' +description: 'default default_deepseek_llm llm with spi' +model_name: 'deepseek-chat' +max_tokens: 1000 +metadata: + type: 'LLM' + module: 'agentuniverse.llm.default.deep_seek_openai_style_llm' + class: 'DefaultDeepSeekLLM' \ No newline at end of file diff --git a/agentuniverse/llm/llm.py b/agentuniverse/llm/llm.py index 53c4fd67..4d33c629 100644 --- a/agentuniverse/llm/llm.py +++ b/agentuniverse/llm/llm.py @@ -103,6 +103,8 @@ def initialize_by_component_configer(self, component_configer: LLMConfiger) -> ' if component_configer.ext_info: self.ext_info = component_configer.ext_info self.tracing = component_configer.tracing + if 'max_context_length' in component_configer.configer.value: + self._max_context_length = component_configer.configer.value['max_context_length'] return self def set_by_agent_model(self, **kwargs) -> None: diff --git a/agentuniverse/llm/openai_style_llm.py b/agentuniverse/llm/openai_style_llm.py index 2d5a3ea0..0981e47a 100644 --- a/agentuniverse/llm/openai_style_llm.py +++ b/agentuniverse/llm/openai_style_llm.py @@ -14,6 +14,8 @@ from langchain_core.language_models.base import BaseLanguageModel from openai import OpenAI, AsyncOpenAI +from agentuniverse.base.config.component_configer.configers.llm_configer import LLMConfiger +from agentuniverse.base.util.env_util import get_from_env from agentuniverse.llm.llm import LLM, LLMOutput from agentuniverse.llm.openai_style_langchain_instance import LangchainOpenAIStyleInstance @@ -161,6 +163,17 @@ async def agenerate_stream_result(self, stream: AsyncIterator) -> AsyncIterator[ await self.aclose() + def initialize_by_component_configer(self, component_configer: LLMConfiger) -> 'LLM': + if 'api_base' in component_configer.configer.value: + self.api_base = component_configer.configer.value.get('api_base') + elif 'api_base_env' in component_configer.configer.value: + self.api_base = get_from_env(component_configer.configer.value.get('api_base_env')) + if 'api_key' in component_configer .configer.value: + self.api_key = component_configer.configer.value.get('api_key') + elif 'api_key_env' in component_configer.configer.value: + self.api_key = get_from_env(component_configer.configer.value.get('api_key_env')) + return super().initialize_by_component_configer(component_configer) + def get_num_tokens(self, text: str) -> int: """Get the number of tokens present in the text. @@ -187,3 +200,9 @@ async def aclose(self): """Async close the client.""" if hasattr(self, 'async_client') and self.async_client: await self.async_client.close() + + def max_context_length(self) -> int: + """Return the maximum length of the context.""" + if super().max_context_length(): + return super().max_context_length() + return 4000 diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index 12057d5d..ceba884c 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -22,7 +22,7 @@ metadata: module: 'sample_standard_app.app.core.tool.google_search_tool' class: 'GoogleSearchTool' ``` -为了使用该api,你必须申请(https://serper.dev)BING_SUBSCRIPTION_KEY,并配置在环境变量当中。 +为了使用该api,你必须申请(https://serper.dev)SERPER_API_KEY并配置在环境变量当中。 配置方法： 1. 通过python代码配置 必须配置：SERPER_API_KEY diff --git a/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml b/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml index 2fd13785..8732f8a1 100644 --- a/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml +++ b/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml @@ -15,8 +15,10 @@ profile: # model_name: 'Baichuan2-Turbo' # name: 'qwen_llm' # model_name: 'qwen-turbo' - name: 'wenxin_llm' - model_name: 'ERNIE-3.5-8K' +# name: 'wenxin_llm' +# model_name: 'ERNIE-3.5-8K' +# name: 'deep_seek_llm' + name: 'default_deepseek_llm' plan: planner: name: 'rag_planner' diff --git a/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml b/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml index bf917fcd..03ced866 100644 --- a/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml +++ b/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml @@ -1,6 +1,6 @@ info: name: 'law_rag_agent' - description: 'law rag agent' + description: '一个法律顾问，可以根据给出的事件，以及提供的背景知识做出客观的司法判断。' profile: introduction: 你是一位精通信息分析的ai法律顾问。 target: 你的目标是根据给出的事件，以及提供的背景知识做出客观的司法判断。 diff --git a/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml b/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml index 8a38838d..b9a4e02a 100644 --- a/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml +++ b/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml @@ -5,11 +5,17 @@ profile: prompt_version: qwen_react_agent.cn llm_model: name: 'default_qwen_llm' + model_name: 'qwen-max' temperature: 0 action: tool: - 'google_search_tool' - 'python_runner' + knowledge: + - 'civil_law_knowledge' + - 'criminal_law_knowledge' + agent: + - 'law_rag_agent' plan: planner: name: 'react_planner' diff --git a/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml b/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml index 86ddd6b0..3cf789e9 100644 --- a/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml +++ b/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml @@ -1,5 +1,5 @@ name: 'civil_law_knowledge' -description: 'civil law knowledge' +description: 民法知识库，可以查询民法典法律相关内容' metadata: type: 'KNOWLEDGE' module: 'sample_standard_app.app.core.knowledge.civil_law_knowledge' diff --git a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml b/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml index 5a1b2a8c..184b16f9 100644 --- a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml +++ b/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml @@ -1,5 +1,5 @@ name: 'criminal_law_knowledge' -description: 'criminal law knowledge' +description: '刑法知识库，可以查询与刑法相关的内容' metadata: type: 'KNOWLEDGE' module: 'sample_standard_app.app.core.knowledge.criminal_law_knowledge' diff --git a/sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml b/sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml new file mode 100644 index 00000000..a4aebb15 --- /dev/null +++ b/sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml @@ -0,0 +1,11 @@ +name: 'deep_seek_llm' +description: 'demo deep_seek llm with spi' +model_name: 'deepseek-chat' +max_tokens: 4000 +max_context_length: 32000 +api_key_env: 'DEEPSEEK_API_KEY' +api_base_env: 'DEEPSEEK_API_BASE' +metadata: + type: 'LLM' + module: 'agentuniverse.llm.openai_style_llm' + class: 'OpenAIStyleLLM' \ No newline at end of file diff --git a/sample_standard_app/app/test/test_deep_seek_llm.py b/sample_standard_app/app/test/test_deep_seek_llm.py new file mode 100644 index 00000000..8afb55c8 --- /dev/null +++ b/sample_standard_app/app/test/test_deep_seek_llm.py @@ -0,0 +1,56 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/5/22 16:25 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: test_wenxin_llm.py + + +import asyncio +import time +import unittest + +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.llm.default.wenxin_llm import WenXinLLM +from agentuniverse.llm.llm_manager import LLMManager + + +class DeepSeekLLMTest(unittest.TestCase): + """Test cases for the reviewing agent""" + + def setUp(self) -> None: + AgentUniverse().start(config_path='../../config/config.toml') + + def test_reviewing_agent(self): + llm: WenXinLLM = LLMManager().get_instance_obj("deep_seek_llm") + res = llm.call( + messages=[{"role": "user", "content": "你好"}], + streaming=True + ) + print(res) + for item in res: + print(item) + + langchain_llm = llm.as_langchain() + print(langchain_llm.invoke(input="你好")) + time.sleep(1) + asyncio.run(self.call_stream()) + + async def call_stream(self): + llm: WenXinLLM = LLMManager().get_instance_obj("deep_seek_llm") + res = await llm.acall( + messages=[{"role": "user", "content": "你好"}], + streaming=True + ) + print(res) + async for item in res: + print(item) + + langchain_llm = llm.as_langchain() + res = await langchain_llm.ainvoke(input="你好") + print(res) + + +if __name__ == '__main__': + unittest.main() From 3d37dfdd9ec8a37855ee336c0d3e71441bef691f Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 19 Jun 2024 19:28:18 +0800 Subject: [PATCH 021/118] feat: add data agent in the agentUniverse. --- .../__init__.py | 0 .../flow/__init__.py | 0 .../flow/dataflow.py | 2 +- .../node/__init__.py | 0 .../node/base/__init__.py | 0 .../node/base/data_node_base.py | 2 +- .../node/base/eval_node_base.py | 4 ++-- .../node/base/model_node_base.py | 4 ++-- .../node/base/node_base.py | 4 ++-- .../node/data/__init__.py | 0 .../node/data/answer/__init__.py | 0 .../node/data/answer/answer.py | 4 ++-- .../node/data/answer/filter.py | 6 +++--- .../node/data/base/__init__.py | 0 .../node/data/base/prompt_answer_base.py | 4 ++-- .../node/data/base/prompt_base.py | 4 ++-- .../node/data/event/__init__.py | 0 .../node/data/event/executor.py | 6 +++--- .../node/data/event/perceiver.py | 6 +++--- .../node/data/event/planner.py | 8 ++++---- .../node/data/prompt/__init__.py | 0 .../node/data/prompt/input/__init__.py | 0 .../node/data/prompt/input/dedupe.py | 2 +- .../node/data/prompt/input/extend.py | 4 ++-- .../node/data/prompt/input/rewrite.py | 4 ++-- .../node/data/prompt/input/seed.py | 4 ++-- .../node/data/prompt/instruct/__init__.py | 0 .../node/data/prompt/instruct/prompt_gen.py | 4 ++-- .../node/data/prompt/instruct/seed.py | 4 ++-- .../node/enum/__init__.py | 0 .../node/enum/enum.py | 0 .../node/eval/__init__.py | 0 .../node/eval/eval.py | 6 +++--- .../node/eval/report.py | 4 ++-- .../node/model/__init__.py | 0 .../node/model/base/__init__.py | 0 .../node/model/base/deploy_base.py | 2 +- .../node/model/base/dump_base.py | 2 +- .../node/model/base/pre_deploy_base.py | 2 +- .../node/model/base/train_base.py | 2 +- .../node/model/base/upload_base.py | 2 +- .../prompt/__init__.py | 0 .../prompt/dataset_extend_node_cn.yaml | 0 .../prompt/dataset_filter_node_cn.yaml | 5 +++-- .../prompt/dataset_input_seed_node_cn.yaml | 0 .../prompt/dataset_rewrite_node_cn.yaml | 0 .../prompt/eval_node_cn.yaml | 2 +- .../prompt/event_planner_node_cn.yaml | 0 .../prompt/instruct_prompt_gen_cn.yaml | 0 .../prompt/instruct_seed_node_cn.yaml | 0 .../util/__init__.py | 0 .../util/constant/__init__.py | 0 .../util/constant/eval_node_dimensions.py | 0 .../util/fileio/__init__.py | 0 .../util/fileio/node_msg_jsonl.py | 0 .../util/llm/__init__.py | 0 .../util/llm/llm_call.py | 0 sample_standard_app/{data => dataflow}/__init__.py | 0 .../{data => dataflow}/dispatch/__init__.py | 0 .../dispatch/data/demo_jsonl.jsonl | 0 .../{data => dataflow}/dispatch/dispatch.py | 2 +- .../{data => dataflow}/dispatch/dispatch.yaml | 2 +- .../{data => dataflow}/flow/__init__.py | 0 .../{data => dataflow}/flow/auto_event.yaml | 6 +++--- .../{data => dataflow}/flow/dataset_build.yaml | 12 ++++++------ .../{data => dataflow}/flow/eval_report.yaml | 4 ++-- .../{data => dataflow}/flow/instruct_select.yaml | 6 +++--- .../{data => dataflow}/flow/model_deploy.yaml | 0 .../{data => dataflow}/flow/train_dump.yaml | 0 69 files changed, 68 insertions(+), 67 deletions(-) rename {agentuniverse_data => agentuniverse_dataflow}/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/flow/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/flow/dataflow.py (97%) rename {agentuniverse_data => agentuniverse_dataflow}/node/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/base/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/base/data_node_base.py (86%) rename {agentuniverse_data => agentuniverse_dataflow}/node/base/eval_node_base.py (77%) rename {agentuniverse_data => agentuniverse_dataflow}/node/base/model_node_base.py (88%) rename {agentuniverse_data => agentuniverse_dataflow}/node/base/node_base.py (94%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/answer/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/answer/answer.py (90%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/answer/filter.py (94%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/base/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/base/prompt_answer_base.py (87%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/base/prompt_base.py (86%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/event/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/event/executor.py (94%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/event/perceiver.py (90%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/event/planner.py (96%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/input/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/input/dedupe.py (97%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/input/extend.py (93%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/input/rewrite.py (91%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/input/seed.py (94%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/instruct/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/instruct/prompt_gen.py (92%) rename {agentuniverse_data => agentuniverse_dataflow}/node/data/prompt/instruct/seed.py (91%) rename {agentuniverse_data => agentuniverse_dataflow}/node/enum/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/enum/enum.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/eval/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/eval/eval.py (94%) rename {agentuniverse_data => agentuniverse_dataflow}/node/eval/report.py (93%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/base/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/base/deploy_base.py (86%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/base/dump_base.py (91%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/base/pre_deploy_base.py (87%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/base/train_base.py (87%) rename {agentuniverse_data => agentuniverse_dataflow}/node/model/base/upload_base.py (91%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/dataset_extend_node_cn.yaml (100%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/dataset_filter_node_cn.yaml (94%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/dataset_input_seed_node_cn.yaml (100%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/dataset_rewrite_node_cn.yaml (100%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/eval_node_cn.yaml (81%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/event_planner_node_cn.yaml (100%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/instruct_prompt_gen_cn.yaml (100%) rename {agentuniverse_data => agentuniverse_dataflow}/prompt/instruct_seed_node_cn.yaml (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/constant/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/constant/eval_node_dimensions.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/fileio/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/fileio/node_msg_jsonl.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/llm/__init__.py (100%) rename {agentuniverse_data => agentuniverse_dataflow}/util/llm/llm_call.py (100%) rename sample_standard_app/{data => dataflow}/__init__.py (100%) rename sample_standard_app/{data => dataflow}/dispatch/__init__.py (100%) rename sample_standard_app/{data => dataflow}/dispatch/data/demo_jsonl.jsonl (100%) rename sample_standard_app/{data => dataflow}/dispatch/dispatch.py (94%) rename sample_standard_app/{data => dataflow}/dispatch/dispatch.yaml (80%) rename sample_standard_app/{data => dataflow}/flow/__init__.py (100%) rename sample_standard_app/{data => dataflow}/flow/auto_event.yaml (88%) rename sample_standard_app/{data => dataflow}/flow/dataset_build.yaml (83%) rename sample_standard_app/{data => dataflow}/flow/eval_report.yaml (86%) rename sample_standard_app/{data => dataflow}/flow/instruct_select.yaml (88%) rename sample_standard_app/{data => dataflow}/flow/model_deploy.yaml (100%) rename sample_standard_app/{data => dataflow}/flow/train_dump.yaml (100%) diff --git a/agentuniverse_data/__init__.py b/agentuniverse_dataflow/__init__.py similarity index 100% rename from agentuniverse_data/__init__.py rename to agentuniverse_dataflow/__init__.py diff --git a/agentuniverse_data/flow/__init__.py b/agentuniverse_dataflow/flow/__init__.py similarity index 100% rename from agentuniverse_data/flow/__init__.py rename to agentuniverse_dataflow/flow/__init__.py diff --git a/agentuniverse_data/flow/dataflow.py b/agentuniverse_dataflow/flow/dataflow.py similarity index 97% rename from agentuniverse_data/flow/dataflow.py rename to agentuniverse_dataflow/flow/dataflow.py index 422383b1..2784d82c 100644 --- a/agentuniverse_data/flow/dataflow.py +++ b/agentuniverse_dataflow/flow/dataflow.py @@ -14,7 +14,7 @@ from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse.base.config.configer import Configer -from agentuniverse_data.node.base.node_base import NodeBase +from agentuniverse_dataflow.node.base.node_base import NodeBase class Dataflow(BaseModel): diff --git a/agentuniverse_data/node/__init__.py b/agentuniverse_dataflow/node/__init__.py similarity index 100% rename from agentuniverse_data/node/__init__.py rename to agentuniverse_dataflow/node/__init__.py diff --git a/agentuniverse_data/node/base/__init__.py b/agentuniverse_dataflow/node/base/__init__.py similarity index 100% rename from agentuniverse_data/node/base/__init__.py rename to agentuniverse_dataflow/node/base/__init__.py diff --git a/agentuniverse_data/node/base/data_node_base.py b/agentuniverse_dataflow/node/base/data_node_base.py similarity index 86% rename from agentuniverse_data/node/base/data_node_base.py rename to agentuniverse_dataflow/node/base/data_node_base.py index cbe203e0..43a5cb43 100644 --- a/agentuniverse_data/node/base/data_node_base.py +++ b/agentuniverse_dataflow/node/base/data_node_base.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: data_node.py -from agentuniverse_data.node.base.node_base import NodeBase +from agentuniverse_dataflow.node.base.node_base import NodeBase class DataNodeBase(NodeBase): diff --git a/agentuniverse_data/node/base/eval_node_base.py b/agentuniverse_dataflow/node/base/eval_node_base.py similarity index 77% rename from agentuniverse_data/node/base/eval_node_base.py rename to agentuniverse_dataflow/node/base/eval_node_base.py index 5084edf0..59e7223c 100644 --- a/agentuniverse_data/node/base/eval_node_base.py +++ b/agentuniverse_dataflow/node/base/eval_node_base.py @@ -5,8 +5,8 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: eval_node_base.py -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.node_base import NodeBase +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.node_base import NodeBase class EvalNodeBase(NodeBase): diff --git a/agentuniverse_data/node/base/model_node_base.py b/agentuniverse_dataflow/node/base/model_node_base.py similarity index 88% rename from agentuniverse_data/node/base/model_node_base.py rename to agentuniverse_dataflow/node/base/model_node_base.py index 7caacccd..f07e59c4 100644 --- a/agentuniverse_data/node/base/model_node_base.py +++ b/agentuniverse_dataflow/node/base/model_node_base.py @@ -7,8 +7,8 @@ # @FileName: model_node_base.py import json -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.node_base import NodeBase +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.node_base import NodeBase class ModelNodeBase(NodeBase): diff --git a/agentuniverse_data/node/base/node_base.py b/agentuniverse_dataflow/node/base/node_base.py similarity index 94% rename from agentuniverse_data/node/base/node_base.py rename to agentuniverse_dataflow/node/base/node_base.py index 7a77f6d3..0c5da8ca 100644 --- a/agentuniverse_data/node/base/node_base.py +++ b/agentuniverse_dataflow/node/base/node_base.py @@ -11,8 +11,8 @@ from pydantic import BaseModel -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileWriter, JsonFileReader +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileWriter, JsonFileReader class NodeBase(BaseModel): diff --git a/agentuniverse_data/node/data/__init__.py b/agentuniverse_dataflow/node/data/__init__.py similarity index 100% rename from agentuniverse_data/node/data/__init__.py rename to agentuniverse_dataflow/node/data/__init__.py diff --git a/agentuniverse_data/node/data/answer/__init__.py b/agentuniverse_dataflow/node/data/answer/__init__.py similarity index 100% rename from agentuniverse_data/node/data/answer/__init__.py rename to agentuniverse_dataflow/node/data/answer/__init__.py diff --git a/agentuniverse_data/node/data/answer/answer.py b/agentuniverse_dataflow/node/data/answer/answer.py similarity index 90% rename from agentuniverse_data/node/data/answer/answer.py rename to agentuniverse_dataflow/node/data/answer/answer.py index c4605a5a..bda85155 100644 --- a/agentuniverse_data/node/data/answer/answer.py +++ b/agentuniverse_dataflow/node/data/answer/answer.py @@ -4,8 +4,8 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: answer.py -from agentuniverse_data.node.data.base.prompt_answer_base import PromptAnswerBase -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.node.data.base.prompt_answer_base import PromptAnswerBase +from agentuniverse_dataflow.util.llm.llm_call import batch_call class AnswerNode(PromptAnswerBase): diff --git a/agentuniverse_data/node/data/answer/filter.py b/agentuniverse_dataflow/node/data/answer/filter.py similarity index 94% rename from agentuniverse_data/node/data/answer/filter.py rename to agentuniverse_dataflow/node/data/answer/filter.py index 02d9f0ce..41212ab4 100644 --- a/agentuniverse_data/node/data/answer/filter.py +++ b/agentuniverse_dataflow/node/data/answer/filter.py @@ -12,9 +12,9 @@ from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.data.base.prompt_answer_base import PromptAnswerBase -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader, JsonFileWriter -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.node.data.base.prompt_answer_base import PromptAnswerBase +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader, JsonFileWriter +from agentuniverse_dataflow.util.llm.llm_call import batch_call class FilterNode(PromptAnswerBase): diff --git a/agentuniverse_data/node/data/base/__init__.py b/agentuniverse_dataflow/node/data/base/__init__.py similarity index 100% rename from agentuniverse_data/node/data/base/__init__.py rename to agentuniverse_dataflow/node/data/base/__init__.py diff --git a/agentuniverse_data/node/data/base/prompt_answer_base.py b/agentuniverse_dataflow/node/data/base/prompt_answer_base.py similarity index 87% rename from agentuniverse_data/node/data/base/prompt_answer_base.py rename to agentuniverse_dataflow/node/data/base/prompt_answer_base.py index 24a0b8da..7afc79a6 100644 --- a/agentuniverse_data/node/data/base/prompt_answer_base.py +++ b/agentuniverse_dataflow/node/data/base/prompt_answer_base.py @@ -7,8 +7,8 @@ # @FileName: prompt_answer_base.py from typing import List, Tuple -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.data_node_base import DataNodeBase +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase class PromptAnswerBase(DataNodeBase): diff --git a/agentuniverse_data/node/data/base/prompt_base.py b/agentuniverse_dataflow/node/data/base/prompt_base.py similarity index 86% rename from agentuniverse_data/node/data/base/prompt_base.py rename to agentuniverse_dataflow/node/data/base/prompt_base.py index ceac9db5..1819a4b0 100644 --- a/agentuniverse_data/node/data/base/prompt_base.py +++ b/agentuniverse_dataflow/node/data/base/prompt_base.py @@ -7,8 +7,8 @@ # @FileName: prompt_base.py from typing import List -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.data_node_base import DataNodeBase +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase class PromptBase(DataNodeBase): diff --git a/agentuniverse_data/node/data/event/__init__.py b/agentuniverse_dataflow/node/data/event/__init__.py similarity index 100% rename from agentuniverse_data/node/data/event/__init__.py rename to agentuniverse_dataflow/node/data/event/__init__.py diff --git a/agentuniverse_data/node/data/event/executor.py b/agentuniverse_dataflow/node/data/event/executor.py similarity index 94% rename from agentuniverse_data/node/data/event/executor.py rename to agentuniverse_dataflow/node/data/event/executor.py index c063cb98..78d996dd 100644 --- a/agentuniverse_data/node/data/event/executor.py +++ b/agentuniverse_dataflow/node/data/event/executor.py @@ -9,10 +9,10 @@ import pandas as pd -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.data_node_base import DataNodeBase +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader class ExecutorNode(DataNodeBase): diff --git a/agentuniverse_data/node/data/event/perceiver.py b/agentuniverse_dataflow/node/data/event/perceiver.py similarity index 90% rename from agentuniverse_data/node/data/event/perceiver.py rename to agentuniverse_dataflow/node/data/event/perceiver.py index 89b5dcce..c9384c7b 100644 --- a/agentuniverse_data/node/data/event/perceiver.py +++ b/agentuniverse_dataflow/node/data/event/perceiver.py @@ -7,9 +7,9 @@ # @FileName: perceiver.py from typing import List, Dict -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.data_node_base import DataNodeBase -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader class PerceiverNode(DataNodeBase): diff --git a/agentuniverse_data/node/data/event/planner.py b/agentuniverse_dataflow/node/data/event/planner.py similarity index 96% rename from agentuniverse_data/node/data/event/planner.py rename to agentuniverse_dataflow/node/data/event/planner.py index 42b8e766..a6996239 100644 --- a/agentuniverse_data/node/data/event/planner.py +++ b/agentuniverse_dataflow/node/data/event/planner.py @@ -13,10 +13,10 @@ from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader -from agentuniverse_data.node.enum.enum import NodeEnum -from agentuniverse_data.node.base.data_node_base import DataNodeBase -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader +from agentuniverse_dataflow.node.enum.enum import NodeEnum +from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase +from agentuniverse_dataflow.util.llm.llm_call import batch_call class PlannerNode(DataNodeBase): diff --git a/agentuniverse_data/node/data/prompt/__init__.py b/agentuniverse_dataflow/node/data/prompt/__init__.py similarity index 100% rename from agentuniverse_data/node/data/prompt/__init__.py rename to agentuniverse_dataflow/node/data/prompt/__init__.py diff --git a/agentuniverse_data/node/data/prompt/input/__init__.py b/agentuniverse_dataflow/node/data/prompt/input/__init__.py similarity index 100% rename from agentuniverse_data/node/data/prompt/input/__init__.py rename to agentuniverse_dataflow/node/data/prompt/input/__init__.py diff --git a/agentuniverse_data/node/data/prompt/input/dedupe.py b/agentuniverse_dataflow/node/data/prompt/input/dedupe.py similarity index 97% rename from agentuniverse_data/node/data/prompt/input/dedupe.py rename to agentuniverse_dataflow/node/data/prompt/input/dedupe.py index ecb041fd..d9d5f454 100644 --- a/agentuniverse_data/node/data/prompt/input/dedupe.py +++ b/agentuniverse_dataflow/node/data/prompt/input/dedupe.py @@ -8,7 +8,7 @@ from collections import Counter from simhash import Simhash -from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase class DedupeNode(PromptBase): diff --git a/agentuniverse_data/node/data/prompt/input/extend.py b/agentuniverse_dataflow/node/data/prompt/input/extend.py similarity index 93% rename from agentuniverse_data/node/data/prompt/input/extend.py rename to agentuniverse_dataflow/node/data/prompt/input/extend.py index f3979c34..2af1732e 100644 --- a/agentuniverse_data/node/data/prompt/input/extend.py +++ b/agentuniverse_dataflow/node/data/prompt/input/extend.py @@ -12,9 +12,9 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.util.llm.llm_call import batch_call class ExtendNode(PromptBase): diff --git a/agentuniverse_data/node/data/prompt/input/rewrite.py b/agentuniverse_dataflow/node/data/prompt/input/rewrite.py similarity index 91% rename from agentuniverse_data/node/data/prompt/input/rewrite.py rename to agentuniverse_dataflow/node/data/prompt/input/rewrite.py index d8588c6e..c087f942 100644 --- a/agentuniverse_data/node/data/prompt/input/rewrite.py +++ b/agentuniverse_dataflow/node/data/prompt/input/rewrite.py @@ -11,8 +11,8 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.data.base.prompt_base import PromptBase -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase +from agentuniverse_dataflow.util.llm.llm_call import batch_call class RewriteNode(PromptBase): diff --git a/agentuniverse_data/node/data/prompt/input/seed.py b/agentuniverse_dataflow/node/data/prompt/input/seed.py similarity index 94% rename from agentuniverse_data/node/data/prompt/input/seed.py rename to agentuniverse_dataflow/node/data/prompt/input/seed.py index 4ba57071..7f02dbb6 100644 --- a/agentuniverse_data/node/data/prompt/input/seed.py +++ b/agentuniverse_dataflow/node/data/prompt/input/seed.py @@ -11,9 +11,9 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.util.llm.llm_call import batch_call class SeedNode(PromptBase): diff --git a/agentuniverse_data/node/data/prompt/instruct/__init__.py b/agentuniverse_dataflow/node/data/prompt/instruct/__init__.py similarity index 100% rename from agentuniverse_data/node/data/prompt/instruct/__init__.py rename to agentuniverse_dataflow/node/data/prompt/instruct/__init__.py diff --git a/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py b/agentuniverse_dataflow/node/data/prompt/instruct/prompt_gen.py similarity index 92% rename from agentuniverse_data/node/data/prompt/instruct/prompt_gen.py rename to agentuniverse_dataflow/node/data/prompt/instruct/prompt_gen.py index e14043e9..ef503f7c 100644 --- a/agentuniverse_data/node/data/prompt/instruct/prompt_gen.py +++ b/agentuniverse_dataflow/node/data/prompt/instruct/prompt_gen.py @@ -9,8 +9,8 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.data.base.prompt_base import PromptBase -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader +from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader class PromptGenNode(PromptBase): diff --git a/agentuniverse_data/node/data/prompt/instruct/seed.py b/agentuniverse_dataflow/node/data/prompt/instruct/seed.py similarity index 91% rename from agentuniverse_data/node/data/prompt/instruct/seed.py rename to agentuniverse_dataflow/node/data/prompt/instruct/seed.py index b7ed4a9b..bb48270d 100644 --- a/agentuniverse_data/node/data/prompt/instruct/seed.py +++ b/agentuniverse_dataflow/node/data/prompt/instruct/seed.py @@ -9,9 +9,9 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.data.base.prompt_base import PromptBase +from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.util.llm.llm_call import batch_call class SeedNode(PromptBase): diff --git a/agentuniverse_data/node/enum/__init__.py b/agentuniverse_dataflow/node/enum/__init__.py similarity index 100% rename from agentuniverse_data/node/enum/__init__.py rename to agentuniverse_dataflow/node/enum/__init__.py diff --git a/agentuniverse_data/node/enum/enum.py b/agentuniverse_dataflow/node/enum/enum.py similarity index 100% rename from agentuniverse_data/node/enum/enum.py rename to agentuniverse_dataflow/node/enum/enum.py diff --git a/agentuniverse_data/node/eval/__init__.py b/agentuniverse_dataflow/node/eval/__init__.py similarity index 100% rename from agentuniverse_data/node/eval/__init__.py rename to agentuniverse_dataflow/node/eval/__init__.py diff --git a/agentuniverse_data/node/eval/eval.py b/agentuniverse_dataflow/node/eval/eval.py similarity index 94% rename from agentuniverse_data/node/eval/eval.py rename to agentuniverse_dataflow/node/eval/eval.py index 2953a377..b87cae3d 100644 --- a/agentuniverse_data/node/eval/eval.py +++ b/agentuniverse_dataflow/node/eval/eval.py @@ -11,10 +11,10 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_data.node.base.eval_node_base import EvalNodeBase +from agentuniverse_dataflow.node.base.eval_node_base import EvalNodeBase from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_data.util.constant.eval_node_dimensions import get_eval_dims -from agentuniverse_data.util.llm.llm_call import batch_call +from agentuniverse_dataflow.util.constant.eval_node_dimensions import get_eval_dims +from agentuniverse_dataflow.util.llm.llm_call import batch_call class EvalNode(EvalNodeBase): diff --git a/agentuniverse_data/node/eval/report.py b/agentuniverse_dataflow/node/eval/report.py similarity index 93% rename from agentuniverse_data/node/eval/report.py rename to agentuniverse_dataflow/node/eval/report.py index 3efbb1d8..e616c8de 100644 --- a/agentuniverse_data/node/eval/report.py +++ b/agentuniverse_dataflow/node/eval/report.py @@ -7,8 +7,8 @@ # @FileName: report.py from typing import List -from agentuniverse_data.node.base.eval_node_base import EvalNodeBase -from agentuniverse_data.util.fileio.node_msg_jsonl import JsonFileReader +from agentuniverse_dataflow.node.base.eval_node_base import EvalNodeBase +from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader class ReportNode(EvalNodeBase): diff --git a/agentuniverse_data/node/model/__init__.py b/agentuniverse_dataflow/node/model/__init__.py similarity index 100% rename from agentuniverse_data/node/model/__init__.py rename to agentuniverse_dataflow/node/model/__init__.py diff --git a/agentuniverse_data/node/model/base/__init__.py b/agentuniverse_dataflow/node/model/base/__init__.py similarity index 100% rename from agentuniverse_data/node/model/base/__init__.py rename to agentuniverse_dataflow/node/model/base/__init__.py diff --git a/agentuniverse_data/node/model/base/deploy_base.py b/agentuniverse_dataflow/node/model/base/deploy_base.py similarity index 86% rename from agentuniverse_data/node/model/base/deploy_base.py rename to agentuniverse_dataflow/node/model/base/deploy_base.py index 9a871d6a..bc1afdfc 100644 --- a/agentuniverse_data/node/model/base/deploy_base.py +++ b/agentuniverse_dataflow/node/model/base/deploy_base.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: deploy_base.py -from agentuniverse_data.node.base.model_node_base import ModelNodeBase +from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase class DeployBase(ModelNodeBase): diff --git a/agentuniverse_data/node/model/base/dump_base.py b/agentuniverse_dataflow/node/model/base/dump_base.py similarity index 91% rename from agentuniverse_data/node/model/base/dump_base.py rename to agentuniverse_dataflow/node/model/base/dump_base.py index adb59b48..34d1f00c 100644 --- a/agentuniverse_data/node/model/base/dump_base.py +++ b/agentuniverse_dataflow/node/model/base/dump_base.py @@ -7,7 +7,7 @@ # @FileName: dump_base.py from typing import List, Tuple -from agentuniverse_data.node.base.model_node_base import ModelNodeBase +from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase class DumpBase(ModelNodeBase): diff --git a/agentuniverse_data/node/model/base/pre_deploy_base.py b/agentuniverse_dataflow/node/model/base/pre_deploy_base.py similarity index 87% rename from agentuniverse_data/node/model/base/pre_deploy_base.py rename to agentuniverse_dataflow/node/model/base/pre_deploy_base.py index bca5712b..b46bcaf5 100644 --- a/agentuniverse_data/node/model/base/pre_deploy_base.py +++ b/agentuniverse_dataflow/node/model/base/pre_deploy_base.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: pre_deploy_base.py -from agentuniverse_data.node.base.model_node_base import ModelNodeBase +from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase class PreDeployBase(ModelNodeBase): diff --git a/agentuniverse_data/node/model/base/train_base.py b/agentuniverse_dataflow/node/model/base/train_base.py similarity index 87% rename from agentuniverse_data/node/model/base/train_base.py rename to agentuniverse_dataflow/node/model/base/train_base.py index b2339510..5d4f09c0 100644 --- a/agentuniverse_data/node/model/base/train_base.py +++ b/agentuniverse_dataflow/node/model/base/train_base.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: train_base.py -from agentuniverse_data.node.base.model_node_base import ModelNodeBase +from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase class TrainBase(ModelNodeBase): diff --git a/agentuniverse_data/node/model/base/upload_base.py b/agentuniverse_dataflow/node/model/base/upload_base.py similarity index 91% rename from agentuniverse_data/node/model/base/upload_base.py rename to agentuniverse_dataflow/node/model/base/upload_base.py index e916bd3d..2b00d9d1 100644 --- a/agentuniverse_data/node/model/base/upload_base.py +++ b/agentuniverse_dataflow/node/model/base/upload_base.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: upload_base.py -from agentuniverse_data.node.base.model_node_base import ModelNodeBase +from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase class UploadBase(ModelNodeBase): diff --git a/agentuniverse_data/prompt/__init__.py b/agentuniverse_dataflow/prompt/__init__.py similarity index 100% rename from agentuniverse_data/prompt/__init__.py rename to agentuniverse_dataflow/prompt/__init__.py diff --git a/agentuniverse_data/prompt/dataset_extend_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_extend_node_cn.yaml similarity index 100% rename from agentuniverse_data/prompt/dataset_extend_node_cn.yaml rename to agentuniverse_dataflow/prompt/dataset_extend_node_cn.yaml diff --git a/agentuniverse_data/prompt/dataset_filter_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_filter_node_cn.yaml similarity index 94% rename from agentuniverse_data/prompt/dataset_filter_node_cn.yaml rename to agentuniverse_dataflow/prompt/dataset_filter_node_cn.yaml index b7f22c65..bd63c65a 100644 --- a/agentuniverse_data/prompt/dataset_filter_node_cn.yaml +++ b/agentuniverse_dataflow/prompt/dataset_filter_node_cn.yaml @@ -1,6 +1,6 @@ introduction: 你是一个极致严谨的金融专家，需要帮助按照如下要求评估answer在各维度的质量打分。 instruction: | - 各维度质量打分请分别以0~100分来评估并确保严格要求不轻易给出高分： + 各维度质量打分请分别以0~100分来评估： 1.不符合要求的打0～20分 2.基本符合要求的打20～40分 3.比较优秀的打40～60分 @@ -61,7 +61,8 @@ instruction: | c.加分项 能够get到用户在问什么后：对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应;回应的内容整体相关性都很高;回复的问题全面且易懂 - 请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: + + 请确保严格要求，不轻易给出高分，请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: { "dimensions": [ { diff --git a/agentuniverse_data/prompt/dataset_input_seed_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_input_seed_node_cn.yaml similarity index 100% rename from agentuniverse_data/prompt/dataset_input_seed_node_cn.yaml rename to agentuniverse_dataflow/prompt/dataset_input_seed_node_cn.yaml diff --git a/agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_rewrite_node_cn.yaml similarity index 100% rename from agentuniverse_data/prompt/dataset_rewrite_node_cn.yaml rename to agentuniverse_dataflow/prompt/dataset_rewrite_node_cn.yaml diff --git a/agentuniverse_data/prompt/eval_node_cn.yaml b/agentuniverse_dataflow/prompt/eval_node_cn.yaml similarity index 81% rename from agentuniverse_data/prompt/eval_node_cn.yaml rename to agentuniverse_dataflow/prompt/eval_node_cn.yaml index 9824ba32..7db7aebf 100644 --- a/agentuniverse_data/prompt/eval_node_cn.yaml +++ b/agentuniverse_dataflow/prompt/eval_node_cn.yaml @@ -18,7 +18,7 @@ instruction: | 输出格式要求： - 请确保输出只包含如下的json格式字段，且每个字段不能为空长度也不超过200，不包含其他内容: + 请确保严格要求，不轻易给出高分，请确保输出只包含如下的json格式字段，且每个字段不能为空长度也不超过200，不包含其他内容: { "dimensions": [ { diff --git a/agentuniverse_data/prompt/event_planner_node_cn.yaml b/agentuniverse_dataflow/prompt/event_planner_node_cn.yaml similarity index 100% rename from agentuniverse_data/prompt/event_planner_node_cn.yaml rename to agentuniverse_dataflow/prompt/event_planner_node_cn.yaml diff --git a/agentuniverse_data/prompt/instruct_prompt_gen_cn.yaml b/agentuniverse_dataflow/prompt/instruct_prompt_gen_cn.yaml similarity index 100% rename from agentuniverse_data/prompt/instruct_prompt_gen_cn.yaml rename to agentuniverse_dataflow/prompt/instruct_prompt_gen_cn.yaml diff --git a/agentuniverse_data/prompt/instruct_seed_node_cn.yaml b/agentuniverse_dataflow/prompt/instruct_seed_node_cn.yaml similarity index 100% rename from agentuniverse_data/prompt/instruct_seed_node_cn.yaml rename to agentuniverse_dataflow/prompt/instruct_seed_node_cn.yaml diff --git a/agentuniverse_data/util/__init__.py b/agentuniverse_dataflow/util/__init__.py similarity index 100% rename from agentuniverse_data/util/__init__.py rename to agentuniverse_dataflow/util/__init__.py diff --git a/agentuniverse_data/util/constant/__init__.py b/agentuniverse_dataflow/util/constant/__init__.py similarity index 100% rename from agentuniverse_data/util/constant/__init__.py rename to agentuniverse_dataflow/util/constant/__init__.py diff --git a/agentuniverse_data/util/constant/eval_node_dimensions.py b/agentuniverse_dataflow/util/constant/eval_node_dimensions.py similarity index 100% rename from agentuniverse_data/util/constant/eval_node_dimensions.py rename to agentuniverse_dataflow/util/constant/eval_node_dimensions.py diff --git a/agentuniverse_data/util/fileio/__init__.py b/agentuniverse_dataflow/util/fileio/__init__.py similarity index 100% rename from agentuniverse_data/util/fileio/__init__.py rename to agentuniverse_dataflow/util/fileio/__init__.py diff --git a/agentuniverse_data/util/fileio/node_msg_jsonl.py b/agentuniverse_dataflow/util/fileio/node_msg_jsonl.py similarity index 100% rename from agentuniverse_data/util/fileio/node_msg_jsonl.py rename to agentuniverse_dataflow/util/fileio/node_msg_jsonl.py diff --git a/agentuniverse_data/util/llm/__init__.py b/agentuniverse_dataflow/util/llm/__init__.py similarity index 100% rename from agentuniverse_data/util/llm/__init__.py rename to agentuniverse_dataflow/util/llm/__init__.py diff --git a/agentuniverse_data/util/llm/llm_call.py b/agentuniverse_dataflow/util/llm/llm_call.py similarity index 100% rename from agentuniverse_data/util/llm/llm_call.py rename to agentuniverse_dataflow/util/llm/llm_call.py diff --git a/sample_standard_app/data/__init__.py b/sample_standard_app/dataflow/__init__.py similarity index 100% rename from sample_standard_app/data/__init__.py rename to sample_standard_app/dataflow/__init__.py diff --git a/sample_standard_app/data/dispatch/__init__.py b/sample_standard_app/dataflow/dispatch/__init__.py similarity index 100% rename from sample_standard_app/data/dispatch/__init__.py rename to sample_standard_app/dataflow/dispatch/__init__.py diff --git a/sample_standard_app/data/dispatch/data/demo_jsonl.jsonl b/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl similarity index 100% rename from sample_standard_app/data/dispatch/data/demo_jsonl.jsonl rename to sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl diff --git a/sample_standard_app/data/dispatch/dispatch.py b/sample_standard_app/dataflow/dispatch/dispatch.py similarity index 94% rename from sample_standard_app/data/dispatch/dispatch.py rename to sample_standard_app/dataflow/dispatch/dispatch.py index de8214b7..91443e4e 100644 --- a/sample_standard_app/data/dispatch/dispatch.py +++ b/sample_standard_app/dataflow/dispatch/dispatch.py @@ -10,7 +10,7 @@ from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse.base.agentuniverse import AgentUniverse -from agentuniverse_data.flow.dataflow import Dataflow +from agentuniverse_dataflow.flow.dataflow import Dataflow AgentUniverse().start(config_path='../../config/config.toml') diff --git a/sample_standard_app/data/dispatch/dispatch.yaml b/sample_standard_app/dataflow/dispatch/dispatch.yaml similarity index 80% rename from sample_standard_app/data/dispatch/dispatch.yaml rename to sample_standard_app/dataflow/dispatch/dispatch.yaml index 7bdb0de4..49d65509 100644 --- a/sample_standard_app/data/dispatch/dispatch.yaml +++ b/sample_standard_app/dataflow/dispatch/dispatch.yaml @@ -1,4 +1,4 @@ name: 'main_dispatch' description: 'dispatch with multi-dataflows which will execute one after another' dataflows: - - ../flow/auto_event.yaml + - ../flow/auto_event.yaml \ No newline at end of file diff --git a/sample_standard_app/data/flow/__init__.py b/sample_standard_app/dataflow/flow/__init__.py similarity index 100% rename from sample_standard_app/data/flow/__init__.py rename to sample_standard_app/dataflow/flow/__init__.py diff --git a/sample_standard_app/data/flow/auto_event.yaml b/sample_standard_app/dataflow/flow/auto_event.yaml similarity index 88% rename from sample_standard_app/data/flow/auto_event.yaml rename to sample_standard_app/dataflow/flow/auto_event.yaml index 16201770..490f92fe 100644 --- a/sample_standard_app/data/flow/auto_event.yaml +++ b/sample_standard_app/dataflow/flow/auto_event.yaml @@ -1,7 +1,7 @@ name: 'auto_event_flow' description: "event collect agent via perceiver planner executor" nodes: - - module: 'agentuniverse_data.node.data.event.perceiver' + - module: 'agentuniverse_dataflow.node.data.event.perceiver' # data perceiver in event agent which could perceive tables in jsonl .etc class: 'PerceiverNode' param_in_jsonl: @@ -18,7 +18,7 @@ nodes: prompt_col: prompt answer_col: answer - - module: 'agentuniverse_data.node.data.event.planner' + - module: 'agentuniverse_dataflow.node.data.event.planner' # data planner in event agent which could plan python code to extract with reflections class: 'PlannerNode' llm: 'qwen_llm' @@ -31,7 +31,7 @@ nodes: node_param: verify_lines: 1 - - module: 'agentuniverse_data.node.data.event.executor' + - module: 'agentuniverse_dataflow.node.data.event.executor' # data executor in event agent which execute python code from planner class: 'ExecutorNode' param_in_jsonl: 'auto_event_flow.planner.plan.out' diff --git a/sample_standard_app/data/flow/dataset_build.yaml b/sample_standard_app/dataflow/flow/dataset_build.yaml similarity index 83% rename from sample_standard_app/data/flow/dataset_build.yaml rename to sample_standard_app/dataflow/flow/dataset_build.yaml index 39782dfc..ae1c7f34 100644 --- a/sample_standard_app/data/flow/dataset_build.yaml +++ b/sample_standard_app/dataflow/flow/dataset_build.yaml @@ -1,7 +1,7 @@ name: 'dataset_build_flow' description: "train dataset build agent flow" nodes: - - module: 'agentuniverse_data.node.data.prompt.input.seed' + - module: 'agentuniverse_dataflow.node.data.prompt.input.seed' # query seed from LLM class: 'SeedNode' llm: 'qwen_llm' @@ -14,7 +14,7 @@ nodes: seeds_num: 5 seed_gen_requirement: '金融领域' - - module: 'agentuniverse_data.node.data.prompt.input.rewrite' + - module: 'agentuniverse_dataflow.node.data.prompt.input.rewrite' # rewrite query from LLM class: 'RewriteNode' llm: 'qwen_llm' @@ -25,7 +25,7 @@ nodes: - 'dataset_build_flow.seed.out' dataset_out_jsonl: 'dataset_build_flow.rewrite.out' - - module: 'agentuniverse_data.node.data.prompt.input.extend' + - module: 'agentuniverse_dataflow.node.data.prompt.input.extend' # extend query from LLM class: 'ExtendNode' llm: 'qwen_llm' @@ -38,7 +38,7 @@ nodes: node_param: extend_times: 2 - - module: 'agentuniverse_data.node.data.prompt.input.dedupe' + - module: 'agentuniverse_dataflow.node.data.prompt.input.dedupe' # dedupe queries which improve sft result class: 'DedupeNode' param_in_jsonl: @@ -51,7 +51,7 @@ nodes: freq_top_percent: 1.0 freq_least_count: 100 - - module: 'agentuniverse_data.node.data.answer.answer' + - module: 'agentuniverse_dataflow.node.data.answer.answer' # build answers from the prompt via LLM as training data class: 'AnswerNode' llm: 'qwen_llm' @@ -61,7 +61,7 @@ nodes: - 'dataset_build_flow.dedupe.out' dataset_out_jsonl: 'dataset_build_flow.answer.out' - - module: 'agentuniverse_data.node.data.answer.filter' + - module: 'agentuniverse_dataflow.node.data.answer.filter' # filter training data to improve data quality class: 'FilterNode' llm: 'qwen_llm' diff --git a/sample_standard_app/data/flow/eval_report.yaml b/sample_standard_app/dataflow/flow/eval_report.yaml similarity index 86% rename from sample_standard_app/data/flow/eval_report.yaml rename to sample_standard_app/dataflow/flow/eval_report.yaml index e73d0df4..8c89643c 100644 --- a/sample_standard_app/data/flow/eval_report.yaml +++ b/sample_standard_app/dataflow/flow/eval_report.yaml @@ -1,7 +1,7 @@ name: 'eval_report_flow' description: "eval for nodes and summarize report" nodes: - - module: 'agentuniverse_data.node.eval.eval' + - module: 'agentuniverse_dataflow.node.eval.eval' # eval dump result from LLM infer class: 'EvalNode' llm: 'qwen_llm' @@ -15,7 +15,7 @@ nodes: node_param: eval_lines: 100 - - module: 'agentuniverse_data.node.eval.report' + - module: 'agentuniverse_dataflow.node.eval.report' # compare multi-eval result as a report class: 'ReportNode' param_in_jsonl: diff --git a/sample_standard_app/data/flow/instruct_select.yaml b/sample_standard_app/dataflow/flow/instruct_select.yaml similarity index 88% rename from sample_standard_app/data/flow/instruct_select.yaml rename to sample_standard_app/dataflow/flow/instruct_select.yaml index 7fcdfad9..9e30116f 100644 --- a/sample_standard_app/data/flow/instruct_select.yaml +++ b/sample_standard_app/dataflow/flow/instruct_select.yaml @@ -1,7 +1,7 @@ name: 'instruct_select_flow' description: "extend instruct from sample and choose the best via pre-deploy model" nodes: - - module: 'agentuniverse_data.node.data.prompt.instruct.seed' + - module: 'agentuniverse_dataflow.node.data.prompt.instruct.seed' # produce multiple instruct seed candidates class: 'SeedNode' llm: 'qwen_llm' @@ -13,7 +13,7 @@ nodes: node_param: extend_times: 10 - - module: 'agentuniverse_data.node.data.prompt.instruct.prompt_gen' + - module: 'agentuniverse_dataflow.node.data.prompt.instruct.prompt_gen' # produce prompt from instruct and query class: 'PromptGenNode' llm: 'qwen_llm' @@ -38,7 +38,7 @@ nodes: # - 'instruct_select_flow.prompt_gen.out' # dataset_out_jsonl: 'instruct_select_flow.dump.out' -# - module: 'agentuniverse_data.node.eval.eval' +# - module: 'agentuniverse_dataflow.node.eval.eval' # # eval dump result through 7 dimensions # class: 'EvalNode' # llm: 'qwen_llm' diff --git a/sample_standard_app/data/flow/model_deploy.yaml b/sample_standard_app/dataflow/flow/model_deploy.yaml similarity index 100% rename from sample_standard_app/data/flow/model_deploy.yaml rename to sample_standard_app/dataflow/flow/model_deploy.yaml diff --git a/sample_standard_app/data/flow/train_dump.yaml b/sample_standard_app/dataflow/flow/train_dump.yaml similarity index 100% rename from sample_standard_app/data/flow/train_dump.yaml rename to sample_standard_app/dataflow/flow/train_dump.yaml From ec55462643b51442225b7cfad1066f2cbf161fda Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Thu, 20 Jun 2024 14:31:41 +0800 Subject: [PATCH 022/118] feat: add data agent in the agentUniverse. --- agentuniverse_dataflow/node/data/prompt/input/dedupe.py | 8 +++++++- pyproject.toml | 2 ++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/agentuniverse_dataflow/node/data/prompt/input/dedupe.py b/agentuniverse_dataflow/node/data/prompt/input/dedupe.py index d9d5f454..bd6f4a8d 100644 --- a/agentuniverse_dataflow/node/data/prompt/input/dedupe.py +++ b/agentuniverse_dataflow/node/data/prompt/input/dedupe.py @@ -7,7 +7,6 @@ # @FileName: dedupe.py from collections import Counter -from simhash import Simhash from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase @@ -29,6 +28,13 @@ def _node_process(self) -> None: if not self._prompt_list or len(self._prompt_list) == 0: return + try: + from simhash import Simhash + except ImportError: + raise ImportError( + "simhash is required at the DedupeNode. Please: `pip install simhash`" + ) + # calculate the simhash value for each document. simhashes = [(doc, Simhash(doc)) for doc in self._prompt_list] diff --git a/pyproject.toml b/pyproject.toml index 5229cb0e..b46a4103 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,8 @@ dashscope = "^1.19.1" anthropic = "^0.26.0" ollama = '^0.2.1' langchain-anthropic = '^0.1.13' +pandas = "^2.2.2" +pyarrow = "^16.1.0" [tool.poetry.extras] log_ext = ["aliyun-log-python-sdk"] From 5e1488b1008a811d5d264888084a02eec17d50fe Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 21 Jun 2024 15:09:50 +0800 Subject: [PATCH 023/118] add deepseek doc --- docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md | 35 +++++++++++++++++ docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md | 37 ++++++++++++++++++ ...3_1_2_DeepSeek\344\275\277\347\224\250.md" | 35 +++++++++++++++++ ...OpenAIStyleLLM\344\275\277\347\224\250.md" | 39 +++++++++++++++++++ 4 files changed, 146 insertions(+) create mode 100644 docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md create mode 100644 docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md create mode 100644 "docs/guidebook/zh/3_1_2_DeepSeek\344\275\277\347\224\250.md" create mode 100644 "docs/guidebook/zh/3_1_2_OpenAIStyleLLM\344\275\277\347\224\250.md" diff --git a/docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md b/docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md new file mode 100644 index 00000000..99622433 --- /dev/null +++ b/docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md @@ -0,0 +1,35 @@ +# DeepSeek Usage +## 1. Create the relevant file. +Create a YAML file, for example, user_deepseek_llm.yaml +Paste the following content into your user_deepseek_llm.yaml file. +```yaml +name: 'user_deepseek_llm' +description: 'default default_deepseek_llm llm with spi' +model_name: 'deepseek-chat' +max_tokens: 1000 +metadata: + type: 'LLM' + module: 'agentuniverse.llm.default.deep_seek_openai_style_llm' + class: 'DefaultDeepSeekLLM' +``` +## 2. Environment Setup +Must be configured: DEEPSEEK_API_KEY +Optional: DEEPSEEK_API_BASE +### 2.1 Configure through Python code +```python +import os +os.environ['DEEPSEEK_API_KEY'] = 'sk-***' +os.environ['DEEPSEEK_API_BASE'] = 'https://xxxxxx' +``` +### 2.2 Configure through the configuration file +In the custom_key.toml file under the config directory of the project, add the configuration: +```toml +DEEPSEEK_API_KEY="sk-******" +DEEPSEEK_API_BASE="https://xxxxxx" +``` +## 3. Obtaining the DeepSeek API KEY +Please refer to the official documentation of DEEPSEEK:https://platform.deepseek.com/api_keys + +## 4. Tips +In the agentuniverse, we have established an LLM named default_deepseek_llm. Users can directly utilize it after configuring their DEEPSEEK_API_KEY. + diff --git a/docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md b/docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md new file mode 100644 index 00000000..ab712fc3 --- /dev/null +++ b/docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md @@ -0,0 +1,37 @@ +# OpenAIStyleLLM Usage +## 1. Create the relevant file. +Create a YAML file, for example, user_openai_style_llm.yaml +Paste the following content into your user_openai_style_llm.yaml file. +```yaml +name: 'deep_seek_llm' +description: 'demo deep_seek llm with spi' +model_name: 'deepseek-chat' +max_tokens: 4000 +max_context_length: 32000 +api_key_env: 'DEEPSEEK_API_KEY' +api_base_env: 'DEEPSEEK_API_BASE' +metadata: + type: 'LLM' + module: 'agentuniverse.llm.openai_style_llm' + class: 'OpenAIStyleLLM' +``` +Parameter Description: +max_context_length: The context length that the model can handle. +api_key_env: The name of the environment variable for the API key. +api_base_env: The name of the environment variable for the API base. +These parameters must be configured, and upon service startup, the corresponding values will be loaded from the environment variables. +## 2. Environment Setup +Mandatory Configuration: $api_key_env, $api_base_env. +In the configuration file, api_key_env and api_base_env are set as DEEPSEEK_API_KEY and DEEPSEEK_API_BASE respectively. Therefore, the configuration should be as follows: +### 2.1 Configure through Python code +```python +import os +os.environ['DEEPSEEK_API_KEY'] = 'sk-***' +os.environ['DEEPSEEK_API_BASE'] = 'https://xxxxxx' +``` +### 2.2 Configure through the configuration file +In the custom_key.toml file under the config directory of the project, add the configuration: +```toml +DEEPSEEK_API_KEY="DEEPSEEK_API_KEY" +DEEPSEEK_API_BASE="https://xxxxxx" +``` diff --git "a/docs/guidebook/zh/3_1_2_DeepSeek\344\275\277\347\224\250.md" "b/docs/guidebook/zh/3_1_2_DeepSeek\344\275\277\347\224\250.md" new file mode 100644 index 00000000..7a243ed2 --- /dev/null +++ "b/docs/guidebook/zh/3_1_2_DeepSeek\344\275\277\347\224\250.md" @@ -0,0 +1,35 @@ +# DeepSeek 使用 +## 1. 创建相关文件 +创建一个yaml文件，例如 user_deepseek_llm.yaml +将以下内容粘贴到您的user_deepseek_llm.yaml文件当中 +```yaml +name: 'user_deepseek_llm' +description: 'default default_deepseek_llm llm with spi' +model_name: 'deepseek-chat' +max_tokens: 1000 +metadata: + type: 'LLM' + module: 'agentuniverse.llm.default.deep_seek_openai_style_llm' + class: 'DefaultDeepSeekLLM' +``` +## 2. 环境设置 +### 2.1 通过python代码配置 +必须配置：DEEPSEEK_API_KEY +可选配置：DEEPSEEK_API_BASE +```python +import os +os.environ['DEEPSEEK_API_KEY'] = 'sk-***' +os.environ['DEEPSEEK_API_BASE'] = 'https://xxxxxx' +``` +### 2.2 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +DEEPSEEK_API_KEY="sk-******" +DEEPSEEK_API_BASE="https://xxxxxx" +``` +## 3. DEEPSEEK API KEY 获取 +参考 DEEPSEEK 官方文档：https://platform.deepseek.com/api_keys + +## 4. Tips +在agentuniverse中，我们已经创建了一个name为default_deepseek_llm的llm,用户在配置DEEPSEEK_API_KEY之后可以直接使用。 + diff --git "a/docs/guidebook/zh/3_1_2_OpenAIStyleLLM\344\275\277\347\224\250.md" "b/docs/guidebook/zh/3_1_2_OpenAIStyleLLM\344\275\277\347\224\250.md" new file mode 100644 index 00000000..e2078d9c --- /dev/null +++ "b/docs/guidebook/zh/3_1_2_OpenAIStyleLLM\344\275\277\347\224\250.md" @@ -0,0 +1,39 @@ +# OpenAIStyleLLM 使用 +通过该配置，您可以连接任何遵守OpenAI标准的模型服务 +## 1. 创建相关文件 +创建一个yaml文件，例如 user_openai_style_llm.yaml +将以下内容粘贴到您的user_openai_style_llm.yaml文件当中 +```yaml +name: 'deep_seek_llm' +description: 'demo deep_seek llm with spi' +model_name: 'deepseek-chat' +max_tokens: 4000 +max_context_length: 32000 +api_key_env: 'DEEPSEEK_API_KEY' +api_base_env: 'DEEPSEEK_API_BASE' +metadata: + type: 'LLM' + module: 'agentuniverse.llm.openai_style_llm' + class: 'OpenAIStyleLLM' +``` +参数说明： + max_context_length: 模型所能承接的上下文 + api_key_env: api key的env变量名 + api_base_env: api base的env变量名 +上述参数必须配置,服务启动时会从环境变量中加载相关的值 +## 2. 环境设置 +必须配置：$api_key_env, $api_base_env +配置文件中api_key_env与api_base_env分别为DEEPSEEK_API_KEY、DEEPSEEK_API_BASE，所以配置如下： +### 2.1 通过python代码配置 +```python +import os +os.environ['DEEPSEEK_API_KEY'] = 'sk-***' +os.environ['DEEPSEEK_API_BASE'] = 'https://xxxxxx' +``` +### 2.2 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +DEEPSEEK_API_KEY="DEEPSEEK_API_KEY" +DEEPSEEK_API_BASE="https://xxxxxx" +``` +注意，这里必须与配置文件中锁配置的api_key_env、api_base_env一致 From 7ae0bd8a2b1d57e89ebf2782f68dde38fc3e17ef Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 14 Jun 2024 17:52:58 +0800 Subject: [PATCH 024/118] docs: update readme & guidebook --- README.md | 5 +++++ README_jp.md | 5 +++++ README_zh.md | 5 +++++ docs/guidebook/en/0_index.md | 16 +++++++++++++--- docs/guidebook/en/3_1_2_0_List_Of_LLMs.md | 4 +++- ...se .md => 7_1_1_Legal_Consultation_Case.md} | 0 .../zh/0_\347\233\256\345\275\225.md" | 16 +++++++++++++--- ...241\345\236\213\345\210\227\350\241\250.md" | 18 ++++++++++-------- 8 files changed, 54 insertions(+), 15 deletions(-) rename docs/guidebook/en/{7_1_1_Legal_Consultation_Case .md => 7_1_1_Legal_Consultation_Case.md} (100%) diff --git a/README.md b/README.md index 977ad774..b09a0f4c 100644 --- a/README.md +++ b/README.md @@ -44,6 +44,11 @@ We will show you how to: * Quickly serve the agent For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md). +## Use Cases +[Legal Consultation Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) +[Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) +[Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) + ## Guidebook For more detailed information, please refer to the [Guidebook](docs/guidebook/en/0_index.md). diff --git a/README_jp.md b/README_jp.md index 967e2154..72168a33 100644 --- a/README_jp.md +++ b/README_jp.md @@ -43,6 +43,11 @@ pip install agentUniverse * エージェントを迅速に提供する 詳細は、[クイックスタート](docs/guidebook/en/1_3_Quick_Start.md)をご覧ください。 +## 使用ケース +[法律相談Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) +[Pythonコード生成と実行Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) +[多回多Agentによるディスカッショングループ](./docs/guidebook/en/6_2_1_Discussion_Group.md) + ## ガイドブック 詳細情報については、[ガイドブック](docs/guidebook/en/0_index.md)を参照してください。 diff --git a/README_zh.md b/README_zh.md index 17763f6e..f7b474b5 100644 --- a/README_zh.md +++ b/README_zh.md @@ -47,6 +47,11 @@ pip install agentUniverse 详情请阅读[快速开始](docs/guidebook/zh/1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。 +## 使用案例 +[法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) +[Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) +[基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) + ## 用户指南 更多详细信息，请参阅[指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 diff --git a/docs/guidebook/en/0_index.md b/docs/guidebook/en/0_index.md index 36bb852b..6fa4e741 100644 --- a/docs/guidebook/en/0_index.md +++ b/docs/guidebook/en/0_index.md @@ -52,6 +52,8 @@ * 3.1.2.3 [WenXin Usage Instructions](3_1_2_WenXin_LLM_Use.md) * 3.1.2.4 [Kimi Usage Instructions](3_1_2_Kimi_LLM_Use.md) * 3.1.2.5 [BaiChuan Usage Instructions](3_1_2_BaiChuan_LLM_Use.md) + * 3.1.2.6 [Claude Usage Instructions](3_1_2_Claude_LLM_Use.md) + * 3.1.2.7 [Ollama Usage Instructions](3_1_2_Ollama_LLM_Use.md) * 3.1.3 List of Tools * 3.1.4 List of Knowledge * 3.1.5 List of Memories @@ -62,6 +64,7 @@ * 3.2.2 Store * 3.2.2.1 [SQLDBWrapper](2_3_1_SQLDB_WRAPPER.md) * 3.2.2.2 [Milvus](3_3_1_Milvus.md) + * 3.2.2.3 [ChromaDB](3_3_2_ChromaDB.md) * 3.2.3 Msg * 3.2.4 Logging * 3.2.4.1 [Alibaba Cloud SLS](3_2_4_Alibaba_Cloud_SLS.md) @@ -73,8 +76,15 @@ * 5.1.1 [Docker Containerization Solution](5_1_1_Docker_Container_Deployment.md) * 5.1.2 [K8S Solution](5_1_2_K8S_Deployment.md) -**6. Series of Articles** +**6. Use Cases** +* 6.1 RAG-Type Agent Examples + * 6.1.1 [Legal Consultation Agent](7_1_1_Legal_Consultation_Case.md) +* 6.2 ReAct-Type Agent Examples + * 6.2.1 [Python Code Generation and Execution Agent](7_1_1_Python_Auto_Runner.md) +* 6.3 [Discussion Group Based on Multi-Turn Multi-Agent Mode](6_2_1_Discussion_Group.md) -**7. Frequently Asked Questions (FAQ)** +**7. Series of Articles** -**[8. Contact Us](6_1_Contact_Us.md)** +**8. Frequently Asked Questions (FAQ)** + +**[9. Contact Us](6_1_Contact_Us.md)** diff --git a/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md b/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md index 5d5975eb..a56aa83f 100644 --- a/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md +++ b/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md @@ -8,7 +8,9 @@ The platform has currently integrated the following list of models; please refer | [WenXin](3_1_2_WenXin_LLM_Use.md) | | [Kimi](3_1_2_Kimi_LLM_Use.md) | | [BaiChuan](3_1_2_BaiChuan_LLM_Use.md) | +| [Claude](3_1_2_Claude_LLM_Use.md) | +| [ollama](3_1_2_Ollama_LLM_Use.md) | For LLM not included in the list, you can customize the integration of any LLM according to the steps provided in the [LLM definition](2_2_3_Tool_Create_And_Use.md). -At the same time, we are adding the integration methods for common LLMs such as Claude, Gemini, ollama, Llama, ZhipuAI, etc. If you have used these LLMs in your projects and have created your own instances, we welcome you to submit a PR to become a contributor to agentUniverse. \ No newline at end of file +At the same time, we are adding the integration methods for common LLMs such as Gemini, Llama, ZhipuAI, etc. If you have used these LLMs in your projects and have created your own instances, we welcome you to submit a PR to become a contributor to agentUniverse. \ No newline at end of file diff --git a/docs/guidebook/en/7_1_1_Legal_Consultation_Case .md b/docs/guidebook/en/7_1_1_Legal_Consultation_Case.md similarity index 100% rename from docs/guidebook/en/7_1_1_Legal_Consultation_Case .md rename to docs/guidebook/en/7_1_1_Legal_Consultation_Case.md diff --git "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" index 4b26c3de..43a9b654 100644 --- "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" +++ "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" @@ -56,6 +56,8 @@ * 3.1.2.3 [文心一言使用说明](3_1_2_文心使用.md) * 3.1.2.4 [Kimi使用说明](3_1_2_Kimi使用.md) * 3.1.2.5 [百川使用说明](3_1_2_百川使用.md) + * 3.1.2.6 [Claude使用说明](3_1_2_Claude使用.md) + * 3.1.2.7 [Ollama使用说明](3_1_2_Ollama使用.md) * 3.1.3 工具列表(Tool) * 3.1.4 知识列表(Knowledge) * 3.1.5 记忆列表(Memory) @@ -66,6 +68,7 @@ * 3.2.2 存储 (Store) * 3.2.2.1 [SQLDBWrapper](2_3_1_SQLDB_WRAPPER.md) * 3.2.2.2 [Milvus](3_3_1_Milvus.md) + * 3.2.2.3 [ChromaDB](3_3_2_ChromaDB.md) * 3.2.3 消息(Msg) * 3.2.4 日志(Logging) * 3.2.4.1 [阿里云SLS](3_2_4_阿里云SLS.md) @@ -77,8 +80,15 @@ * 5.1.1 [Docker容器化方案](5_1_1_Docker容器化部署.md) * 5.1.2 [K8S部署方案](5_1_2_K8S部署.md) -**6.系列文章** +**6.使用案例** +* 6.1 RAG类Agent案例 + * 6.1.1 [法律咨询Agent](7_1_1_法律咨询案例.md) +* 6.2 ReAct类Agent案例 + * 6.2.1 [Python代码生成与执行Agent](7_1_1_Python自动执行案例.md) +* 6.3 [基于多轮多Agent的讨论小组](6_2_1_讨论组.md) -**7.常见问题FAQ** +**7.系列文章** -**[8.联系我们](6_1_联系我们.md)** +**8.常见问题FAQ** + +**[9.联系我们](6_1_联系我们.md)** diff --git "a/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" "b/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" index 4791894c..5e62a724 100644 --- "a/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" +++ "b/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" @@ -1,14 +1,16 @@ # 模型（LLM）列表 目前平台已经接入了如下模型列表，详细使用方式请关注本章节各LLM的说明。 -| 模型LLM(系列)名称 | -|------------------------------| -| [OpenAI](3_1_2_OpenAI使用.md) | -| [通义千问](3_1_2_Qwen使用.md) | -| [文心一言](3_1_2_文心使用.md) | -| [Kimi](3_1_2_Kimi使用.md) | -| [百川](3_1_2_百川使用.md) | +| 模型LLM(系列)名称 | +|----------------------------------| +| [OpenAI](3_1_2_OpenAI使用.md) | +| [通义千问](3_1_2_Qwen使用.md) | +| [文心一言](3_1_2_文心使用.md) | +| [Kimi](3_1_2_Kimi使用.md) | +| [百川](3_1_2_百川使用.md) | +| [Claude](3_1_2_Claude使用.md) | +| [ollama](3_1_2_Ollama使用.md) | 未在列表中的模型您可以根据[模型定义](2_2_2_模型定义与使用.md)中的步骤自定义接入任何模型。 -同时我们正在添加Claude、Gemini、ollama、Llama、ZhipuAI等常见LLM的接入方式，如果您在工程中使用了这些LLM并创建了属于自己的实例，欢迎您提交pr成为agentUniverse的contributor。 \ No newline at end of file +同时我们正在添加Gemini、Llama、ZhipuAI等常见LLM的接入方式，如果您在工程中使用了这些LLM并创建了属于自己的实例，欢迎您提交pr成为agentUniverse的contributor。 \ No newline at end of file From da761da3c44173efc3f2fdc79ca67626f4da395b Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 14 Jun 2024 18:27:56 +0800 Subject: [PATCH 025/118] feat: Release version 0.0.9 --- .../knowledge/embedding/qwen_embedding.py | 82 ------------------- 1 file changed, 82 deletions(-) delete mode 100644 agentuniverse/agent/action/knowledge/embedding/qwen_embedding.py diff --git a/agentuniverse/agent/action/knowledge/embedding/qwen_embedding.py b/agentuniverse/agent/action/knowledge/embedding/qwen_embedding.py deleted file mode 100644 index 74455f9a..00000000 --- a/agentuniverse/agent/action/knowledge/embedding/qwen_embedding.py +++ /dev/null @@ -1,82 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/11 16:30 -# @Author : weizjajj -# @Email : weizhongjie.wzj@antgroup.com -# @FileName: qwen_embedding.py - - -from typing import List, Optional - -import dashscope -from langchain_community.embeddings import DashScopeEmbeddings -from pydantic import Field - -from agentuniverse.agent.action.knowledge.embedding.embedding import Embedding -from agentuniverse.base.util.env_util import get_from_env - - -class QwenEmbedding(Embedding): - """The openai embedding class.""" - - dashscope_api_key: Optional[str] = Field(default_factory=lambda: get_from_env("DASHSCOPE_API_KEY")) - """The DashScope client.""" - model: Optional[str] = "text-embedding-v1" - - def get_embeddings(self, texts: List[str]) -> List[List[float]]: - """Get the OpenAI embeddings. - - Note: - The `embedding_model_name` parameter of the openai embedding class must be provided. - The `dimensions` parameter of the openai embedding class is optional. - - Args: - texts (List[str]): A list of texts that need to be embedded. - - Returns: - List[List[float]]: Each text gets a float list, and the result is a list of the results for each text. - - Raises: - ValueError: If texts exceed the embedding model token limit or missing some required parameters. - """ - result = [] - resp = dashscope.TextEmbedding.call( - model=self.model, - input=texts, - api_key=self.dashscope_api_key - ) - if resp.status_code == 200: - result = [item['embedding'] for item in resp.output.get('embeddings')] - elif resp.status_code in [400, 401]: - raise ValueError( - f"status_code: {resp.status_code} \n " - f"code: {resp.code} \n message: {resp.message}" - ) - else: - raise Exception(f"status_code: {resp.status_code} \n code: {resp.code} \n message: {resp.message}") - return result - - async def async_get_embeddings(self, texts: List[str]) -> List[List[float]]: - """Asynchronously get the OpenAI embeddings. - - Note: - The `embedding_model_name` parameter of the openai embedding class must be provided. - The `dimensions` parameter of the openai embedding class is optional. - - Args: - texts (List[str]): A list of texts that need to be embedded. - - Returns: - List[List[float]]: Each text gets a float list, and the result is a list of the results for each text. - Raises: - ValueError: If texts exceed the embedding model token limit or missing some required parameters. - """ - raise NotImplementedError - - def as_langchain(self) -> DashScopeEmbeddings: - """Convert the agentUniverse(aU) openai embedding class to the langchain openai embedding class.""" - return DashScopeEmbeddings( - model=self.model, - dashscope_api_key=self.dashscope_api_key, - ) From 53fee05bc8648b9982b6619d0be04fe59315e9cc Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 14 Jun 2024 18:29:12 +0800 Subject: [PATCH 026/118] feat: Release version 0.0.9 --- CHANGELOG.md | 15 +++++++++++++++ CHANGELOG_zh.md | 15 +++++++++++++++ README.md | 2 +- README_jp.md | 2 +- README_zh.md | 2 +- pyproject.toml | 2 +- 6 files changed, 34 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 54bfed04..7ec09f51 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -24,6 +24,21 @@ Note - Additional remarks regarding the version. *************************************************** # Version Update History +## [0.0.9] - 2024-06-14 +### Added +- Added standard integration for Claude and Ollama LLM components +- Added new Qwen embedding module +- Added default agents for ReAct-Type and NL2API-Type + +### Note +- Added new use cases + - RAG-Type Agent Examples: Legal Consultation Agent + - ReAct-Type Agent Examples: Python Code Generation and Execution Agent + - Multi-Agent: Discussion Group Based on Multi-Turn Multi-Agent Mode + + For more details, please refer to the use case section in the user documentation. +- Some code optimizations and documentation updates. + ## [0.0.8] - 2024-06-06 ### Added - Introduced a new monitor module diff --git a/CHANGELOG_zh.md b/CHANGELOG_zh.md index f938da24..600479be 100644 --- a/CHANGELOG_zh.md +++ b/CHANGELOG_zh.md @@ -24,6 +24,21 @@ Note - 对于版本的额外说明。 *************************************************** # 版本更新记录 +## [0.0.9] - 2024-06-14 +### Added +- LLM组件新增claude、ollama标准接入 +- 新增qwen embedding模块 +- 新增ReAct、nl2api默认agent + +### Note +- 新增使用案例 + - RAG类Agent案例-法律咨询Agent + - ReAct类Agent案例-Python代码生成与执行Agent + - 多智能体案例-基于多轮多Agent的讨论小组 + + 详情请看用户文档案例部分。 +- 部分代码优化与文档更新 + ## [0.0.8] - 2024-06-06 ### Added - 新增monitor模块 diff --git a/README.md b/README.md index b09a0f4c..ee6656b2 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語 ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.8-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** diff --git a/README_jp.md b/README_jp.md index 72168a33..ec284e38 100644 --- a/README_jp.md +++ b/README_jp.md @@ -5,7 +5,7 @@ ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.8-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** diff --git a/README_zh.md b/README_zh.md index f7b474b5..d35db732 100644 --- a/README_zh.md +++ b/README_zh.md @@ -5,7 +5,7 @@ ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.8-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** diff --git a/pyproject.toml b/pyproject.toml index b46a4103..30e82d41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "agentUniverse" -version = "0.0.8" +version = "0.0.9" description = "agentUniverse is a framework for developing applications powered by multi-agent base on large language model." authors = ["AntGroup "] From 188695a535c2f3bbb33182a14b67b1f38b6401d9 Mon Sep 17 00:00:00 2001 From: "fanen.lhy" Date: Tue, 18 Jun 2024 12:22:45 +0800 Subject: [PATCH 027/118] =?UTF-8?q?Add:=20peer=20case=20docs=20Fix?= =?UTF-8?q?=EF=BC=9Aonly=20check=20dashscope=20key=20when=20used=20instead?= =?UTF-8?q?=20of=20init?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../embedding/dashscope_embedding.py | 18 ++--- .../_picture/6_4_1_executing_result.png | Bin 0 -> 1087940 bytes .../_picture/6_4_1_expressing_result.png | Bin 0 -> 606093 bytes .../_picture/6_4_1_planning_result.png | Bin 0 -> 141901 bytes .../_picture/6_4_1_reviewing_result.png | Bin 0 -> 72788 bytes .../en/6_4_1_Financial_Event_Analysis_Case.md | 75 ++++++++++++++++++ .../zh/0_\347\233\256\345\275\225.md" | 2 + ...06\346\236\220\346\241\210\344\276\213.md" | 75 ++++++++++++++++++ 8 files changed, 160 insertions(+), 10 deletions(-) create mode 100644 docs/guidebook/_picture/6_4_1_executing_result.png create mode 100644 docs/guidebook/_picture/6_4_1_expressing_result.png create mode 100644 docs/guidebook/_picture/6_4_1_planning_result.png create mode 100644 docs/guidebook/_picture/6_4_1_reviewing_result.png create mode 100644 docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md create mode 100644 "docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md" diff --git a/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py b/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py index 7cab3b0b..e46ee14b 100644 --- a/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py +++ b/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py @@ -8,6 +8,7 @@ import aiohttp import requests from typing import List, Generator, Optional +from pydantic import Field import json from agentuniverse.base.util.env_util import get_from_env @@ -27,14 +28,9 @@ def batched(inputs: List, class DashscopeEmbedding(Embedding): """The Dashscope embedding class.""" - dashscope_api_key: Optional[str] = None - - def __init__(self, **kwargs): - """Initialize the dashscope embedding class, need dashscope api key.""" - super().__init__(**kwargs) - self.dashscope_api_key = get_from_env("DASHSCOPE_API_KEY") - if not self.dashscope_api_key: - raise Exception("No DASHSCOPE_API_KEY in your environment.") + dashscope_api_key: Optional[str] = Field( + default_factory=lambda: get_from_env("DASHSCOPE_API_KEY") + ) def get_embeddings(self, texts: List[str]) -> List[List[float]]: @@ -69,7 +65,8 @@ def post(post_params): ) resp_json = response.json() return resp_json - + if not self.dashscope_api_key: + raise Exception("No DASHSCOPE_API_KEY in your environment.") result = [] post_params = { "model": self.embedding_model_name, @@ -128,7 +125,8 @@ async def async_post(post_params): ) as resp: resp_json = await resp.json() return resp_json - + if not self.dashscope_api_key: + raise Exception("No DASHSCOPE_API_KEY in your environment.") result = [] post_params = { "model": self.embedding_model_name, diff --git a/docs/guidebook/_picture/6_4_1_executing_result.png b/docs/guidebook/_picture/6_4_1_executing_result.png new file mode 100644 index 0000000000000000000000000000000000000000..5456c546f7c30d1900b262358b83a7821bbb03d6 GIT binary patch literal 1087940 zcmbrmcUV*1wmpoBU_f^?}7QBe_45fCXsY0^cSlw^yDSb!kC2&gEil+ckH 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z=6#P77WBAFN3ktDzjcl7No}{^e!qHe-nh=+@uxkcXjZ-4hxq>tx!LRooBsUOE@EKWsVOE!r`*1_djtXf2qporuQouXJ=AWzeiO%uMKOPZMoZi8_cK7l5S)8-VoJ`GYPcX1QcM8Z{ zF0`e(?_Po9!2<$$eCw)L%yBtXryjJVW#iI!53OtdmBby}yL6p6tK0XW{n>TD`B}aw zI5ITXRZa_YIrLnVCx4#rF2}S!jz2F?3aeyhH)StdVphIa-YaFz#D!-IY~I_36!Mmr zI|Lq7IVa@1`oR^}_U8c&jw~_i7k4h%#s1WmBg|8B%fQ> zrCwiWd|^plHNWA`+Lu~fZtm~pEAOg*-Vi0D<9eu8gx{R^^n+gR+XvF;m;e9( diff --git a/docs/guidebook/en/8_1_agentUniverse-dataflow.md b/docs/guidebook/en/8_1_agentUniverse-dataflow.md deleted file mode 100644 index b9a197ab..00000000 --- a/docs/guidebook/en/8_1_agentUniverse-dataflow.md +++ /dev/null @@ -1,115 +0,0 @@ -# Project Introduction -agentUniverse-dataflow is the project based on the idea of **Agentic Workflow**, utilizing the multi-agent and flow orchestration approach. It enhances the data processing workflow of LLM and agents through **offline adaptive LLM data collection, fine-tuning dataset generation, model inference training, and multi-dimensional automatic dataset evaluation, etc.** - -Including: -1. A foundational data framework: **DataFramework**, structured around the Dispatch-Flow-Node-Data/Event/Prompt/Answer/Eval, with capabilities for sustainable integration and expansion. -2. An agent framework: **PVRP** (Planner-Verifier-Reflector-Producer). -3. A set of capabilities: - - Adaptive data event collection: **auto_event** - - Fine-tuning dataset generation: **dataset_build** - - Uploading for training and inference: **train_dump** - - Multi-dimensional objective automatic evaluation: **eval_report** - - Model deployment and activation: **model_deploy** - - System instruction optimization: **instruct_select** - -# Quick Start -In the agentUniverse standard project template `sample_standard_app`: -## step1 -Configure the loading path of prompt in config.toml, add `agentuniverse_dataflow.prompt`, and read the built-in prompt files of agentuniverse_dataflow. -```toml -[CORE_PACKAGE] -# Perform a full component scan and registration for all the paths under this list. -default = ['sample_standard_app.app.core'] -# Scan and register agent components for all paths under this list, with priority over the default. -agent = ['sample_standard_app.app.core.agent'] -# Scan and register agent components for all paths under this list, with priority over the default. -knowledge = ['sample_standard_app.app.core.knowledge'] -# Scan and register knowledge components for all paths under this list, with priority over the default. -llm = ['sample_standard_app.app.core.llm'] -# Scan and register llm components for all paths under this list, with priority over the default. -planner = ['sample_standard_app.app.core.planner'] -# Scan and register planner components for all paths under this list, with priority over the default. -tool = ['sample_standard_app.app.core.tool'] -# Scan and register memory components for all paths under this list, with priority over the default. -memory = ['sample_standard_app.app.core.memory'] -# Scan and register service components for all paths under this list, with priority over the default. -service = ['sample_standard_app.app.core.service'] -# Scan and register prompt components for all paths under this list, with priority over the default. -prompt = ['sample_standard_app.app.core.prompt', 'agentuniverse_dataflow.prompt'] -``` -## step2 -Under the dataflow directory of sample_standard_app, dispatch is used as the entry, configure the dataflow process in `dispatch.yaml`, and then run the `dispatch.py` file (the results of the dataflow run are stored in the dispatch/data directory as jsonl). - -![picture](../_picture/dataflow_dispatch.png) - -In the `dispatch.yaml` file, users can customize and edit the dataflow task nodes that need to be run, such as the following configuration file. After dataflow runs, it performs two tasks: **adaptive data event collection and fine-tuning dataset generation**. -```yaml -name: 'main_dispatch' -description: 'dispatch with multi-dataflows which will execute one after another' -dataflows: - - ../flow/auto_event.yaml - - ../flow/dataset_build.yaml -``` - -# Dataflow Introduction -agentUniverse-dataflow currently supports six kinds of flow, namely `auto_event/dataset_build/train_dump/eval_report/model_deploy/instruct_select`, and each flow is combined through nodes to form a corresponding pipeline. -## Dataflow Flowchart -![picture](../_picture/dataflow_flowchart.jpg) - -In the yaml configuration file of each flow, each node contains llm and prompt version configuration information. **Users can customize the llm and prompt to achieve version management and quick switching.** -For example, the node in the following figure uses the QWen model of the sample project and the built-in prompt file of agentuniverse_dataflow: - -![picture](../_picture/dataflow_dataset_build.png) -## Flow Detailed Introduction -**Special instruction:** -`Auto event/dataset build/eval report/instruct select` in dataflow is a flow node that **users can directly run and experience**. The `train dump and model deploy` node mainly **provide the flow concept**. At present, they have been deployed and run through the standard model and training platform in Ant Group, and the open source community version will consider opening up later. -### Auto Event -The main function of Auto Event is to collect different data sources, perceive the log information of the corresponding agent runtime in the data source, extract valid input and output to generate specific jsonl files (currently the collection of jsonl data source is supported, and more data source types remain open. - -Auto Event contains three nodes, `Perceiver/Planner/Executor`, configured through yaml, and the example configuration file is `auto_event.yaml` in sample_standard_app. - - Perceiver Node: perceive the data source and extracting the original input and output from the data source. - - Planner Node: generate code to extract valid model input and output from the data source. - - Executor Node: execute code to extract valid model input and output from the data source. - -### Dataset Build -The main function of Dataset Build is to generate domain-specific high-quality q&a datasets. - -Dataset Build contains six nodes, `Seed/Rewrite/Extend/Dedupe/Answer/Filter`, configured through yaml, and the example configuration file is `dataset_build.yaml` in sample_standard_app. - - Seed Node: user-specified domain to produce rough query sets. - - Rewrite Node: rewrite rough query sets to standard query sets. - - Extend Node: extent query sets. - - Dedupe Node: de-duplicate query sets. - - Answer Node: LLM calls the query sets to generate the q&a datasets. - - Filter Node: professional domain evaluation criteria to filter valid datasets. - -### Train Dump -The main function of Train Dump is to upload specified data sets, sft training model, and dump q&a result sets. - -Train Dump contains four nodes, `Upload/Train/PreDeploy/Dump`, configured through yaml, and the example configuration file is `train_dump.yaml` in sample_standard_app. - - Upload Node: upload datasets to the model training platform. - - Train Node: train model according to fine-tuning data sets. - - PreDeploy Node: pre-deploy the model after training. - - Dump Node: dump q&a result sets according to query sets. - -### Eval Report -The main function of Eval Report is to evaluate the quality of q&a result sets and generate evaluation reports. - -Eval Report contains two nodes, `Eval/Report`, configured through yaml, and the example configuration file is `eval_report.yaml` in sample_standard_app. - - Eval Node: multidimensional evaluation of the quality of q&a result sets. - - Report Node: generate quality assessment reports. - -### Model Deploy -The main function of Model Deploy is to formally deploy the post-training model, dump q&a result sets. - -Model Deploy contains two nodes, `Deploy/Dump`, configured through yaml, and the example configuration file is `model_deploy.yaml` in sample_standard_app. - - Deploy Node: formal deployment of the trained model. - - Dump Node: dump q&a result sets according to query sets. - -### Instruct Select -The main function of Instruct Select is to generate instruction sets, assemble complete prompt sets, dump q&a result sets, and evaluate the quality of result sets. - -Instruct Select contains four nodes, `Seed/PromptGen/Dump/Eval`, configured through yaml, and the example configuration file is `instruct_select.yaml` in sample_standard_app. - - Seed Node: generate instruction sets based on user-specified domain. - - PromptGen Node: merge instruction sets and query sets into complete prompt sets. - - Dump Node: dump q&a result sets according to query sets. - - Eval Node: multi-dimensional evaluation of the quality of q&a result sets. \ No newline at end of file diff --git a/docs/guidebook/zh/8_1_agentUniverse-dataflow.md b/docs/guidebook/zh/8_1_agentUniverse-dataflow.md deleted file mode 100644 index f542dcf4..00000000 --- a/docs/guidebook/zh/8_1_agentUniverse-dataflow.md +++ /dev/null @@ -1,115 +0,0 @@ -# 项目介绍 -agentUniverse-dataflow基于**Agentic Workflow**思想，以多智能体及flow编排的形式，通过**离线自适应数据采集、微调数据集生成、模型推理训练、数据集多维度自动评估**等工作，强化大模型和Agent的数据处理流程。 - -具体包括： -1. 一套基础数据框架：**DataFramework**, 以Dispatch-Flow-Node-Data/Event/Prompt/Answer/Eval为框架，具有可持续集成和扩展能力 -2. 一套智能体框架：**PVRP**「Planner-Verifier-Reflector-Producer」 -3. 一套能力： - - 自适应数据事件采集 **auto_event** - - 微调数据集生成 **dataset_build** - - 上传训练推理 **train_dump** - - 多维客观自动评估 **eval_report** - - 模型部署生效 **model_deploy** - - 系统指令优选 **instruct_select** - -# 快速开始 -在agentUniverse标准工程模版`sample_standard_app`中： -## step1 -配置config.toml中prompt的加载路径，增加`agentuniverse_dataflow.prompt`，读取agentuniverse_dataflow系统内置prompt文件。 -```toml -[CORE_PACKAGE] -# Perform a full component scan and registration for all the paths under this list. -default = ['sample_standard_app.app.core'] -# Scan and register agent components for all paths under this list, with priority over the default. -agent = ['sample_standard_app.app.core.agent'] -# Scan and register agent components for all paths under this list, with priority over the default. -knowledge = ['sample_standard_app.app.core.knowledge'] -# Scan and register knowledge components for all paths under this list, with priority over the default. -llm = ['sample_standard_app.app.core.llm'] -# Scan and register llm components for all paths under this list, with priority over the default. -planner = ['sample_standard_app.app.core.planner'] -# Scan and register planner components for all paths under this list, with priority over the default. -tool = ['sample_standard_app.app.core.tool'] -# Scan and register memory components for all paths under this list, with priority over the default. -memory = ['sample_standard_app.app.core.memory'] -# Scan and register service components for all paths under this list, with priority over the default. -service = ['sample_standard_app.app.core.service'] -# Scan and register prompt components for all paths under this list, with priority over the default. -prompt = ['sample_standard_app.app.core.prompt', 'agentuniverse_dataflow.prompt'] -``` -## step2 -sample_standard_app的dataflow目录下，dispatch作为入口，在`dispatch.yaml`中配置dataflow流程，之后运行`dispatch.py`文件即可（dataflow运行结果以jsonl形式存储在dispatch/data目录下）。 - -![图片](../_picture/dataflow_dispatch.png) - -在dispatch.yaml文件中，用户可以自定义编辑需要运行的dataflow任务节点，如下文配置文件dataflow运行后，执行**自适应数据事件采集和微调数据集生成**两个任务。 -```yaml -name: 'main_dispatch' -description: 'dispatch with multi-dataflows which will execute one after another' -dataflows: - - ../flow/auto_event.yaml - - ../flow/dataset_build.yaml -``` -# Dataflow介绍 -agentUniverse-dataflow目前支持6种flow，分别为`auto_event/dataset_build/train_dump/eval_report/model_deploy/instruct_select`，每个flow通过node组合形成对应的pipeline。 -## Dataflow流程图 -![图片](../_picture/dataflow_flowchart.jpg) - -在每个flow的yaml配置文件，各node节点包含llm、prompt version配置信息，用户可自定义llm基座模型和prompt，实现版本管理和快捷切换。 -例如，下图中node节点使用sample工程的qwen基座模型和agentuniverse-dataflow系统内置的prompt文件： - -![图片](../_picture/dataflow_dataset_build.png) -## 各Flow具体详情 -**特殊说明：** -dataflow中`auto event/dataset build/eval report/instruce select`为用户可以**直接运行体验的flow节点**，`train dump/model deploy`节点**理念先行**，目前在蚂蚁集团内已通过标准的模型部署和训练平台运行，开源社区版本后续考虑开放。 - -### Auto Event -Auto Event主要功能是通过采集不同数据源，感知数据源中对应agent运行时的日志信息， 提取有效输入输出生成特定jsonl文件（当前支持采集jsonl数据源，更多数据源类型持续开放）。 - -Auto Event包含3个node，`Perceiver/Planner/Executor`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的auto_event.yaml。 - - Perceiver Node: 感知数据源，提取数据源中的原始输入输出 - - Planner Node: 生成代码用来提取数据源中的有效模型输入输出 - - Executor Node: 执行代码提取数据源中的有效模型输入输出 - -### Dataset Build -Dataset Build主要功能是生成特定领域高质量模型问答数据集。 - -Dataset Build包含6个node，`Seed/Rewrite/Extend/Dedupe/Answer/Filter`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的dataset_build.yaml。 - - Seed Node: 用户指定领域，生产粗略query集 - - Rewrite Node: 粗版query集改写为标准query集 - - Extend Node: 扩充query集 - - Dedupe Node: 对query集内容去重 - - Answer Node: query集，模型调用生成问答结果集 - - Filter Node: 专业领域评价标准，过滤有效数据集 - -### Train Dump -Train Dump主要功能是上传指定数据集，sft训练模型，dump问答结果集。 - -Train Dump包含4个node，`Upload/Train/PreDeploy/Dump`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的train_dump.yaml。 - - Upload Node: 上传数据集到模型训练平台 - - Train Node: 根据微调数据集训练模型 - - PreDeploy Node: 预部署训练后模型 - - Dump Node: 指定query集，dump问答结果 - -### Eval Report -Eval Report主要功能是多维度评估问答结果集质量，生成评估报告。 - -Eval Report包含2个node，`Eval/Report`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的eval_report.yaml。 - - Eval Node: 多维度评估问答结果集质量 - - Report Node: 生产质量评估报告 - -### Model Deploy -Model Deploy主要功能是正式部署训练后模型，dump问答数据集。 - -Model Deploy包含2个node，`Deploy/Dump`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的model_deploy.yaml。 - - Deploy Node: 正式部署训练后模型 - - Dump Node: 指定query集，dump问答结果 - -### Instruct Select -Instruct Select主要功能是生成指令集，拼接完整prompt集，dump问答数据集，评估问答质量。 - -Instruct Select包含4个node，`Seed/PromptGen/Dump/Eval`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的instruct_select.yaml。 - - Seed Node: 用户指定领域，生产指令集 - - PromptGen Node: 指令集+query集合并完整prompt集 - - Dump Node: 指定prompt集，dump问答结果 - - Eval Node: 多维度评估问答结果集质量 \ No newline at end of file diff --git a/sample_standard_app/dataflow/__init__.py b/sample_standard_app/dataflow/__init__.py deleted file mode 100644 index 8f9304d1..00000000 --- a/sample_standard_app/dataflow/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/18 16:32 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/sample_standard_app/dataflow/dispatch/__init__.py b/sample_standard_app/dataflow/dispatch/__init__.py deleted file mode 100644 index 6d7ca694..00000000 --- a/sample_standard_app/dataflow/dispatch/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/18 16:35 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl b/sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl deleted file mode 100644 index de1ee0ec..00000000 --- a/sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"prompt": "怎样判断一个企业的经济状况稳健与否？"} -{"prompt": "做出资本配置决定时，有哪些核心标志可以体现公司的盈利表现？"} -{"prompt": "金融投资领域中，风险控制措施的重要性及其实行方法是怎样的？"} -{"prompt": "加密货币对常规金融市场的冲击与后续走向如何？"} -{"prompt": "在公司收购过程中，财务审查需着重检查哪几个关键点？"} -{"prompt": "如何评估一家银行的信贷风险并制定相应的风险管理策略？"} -{"prompt": "在进行跨国投资时，应考虑哪些宏观经济指标来预测市场趋势？"} -{"prompt": "保险业中的精算模型如何影响产品定价和企业利润？"} -{"prompt": "绿色金融的发展趋势及其对传统能源行业的影响是什么？"} -{"prompt": "金融科技如何改变个人理财规划和财富管理服务？"} diff --git a/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl b/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl deleted file mode 100644 index 08b84822..00000000 --- a/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl +++ /dev/null @@ -1 +0,0 @@ -{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} \ No newline at end of file diff --git a/sample_standard_app/dataflow/dispatch/dispatch.py b/sample_standard_app/dataflow/dispatch/dispatch.py deleted file mode 100644 index 91443e4e..00000000 --- a/sample_standard_app/dataflow/dispatch/dispatch.py +++ /dev/null @@ -1,38 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/11 20:52 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: dispatch.py -import yaml - -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.base.agentuniverse import AgentUniverse - -from agentuniverse_dataflow.flow.dataflow import Dataflow - -AgentUniverse().start(config_path='../../config/config.toml') - - -def load_dataflows_from_yaml(conf_file): - with open(conf_file, 'r') as file: - config = yaml.safe_load(file) - dataflows = [] - yaml_list = config.get('dataflows') - for item in yaml_list: - dataflows.append(Dataflow(item)) - return dataflows - - -def dispatch(conf_file='dispatch.yaml'): - dataflows = load_dataflows_from_yaml(conf_file) - try: - for dataflow in dataflows: - dataflow.execute() - except Exception as e: - LOGGER.error(f"An error occurred: {e}") - - -if __name__ == '__main__': - dispatch('dispatch.yaml') diff --git a/sample_standard_app/dataflow/dispatch/dispatch.yaml b/sample_standard_app/dataflow/dispatch/dispatch.yaml deleted file mode 100644 index f73410b9..00000000 --- a/sample_standard_app/dataflow/dispatch/dispatch.yaml +++ /dev/null @@ -1,5 +0,0 @@ -name: 'main_dispatch' -description: 'dispatch with multi-dataflows which will execute one after another' -dataflows: - - ../flow/eval_report.yaml - - ../flow/instruct_select.yaml \ No newline at end of file diff --git a/sample_standard_app/dataflow/flow/__init__.py b/sample_standard_app/dataflow/flow/__init__.py deleted file mode 100644 index 8f9304d1..00000000 --- a/sample_standard_app/dataflow/flow/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/18 16:32 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/sample_standard_app/dataflow/flow/auto_event.yaml b/sample_standard_app/dataflow/flow/auto_event.yaml deleted file mode 100644 index 490f92fe..00000000 --- a/sample_standard_app/dataflow/flow/auto_event.yaml +++ /dev/null @@ -1,42 +0,0 @@ -name: 'auto_event_flow' -description: "event collect agent via perceiver planner executor" -nodes: - - module: 'agentuniverse_dataflow.node.data.event.perceiver' - # data perceiver in event agent which could perceive tables in jsonl .etc - class: 'PerceiverNode' - param_in_jsonl: - param_out_jsonl: 'auto_event_flow.perceiver.param.out' - datasets_in_jsonl: - dataset_out_jsonl: 'auto_event_flow.perceiver.dataset.out' - node_param: - # supported event_db: jsonl - event_db: jsonl - # if jsonl, please input jsonl file prefix as event_uri and make sure jsonl file in path data/; then event_sql is useless - event_uri: demo_jsonl - # perceive limited lines - event_sql: - prompt_col: prompt - answer_col: answer - - - module: 'agentuniverse_dataflow.node.data.event.planner' - # data planner in event agent which could plan python code to extract with reflections - class: 'PlannerNode' - llm: 'qwen_llm' - prompt_version: 'event_planner_node.cn' - param_in_jsonl: 'auto_event_flow.perceiver.param.out' - param_out_jsonl: 'auto_event_flow.planner.plan.out' - datasets_in_jsonl: - - 'auto_event_flow.perceiver.dataset.out' - dataset_out_jsonl: - node_param: - verify_lines: 1 - - - module: 'agentuniverse_dataflow.node.data.event.executor' - # data executor in event agent which execute python code from planner - class: 'ExecutorNode' - param_in_jsonl: 'auto_event_flow.planner.plan.out' - param_out_jsonl: - datasets_in_jsonl: - dataset_out_jsonl: 'auto_event_flow.executor.out' - - diff --git a/sample_standard_app/dataflow/flow/dataset_build.yaml b/sample_standard_app/dataflow/flow/dataset_build.yaml deleted file mode 100644 index 74204632..00000000 --- a/sample_standard_app/dataflow/flow/dataset_build.yaml +++ /dev/null @@ -1,77 +0,0 @@ -name: 'dataset_build_flow' -description: "train dataset build agent flow" -nodes: - - module: 'agentuniverse_dataflow.node.data.prompt.input.seed' - # query seed from LLM - class: 'SeedNode' - llm: 'qwen_llm' - prompt_version: 'dataset_input_seed_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - dataset_out_jsonl: 'dataset_build_flow.seed.out' - node_param: - seeds_num: 5 - seed_gen_requirement: '金融领域' - - - module: 'agentuniverse_dataflow.node.data.prompt.input.rewrite' - # rewrite query from LLM - class: 'RewriteNode' - llm: 'qwen_llm' - prompt_version: 'dataset_rewrite_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.seed.out' - dataset_out_jsonl: 'dataset_build_flow.rewrite.out' - - - module: 'agentuniverse_dataflow.node.data.prompt.input.extend' - # extend query from LLM - class: 'ExtendNode' - llm: 'qwen_llm' - prompt_version: 'dataset_extend_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.rewrite.out' - dataset_out_jsonl: 'dataset_build_flow.extend.out' - node_param: - extend_times: 1 - - - module: 'agentuniverse_dataflow.node.data.prompt.input.dedupe' - # dedupe queries which improve sft result - class: 'DedupeNode' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.extend.out' - dataset_out_jsonl: 'dataset_build_flow.dedupe.out' - node_param: - diversify_hamming_threshold: 18 - freq_top_percent: 1.0 - freq_least_count: 100 - - - module: 'agentuniverse_dataflow.node.data.answer.answer' - # build answers from the prompt via LLM as training data - class: 'AnswerNode' - llm: 'qwen_llm' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.dedupe.out' - dataset_out_jsonl: 'dataset_build_flow.answer.out' - - - module: 'agentuniverse_dataflow.node.data.answer.filter' - # filter training data to improve data quality - class: 'FilterNode' - llm: 'qwen_llm' - prompt_version: 'dataset_filter_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.answer.out' - dataset_out_jsonl: 'dataset_build_flow.filter.out' - node_param: - dimscore_threshold: 60 - avgscore_threshold: 75 - diff --git a/sample_standard_app/dataflow/flow/eval_report.yaml b/sample_standard_app/dataflow/flow/eval_report.yaml deleted file mode 100644 index 43fd585d..00000000 --- a/sample_standard_app/dataflow/flow/eval_report.yaml +++ /dev/null @@ -1,28 +0,0 @@ -name: 'eval_report_flow' -description: "eval for nodes and summarize report" -nodes: - - module: 'agentuniverse_dataflow.node.eval.eval' - # eval dump result from LLM infer - class: 'EvalNode' - llm: 'qwen_llm' - prompt_version: 'eval_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: -# - 'train_dump_flow.aci_dump.out' -# - 'dataset_build_flow.filter.out' - - 'demo_jsonl' - dataset_out_jsonl: 'eval_report_flow.eval.out' - node_param: - eval_lines: 100 - - - module: 'agentuniverse_dataflow.node.eval.report' - # compare multi-eval result as a report - class: 'ReportNode' - param_in_jsonl: - param_out_jsonl: - # multiple datasets - datasets_in_jsonl: - - 'eval_report_flow.eval.out' - dataset_out_jsonl: 'eval_report_flow.report.out' - diff --git a/sample_standard_app/dataflow/flow/instruct_select.yaml b/sample_standard_app/dataflow/flow/instruct_select.yaml deleted file mode 100644 index 9e30116f..00000000 --- a/sample_standard_app/dataflow/flow/instruct_select.yaml +++ /dev/null @@ -1,53 +0,0 @@ -name: 'instruct_select_flow' -description: "extend instruct from sample and choose the best via pre-deploy model" -nodes: - - module: 'agentuniverse_dataflow.node.data.prompt.instruct.seed' - # produce multiple instruct seed candidates - class: 'SeedNode' - llm: 'qwen_llm' - prompt_version: 'instruct_seed_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - dataset_out_jsonl: 'instruct_select_flow.seed.out' - node_param: - extend_times: 10 - - - module: 'agentuniverse_dataflow.node.data.prompt.instruct.prompt_gen' - # produce prompt from instruct and query - class: 'PromptGenNode' - llm: 'qwen_llm' - prompt_version: 'instruct_prompt_gen_node.cn' - param_in_jsonl: - param_out_jsonl: - datasets_in_jsonl: - # instruct jsonl - - 'instruct_select_flow.seed.out' - # input jsonl - - 'dataset_build_flow.dedupe.out' - dataset_out_jsonl: 'instruct_select_flow.prompt_gen.out' - node_param: - instruct_num: 10 - -# - module: '' -# # dump answer from pre deployed LLM infer via prompt as input -# class: 'DumpNode' -# param_in_jsonl: 'train_dump_flow.aci_pre_deploy.out' -# param_out_jsonl: -# datasets_in_jsonl: -# - 'instruct_select_flow.prompt_gen.out' -# dataset_out_jsonl: 'instruct_select_flow.dump.out' - -# - module: 'agentuniverse_dataflow.node.eval.eval' -# # eval dump result through 7 dimensions -# class: 'EvalNode' -# llm: 'qwen_llm' -# prompt_version: 'eval_node.cn' -# param_in_jsonl: -# param_out_jsonl: -# datasets_in_jsonl: -# - 'instruct_select_flow.dump.out' -# dataset_out_jsonl: 'instruct_select_flow.eval.out' -# node_param: -# eval_lines: 100 - diff --git a/sample_standard_app/dataflow/flow/model_deploy.yaml b/sample_standard_app/dataflow/flow/model_deploy.yaml deleted file mode 100644 index 9cc35645..00000000 --- a/sample_standard_app/dataflow/flow/model_deploy.yaml +++ /dev/null @@ -1,21 +0,0 @@ -name: 'model_deploy_flow' -description: "deploy with artifact" -nodes: - - module: '' - # deploy LLM from artifact - class: 'DeployNode' - param_in_jsonl: 'train_dump_flow.train.out' - param_out_jsonl: 'train_dump_flow.deploy.out' - datasets_in_jsonl: - dataset_out_jsonl: - - - module: '' - # dump answer from deploy via prompt - class: 'DumpNode' - param_in_jsonl: 'train_dump_flow.deploy.out' - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.filter.out' - dataset_out_jsonl: 'train_dump_flow.dump.deploy.out' -train_dump: - yaml: \ No newline at end of file diff --git a/sample_standard_app/dataflow/flow/train_dump.yaml b/sample_standard_app/dataflow/flow/train_dump.yaml deleted file mode 100644 index 21d847fe..00000000 --- a/sample_standard_app/dataflow/flow/train_dump.yaml +++ /dev/null @@ -1,42 +0,0 @@ -name: 'train_dump_flow' -description: "train then dump instructions from dataset input" -nodes: - - module: '' - # upload sft dataset to training platform - class: 'UploadNode' - param_in_jsonl: - param_out_jsonl: 'train_dump_flow.upload.out' - datasets_in_jsonl: - - 'dataset_build_flow.filter.out' - dataset_out_jsonl: - node_param: - datagraph_token_name: 'dataflow token' - dataset_name: 'dataagent_dataset' - dataset_desc: 'dataagent dataset common' - - - module: '' - # train sft model via sfs dataset - class: 'TrainNode' - param_in_jsonl: 'train_dump_flow.upload.out' - param_out_jsonl: 'train_dump_flow.aci_train.out' - datasets_in_jsonl: - dataset_out_jsonl: - - - module: '' - # pre deployed LLM infer service via artifact from train stage via platform with deploy func - class: 'PreDeployNode' - param_in_jsonl: 'train_dump_flow.aci_train.out' - param_out_jsonl: 'train_dump_flow.aci_pre_deploy.out' - datasets_in_jsonl: - dataset_out_jsonl: - - - module: '' - # dump answers from LLM inference service via prompts from platform - class: 'DumpNode' - param_in_jsonl: 'train_dump_flow.aci_pre_deploy.out' - param_out_jsonl: - datasets_in_jsonl: - - 'dataset_build_flow.filter.out' - dataset_out_jsonl: 'train_dump_flow.aci_dump.out' - node_param: - dump_lines: 100 \ No newline at end of file From c21ef59f25353ebcb7c768894f35743f5b0cd901 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Mon, 24 Jun 2024 17:26:01 +0800 Subject: [PATCH 045/118] docs: add monitor module docs in the agentUniverse. --- docs/guidebook/en/2_5_1_Monitor_Module.md | 119 ++++++++++++++++++ ...21\346\216\247\346\250\241\345\235\227.md" | 89 +++++++++++++ 2 files changed, 208 insertions(+) create mode 100644 docs/guidebook/en/2_5_1_Monitor_Module.md create mode 100644 "docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" diff --git a/docs/guidebook/en/2_5_1_Monitor_Module.md b/docs/guidebook/en/2_5_1_Monitor_Module.md new file mode 100644 index 00000000..e68cec07 --- /dev/null +++ b/docs/guidebook/en/2_5_1_Monitor_Module.md @@ -0,0 +1,119 @@ +# Monitor Module + +agentUniverse monitor module, mainly around the agent runtime LLM invocation/token expenses for data tracking and +recording. In this document, we will provide a detailed introduction on how to use the **LLM invocation tracking +capability** that is currently open in the agentUniverse. + +## Monitor Configuration + +Configure the monitor module settings in the main configuration file `config.toml` of agentUniverse, as follows: + +```toml +[MONITOR] +activate = true +dir = './monitor' +``` + +- **`activate`**: The master switch for the monitor module is set to off by default. When set to true, it will turn on + the LLM invocation tracking function of the agent runtime. +- **`dir`**: The local storage directory corresponding to the monitor module is, by default, the 'monitor' directory one + level above the runtime directory. Users can customize the configuration of the directory path. + +### LLM tracing configuration + +For the LLM invocation tracking capability, agentUniverse also supports model granularity configuration. Once the main +switch of the monitor module is turned on, the invocation tracking function for specific models can be selectively +disabled through the LLM's yaml file. + +For example, for a user-defined model such as `demo_llm`, if the tracing is set to false, then the invocation tracking +function for this model will be turned off. + +```yaml +name: 'demo_llm' +description: 'demo openai' +model_name: 'gpt-4o' +max_tokens: 1000 +max_retries: 2 +tracing: false +metadata: + type: 'LLM' + module: 'agentuniverse.llm.default.default_openai_llm' + class: 'DefaultOpenAILLM' +``` + +Special note: In the main configuration file of agentUniverse, the monitor module's master switch has the **highest +priority**. If the `activate` configuration is set to false, the model granularity configuration will not take effect. + +## Monitor Module Introduction + +### LLM Invocation Tracking + +If the user utilizes the **default model class** supported by the agentUniverse, the feature can be used after the +configuration as described above is completed. + +#### trace_llm Decorator + +If a user customizes the extension based on the agentUniverse's model base class `LLM` to implement a custom model +invocation, it is necessary to add the `@trace_llm` decorator to the call/acall method of the custom model class. + +Custom model example code as follows: + +```python +from typing import Any, Iterator, Union, AsyncIterator + +from agentuniverse.llm.llm import LLM, LLMOutput +from agentuniverse.base.annotation.trace import trace_llm + + +class DemoLLM(LLM): + + @trace_llm + def call(self, messages: list, **kwargs: Any) -> Union[LLMOutput, Iterator[LLMOutput]]: + pass + + @trace_llm + async def acall(self, *args: Any, **kwargs: Any) -> Union[LLMOutput, AsyncIterator[LLMOutput]]: + pass +``` + +agentUniverse implements the `@trace_llm` decorator, which, when the agent calls the corresponding LLM call/acall +methods, collects the model's prompt and response, serializes the information into JSON, and stores it in the local +storage directory corresponding to the monitor configuration in the form of a jsonl file, divided by the hour. + +#### Demo Effect + +Call the `demo_rag_agent` in the sample project of agentUniverse, using the gpt-4o model, with the query to analyze the +reasons behind Warren Buffett's reduction of his stake in BYD. The model invocation tracking function captures the +following data: + +```json +{ + "source": "DefaultOpenAILLM.acall", + "date": "2024-06-24 16:27:14", + "llm_input": { + "messages": [ + { + "role": "system", + "content": "You are an AI assistant proficient in information analysis.\nYour goal is to determine whether the corresponding answers to the questions provide valuable information, and to make suggestions and evaluations on the answers to the questions." + }, + { + "role": "user", + "content": "The rules you need to follow are:\n1. You must answer the user's questions using English, combining the background information of the query with the knowledge you possess.\n2. Generate structured answers, and use blank lines to enhance the reading experience when necessary.\\n the question needs to be answered is: the reasons behind Warren Buffett's reduction of his stake in BYD\\n" + } + ] + }, + "kwargs": { + "model": "gpt-4o", + "stream": false, + "n": 1, + "temperature": 0.5, + "max_tokens": 1000 + }, + "llm_output": "Reasons Behind Warren Buffett's Reduction of His Stake in BYD\n\nWarren Buffett, through his company Berkshire Hathaway, has been reducing his stake in BYD since August 24, 2022. There are several potential reasons behind this decision:\n\n1. **Portfolio Rebalancing**:\n - **Investment Strategy**: Buffett is known for his long-term investment strategy and value investing. Reducing his stake in BYD could be part of a broader portfolio rebalancing effort to diversify or allocate capital to other investment opportunities.\n - **Profit Realization**: Given that Buffett's initial investment in BYD has appreciated significantly (approximately 31 times the initial value), selling some shares allows him to realize substantial profits.\n\n2. **Market Conditions**:\n - **Stock Performance**: Despite the reduction in stake, BYD's stock price has remained relatively stable. This suggests that Buffett's decision to sell shares is not necessarily a reflection of a negative outlook on the company's stock performance.\n - **Economic Factors**: The broader economic environment, including interest rates, inflation, and market volatility, may influence investment decisions. Buffett might be adjusting his holdings in response to these macroeconomic factors.\n\n3. **Company Evaluation**:\n - **Intrinsic Value**: Buffett's investment philosophy focuses on the intrinsic value of a company rather than short-term stock price movements. He might believe that the current market price of BYD reflects its intrinsic value, prompting a partial exit.\n - **Sector Analysis**: While BYD remains a leading player in the Chinese electric vehicle (EV) market, Buffett might be evaluating the competitive landscape and future growth prospects, leading to a strategic reduction in holdings.\n\n" +} +``` + +- **`source`**: the source of LLM invocation during agent runtime. +- **`date`**: LLM invocation time +- **`llm_input`**: LLM invocation messages and model parameters +- **`llm_output`**: LLM output text \ No newline at end of file diff --git "a/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" "b/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" new file mode 100644 index 00000000..a2810476 --- /dev/null +++ "b/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" @@ -0,0 +1,89 @@ +# 监控模块 +agentUniverse监控模块，主要围绕agent运行时的模型调用/token开销等进行数据追踪与记录。针对**目前框架已开放的模型调用追踪能力**，本文将详细介绍如何使用该功能。 + +## 监控配置 +在agentUniverse的主配置文件`config.toml`中配置监控模块配置信息，如下： +```toml +[MONITOR] +activate = true +dir = './monitor' +``` +- **`activate`**: 监控模块总开关，默认关闭，设置为true后将开启agent运行时的LLM调用追踪功能。 +- **`dir`**: 监控模块对应的本地记录存储目录，默认为运行时，上一层级下的monitor目录，用户可自定义配置目录路径。 + +### 模型粒度配置 +针对LLM调用追踪能力，agentUniverse同样支持模型粒度配置，当监控模块主开关打开后，可以通过LLM的yaml文件选择关闭特定模型调用追踪功能。 + +比如用户自定义的`demo_llm`模型，tracing设置为false，则关闭该模型的调用追踪功能。 +```yaml +name: 'demo_llm' +description: 'demo openai' +model_name: 'gpt-4o' +max_tokens: 1000 +max_retries: 2 +tracing: false +metadata: + type: 'LLM' + module: 'agentuniverse.llm.default.default_openai_llm' + class: 'DefaultOpenAILLM' +``` +特别说明：agentUniverse主配置文件的监控模块总开关**优先级最高**，若activate配置为false的情况下，模型粒度配置不生效。 + +## 监控模块说明 +### 模型调用追踪 +用户若使用当前agentUniverse支持的**默认模型类**，则如上文所述配置完成后即可使用该功能。 + +#### trace_llm装饰器 + +若用户根据agentUniverse的模型基类LLM做自定义扩展，实现自定义模型调用，则需要在自定义模型类的调用方法call/acall中增加@trace_llm装饰器功能。 + +如下自定义模型示例代码： +```python +from typing import Any, Iterator, Union, AsyncIterator + +from agentuniverse.llm.llm import LLM, LLMOutput +from agentuniverse.base.annotation.trace import trace_llm + +class DemoLLM(LLM): + + @trace_llm + def call(self, messages: list, **kwargs: Any) -> Union[LLMOutput, Iterator[LLMOutput]]: + pass + + @trace_llm + async def acall(self, *args: Any, **kwargs: Any) -> Union[LLMOutput, AsyncIterator[LLMOutput]]: + pass +``` +agentUniverse通过`@trace_llm`装饰器实现，agent在调用对应LLM call/acall方法时，采集模型prompt和response，将信息json序列化后存储到上文监控配置对应的本地存储目录下，以jsonl文件的形式按小时为单位分割。 + +#### 具体效果 +调用agentUniverse sample工程中的demo_rag_agent，使用qwen-turbo模型，query为分析下巴菲特减持比亚迪的原因，模型调用追踪功能捕捉到的数据如下： +```json +{ + "source": "QWenOpenAIStyleLLM.acall", + "date": "2024-06-24 16:27:14", + "llm_input": + {"messages": [ + { + "role": "system", + "content": "你是一位精通信息分析的ai助手。\\n 你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价" + }, + { + "role": "user", + "content": "你需要遵守的规则是:\\n1. 必须使用中文结合查询的背景信息结合你所拥有的知识回答用户提出的问题。\\n2. 结构化答案生成，必要时通过空行提升阅读体验。\\n 需要回答的问题是: 分析下巴菲特减持比亚迪的原因\\n" + } + ]}, + "kwargs": { + "model": "qwen-turbo", + "stream": false, + "n": 1, + "temperature": 0.5, + "max_tokens": 1000 + }, + "llm_output": "巴菲特减持比亚迪股票的原因可以从几个方面来分析：\\n\\n1. **投资策略调整**：尽管巴菲特通常倾向于长期持有优质股票，但投资组合管理需要灵活性。他可能会根据市场动态、行业趋势和个人评估的变化，适时调整持仓以优化资产配置或分散风险。\\n\\n2. **估值与预期回报**：减持可能反映了巴菲特对比亚迪当前股价与他内在价值评估的差距。尽管比亚迪业绩强劲，但如果他认为股价过高，可能会选择部分兑现利润。\\n 综上所述，巴菲特减持比亚迪股票可能是基于上述因素的综合考虑，但具体原因只有巴菲特和芒格本人了解。值得注意的是，尽管减持，他们对比亚迪的长期看好并未改变，这从芒格的演讲和持续的股东大会支持可以看出。" +} +``` +- **`source`**: agent运行时LLM的调用来源 +- **`date`**: LLM调用时间 +- **`llm_input`**: LLM调用message及模型参数 +- **`llm_output`**: LLM输出文本段信息 \ No newline at end of file From 17f41cba063add17b87f545667b1068fa8542878 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Mon, 24 Jun 2024 17:30:12 +0800 Subject: [PATCH 046/118] docs: add monitor module docs in the agentUniverse. --- docs/guidebook/en/2_5_1_Monitor_Module.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guidebook/en/2_5_1_Monitor_Module.md b/docs/guidebook/en/2_5_1_Monitor_Module.md index e68cec07..62f56663 100644 --- a/docs/guidebook/en/2_5_1_Monitor_Module.md +++ b/docs/guidebook/en/2_5_1_Monitor_Module.md @@ -19,7 +19,7 @@ dir = './monitor' - **`dir`**: The local storage directory corresponding to the monitor module is, by default, the 'monitor' directory one level above the runtime directory. Users can customize the configuration of the directory path. -### LLM tracing configuration +### LLM Tracing Configuration For the LLM invocation tracking capability, agentUniverse also supports model granularity configuration. Once the main switch of the monitor module is turned on, the invocation tracking function for specific models can be selectively From 6fadcad416ffa96188ad8048b9351d7eca33c1af Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 24 Jun 2024 20:54:32 +0800 Subject: [PATCH 047/118] add requesttool --- .../app/core/tool/request_get_tool.yaml | 16 +++++ .../app/core/tool/request_post_tool.yaml | 19 +++++ .../app/core/tool/request_tool.py | 69 +++++++++++++++++++ 3 files changed, 104 insertions(+) create mode 100644 sample_standard_app/app/core/tool/request_get_tool.yaml create mode 100644 sample_standard_app/app/core/tool/request_post_tool.yaml create mode 100644 sample_standard_app/app/core/tool/request_tool.py diff --git a/sample_standard_app/app/core/tool/request_get_tool.yaml b/sample_standard_app/app/core/tool/request_get_tool.yaml new file mode 100644 index 00000000..ed0dd400 --- /dev/null +++ b/sample_standard_app/app/core/tool/request_get_tool.yaml @@ -0,0 +1,16 @@ +name: 'requests_get' +description: 'A portal to the internet. Use this when you need to get specific + content from a website. Input should be a url (i.e. https://www.google.com). + The output will be the text response of the GET request. + ```' +headers: + content-type: 'application/json' +method: 'POST' +json_parser: true +response_content_type: json +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.request_tool' + class: 'RequestTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/request_post_tool.yaml b/sample_standard_app/app/core/tool/request_post_tool.yaml new file mode 100644 index 00000000..a141dd3d --- /dev/null +++ b/sample_standard_app/app/core/tool/request_post_tool.yaml @@ -0,0 +1,19 @@ +name: 'requests_post' +description: 'Use this when you want to POST to a website. + Input should be a json string with two keys: "url" and "data". + The value of "url" should be a string, and the value of "data" should be a dictionary of + key-value pairs you want to POST to the url. + Be careful to always use double quotes for strings in the json string + The output will be the text response of the POST request. + ```' +headers: + content-type: 'application/json' +method: 'GET' +json_parser: false +response_content_type: json +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.request_tool' + class: 'RequestTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/request_tool.py b/sample_standard_app/app/core/tool/request_tool.py new file mode 100644 index 00000000..ae007614 --- /dev/null +++ b/sample_standard_app/app/core/tool/request_tool.py @@ -0,0 +1,69 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/24 10:19 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: request_tool.py + + +from typing import Optional + +from langchain_community.utilities.requests import GenericRequestsWrapper +from langchain_core.utils.json import parse_json_markdown + +from agentuniverse.agent.action.tool.tool import Tool, ToolInput +from agentuniverse.base.config.component_configer.configers.tool_configer import ToolConfiger +from agentuniverse.base.util.logging.logging_util import LOGGER + + +class RequestTool(Tool): + method:Optional[str] = 'GET' + headers: Optional[dict]= {} + response_content_type:Optional[str] = 'text' + requests_wrapper: Optional[GenericRequestsWrapper] = None + json_parser: Optional[bool] = False + + @staticmethod + def _clean_url(url: str) -> str: + """Strips quotes from the url.""" + return url.strip("\"'") + + def execute(self, tool_input: ToolInput): + input_params: str = tool_input.get_data('input') + if self.json_parser: + try: + parse_data = parse_json_markdown(input_params) + return self.execute_by_method(**parse_data) + except Exception as e: + LOGGER.error(f'execute request error input{input_params} error{e}') + return str(e) + else: + return self.execute_by_method(input_params) + + def execute_by_method(self, url: str, data: dict = None, **kwargs): + url = self._clean_url(url) + if self.method == 'GET': + return self.requests_wrapper.get(url) + elif self.method == 'POST': + return self.requests_wrapper.post(url, data=data) + elif self.method == 'PUT': + return self.requests_wrapper.put(url, data=data) + elif self.method == 'DELETE': + return self.requests_wrapper.delete(url) + else: + raise ValueError(f"Unsupported method: {self.method}") + + def initialize_by_component_configer(self, component_configer: ToolConfiger) -> 'Tool': + """ + :param component_configer: + :return: + """ + self.headers = component_configer.configer.value.get('headers') + self.method = component_configer.configer.value.get('method') + self.response_content_type = component_configer.configer.value.get('response_content_type') + self.requests_wrapper = GenericRequestsWrapper( + headers=self.headers, + response_content_type=self.response_content_type + ) + return super().initialize_by_component_configer(component_configer) From 82134282fcd15272f762d4bc7380f48d417344f4 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Tue, 25 Jun 2024 15:01:51 +0800 Subject: [PATCH 048/118] add langchain_tool --- .../app/core/tool/langchain_tool.py | 51 +++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 sample_standard_app/app/core/tool/langchain_tool.py diff --git a/sample_standard_app/app/core/tool/langchain_tool.py b/sample_standard_app/app/core/tool/langchain_tool.py new file mode 100644 index 00000000..089d7b80 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool.py @@ -0,0 +1,51 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- +import importlib +import json +# @Time : 2024/6/24 11:42 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: langchain_tool.py + +from typing import Optional, Type + +from langchain_core.tools import BaseTool, Tool as LangchainTool + +from agentuniverse.agent.action.tool.tool import Tool, ToolInput +from agentuniverse.base.config.component_configer.configers.tool_configer import ToolConfiger + + +class LangChainTool(Tool): + name: Optional[str] = "" + description: Optional[str] = "" + tool: Optional[BaseTool] = None + + def execute(self, tool_input: ToolInput): + input = tool_input.get_data("input") + callbacks = tool_input.get_data("callbacks", None) + return self.tool.run(json.loads(input), callbacks=callbacks) + + def initialize_by_component_configer(self, component_configer: ToolConfiger) -> 'Tool': + super().initialize_by_component_configer(component_configer) + self.init_langchain_tool(component_configer) + self.description = self.tool.description + return self + + def init_langchain_tool(self, component_configer): + langchain_info = component_configer.configer.value.get('langchain') + module = langchain_info.get("module") + class_name = langchain_info.get("class_name") + module = importlib.import_module(module) + clz = getattr(module, class_name) + init_params = langchain_info.get("init_params") + self.get_langchain_tool(init_params, clz) + return self.tool + + def get_langchain_tool(self, init_params: dict, clz: Type[BaseTool]): + if init_params: + self.tool = clz(**init_params) + else: + self.tool = clz() + + def as_langchain(self): + return self.tool From 474d45949d6d447b7bcac70a602407e31879edcf Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 25 Jun 2024 19:32:51 +0800 Subject: [PATCH 049/118] fix: add process_input_object in the executing agent --- .../executing_agent/executing_agent.py | 28 ++++++++++++++++++- .../agent/memory/langchain_instance.py | 6 ++-- .../plan/planner/peer_planner/peer_planner.py | 4 +-- 3 files changed, 32 insertions(+), 6 deletions(-) diff --git a/agentuniverse/agent/default/executing_agent/executing_agent.py b/agentuniverse/agent/default/executing_agent/executing_agent.py index 94e4143d..2ac27143 100644 --- a/agentuniverse/agent/default/executing_agent/executing_agent.py +++ b/agentuniverse/agent/default/executing_agent/executing_agent.py @@ -8,6 +8,8 @@ import copy from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED from typing import Optional, Any + +from agentuniverse.agent.action.tool.tool_manager import ToolManager from agentuniverse.agent.agent import Agent from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.plan.planner.planner_manager import PlannerManager @@ -78,6 +80,30 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: futures.append( self.executor.submit( PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')).invoke, - self.agent_model, agent_input_copy, input_object)) + self.agent_model, agent_input_copy, self.process_intput_object(input_object, task))) wait(futures, return_when=ALL_COMPLETED) return {'futures': futures} + + def process_intput_object(self, input_object: InputObject, planning_task: str) -> InputObject: + """Process input object for the executing agent. + + Args: + input_object (InputObject): input parameters passed by the user. + planning_task (str): planning task to be executed. + Returns: + InputObject: Processed input object + """ + # get agent toolsets + action: dict = self.agent_model.action or dict() + tools: list = action.get('tool') or list() + if len(tools) < 1: + return input_object + input_object_copy: InputObject = copy.deepcopy(input_object) + # wrap input_object for agent toolsets + for tool_name in tools: + tool = ToolManager().get_instance_obj(tool_name) + if tool is None: + continue + # note: only insert the first key of tool input + input_object_copy.add_data(tool.input_keys[0], planning_task) + return input_object_copy diff --git a/agentuniverse/agent/memory/langchain_instance.py b/agentuniverse/agent/memory/langchain_instance.py index bbbe5258..4b3936c6 100644 --- a/agentuniverse/agent/memory/langchain_instance.py +++ b/agentuniverse/agent/memory/langchain_instance.py @@ -9,6 +9,7 @@ from langchain_core.messages import BaseMessage, get_buffer_string from langchain.memory import ConversationSummaryBufferMemory, ConversationTokenBufferMemory +from langchain_core.output_parsers import StrOutputParser from agentuniverse.agent.memory.enum import ChatMessageEnum from agentuniverse.agent.memory.message import Message @@ -115,9 +116,8 @@ def predict_new_summary( ) prompt_version = self.prompt_version if self.prompt_version else 'chat_memory.summarizer_cn' prompt: Prompt = PromptManager().get_instance_obj(prompt_version) - chain = prompt.as_langchain() | self.llm - res = chain.invoke(input={'summary': existing_summary, 'new_lines': new_lines}) - return res.content + chain = prompt.as_langchain() | self.llm | StrOutputParser() + return chain.invoke(input={'summary': existing_summary, 'new_lines': new_lines}) class AuConversationTokenBufferMemory(ConversationTokenBufferMemory): diff --git a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py index a25fb3bc..e552a876 100644 --- a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py +++ b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py @@ -146,7 +146,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu if not expressing_result or jump_step in ["planning", "executing", "expressing"]: if not expressingAgent: - LOGGER.warn("no expression agent.") + LOGGER.warn("no expressing agent.") expressing_result = OutputObject({}) else: LOGGER.info(f"Starting expressing agent.") @@ -160,7 +160,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu if not reviewing_result or jump_step in ["planning", "executing", "expressing", "reviewing"]: if not reviewingAgent: - LOGGER.warn("no expression agent.") + LOGGER.warn("no reviewing agent.") loopResults.append({ "planning_result": planning_result, "executing_result": executing_result, From f6b166a2cb020588b0e1ae539ac3e417d341aad4 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 25 Jun 2024 20:18:43 +0800 Subject: [PATCH 050/118] fix: add process_input_object in the executing agent --- .../executing_agent/executing_agent.py | 24 ++++++++++--------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/agentuniverse/agent/default/executing_agent/executing_agent.py b/agentuniverse/agent/default/executing_agent/executing_agent.py index 2ac27143..318e4eef 100644 --- a/agentuniverse/agent/default/executing_agent/executing_agent.py +++ b/agentuniverse/agent/default/executing_agent/executing_agent.py @@ -12,6 +12,7 @@ from agentuniverse.agent.action.tool.tool_manager import ToolManager from agentuniverse.agent.agent import Agent from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.plan.planner.planner import Planner from agentuniverse.agent.plan.planner.planner_manager import PlannerManager @@ -77,33 +78,34 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: for task in framework: agent_input_copy: dict = copy.deepcopy(agent_input) agent_input_copy['input'] = task + planner: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')) futures.append( - self.executor.submit( - PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')).invoke, - self.agent_model, agent_input_copy, self.process_intput_object(input_object, task))) + self.executor.submit(planner.invoke, self.agent_model, agent_input_copy, + self.process_intput_object(input_object, task, planner.input_key))) wait(futures, return_when=ALL_COMPLETED) return {'futures': futures} - def process_intput_object(self, input_object: InputObject, planning_task: str) -> InputObject: + def process_intput_object(self, input_object: InputObject, subtask: str, planner_input_key: str) -> InputObject: """Process input object for the executing agent. Args: input_object (InputObject): input parameters passed by the user. - planning_task (str): planning task to be executed. + subtask (str): subtask to be executed. + planner_input_key (str): planner input key. Returns: InputObject: Processed input object """ - # get agent toolsets + # get agent toolsets. action: dict = self.agent_model.action or dict() tools: list = action.get('tool') or list() - if len(tools) < 1: - return input_object input_object_copy: InputObject = copy.deepcopy(input_object) - # wrap input_object for agent toolsets + # wrap input_object for agent knowledge. + input_object_copy.add_data(planner_input_key, subtask) + # wrap input_object for agent toolsets. for tool_name in tools: tool = ToolManager().get_instance_obj(tool_name) if tool is None: continue - # note: only insert the first key of tool input - input_object_copy.add_data(tool.input_keys[0], planning_task) + # note: only insert the first key of tool input. + input_object_copy.add_data(tool.input_keys[0], subtask) return input_object_copy From 2e85e986488c69df1d0c415f12f7b627d4aec590 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 25 Jun 2024 20:45:36 +0800 Subject: [PATCH 051/118] fix: add process_input_object in the executing agent --- agentuniverse/agent/default/executing_agent/executing_agent.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/agentuniverse/agent/default/executing_agent/executing_agent.py b/agentuniverse/agent/default/executing_agent/executing_agent.py index 318e4eef..63ac6a44 100644 --- a/agentuniverse/agent/default/executing_agent/executing_agent.py +++ b/agentuniverse/agent/default/executing_agent/executing_agent.py @@ -92,8 +92,9 @@ def process_intput_object(self, input_object: InputObject, subtask: str, planner input_object (InputObject): input parameters passed by the user. subtask (str): subtask to be executed. planner_input_key (str): planner input key. + Returns: - InputObject: Processed input object + input_object (InputObject): processed input object. """ # get agent toolsets. action: dict = self.agent_model.action or dict() From 0daff059dc4bdaa9a8d2fbaa33a93ec48a1b1568 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 26 Jun 2024 14:52:28 +0800 Subject: [PATCH 052/118] add translation_agent.py --- .../multi_improve_translation_agent.yaml | 59 ++++++++++ .../multi_reflection_translation_agent.yaml | 52 +++++++++ .../multi_work_translation_agent.yaml | 39 +++++++ .../translation_agent.py | 27 +++++ .../translation_agent.yaml | 24 ++++ .../translation_by_token_agent.py | 109 ++++++++++++++++++ .../translation_improve_agent.yaml | 50 ++++++++ .../translation_reflection_agent.yaml | 44 +++++++ .../translation_work_agent.yaml | 28 +++++ .../translation_planner/reflection_planner.py | 48 ++++++++ .../reflection_planner.yaml | 6 + .../app/test/test_translation_agent.py | 61 ++++++++++ 12 files changed, 547 insertions(+) create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml create mode 100644 sample_standard_app/app/core/planner/translation_planner/reflection_planner.py create mode 100644 sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml create mode 100644 sample_standard_app/app/test/test_translation_agent.py diff --git a/sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml new file mode 100644 index 00000000..ba6ac476 --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml @@ -0,0 +1,59 @@ +info: + name: 'multi_improve_translation_work_agent' + description: '翻译测试Agent' +profile: + introduction: You are an expert linguist, specializing in translation editing from {source_lang} to {target_lang}. + target: ' ' + instruction: | + Your task is to carefully read, then improve, a translation from {source_lang} to {target_lang}, taking into + account a set of expert suggestions and constructive critisms. Below, the source text, initial translation, and expert suggestions are provided. + + The source text is below, delimited by XML tags and , and the part that has been translated + is delimited by and within the source text. You can use the rest of the source text + as context, but need to provide a translation only of the part indicated by and . + + + {tagged_text} + + + To reiterate, only part of the text is being translated, shown here again between and : + + {chunk_to_translate} + + + The translation of the indicated part, delimited below by and , is as follows: + + {init_agent_result} + + + The expert translations of the indicated part, delimited below by and , is as follows: + + {reflection_agent_result} + + + Taking into account the expert suggestions rewrite the translation to improve it, paying attention + to whether there are ways to improve the translation's + + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules and ensuring there are no unnecessary repetitions), \ + (iii) style (by ensuring the translations reflect the style of the source text) + (iv) terminology (inappropriate for context, inconsistent use), or + (v) other errors. + + Output only the new translation of the indicated part and nothing else. + input_keys: [ 'source_lang','target_lang','tagged_text','chunk_to_translate','init_agent_result','reflection_agent_result'] + output_keys: [ 'output' ] + llm_model: + name: 'default_deepseek_llm' + max_tokens: 4000 +plan: + planner: + name: 'rag_planner' +action: + tool: +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml new file mode 100644 index 00000000..d2f6f7eb --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml @@ -0,0 +1,52 @@ +info: + name: 'multi_reflection_translation_work_agent' + description: '翻译测试Agent' +profile: + introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. + target: You will be provided with a source text and its translation and your goal is to improve the translation. + instruction: | + Your task is to carefully read a source text and part of a translation of that text from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions for improving the translation. + + The source text is below, delimited by XML tags and , and the part that has been translated + is delimited by and within the source text. You can use the rest of the source text + as context for critiquing the translated part. + + + {tagged_text} + + + To reiterate, only part of the text is being translated, shown here again between and : + + {chunk_to_translate} + + + The translation of the indicated part, delimited below by and , is as follows: + + {init_agent_result} + + + When writing suggestions, pay attention to whether there are ways to improve the translation's:\n\ + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n\ + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions),\n\ + (iii) style (by ensuring the translations reflect the style of the source text and takes into account any cultural context),\n\ + (iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}).\n\ + + Write a list of specific, helpful and constructive suggestions for improving the translation. + Each suggestion should address one specific part of the translation. + Output only the suggestions and nothing else. + input_keys: [ 'source_lang','target_lang','tagged_text','chunk_to_translate','init_agent_result'] + output_keys: [ 'output' ] + llm_model: + name: 'default_deepseek_llm' + max_tokens: 4000 +plan: + planner: + name: 'rag_planner' +action: + tool: +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml new file mode 100644 index 00000000..09cf1b59 --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml @@ -0,0 +1,39 @@ +info: + name: 'multi_translation_work_agent' + description: '翻译测试Agent' +profile: + introduction: 'You are an expert linguist, specializing in translation from {source_lang} to {target_lang}.' + target: ' ' + instruction: | + Your task is provide a professional translation from {source_lang} to {target_lang} of PART of a text. + + The source text is below, delimited by XML tags and . Translate only the part within the source text + delimited by and . You can use the rest of the source text as context, but do not translate any + of the other text. Do not output anything other than the translation of the indicated part of the text. + + + {tagged_text} + + + To reiterate, you should translate only this part of the text, shown here again between and : + + {chunk_to_translate} + + + Output only the translation of the portion you are asked to translate, and nothing else. + input_keys: [ 'source_lang','target_lang','tagged_text','chunk_to_translate'] + output_keys: [ 'output' ] + llm_model: + name: 'default_deepseek_llm' + max_tokens: 4000 +plan: + planner: + name: 'rag_planner' +action: + tool: +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py new file mode 100644 index 00000000..7a09741a --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py @@ -0,0 +1,27 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- +from langchain_text_splitters import RecursiveCharacterTextSplitter + +# @Time : 2024/6/25 16:56 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: translation_planner.py + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject + + +class TranslationAgent(Agent): + def input_keys(self) -> list[str]: + return self.agent_model.profile.get('input_keys') + + def output_keys(self) -> list[str]: + return self.agent_model.profile.get('output_keys') + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + for key in self.input_keys(): + agent_input[key] = input_object.get_data(key) + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + return planner_result diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml new file mode 100644 index 00000000..7e35eaf3 --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml @@ -0,0 +1,24 @@ +info: + name: 'translation_by_token_agent' + description: '翻译Agent' +profile: + prompt_version: 'one_chunk_improve.en' + input_keys: ['source_lang','target_lang','source_text'] + output_keys: ['output'] + llm_model: + name: 'default_deepseek_llm' +plan: + work_agent: 'translation_work_agent' + reflection_agent: 'translation_reflection_agent' + improve_agent: 'translation_improve_agent' + planner: + name: 'reflection_planner' +action: + tool: +# - 'google_search_tool' +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_by_token_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py new file mode 100644 index 00000000..45931410 --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -0,0 +1,109 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- +from langchain_text_splitters import RecursiveCharacterTextSplitter + +# @Time : 2024/6/25 16:56 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: translation_planner.py + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject +from agentuniverse.llm.llm import LLM +from agentuniverse.llm.llm_manager import LLMManager + + +def calculate_chunk_size(token_count: int, token_limit: int) -> int: + """ + Calculate the chunk size based on the token count and token limit. + + Args: + token_count (int): The total number of tokens. + token_limit (int): The maximum number of tokens allowed per chunk. + + Returns: + int: The calculated chunk size. + + Description: + This function calculates the chunk size based on the given token count and token limit. + If the token count is less than or equal to the token limit, the function returns the token count as the chunk size. + Otherwise, it calculates the number of chunks needed to accommodate all the tokens within the token limit. + The chunk size is determined by dividing the token limit by the number of chunks. + If there are remaining tokens after dividing the token count by the token limit, + the chunk size is adjusted by adding the remaining tokens divided by the number of chunks. + + Example: + >>> calculate_chunk_size(1000, 500) + 500 + >>> calculate_chunk_size(1530, 500) + 389 + >>> calculate_chunk_size(2242, 500) + 496 + """ + + if token_count <= token_limit: + return token_count + + num_chunks = (token_count + token_limit - 1) // token_limit + chunk_size = token_count // num_chunks + + remaining_tokens = token_count % token_limit + if remaining_tokens > 0: + chunk_size += remaining_tokens // num_chunks + + return chunk_size + + +class TranslationAgent(Agent): + def input_keys(self) -> list[str]: + return self.agent_model.profile.get('input_keys') + + def output_keys(self) -> list[str]: + return self.agent_model.profile.get('output_keys') + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + for key in self.input_keys(): + agent_input[key] = input_object.get_data(key) + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + return planner_result + + def execute(self, input_object: InputObject, agent_input: dict) -> dict: + llm_name = self.agent_model.profile.get('llm_model').get('name') + llm: LLM = LLMManager().get_instance_obj(llm_name) + source_text = agent_input.get('source_text') + text_tokens = llm.get_num_tokens(source_text) + # 这里使用最大输入token，因为必须要保证有足够的token输出翻译结果 + max_context_length = llm.max_tokens + if text_tokens < max_context_length: + return super().execute(input_object, agent_input) + + chunk_result = list[str]() + chunk_size = calculate_chunk_size(text_tokens, max_context_length) + source_text_chunks = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0).split_text( + source_text) + + input_object.add_data('work_agent', 'multi_translation_work_agent') + input_object.add_data('reflection_agent', 'multi_reflection_translation_work_agent') + input_object.add_data('improve_agent', 'multi_improve_translation_work_agent') + + for i in range(len(source_text_chunks)): + tagged_text = "" + if i - 1 > 0: + tagged_text += source_text_chunks[i - 1] + tagged_text += ( + "" + + source_text_chunks[i] + + "" + ) + if i + 1 < len(source_text_chunks): + tagged_text += source_text_chunks[i + 1] + agent_input['chunk_to_translate'] = source_text_chunks[i] + agent_input['tagged_text'] = tagged_text + result = super().execute(input_object, agent_input) + chunk_result.append(result.get('output')) + + return { + 'output': "".join(chunk_result) + } diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml new file mode 100644 index 00000000..ac3dc211 --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml @@ -0,0 +1,50 @@ +info: + name: 'translation_improve_agent' + description: '翻译反思Agent' +profile: + introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. + target: You will be provided with a source text and its translation and your goal is to improve the translation. + instruction: | + Your task is to carefully read, then edit, a translation from {source_lang} to {target_lang}, taking into + account a list of expert suggestions and constructive criticisms. + + The source text, the initial translation, and the expert linguist suggestions are delimited by XML tags , and \ + as follows: + + + {source_text} + + + + {init_agent_result} + + + + {reflection_agent_result} + + + Please take into account the expert suggestions when editing the translation. Edit the translation by ensuring: + + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules and ensuring there are no unnecessary repetitions), + (iii) style (by ensuring the translations reflect the style of the source text) + (iv) terminology (inappropriate for context, inconsistent use), or + (v) other errors. + + Output only the new translation and nothing else. + input_keys: ['source_lang','target_lang','source_text','init_agent_result','reflection_agent_result'] + output_keys: ['output'] + llm_model: + name: 'default_deepseek_llm' + max_tokens: 4000 +plan: + planner: + name: 'rag_planner' +action: + tool: +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml new file mode 100644 index 00000000..d9a65f78 --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml @@ -0,0 +1,44 @@ +info: + name: 'translation_reflection_agent' + description: '翻译反思Agent' +profile: + introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. + target: You will be provided with a source text and its translation and your goal is to improve the translation. + instruction: | + Your task is to carefully read a source text and a translation from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions to improve the translation. + + The source text and initial translation, delimited by XML tags and , are as follows: + + + {source_text} + + + + {init_agent_result} + + + When writing suggestions, pay attention to whether there are ways to improve the translation's + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions), + (iii) style (by ensuring the translations reflect the style of the source text and takes into account any cultural context), + (iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}). + + Write a list of specific, helpful and constructive suggestions for improving the translation. + Each suggestion should address one specific part of the translation. + Output only the suggestions and nothing else. + input_keys: ['source_lang','target_lang','source_text','init_agent_result'] + output_keys: ['output'] + llm_model: + name: 'default_deepseek_llm' + max_tokens: 4000 +plan: + planner: + name: 'rag_planner' +action: + tool: +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml new file mode 100644 index 00000000..870bb4ae --- /dev/null +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml @@ -0,0 +1,28 @@ +info: + name: 'translation_work_agent' + description: '翻译测试Agent' +profile: + introduction: You are an expert linguist, specializing in translation from {source_lang} to {target_lang}. + target: ' ' + instruction: | + This is an {source_lang} to {target_lang} translation, please provide the {target_lang} translation for this text. + Do not provide any explanations or text apart from the translation. + {source_lang}: {source_text} + + {target_lang}: + input_keys: ['source_lang','target_lang','source_text'] + output_keys: ['output'] + llm_model: + name: 'default_deepseek_llm' + max_tokens: 4000 +plan: + planner: + name: 'rag_planner' +action: + tool: +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' + class: 'TranslationAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/planner/translation_planner/reflection_planner.py b/sample_standard_app/app/core/planner/translation_planner/reflection_planner.py new file mode 100644 index 00000000..eb452416 --- /dev/null +++ b/sample_standard_app/app/core/planner/translation_planner/reflection_planner.py @@ -0,0 +1,48 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/25 16:33 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: translation_planner.py + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager + +from agentuniverse.agent.agent_model import AgentModel +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.plan.planner.planner import Planner +from agentuniverse.base.util.logging.logging_util import LOGGER + + +class ReflectionPlanner(Planner): + + def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: InputObject) -> dict: + work_agent = agent_model.plan.get('work_agent') + reflection_agent = agent_model.plan.get('reflection_agent') + improve_agent = agent_model.plan.get('improve_agent') + + work_agent = input_object.get_data('work_agent', work_agent) + reflection_agent = input_object.get_data('reflection_agent', reflection_agent) + improve_agent = input_object.get_data('improve_agent', improve_agent) + + init_agent_result = self.execute_agent(work_agent, planner_input) + LOGGER.info(f"init_agent_result: {init_agent_result.to_json_str()}") + + planner_input['init_agent_result'] = init_agent_result.get_data('output') + + reflection_result = self.execute_agent(reflection_agent, planner_input) + LOGGER.info(f"reflection_result: {reflection_result.to_json_str()}") + + planner_input['reflection_agent_result'] = reflection_result.get_data('output') + + improve_result = self.execute_agent(improve_agent, planner_input) + LOGGER.info(f"improve_agent_result: {improve_result.to_json_str()}") + + return improve_result.to_dict() + + @staticmethod + def execute_agent(agent_name: str, agent_input: dict): + agent: Agent = AgentManager().get_instance_obj(agent_name) + result = agent.run(**agent_input) + return result diff --git a/sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml b/sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml new file mode 100644 index 00000000..c567d4d2 --- /dev/null +++ b/sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml @@ -0,0 +1,6 @@ +name: 'reflection_planner' +description: '反思规划' +metadata: + type: 'PLANNER' + module: 'sample_standard_app.app.core.planner.translation_planner.reflection_planner' + class: 'ReflectionPlanner' \ No newline at end of file diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py new file mode 100644 index 00000000..0d4d0e77 --- /dev/null +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -0,0 +1,61 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/4/1 14:32 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: test_rag_agent.py +import unittest + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager +from agentuniverse.agent.output_object import OutputObject +from agentuniverse.base.agentuniverse import AgentUniverse + + +class TranslationAgentTest(unittest.TestCase): + """ + Test cases for the rag agent + """ + + def setUp(self) -> None: + AgentUniverse().start(config_path='../../config/config.toml') + + def test_rag_fgent(self): + data = """ + Last week, I spoke about AI and regulation at the U.S. Capitol at an event that was attended by legislative and business leaders. I’m encouraged by the progress the open source community has made fending off regulations that would have stifled innovation. But opponents of open source are continuing to shift their arguments, with the latest worries centering on open source's impact on national security. I hope we’ll all keep protecting open source! + +Based on my conversations with legislators, I’m encouraged by the progress the U.S. federal government has made getting a realistic grasp of AI’s risks. To be clear, guardrails are needed. But they should be applied to AI applications, not to general-purpose AI technology. + +Nonetheless, as I wrote previously, some companies are eager to limit open source, possibly to protect the value of massive investments they’ve made in proprietary models and to deter competitors. It has been fascinating to watch their arguments change over time. + +For instance, about 12 months ago, the Center For AI Safety’s “Statement on AI Risk” warned that AI could cause human extinction and stoked fears of AI taking over. This alarmed leaders in Washington. But many people in AI pointed out that this dystopian science-fiction scenario has little basis in reality. About six months later, when I testified at the U.S. Senate’s AI Insight forum, legislators no longer worried much about an AI takeover. + +Then the opponents of open source shifted gears. Their leading argument shifted to the risk of AI helping to create bioweapons. Soon afterward, OpenAI and RAND showed that current AI does not significantly increase the ability of malefactors to build bioweapons. This fear of AI-enabled bioweapons has diminished. To be sure, the possibility that bad actors could use bioweapons — with or without AI — remains a topic of great international concern. + + +The latest argument for blocking open source AI has shifted to national security. AI is useful for both economic competition and warfare, and open source opponents say the U.S. should make sure its adversaries don’t have access to the latest foundation models. While I don’t want authoritarian governments to use AI, particularly to wage unjust wars, the LLM cat is out of the bag, and authoritarian countries will fill the vacuum if democratic nations limit access. When, some day, a child somewhere asks an AI system questions about democracy, the role of a free press, or the function of an independent judiciary in preserving the rule of law, I would like the AI to reflect democratic values rather than favor authoritarian leaders’ goals over, say, human rights. + +I came away from Washington optimistic about the progress we’ve made. A year ago, legislators seemed to me to spend 80% of their time talking about guardrails for AI and 20% about investing in innovation. I was delighted that the ratio has flipped, and there was far more talk of investing in innovation. + +Looking beyond the U.S. federal government, there are many jurisdictions globally. Unfortunately, arguments in favor of regulations that would stifle AI development continue to proliferate. But I’ve learned from my trips to Washington and other nations’ capitals that talking to regulators does have an impact. If you get a chance to talk to a regulator at any level, I hope you’ll do what you can to help governments better understand AI. + """ + """Test demo rag agent.""" + with open('/Users/weizj/jobspace/agentUniverse/sample_standard_app/app/test/test_file.txt') as f: + data = f.read() + print(len(data)) + + instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') + output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", + source_text=data + ) + res_info = f"\nRag agent execution result is :\n" + res_info += output_object.get_data('output') + # 创建文件，并写入文件 + with open('./rag_agent_result.txt', 'w') as f: + f.write(res_info) + print(res_info) + + +if __name__ == '__main__': + unittest.main() From cba1e9dd8d01e3998ae94a50f5a388286ab59646 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 26 Jun 2024 16:38:39 +0800 Subject: [PATCH 053/118] add doc --- docs/guidebook/en/2_2_3_Integrated_Tools.md | 62 ++++++++++++++++++- ...20\347\232\204\345\267\245\345\205\267.md" | 62 ++++++++++++++++++- .../agent/translation_agent_case/__init__.py | 0 .../app/core/tool/human_input_run.yaml | 11 ++++ .../app/core/tool/langchain_tool.py | 4 +- .../app/core/tool/request_get_tool.yaml | 4 +- .../app/core/tool/request_post_tool.yaml | 4 +- .../app/test/test_react_agent.py | 2 +- 8 files changed, 139 insertions(+), 10 deletions(-) create mode 100644 sample_standard_app/app/core/agent/translation_agent_case/__init__.py create mode 100644 sample_standard_app/app/core/tool/human_input_run.yaml diff --git a/docs/guidebook/en/2_2_3_Integrated_Tools.md b/docs/guidebook/en/2_2_3_Integrated_Tools.md index 6f7e977e..3761ec39 100644 --- a/docs/guidebook/en/2_2_3_Integrated_Tools.md +++ b/docs/guidebook/en/2_2_3_Integrated_Tools.md @@ -104,7 +104,7 @@ SEARCHAPI_API_KEY="xxxxxx" ``` -## 2. 代码工具 +## 2. Code Tool ### 2.1 PythonRepl [Tool address](../../../sample_standard_app/app/core/tool/python_repl_tool.yaml) @@ -134,5 +134,65 @@ metadata: This tool can be used directly without any key, but for system security, please do not use this tool in production environments. +## 3.HTTP Tool +### 3.1 HTTP GET +[Tool address](../../../sample_standard_app/app/core/tool/request_get_tool.yaml) +The tool can send a GET request, with its configuration information being: +```yaml +name: 'requests_get' +description: 'A portal to the internet. Use this when you need to get specific + content from a website. Input should be a url (i.e. https://www.google.com). + The output will be the text response of the GET request. + ```' +headers: + content-type: 'application/json' +method: 'POST' +json_parser: true +response_content_type: json +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.request_tool' + class: 'RequestTool' +``` +Parameter Description: + method: The method of the request, such as GET, POST, PUT, etc. + headers: The HTTP headers required for sending the request. + json_parse: Specifies whether the input parameters need to be parsed by HTTP. It should be set to True for POST requests and False for GET requests. + response_content_type: The parsing method for the HTTP request result. If set to 'json', the result will be returned in JSON format; if set to 'text', the result will be returned as text. +This tool can be used directly without any keys required. + +## 4. Langchain Tool +Langchain incorporates numerous tools, but different tools may have distinct initialization methods. To address this, we have implemented a base class called LangChainTool. You merely need to implement the init_langchain_tool method of this class to initialize the corresponding Langchain tool object. +Here's an example of a tool implementation: +[Tool address](../../../sample_standard_app/app/core/tool/human_input_run.yaml) +```yaml +name: 'human_input_run' +description: 'human input tool' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: HumanInputRun +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool' + class: 'LangChainTool' +``` +Parameter Explanation: + langchain: The Langchain tool you intend to use, requiring configuration of module and class_name. + langchain.module: The name of the Langchain module, for instance, langchain_community.tools. + langchain.class_name: The class name within Langchain, such as HumanInputRun. + langchain.init_params: Initialization parameters for Langchain, for example: + ```yaml + langchain: + module: langchain_community.tools + class_name: HumanInputRun + init_params: + prompt: '请输入你的问题' + ``` + If you opt to fully override the init_langchain_tool method, then you do not need to configure this part. +This tool can be utilized directly without requiring any keys. diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index ceba884c..fef2338a 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -135,6 +135,64 @@ metadata: 该工具可以直接使用，无需任何key，但是为了系统安全，请不要在生产环境使用该工具 +## 3.HTTP 工具 - - +### 3.1 HTTP GET +[工具地址](../../../sample_standard_app/app/core/tool/request_get_tool.yaml) +该工具可以发送一个GET请求，工具的配置信息 : +```yaml +name: 'requests_get' +description: 'A portal to the internet. Use this when you need to get specific + content from a website. Input should be a url (i.e. https://www.google.com). + The output will be the text response of the GET request. + ```' +headers: + content-type: 'application/json' +method: 'POST' +json_parser: true +response_content_type: json +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.request_tool' + class: 'RequestTool' +``` +参数说明： + method 请求的方式GET/POST/PUT等 + headers 发送请求需要使用的 http的header, + json_parse 输入参数是否需要是要HTTP解析，POST请求时需要设置为True,GET请求需要设置为False + response_content_type http请求结果的解析方式，设置为json时，会返回json结果，设置为text时会返回text结果 +该工具可以直接使用，无需任何keys + +## 4. Langchain 工具 +langchain当中集成了很多工具，但是不同的工具初始化方法会有不同，对此，我们实现了一个LangChainTool基础类，你只需要实现该类的init_langchain_tool方法，初始化对应的langchain工具对象即可 +一个工具示例： +[工具地址](../../../sample_standard_app/app/core/tool/human_input_run.yaml) +```yaml +name: 'human_input_run' +description: 'human input tool' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: HumanInputRun +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool' + class: 'LangChainTool' +``` +参数说明： + langchain: 你打算使用的langchain工具，需要配置module和class_name + langchain.module: langchain的模块名，例如langchain_community.tools + langchain.class_name: langchain的类名，例如HumanInputRun + langchain.init_params： langchain的初始化参数，例如： + ```yaml + langchain: + module: langchain_community.tools + class_name: HumanInputRun + init_params: + prompt: '请输入你的问题' + ``` + 如果需要使用你完全重写了init_langchain_tool方法，那么你不需要配置该部分 +该工具可以直接使用，无需任何keys \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/__init__.py b/sample_standard_app/app/core/agent/translation_agent_case/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/sample_standard_app/app/core/tool/human_input_run.yaml b/sample_standard_app/app/core/tool/human_input_run.yaml new file mode 100644 index 00000000..611944b2 --- /dev/null +++ b/sample_standard_app/app/core/tool/human_input_run.yaml @@ -0,0 +1,11 @@ +name: 'human_input_run' +description: 'human input tool' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: HumanInputRun +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool' + class: 'LangChainTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/langchain_tool.py b/sample_standard_app/app/core/tool/langchain_tool.py index 089d7b80..24444a87 100644 --- a/sample_standard_app/app/core/tool/langchain_tool.py +++ b/sample_standard_app/app/core/tool/langchain_tool.py @@ -9,7 +9,7 @@ from typing import Optional, Type -from langchain_core.tools import BaseTool, Tool as LangchainTool +from langchain_core.tools import BaseTool from agentuniverse.agent.action.tool.tool import Tool, ToolInput from agentuniverse.base.config.component_configer.configers.tool_configer import ToolConfiger @@ -23,7 +23,7 @@ class LangChainTool(Tool): def execute(self, tool_input: ToolInput): input = tool_input.get_data("input") callbacks = tool_input.get_data("callbacks", None) - return self.tool.run(json.loads(input), callbacks=callbacks) + return self.tool.run(input, callbacks=callbacks) def initialize_by_component_configer(self, component_configer: ToolConfiger) -> 'Tool': super().initialize_by_component_configer(component_configer) diff --git a/sample_standard_app/app/core/tool/request_get_tool.yaml b/sample_standard_app/app/core/tool/request_get_tool.yaml index ed0dd400..97426045 100644 --- a/sample_standard_app/app/core/tool/request_get_tool.yaml +++ b/sample_standard_app/app/core/tool/request_get_tool.yaml @@ -5,8 +5,8 @@ description: 'A portal to the internet. Use this when you need to get specific ```' headers: content-type: 'application/json' -method: 'POST' -json_parser: true +method: 'GET' +json_parser: false response_content_type: json tool_type: 'api' input_keys: ['input'] diff --git a/sample_standard_app/app/core/tool/request_post_tool.yaml b/sample_standard_app/app/core/tool/request_post_tool.yaml index a141dd3d..6a5a8c8f 100644 --- a/sample_standard_app/app/core/tool/request_post_tool.yaml +++ b/sample_standard_app/app/core/tool/request_post_tool.yaml @@ -8,8 +8,8 @@ description: 'Use this when you want to POST to a website. ```' headers: content-type: 'application/json' -method: 'GET' -json_parser: false +method: 'POST' +json_parser: true response_content_type: json tool_type: 'api' input_keys: ['input'] diff --git a/sample_standard_app/app/test/test_react_agent.py b/sample_standard_app/app/test/test_react_agent.py index 77534dbd..8f008554 100644 --- a/sample_standard_app/app/test/test_react_agent.py +++ b/sample_standard_app/app/test/test_react_agent.py @@ -21,7 +21,7 @@ def setUp(self) -> None: def test_react_agent(self): """Test demo reAct agent.""" instance: Agent = AgentManager().get_instance_obj('demo_react_agent') - output_object: OutputObject = instance.run(input='请给出一段python代码，可以计算三数之和，给出之前必须验证代码是否可以运行，最少验证三次') + output_object: OutputObject = instance.run(input='我想知道张三今年多大了') if __name__ == '__main__': From 3d90f523597de21fe4b742077e22ecc2850f1ebc Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 26 Jun 2024 16:43:03 +0800 Subject: [PATCH 054/118] delete file --- .../app/core/agent/translation_agent_case/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 sample_standard_app/app/core/agent/translation_agent_case/__init__.py diff --git a/sample_standard_app/app/core/agent/translation_agent_case/__init__.py b/sample_standard_app/app/core/agent/translation_agent_case/__init__.py deleted file mode 100644 index e69de29b..00000000 From 1efab5438b5c3c3b5e3dce859548a9e8d106672f Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 26 Jun 2024 17:15:17 +0800 Subject: [PATCH 055/118] =?UTF-8?q?=E6=92=A4=E9=94=80=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- sample_standard_app/app/test/test_react_agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sample_standard_app/app/test/test_react_agent.py b/sample_standard_app/app/test/test_react_agent.py index 8f008554..77534dbd 100644 --- a/sample_standard_app/app/test/test_react_agent.py +++ b/sample_standard_app/app/test/test_react_agent.py @@ -21,7 +21,7 @@ def setUp(self) -> None: def test_react_agent(self): """Test demo reAct agent.""" instance: Agent = AgentManager().get_instance_obj('demo_react_agent') - output_object: OutputObject = instance.run(input='我想知道张三今年多大了') + output_object: OutputObject = instance.run(input='请给出一段python代码，可以计算三数之和，给出之前必须验证代码是否可以运行，最少验证三次') if __name__ == '__main__': From 9cb47538b10a6aa7416241c6d030c1591bb38304 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Thu, 27 Jun 2024 10:19:52 +0800 Subject: [PATCH 056/118] feat: add trace_agent annotation to trace the agent invocation record. --- agentuniverse/agent/agent.py | 2 + agentuniverse/base/annotation/trace.py | 57 ++++++++++++++++--- agentuniverse/base/util/monitor/monitor.py | 30 ++++++++++ .../app/test/test_rag_agent.py | 2 +- 4 files changed, 82 insertions(+), 9 deletions(-) diff --git a/agentuniverse/agent/agent.py b/agentuniverse/agent/agent.py index 40f34354..d6fe3034 100644 --- a/agentuniverse/agent/agent.py +++ b/agentuniverse/agent/agent.py @@ -18,6 +18,7 @@ from agentuniverse.agent.output_object import OutputObject from agentuniverse.agent.plan.planner.planner import Planner from agentuniverse.agent.plan.planner.planner_manager import PlannerManager +from agentuniverse.base.annotation.trace import trace_agent from agentuniverse.base.component.component_base import ComponentBase from agentuniverse.base.component.component_enum import ComponentEnum from agentuniverse.base.config.application_configer.application_config_manager \ @@ -70,6 +71,7 @@ def parse_result(self, planner_result: dict) -> dict: """ pass + @trace_agent def run(self, **kwargs) -> OutputObject: """Agent instance running entry. diff --git a/agentuniverse/base/annotation/trace.py b/agentuniverse/base/annotation/trace.py index d3333edb..4ec61c56 100644 --- a/agentuniverse/base/annotation/trace.py +++ b/agentuniverse/base/annotation/trace.py @@ -14,8 +14,6 @@ from agentuniverse.base.util.monitor.monitor import Monitor from agentuniverse.llm.llm_output import LLMOutput -monitor = Monitor() - def trace_llm(func): """Annotation: @trace_llm @@ -37,7 +35,7 @@ async def wrapper_async(*args, **kwargs): # not streaming if isinstance(result, LLMOutput): # add llm invocation info to monitor - monitor.trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, llm_output=result.text) + Monitor().trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, llm_output=result.text) return result else: # streaming @@ -47,8 +45,8 @@ async def gen_iterator(): llm_output.append(chunk.text) yield chunk # add llm invocation info to monitor - monitor.trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, - llm_output="".join(llm_output)) + Monitor().trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, + llm_output="".join(llm_output)) return gen_iterator() @@ -66,7 +64,7 @@ def wrapper_sync(*args, **kwargs): # not streaming if isinstance(result, LLMOutput): # add llm invocation info to monitor - monitor.trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, llm_output=result.text) + Monitor().trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, llm_output=result.text) return result else: # streaming @@ -76,8 +74,8 @@ def gen_iterator(): llm_output.append(chunk.text) yield chunk # add llm invocation info to monitor - monitor.trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, - llm_output="".join(llm_output)) + Monitor().trace_llm_invocation(source=func.__qualname__, llm_input=llm_input, + llm_output="".join(llm_output)) return gen_iterator() @@ -95,3 +93,46 @@ def _get_llm_input(func, *args, **kwargs) -> dict: bound_args = sig.bind(*args, **kwargs) bound_args.apply_defaults() return {k: v for k, v in bound_args.arguments.items()} + + +def trace_agent(func): + """Annotation: @trace_agent + + Decorator to trace the agent invocation, add agent input and output to the monitor. + """ + + @functools.wraps(func) + def wrapper_sync(*args, **kwargs): + # get agent input from arguments + agent_input = _get_agent_input(func, *args, **kwargs) + # check whether the tracing switch is enabled + source = func.__qualname__ + self = agent_input.pop('self', None) + + if isinstance(self, object): + agent_model = getattr(self, 'agent_model', None) + if isinstance(agent_model, object): + info = getattr(agent_model, 'info', None) + if isinstance(info, dict): + source = info.get('name', None) + + if self and hasattr(self, 'tracing'): + if not self.tracing: + return func(*args, **kwargs) + + # invoke function + result = func(*args, **kwargs) + # add agent invocation info to monitor + Monitor().trace_agent_invocation(source=source, agent_input=agent_input, agent_output=result.to_dict()) + return result + + # sync function + return wrapper_sync + + +def _get_agent_input(func, *args, **kwargs) -> dict: + """Get the agent input from arguments.""" + sig = inspect.signature(func) + bound_args = sig.bind(*args, **kwargs) + bound_args.apply_defaults() + return {k: v for k, v in bound_args.arguments.items()} diff --git a/agentuniverse/base/util/monitor/monitor.py b/agentuniverse/base/util/monitor/monitor.py index 7b4e942e..6275d498 100644 --- a/agentuniverse/base/util/monitor/monitor.py +++ b/agentuniverse/base/util/monitor/monitor.py @@ -16,6 +16,7 @@ from agentuniverse.base.config.configer import Configer LLM_INVOCATION_SUBDIR = "llm_invocation" +AGENT_INVOCATION_SUBDIR = "agent_invocation" @singleton @@ -62,3 +63,32 @@ def _get_or_create_subdir(self, subdir: str) -> str: path = os.path.join(self.dir, subdir) os.makedirs(path, exist_ok=True) return path + + def trace_agent_invocation(self, source: str, agent_input: Union[str, dict], + agent_output: Union[str, dict]) -> None: + """Trace the agent invocation and save it to the monitor jsonl file.""" + if self.activate: + try: + import jsonlines + except ImportError: + raise ImportError( + "jsonlines is required to trace llm invocation: `pip install jsonlines`" + ) + # get the current time + date = datetime.datetime.now() + agent_invocation = { + "source": source, + "date": date.strftime("%Y-%m-%d %H:%M:%S"), + "agent_input": agent_input, + "agent_output": agent_output, + } + # files are stored in hours + filename = f"agent_{source}_{date.strftime('%Y-%m-%d-%H')}.jsonl" + # file path to save + path_save = os.path.join(str(self._get_or_create_subdir(AGENT_INVOCATION_SUBDIR)), filename) + + # write to jsonl + with jsonlines.open(path_save, 'a') as writer: + json_record = json.dumps(agent_invocation, ensure_ascii=False) + writer.write(json_record) + diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 14ba678f..06c3d9b8 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -21,7 +21,7 @@ class RagAgentTest(unittest.TestCase): def setUp(self) -> None: AgentUniverse().start(config_path='../../config/config.toml') - def test_rag_fgent(self): + def test_rag_agent(self): """Test demo rag agent.""" instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') From 7f38c8fa57b5351f6a1ccc2e7b775c7af3a87359 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Thu, 27 Jun 2024 10:39:27 +0800 Subject: [PATCH 057/118] feat: add trace_agent annotation to trace the agent invocation record. --- agentuniverse/base/util/monitor/monitor.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/agentuniverse/base/util/monitor/monitor.py b/agentuniverse/base/util/monitor/monitor.py index 6275d498..e69388af 100644 --- a/agentuniverse/base/util/monitor/monitor.py +++ b/agentuniverse/base/util/monitor/monitor.py @@ -55,8 +55,7 @@ def trace_llm_invocation(self, source: str, llm_input: Union[str, dict], llm_out # write to jsonl with jsonlines.open(path_save, 'a') as writer: - json_record = json.dumps(llm_invocation, ensure_ascii=False) - writer.write(json_record) + writer.write(llm_invocation) def _get_or_create_subdir(self, subdir: str) -> str: """Get or create a subdirectory if it doesn't exist in the monitor directory.""" @@ -89,6 +88,5 @@ def trace_agent_invocation(self, source: str, agent_input: Union[str, dict], # write to jsonl with jsonlines.open(path_save, 'a') as writer: - json_record = json.dumps(agent_invocation, ensure_ascii=False) - writer.write(json_record) + writer.write(agent_invocation) From b5957cdb50eb0e3be2cd7315cd7b7adfa510edba Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 27 Jun 2024 18:24:05 +0800 Subject: [PATCH 058/118] add langchain tool --- .../en/2_2_3_Integrated_LangChain_Tools.md | 83 ++++++++++++++++++ docs/guidebook/en/2_2_3_Integrated_Tools.md | 55 +++++------- ...0\220LangChain\345\267\245\345\205\267.md" | 84 +++++++++++++++++++ ...20\347\232\204\345\267\245\345\205\267.md" | 56 +++++-------- pyproject.toml | 2 + .../app/core/tool/langchain_tool/__init__.py | 0 .../langchain_tool/duckduckgo_search.yaml | 13 +++ .../{ => langchain_tool}/human_input_run.yaml | 4 +- .../{ => langchain_tool}/langchain_tool.py | 9 +- .../tool/langchain_tool/wikipedia_query.py | 19 +++++ .../tool/langchain_tool/wikipedia_query.yaml | 8 ++ 11 files changed, 261 insertions(+), 72 deletions(-) create mode 100644 docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md create mode 100644 "docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md" create mode 100644 sample_standard_app/app/core/tool/langchain_tool/__init__.py create mode 100644 sample_standard_app/app/core/tool/langchain_tool/duckduckgo_search.yaml rename sample_standard_app/app/core/tool/{ => langchain_tool}/human_input_run.yaml (66%) rename sample_standard_app/app/core/tool/{ => langchain_tool}/langchain_tool.py (87%) create mode 100644 sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.py create mode 100644 sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.yaml diff --git a/docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md b/docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md new file mode 100644 index 00000000..fcaba8f4 --- /dev/null +++ b/docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md @@ -0,0 +1,83 @@ +# Integrated LangChain Tool + +Based on the level of difficulty in initializing tool objects in LangChain, they can be divided into two categories: +The first category involves simple initialization, where initialization can be completed with basic parameter configuration. +The second category involves complex initialization with intricate internal objects that need to be set up. +For the first category of tools, you can use configuration files in aU to directly perform initialization, such as initializing the DuDuckGo search tool. +For the second category of tools, we have implemented a LangChainTool base class. You only need to implement the init_langchain_tool method of this class to initialize the corresponding LangChain tool objects, with reference to the initialization method of Wikipedia. + +Note: If you want to directly use the description from LangChain, the description in the configuration file must be set to empty. + +An Example of Tool Initialization: +[Tool Address](../../../sample_standard_app/app/core/tool/langchain_tool/human_input_run.yaml) +```yaml +name: 'human_input_run' +description: '' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: HumanInputRun +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool' + class: 'LangChainTool' +``` +Parameter Description: + langchain: The LangChain tool you intend to use, which requires the configuration of module and class_name. + langchain.module: The name of the LangChain module, e.g., langchain_community.tools. + langchain.class_name: The name of the LangChain class, e.g., HumanInputRun. + langchain.init_params: The initialization parameters for LangChain, such as: + ```yaml + langchain: + module: langchain_community.tools + class_name: HumanInputRun + init_params: + prompt: 'please Input your question' + ``` + If you completely override the `init_langchain_tool` method, then you do not need to configure this part. + +## 1. Integrate the DuckDuckGo Tool from LangChain +[Tool Address](../../../sample_standard_app/app/core/tool/langchain_tool/duckduckgo_search.yaml) +```yaml +name: 'duckduckgo_search' +description: 'DuckDuckGo Search tool' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain.tools + class_name: DuckDuckGoSearchResults + init_params: + backend: news +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.langchain_tool' + class: 'LangChainTool' +``` +This tool can be used directly without any keys. + +## 2.Integrate Wikipedia Search +Since the definition of LangChain includes an api_wrapper object, define the object file and override the initialization method: +```python +from langchain_community.tools import WikipediaQueryRun +from langchain_community.utilities import WikipediaAPIWrapper + +from sample_standard_app.app.core.tool.langchain_tool.langchain_tool import LangChainTool + + +class WikipediaTool(LangChainTool): + def init_langchain_tool(self, component_configer): + wrapper = WikipediaAPIWrapper() + return WikipediaQueryRun(api_wrapper=wrapper) +``` +Define Configuration: +```yaml +name: 'wikipedia_query' +description: '' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.wikipedia_query' + class: 'WikipediaTool' +``` \ No newline at end of file diff --git a/docs/guidebook/en/2_2_3_Integrated_Tools.md b/docs/guidebook/en/2_2_3_Integrated_Tools.md index 3761ec39..6db58fe8 100644 --- a/docs/guidebook/en/2_2_3_Integrated_Tools.md +++ b/docs/guidebook/en/2_2_3_Integrated_Tools.md @@ -157,42 +157,33 @@ metadata: module: 'sample_standard_app.app.core.tool.request_tool' class: 'RequestTool' ``` -Parameter Description: - method: The method of the request, such as GET, POST, PUT, etc. - headers: The HTTP headers required for sending the request. - json_parse: Specifies whether the input parameters need to be parsed by HTTP. It should be set to True for POST requests and False for GET requests. - response_content_type: The parsing method for the HTTP request result. If set to 'json', the result will be returned in JSON format; if set to 'text', the result will be returned as text. -This tool can be used directly without any keys required. - -## 4. Langchain Tool -Langchain incorporates numerous tools, but different tools may have distinct initialization methods. To address this, we have implemented a base class called LangChainTool. You merely need to implement the init_langchain_tool method of this class to initialize the corresponding Langchain tool object. -Here's an example of a tool implementation: -[Tool address](../../../sample_standard_app/app/core/tool/human_input_run.yaml) +Configuration to Refer to When Sending a POST Request： ```yaml -name: 'human_input_run' -description: 'human input tool' +name: 'requests_post' +# description copy from langchain RequestPOSTTool +description: 'Use this when you want to POST to a website. + Input should be a json string with two keys: "url" and "data". + The value of "url" should be a string, and the value of "data" should be a dictionary of + key-value pairs you want to POST to the url. + Be careful to always use double quotes for strings in the json string + The output will be the text response of the POST request. + ```' +headers: + content-type: 'application/json' +method: 'POST' +json_parser: true +response_content_type: json tool_type: 'api' input_keys: ['input'] -langchain: - module: langchain_community.tools - class_name: HumanInputRun metadata: type: 'TOOL' - module: 'sample_standard_app.app.core.tool.langchain_tool' - class: 'LangChainTool' + module: 'sample_standard_app.app.core.tool.request_tool' + class: 'RequestTool' ``` -Parameter Explanation: - langchain: The Langchain tool you intend to use, requiring configuration of module and class_name. - langchain.module: The name of the Langchain module, for instance, langchain_community.tools. - langchain.class_name: The class name within Langchain, such as HumanInputRun. - langchain.init_params: Initialization parameters for Langchain, for example: - ```yaml - langchain: - module: langchain_community.tools - class_name: HumanInputRun - init_params: - prompt: '请输入你的问题' - ``` - If you opt to fully override the init_langchain_tool method, then you do not need to configure this part. -This tool can be utilized directly without requiring any keys. +Parameter Description: + method: The method of the request, such as GET, POST, PUT, etc. + headers: The HTTP headers required for sending the request. + json_parse: Specifies whether the input parameters need to be parsed by HTTP. It should be set to True for POST requests and False for GET requests. + response_content_type: The parsing method for the HTTP request result. If set to 'json', the result will be returned in JSON format; if set to 'text', the result will be returned as text. +This tool can be used directly without any keys required. diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md" new file mode 100644 index 00000000..d8126469 --- /dev/null +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md" @@ -0,0 +1,84 @@ +# 集成LangChain工具 + +根据langchain中工具对象的初始化的难易程度，可以将其分为两类： +第一类，简单初始化，只需要简单的参数配置即可完成初始化。 +第二类，复杂初始化，内部包含一些复杂的对象需要进行初始化。 +对于一类工具，你可以在aU中直接使用配置文件进行初始化，如DuDuckGo搜索工具的初始化。 +对于第二类工具，我们实现了一个LangChainTool基础类，你只需要实现该类的init_langchain_tool方法，初始化对应的langchain工具对象即可，参考维基百科的初始化方法。 + +注意，如果你想直接使用LangChain中的description，在配置文件中description必须要配置为空 + +一个工具初始化示例： +[工具地址](../../../sample_standard_app/app/core/tool/langchain_tool/human_input_run.yaml) +```yaml +name: 'human_input_run' +description: '' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: HumanInputRun +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool' + class: 'LangChainTool' +``` +参数说明： + langchain: 你打算使用的langchain工具，需要配置module和class_name + langchain.module: langchain的模块名，例如langchain_community.tools + langchain.class_name: langchain的类名，例如HumanInputRun + langchain.init_params： langchain的初始化参数，例如： + ```yaml + langchain: + module: langchain_community.tools + class_name: HumanInputRun + init_params: + prompt: '请输入你的问题' + ``` + 如果需要使用你完全重写了init_langchain_tool方法，那么你不需要配置该部分 +该工具可以直接使用，无需任何keys + +## 1. 集成LangChain中的DuckDuckGo工具 +[工具地址](../../../sample_standard_app/app/core/tool/langchain_tool/duckduckgo_search.yaml) +```yaml +name: 'duckduckgo_search' +description: 'DuckDuckGo Search tool' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain.tools + class_name: DuckDuckGoSearchResults + init_params: + backend: news +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.langchain_tool' + class: 'LangChainTool' +``` +该工具可以直接使用，无需任何keys + +## 2.集成维基百科搜索 +因为LangChain的定义当中，包含一个api_wrapper对象，因此定义对象文件，并重写初始化方法： +```python +from langchain_community.tools import WikipediaQueryRun +from langchain_community.utilities import WikipediaAPIWrapper + +from sample_standard_app.app.core.tool.langchain_tool.langchain_tool import LangChainTool + + +class WikipediaTool(LangChainTool): + def init_langchain_tool(self, component_configer): + wrapper = WikipediaAPIWrapper() + return WikipediaQueryRun(api_wrapper=wrapper) +``` +定义配置 +```yaml +name: 'wikipedia_query' +description: '' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.wikipedia_query' + class: 'WikipediaTool' +``` \ No newline at end of file diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index fef2338a..6e153352 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -136,12 +136,11 @@ metadata: 该工具可以直接使用，无需任何key，但是为了系统安全，请不要在生产环境使用该工具 ## 3.HTTP 工具 - -### 3.1 HTTP GET [工具地址](../../../sample_standard_app/app/core/tool/request_get_tool.yaml) 该工具可以发送一个GET请求，工具的配置信息 : ```yaml name: 'requests_get' +# description copy from langchain RequestGetTool description: 'A portal to the internet. Use this when you need to get specific content from a website. Input should be a url (i.e. https://www.google.com). The output will be the text response of the GET request. @@ -149,7 +148,7 @@ description: 'A portal to the internet. Use this when you need to get specific headers: content-type: 'application/json' method: 'POST' -json_parser: true +json_parser: false response_content_type: json tool_type: 'api' input_keys: ['input'] @@ -158,41 +157,32 @@ metadata: module: 'sample_standard_app.app.core.tool.request_tool' class: 'RequestTool' ``` -参数说明： - method 请求的方式GET/POST/PUT等 - headers 发送请求需要使用的 http的header, - json_parse 输入参数是否需要是要HTTP解析，POST请求时需要设置为True,GET请求需要设置为False - response_content_type http请求结果的解析方式，设置为json时，会返回json结果，设置为text时会返回text结果 -该工具可以直接使用，无需任何keys - -## 4. Langchain 工具 -langchain当中集成了很多工具，但是不同的工具初始化方法会有不同，对此，我们实现了一个LangChainTool基础类，你只需要实现该类的init_langchain_tool方法，初始化对应的langchain工具对象即可 -一个工具示例： -[工具地址](../../../sample_standard_app/app/core/tool/human_input_run.yaml) +发送POST请求时可以参考的配置： ```yaml -name: 'human_input_run' -description: 'human input tool' +name: 'requests_post' +# description copy from langchain RequestPOSTTool +description: 'Use this when you want to POST to a website. + Input should be a json string with two keys: "url" and "data". + The value of "url" should be a string, and the value of "data" should be a dictionary of + key-value pairs you want to POST to the url. + Be careful to always use double quotes for strings in the json string + The output will be the text response of the POST request. + ```' +headers: + content-type: 'application/json' +method: 'POST' +json_parser: true +response_content_type: json tool_type: 'api' input_keys: ['input'] -langchain: - module: langchain_community.tools - class_name: HumanInputRun metadata: type: 'TOOL' - module: 'sample_standard_app.app.core.tool.langchain_tool' - class: 'LangChainTool' + module: 'sample_standard_app.app.core.tool.request_tool' + class: 'RequestTool' ``` 参数说明： - langchain: 你打算使用的langchain工具，需要配置module和class_name - langchain.module: langchain的模块名，例如langchain_community.tools - langchain.class_name: langchain的类名，例如HumanInputRun - langchain.init_params： langchain的初始化参数，例如： - ```yaml - langchain: - module: langchain_community.tools - class_name: HumanInputRun - init_params: - prompt: '请输入你的问题' - ``` - 如果需要使用你完全重写了init_langchain_tool方法，那么你不需要配置该部分 + method 请求的方式GET/POST/PUT等 + headers 发送请求需要使用的 http的header, + json_parse 输入参数是否需要是要HTTP解析，POST请求时需要设置为True,GET请求需要设置为False + response_content_type http请求结果的解析方式，设置为json时，会返回json结果，设置为text时会返回text结果 该工具可以直接使用，无需任何keys \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 302c6b67..84aa9369 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,6 +54,8 @@ langchain-anthropic = '^0.1.13' numpy = '^1.26.0' pandas = "^2.2.2" pyarrow = "^16.1.0" +duckduckgo-search = "^6.1.7" +wikipedia= "^1.4.0" [tool.poetry.extras] log_ext = ["aliyun-log-python-sdk"] diff --git a/sample_standard_app/app/core/tool/langchain_tool/__init__.py b/sample_standard_app/app/core/tool/langchain_tool/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/sample_standard_app/app/core/tool/langchain_tool/duckduckgo_search.yaml b/sample_standard_app/app/core/tool/langchain_tool/duckduckgo_search.yaml new file mode 100644 index 00000000..9bbb2977 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/duckduckgo_search.yaml @@ -0,0 +1,13 @@ +name: 'duckduckgo_search' +description: '' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain.tools + class_name: DuckDuckGoSearchResults + init_params: + backend: news +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.langchain_tool' + class: 'LangChainTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/human_input_run.yaml b/sample_standard_app/app/core/tool/langchain_tool/human_input_run.yaml similarity index 66% rename from sample_standard_app/app/core/tool/human_input_run.yaml rename to sample_standard_app/app/core/tool/langchain_tool/human_input_run.yaml index 611944b2..405b6ea8 100644 --- a/sample_standard_app/app/core/tool/human_input_run.yaml +++ b/sample_standard_app/app/core/tool/langchain_tool/human_input_run.yaml @@ -1,5 +1,5 @@ name: 'human_input_run' -description: 'human input tool' +description: '' tool_type: 'api' input_keys: ['input'] langchain: @@ -7,5 +7,5 @@ langchain: class_name: HumanInputRun metadata: type: 'TOOL' - module: 'sample_standard_app.app.core.tool.langchain_tool' + module: 'sample_standard_app.app.core.tool.langchain_tool.langchain_tool' class: 'LangChainTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/langchain_tool.py b/sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py similarity index 87% rename from sample_standard_app/app/core/tool/langchain_tool.py rename to sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py index 24444a87..5a8a3d7a 100644 --- a/sample_standard_app/app/core/tool/langchain_tool.py +++ b/sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py @@ -9,6 +9,7 @@ from typing import Optional, Type +from langchain_community.tools import DuckDuckGoSearchResults from langchain_core.tools import BaseTool from agentuniverse.agent.action.tool.tool import Tool, ToolInput @@ -27,8 +28,9 @@ def execute(self, tool_input: ToolInput): def initialize_by_component_configer(self, component_configer: ToolConfiger) -> 'Tool': super().initialize_by_component_configer(component_configer) - self.init_langchain_tool(component_configer) - self.description = self.tool.description + self.tool = self.init_langchain_tool(component_configer) + if not component_configer.description: + self.description = self.tool.description return self def init_langchain_tool(self, component_configer): @@ -46,6 +48,3 @@ def get_langchain_tool(self, init_params: dict, clz: Type[BaseTool]): self.tool = clz(**init_params) else: self.tool = clz() - - def as_langchain(self): - return self.tool diff --git a/sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.py b/sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.py new file mode 100644 index 00000000..6c4e65a9 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.py @@ -0,0 +1,19 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/6/27 17:38 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: wikipedia_query.py + + +from langchain_community.tools import WikipediaQueryRun +from langchain_community.utilities import WikipediaAPIWrapper + +from sample_standard_app.app.core.tool.langchain_tool.langchain_tool import LangChainTool + + +class WikipediaTool(LangChainTool): + def init_langchain_tool(self, component_configer): + wrapper = WikipediaAPIWrapper() + return WikipediaQueryRun(api_wrapper=wrapper) diff --git a/sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.yaml b/sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.yaml new file mode 100644 index 00000000..71fafe62 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/wikipedia_query.yaml @@ -0,0 +1,8 @@ +name: 'wikipedia_query' +description: '' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.wikipedia_query' + class: 'WikipediaTool' \ No newline at end of file From 59d5d516bc2c58e8f888e29e284ff5af31b80cca Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 27 Jun 2024 20:28:15 +0800 Subject: [PATCH 059/118] fix --- .../translation_agent.py | 14 +++- .../translation_agent.yaml | 6 +- .../translation_by_token_agent.py | 71 +++++++++---------- .../translation_improve_agent.yaml | 31 +------- .../translation_reflection_agent.yaml | 25 +------ .../translation_work_agent.yaml | 9 +-- .../translation_planner/reflection_planner.py | 48 ------------- .../reflection_planner.yaml | 6 -- .../multi_translation_improve_en.yaml} | 29 ++------ .../multi_translation_init_en.yaml} | 27 ++----- .../multi_translation_reflection_en.yaml} | 28 ++------ .../translation/translation_improve_en.yaml | 33 +++++++++ .../translation/translation_init_en.yaml | 12 ++++ .../translation_reflection_en.yaml | 28 ++++++++ .../app/test/test_translation_agent.py | 3 - sample_standard_app/config/config.toml | 2 +- 16 files changed, 143 insertions(+), 229 deletions(-) delete mode 100644 sample_standard_app/app/core/planner/translation_planner/reflection_planner.py delete mode 100644 sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml rename sample_standard_app/app/core/{agent/translation_agent_case/multi_improve_translation_agent.yaml => prompt/translation/multi_translation_improve_en.yaml} (69%) rename sample_standard_app/app/core/{agent/translation_agent_case/multi_work_translation_agent.yaml => prompt/translation/multi_translation_init_en.yaml} (58%) rename sample_standard_app/app/core/{agent/translation_agent_case/multi_reflection_translation_agent.yaml => prompt/translation/multi_translation_reflection_en.yaml} (71%) create mode 100644 sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml create mode 100644 sample_standard_app/app/core/prompt/translation/translation_init_en.yaml create mode 100644 sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py index 7a09741a..53e314df 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py @@ -1,12 +1,15 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- -from langchain_text_splitters import RecursiveCharacterTextSplitter # @Time : 2024/6/25 16:56 # @Author : weizjajj # @Email : weizhongjie.wzj@antgroup.com # @FileName: translation_planner.py +import copy +from agentuniverse.agent.plan.planner.planner import Planner +from agentuniverse.agent.plan.planner.planner_manager import PlannerManager + from agentuniverse.agent.agent import Agent from agentuniverse.agent.input_object import InputObject @@ -25,3 +28,12 @@ def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: def parse_result(self, planner_result: dict) -> dict: return planner_result + + def execute(self, input_object: InputObject, agent_input: dict) -> dict: + planner_base: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')) + agent_model = copy.deepcopy(self.agent_model) + translation_type = input_object.get_data('type') + if translation_type == "multi": + agent_model.profile['prompt_version'] = translation_type + "_" + agent_model.profile['prompt_version'] + planner_result = planner_base.invoke(agent_model, agent_input, input_object) + return planner_result diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml index 7e35eaf3..3c292391 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml @@ -7,12 +7,8 @@ profile: output_keys: ['output'] llm_model: name: 'default_deepseek_llm' + max_tokens: 4000 plan: - work_agent: 'translation_work_agent' - reflection_agent: 'translation_reflection_agent' - improve_agent: 'translation_improve_agent' - planner: - name: 'reflection_planner' action: tool: # - 'google_search_tool' diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py index 45931410..6545b8a3 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -1,12 +1,16 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- -from langchain_text_splitters import RecursiveCharacterTextSplitter # @Time : 2024/6/25 16:56 # @Author : weizjajj # @Email : weizhongjie.wzj@antgroup.com # @FileName: translation_planner.py +from agentuniverse.agent.agent_manager import AgentManager + +from agentuniverse.base.util.logging.logging_util import LOGGER +from langchain_text_splitters import RecursiveCharacterTextSplitter + from agentuniverse.agent.agent import Agent from agentuniverse.agent.input_object import InputObject from agentuniverse.llm.llm import LLM @@ -14,33 +18,6 @@ def calculate_chunk_size(token_count: int, token_limit: int) -> int: - """ - Calculate the chunk size based on the token count and token limit. - - Args: - token_count (int): The total number of tokens. - token_limit (int): The maximum number of tokens allowed per chunk. - - Returns: - int: The calculated chunk size. - - Description: - This function calculates the chunk size based on the given token count and token limit. - If the token count is less than or equal to the token limit, the function returns the token count as the chunk size. - Otherwise, it calculates the number of chunks needed to accommodate all the tokens within the token limit. - The chunk size is determined by dividing the token limit by the number of chunks. - If there are remaining tokens after dividing the token count by the token limit, - the chunk size is adjusted by adding the remaining tokens divided by the number of chunks. - - Example: - >>> calculate_chunk_size(1000, 500) - 500 - >>> calculate_chunk_size(1530, 500) - 389 - >>> calculate_chunk_size(2242, 500) - 496 - """ - if token_count <= token_limit: return token_count @@ -69,25 +46,47 @@ def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: def parse_result(self, planner_result: dict) -> dict: return planner_result + def execute_agents(self, planner_input: dict) -> dict: + work_agent = 'translation_work_agent' + reflection_agent = 'translation_reflection_agent' + improve_agent = 'translation_improve_agent' + + init_agent_result = self.execute_agent(work_agent, planner_input) + LOGGER.info(f"init_agent_result: {init_agent_result.to_json_str()}") + + planner_input['init_agent_result'] = init_agent_result.get_data('output') + + reflection_result = self.execute_agent(reflection_agent, planner_input) + LOGGER.info(f"reflection_result: {reflection_result.to_json_str()}") + + planner_input['reflection_agent_result'] = reflection_result.get_data('output') + + improve_result = self.execute_agent(improve_agent, planner_input) + LOGGER.info(f"improve_agent_result: {improve_result.to_json_str()}") + + return improve_result.to_dict() + + @staticmethod + def execute_agent(agent_name: str, agent_input: dict): + agent: Agent = AgentManager().get_instance_obj(agent_name) + result = agent.run(**agent_input) + return result + def execute(self, input_object: InputObject, agent_input: dict) -> dict: llm_name = self.agent_model.profile.get('llm_model').get('name') llm: LLM = LLMManager().get_instance_obj(llm_name) source_text = agent_input.get('source_text') - text_tokens = llm.get_num_tokens(source_text) + text_tokens = len(source_text) # 这里使用最大输入token，因为必须要保证有足够的token输出翻译结果 max_context_length = llm.max_tokens if text_tokens < max_context_length: - return super().execute(input_object, agent_input) - + return self.execute_agents(agent_input) + agent_input['execute_type'] = 'multi' chunk_result = list[str]() chunk_size = calculate_chunk_size(text_tokens, max_context_length) source_text_chunks = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0).split_text( source_text) - input_object.add_data('work_agent', 'multi_translation_work_agent') - input_object.add_data('reflection_agent', 'multi_reflection_translation_work_agent') - input_object.add_data('improve_agent', 'multi_improve_translation_work_agent') - for i in range(len(source_text_chunks)): tagged_text = "" if i - 1 > 0: @@ -101,7 +100,7 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: tagged_text += source_text_chunks[i + 1] agent_input['chunk_to_translate'] = source_text_chunks[i] agent_input['tagged_text'] = tagged_text - result = super().execute(input_object, agent_input) + result = self.execute_agents(agent_input) chunk_result.append(result.get('output')) return { diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml index ac3dc211..90b9a336 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml @@ -2,36 +2,7 @@ info: name: 'translation_improve_agent' description: '翻译反思Agent' profile: - introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. - target: You will be provided with a source text and its translation and your goal is to improve the translation. - instruction: | - Your task is to carefully read, then edit, a translation from {source_lang} to {target_lang}, taking into - account a list of expert suggestions and constructive criticisms. - - The source text, the initial translation, and the expert linguist suggestions are delimited by XML tags , and \ - as follows: - - - {source_text} - - - - {init_agent_result} - - - - {reflection_agent_result} - - - Please take into account the expert suggestions when editing the translation. Edit the translation by ensuring: - - (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), - (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules and ensuring there are no unnecessary repetitions), - (iii) style (by ensuring the translations reflect the style of the source text) - (iv) terminology (inappropriate for context, inconsistent use), or - (v) other errors. - - Output only the new translation and nothing else. + prompt_version: 'translation_improve.en' input_keys: ['source_lang','target_lang','source_text','init_agent_result','reflection_agent_result'] output_keys: ['output'] llm_model: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml index d9a65f78..85cbebb3 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml @@ -2,30 +2,7 @@ info: name: 'translation_reflection_agent' description: '翻译反思Agent' profile: - introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. - target: You will be provided with a source text and its translation and your goal is to improve the translation. - instruction: | - Your task is to carefully read a source text and a translation from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions to improve the translation. - - The source text and initial translation, delimited by XML tags and , are as follows: - - - {source_text} - - - - {init_agent_result} - - - When writing suggestions, pay attention to whether there are ways to improve the translation's - (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), - (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions), - (iii) style (by ensuring the translations reflect the style of the source text and takes into account any cultural context), - (iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}). - - Write a list of specific, helpful and constructive suggestions for improving the translation. - Each suggestion should address one specific part of the translation. - Output only the suggestions and nothing else. + prompt_version: 'translation_reflection.en' input_keys: ['source_lang','target_lang','source_text','init_agent_result'] output_keys: ['output'] llm_model: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml index 870bb4ae..e6704d67 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml @@ -2,14 +2,7 @@ info: name: 'translation_work_agent' description: '翻译测试Agent' profile: - introduction: You are an expert linguist, specializing in translation from {source_lang} to {target_lang}. - target: ' ' - instruction: | - This is an {source_lang} to {target_lang} translation, please provide the {target_lang} translation for this text. - Do not provide any explanations or text apart from the translation. - {source_lang}: {source_text} - - {target_lang}: + prompt_version: 'translation_init.en' input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: diff --git a/sample_standard_app/app/core/planner/translation_planner/reflection_planner.py b/sample_standard_app/app/core/planner/translation_planner/reflection_planner.py deleted file mode 100644 index eb452416..00000000 --- a/sample_standard_app/app/core/planner/translation_planner/reflection_planner.py +++ /dev/null @@ -1,48 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/25 16:33 -# @Author : weizjajj -# @Email : weizhongjie.wzj@antgroup.com -# @FileName: translation_planner.py - -from agentuniverse.agent.agent import Agent -from agentuniverse.agent.agent_manager import AgentManager - -from agentuniverse.agent.agent_model import AgentModel -from agentuniverse.agent.input_object import InputObject -from agentuniverse.agent.plan.planner.planner import Planner -from agentuniverse.base.util.logging.logging_util import LOGGER - - -class ReflectionPlanner(Planner): - - def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: InputObject) -> dict: - work_agent = agent_model.plan.get('work_agent') - reflection_agent = agent_model.plan.get('reflection_agent') - improve_agent = agent_model.plan.get('improve_agent') - - work_agent = input_object.get_data('work_agent', work_agent) - reflection_agent = input_object.get_data('reflection_agent', reflection_agent) - improve_agent = input_object.get_data('improve_agent', improve_agent) - - init_agent_result = self.execute_agent(work_agent, planner_input) - LOGGER.info(f"init_agent_result: {init_agent_result.to_json_str()}") - - planner_input['init_agent_result'] = init_agent_result.get_data('output') - - reflection_result = self.execute_agent(reflection_agent, planner_input) - LOGGER.info(f"reflection_result: {reflection_result.to_json_str()}") - - planner_input['reflection_agent_result'] = reflection_result.get_data('output') - - improve_result = self.execute_agent(improve_agent, planner_input) - LOGGER.info(f"improve_agent_result: {improve_result.to_json_str()}") - - return improve_result.to_dict() - - @staticmethod - def execute_agent(agent_name: str, agent_input: dict): - agent: Agent = AgentManager().get_instance_obj(agent_name) - result = agent.run(**agent_input) - return result diff --git a/sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml b/sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml deleted file mode 100644 index c567d4d2..00000000 --- a/sample_standard_app/app/core/planner/translation_planner/reflection_planner.yaml +++ /dev/null @@ -1,6 +0,0 @@ -name: 'reflection_planner' -description: '反思规划' -metadata: - type: 'PLANNER' - module: 'sample_standard_app.app.core.planner.translation_planner.reflection_planner' - class: 'ReflectionPlanner' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml b/sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml similarity index 69% rename from sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml rename to sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml index ba6ac476..675b4255 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/multi_improve_translation_agent.yaml +++ b/sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml @@ -1,10 +1,6 @@ -info: - name: 'multi_improve_translation_work_agent' - description: '翻译测试Agent' -profile: - introduction: You are an expert linguist, specializing in translation editing from {source_lang} to {target_lang}. - target: ' ' - instruction: | +introduction: You are an expert linguist, specializing in translation editing from {source_lang} to {target_lang}. +target: ' ' +instruction: | Your task is to carefully read, then improve, a translation from {source_lang} to {target_lang}, taking into account a set of expert suggestions and constructive critisms. Below, the source text, initial translation, and expert suggestions are provided. @@ -26,7 +22,7 @@ profile: {init_agent_result} - The expert translations of the indicated part, delimited below by and , is as follows: + The expert translations of the indicated part, delimited below by and , is as follows: {reflection_agent_result} @@ -41,19 +37,6 @@ profile: (v) other errors. Output only the new translation of the indicated part and nothing else. - input_keys: [ 'source_lang','target_lang','tagged_text','chunk_to_translate','init_agent_result','reflection_agent_result'] - output_keys: [ 'output' ] - llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 -plan: - planner: - name: 'rag_planner' -action: - tool: -memory: - name: '' metadata: - type: 'AGENT' - module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' - class: 'TranslationAgent' \ No newline at end of file + type: 'PROMPT' + version: 'multi_translation_improve.en' diff --git a/sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml b/sample_standard_app/app/core/prompt/translation/multi_translation_init_en.yaml similarity index 58% rename from sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml rename to sample_standard_app/app/core/prompt/translation/multi_translation_init_en.yaml index 09cf1b59..f1ffdf3d 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/multi_work_translation_agent.yaml +++ b/sample_standard_app/app/core/prompt/translation/multi_translation_init_en.yaml @@ -1,10 +1,6 @@ -info: - name: 'multi_translation_work_agent' - description: '翻译测试Agent' -profile: - introduction: 'You are an expert linguist, specializing in translation from {source_lang} to {target_lang}.' - target: ' ' - instruction: | +introduction: 'You are an expert linguist, specializing in translation from {source_lang} to {target_lang}.' +target: ' ' +instruction: | Your task is provide a professional translation from {source_lang} to {target_lang} of PART of a text. The source text is below, delimited by XML tags and . Translate only the part within the source text @@ -21,19 +17,6 @@ profile: Output only the translation of the portion you are asked to translate, and nothing else. - input_keys: [ 'source_lang','target_lang','tagged_text','chunk_to_translate'] - output_keys: [ 'output' ] - llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 -plan: - planner: - name: 'rag_planner' -action: - tool: -memory: - name: '' metadata: - type: 'AGENT' - module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' - class: 'TranslationAgent' \ No newline at end of file + type: 'PROMPT' + version: 'multi_translation_init.en' diff --git a/sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml b/sample_standard_app/app/core/prompt/translation/multi_translation_reflection_en.yaml similarity index 71% rename from sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml rename to sample_standard_app/app/core/prompt/translation/multi_translation_reflection_en.yaml index d2f6f7eb..0a3e9b95 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/multi_reflection_translation_agent.yaml +++ b/sample_standard_app/app/core/prompt/translation/multi_translation_reflection_en.yaml @@ -1,10 +1,6 @@ -info: - name: 'multi_reflection_translation_work_agent' - description: '翻译测试Agent' -profile: - introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. - target: You will be provided with a source text and its translation and your goal is to improve the translation. - instruction: | +introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. +target: You will be provided with a source text and its translation and your goal is to improve the translation. +instruction: | Your task is to carefully read a source text and part of a translation of that text from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions for improving the translation. The source text is below, delimited by XML tags and , and the part that has been translated @@ -34,19 +30,7 @@ profile: Write a list of specific, helpful and constructive suggestions for improving the translation. Each suggestion should address one specific part of the translation. Output only the suggestions and nothing else. - input_keys: [ 'source_lang','target_lang','tagged_text','chunk_to_translate','init_agent_result'] - output_keys: [ 'output' ] - llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 -plan: - planner: - name: 'rag_planner' -action: - tool: -memory: - name: '' + metadata: - type: 'AGENT' - module: 'sample_standard_app.app.core.agent.translation_agent_case.translation_agent' - class: 'TranslationAgent' \ No newline at end of file + type: 'PROMPT' + version: 'multi_translation_reflection.en' diff --git a/sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml b/sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml new file mode 100644 index 00000000..5480d5e8 --- /dev/null +++ b/sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml @@ -0,0 +1,33 @@ +introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. +target: ' ' +instruction: | + Your task is to carefully read, then edit, a translation from {source_lang} to {target_lang}, taking into + account a list of expert suggestions and constructive criticisms. + + The source text, the initial translation, and the expert linguist suggestions are delimited by XML tags , and \ + as follows: + + + {source_text} + + + + {init_agent_result} + + + + {reflection_agent_result} + + + Please take into account the expert suggestions when editing the translation. Edit the translation by ensuring: + + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules and ensuring there are no unnecessary repetitions), + (iii) style (by ensuring the translations reflect the style of the source text) + (iv) terminology (inappropriate for context, inconsistent use), or + (v) other errors. + + Output only the new translation and nothing else. +metadata: + type: 'PROMPT' + version: 'translation_improve.en' diff --git a/sample_standard_app/app/core/prompt/translation/translation_init_en.yaml b/sample_standard_app/app/core/prompt/translation/translation_init_en.yaml new file mode 100644 index 00000000..e94cb39f --- /dev/null +++ b/sample_standard_app/app/core/prompt/translation/translation_init_en.yaml @@ -0,0 +1,12 @@ +introduction: You are an expert linguist, specializing in translation from {source_lang} to {target_lang}. +target: ' ' +instruction: | + This is an {source_lang} to {target_lang} translation, please provide the {target_lang} translation for this text. + Do not provide any explanations or text apart from the translation. + {source_lang}: {source_text} + + {target_lang}: + +metadata: + type: 'PROMPT' + version: 'translation_init.en' diff --git a/sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml b/sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml new file mode 100644 index 00000000..efd887d7 --- /dev/null +++ b/sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml @@ -0,0 +1,28 @@ +introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. +target: You will be provided with a source text and its translation and your goal is to improve the translation. +instruction: | + Your task is to carefully read a source text and a translation from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions to improve the translation. + + The source text and initial translation, delimited by XML tags and , are as follows: + + + {source_text} + + + + {init_agent_result} + + + When writing suggestions, pay attention to whether there are ways to improve the translation's + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text), + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions), + (iii) style (by ensuring the translations reflect the style of the source text and takes into account any cultural context), + (iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}). + + Write a list of specific, helpful and constructive suggestions for improving the translation. + Each suggestion should address one specific part of the translation. + Output only the suggestions and nothing else. + +metadata: + type: 'PROMPT' + version: 'translation_reflection.en' diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py index 0d4d0e77..8ffc7ab5 100644 --- a/sample_standard_app/app/test/test_translation_agent.py +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -41,9 +41,6 @@ def test_rag_fgent(self): Looking beyond the U.S. federal government, there are many jurisdictions globally. Unfortunately, arguments in favor of regulations that would stifle AI development continue to proliferate. But I’ve learned from my trips to Washington and other nations’ capitals that talking to regulators does have an impact. If you get a chance to talk to a regulator at any level, I hope you’ll do what you can to help governments better understand AI. """ """Test demo rag agent.""" - with open('/Users/weizj/jobspace/agentUniverse/sample_standard_app/app/test/test_file.txt') as f: - data = f.read() - print(len(data)) instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", diff --git a/sample_standard_app/config/config.toml b/sample_standard_app/config/config.toml index 4a662064..6abfc733 100644 --- a/sample_standard_app/config/config.toml +++ b/sample_standard_app/config/config.toml @@ -28,7 +28,7 @@ sqldb_wrapper = ['sample_standard_app.app.core.sqldb_wrapper'] # Log config file path, an absolute path or a relative path based on the dir where the current config file is located. log_config_path = './log_config.toml' # Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore. -#custom_key_path = './custom_key.toml' +custom_key_path = './custom_key.toml' [DB] # A sqlalchemy db uri used for storing various info, for example, service request, generated during application running. From ff8741304de9008720844de5389f26909a002ab1 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 27 Jun 2024 20:37:54 +0800 Subject: [PATCH 060/118] fix --- .../core/agent/translation_agent_case/translation_agent.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py index 53e314df..654a68bf 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py @@ -22,7 +22,7 @@ def output_keys(self) -> list[str]: return self.agent_model.profile.get('output_keys') def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: - for key in self.input_keys(): + for key in input_object.to_dict(): agent_input[key] = input_object.get_data(key) return agent_input @@ -32,7 +32,7 @@ def parse_result(self, planner_result: dict) -> dict: def execute(self, input_object: InputObject, agent_input: dict) -> dict: planner_base: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')) agent_model = copy.deepcopy(self.agent_model) - translation_type = input_object.get_data('type') + translation_type = input_object.get_data('execute_type') if translation_type == "multi": agent_model.profile['prompt_version'] = translation_type + "_" + agent_model.profile['prompt_version'] planner_result = planner_base.invoke(agent_model, agent_input, input_object) From ef221972d13946d4e8eee3278f1bea4a6b31bf5f Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 10:01:34 +0800 Subject: [PATCH 061/118] add texg --- .../translation_by_token_agent.py | 2 +- .../app/test/rag_agent_result.txt | 9 ++++ .../app/test/test_translation_agent.py | 42 +++++++++---------- .../app/test/translation_data/long_text.txt | 30 +++++++++++++ .../translation_data/long_text_result.txt | 10 +++++ .../app/test/translation_data/short_text.txt | 3 ++ .../translation_data/short_text_result.txt | 5 +++ 7 files changed, 78 insertions(+), 23 deletions(-) create mode 100644 sample_standard_app/app/test/rag_agent_result.txt create mode 100644 sample_standard_app/app/test/translation_data/long_text.txt create mode 100644 sample_standard_app/app/test/translation_data/long_text_result.txt create mode 100644 sample_standard_app/app/test/translation_data/short_text.txt create mode 100644 sample_standard_app/app/test/translation_data/short_text_result.txt diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py index 6545b8a3..8ca27988 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -76,7 +76,7 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: llm_name = self.agent_model.profile.get('llm_model').get('name') llm: LLM = LLMManager().get_instance_obj(llm_name) source_text = agent_input.get('source_text') - text_tokens = len(source_text) + text_tokens = llm.get_num_tokens(source_text) # 这里使用最大输入token，因为必须要保证有足够的token输出翻译结果 max_context_length = llm.max_tokens if text_tokens < max_context_length: diff --git a/sample_standard_app/app/test/rag_agent_result.txt b/sample_standard_app/app/test/rag_agent_result.txt new file mode 100644 index 00000000..29e4b3f4 --- /dev/null +++ b/sample_standard_app/app/test/rag_agent_result.txt @@ -0,0 +1,9 @@ + +Rag agent execution result is : +上周，我在美国国会大厦就人工智能与监管问题发表了演讲，该活动吸引了立法和商界领袖的参与。我对开源社区成功抵御那些可能抑制创新的法规所取得的进展感到鼓舞。但反对开源的人士仍在不断调整他们的论点，最新的担忧焦点在于开源对国家安全可能产生的影响。我希望我们都能继续保护开源！ + +基于与立法者的交流，我对美国联邦政府在准确评估人工智能风险方面取得的进展感到鼓舞。明确地说，确实需要设置防护措施。然而，这些防护措施应针对具体的人工智能应用，而不是通用的人工智能技术。尽管如此，正如我之前所述，一些公司积极限制开源，可能是为了保护他们在专有模型上的巨额投资，并遏制竞争对手。观察他们的论点随时间的变化，颇具观察价值。 + +例如，大约12个月前，人工智能安全中心的“人工智能风险声明”警告说，人工智能可能导致人类灭绝，并激起了对人工智能接管的恐惧。这引起了华盛顿领导人的关注。但许多人工智能专家指出，这种反乌托邦的科幻场景缺乏现实基础。大约六个月后，当我在美国参议院的人工智能洞察论坛上作陈述时，立法者不再那么担心人工智能的接管。随后，开源的反对者调整了他们的论点，主要论点转为人工智能可能帮助制造生物武器的风险。不久之后，OpenAI和兰德公司指出，当前的人工智能并未显著增强恶意行为者制造生物武器的能力。因此，对AI辅助生物武器的担忧有所减少。当然，坏人可能使用生物武器（无论是否借助AI）这一可能性仍然是国际上极为关注的话题。最新的反对开放源代码AI的论点已转向国家安全。AI在经济竞争和战争中都具有重要作用，反对开放源代码的人士认为美国应确保其对手无法接触到最新的基础模型。虽然我不希望专制政府使用AI，尤其是用于发动不义之战，但大型语言模型（LLM）的秘密已无法掩盖，如果民主国家限制访问，专制国家将会填补这一空白。将来某日，若有孩子向AI询问关于民主、自由媒体或独立司法在维护法治中的作用，我希望AI能反映民主价值观，而不是偏袒专制领导人的目标，例如忽视人权。我从华盛顿回来时，对我们在AI领域取得的进展感到乐观。一年前，立法者在我看来似乎将80%的时间用于讨论AI的监管措施，而只有20%的时间用于讨论创新投资。我很高兴这一比例已经显著逆转，现在更多的是讨论对创新的投入。 + +放眼美国联邦政府之外，全球各地有许多不同的司法管辖区。不幸的是，那些支持可能阻碍AI发展的监管规定的论点仍在不断增多。但我从访问华盛顿和其他国家首都的经历中学到，与监管者进行对话确实能够产生积极影响。如果你有机会与任何级别的监管者交谈，我希望你能尽力帮助政府更深入地理解AI。 \ No newline at end of file diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py index 8ffc7ab5..afb82440 100644 --- a/sample_standard_app/app/test/test_translation_agent.py +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -21,38 +21,36 @@ class TranslationAgentTest(unittest.TestCase): def setUp(self) -> None: AgentUniverse().start(config_path='../../config/config.toml') - def test_rag_fgent(self): - data = """ - Last week, I spoke about AI and regulation at the U.S. Capitol at an event that was attended by legislative and business leaders. I’m encouraged by the progress the open source community has made fending off regulations that would have stifled innovation. But opponents of open source are continuing to shift their arguments, with the latest worries centering on open source's impact on national security. I hope we’ll all keep protecting open source! - -Based on my conversations with legislators, I’m encouraged by the progress the U.S. federal government has made getting a realistic grasp of AI’s risks. To be clear, guardrails are needed. But they should be applied to AI applications, not to general-purpose AI technology. - -Nonetheless, as I wrote previously, some companies are eager to limit open source, possibly to protect the value of massive investments they’ve made in proprietary models and to deter competitors. It has been fascinating to watch their arguments change over time. - -For instance, about 12 months ago, the Center For AI Safety’s “Statement on AI Risk” warned that AI could cause human extinction and stoked fears of AI taking over. This alarmed leaders in Washington. But many people in AI pointed out that this dystopian science-fiction scenario has little basis in reality. About six months later, when I testified at the U.S. Senate’s AI Insight forum, legislators no longer worried much about an AI takeover. - -Then the opponents of open source shifted gears. Their leading argument shifted to the risk of AI helping to create bioweapons. Soon afterward, OpenAI and RAND showed that current AI does not significantly increase the ability of malefactors to build bioweapons. This fear of AI-enabled bioweapons has diminished. To be sure, the possibility that bad actors could use bioweapons — with or without AI — remains a topic of great international concern. - - -The latest argument for blocking open source AI has shifted to national security. AI is useful for both economic competition and warfare, and open source opponents say the U.S. should make sure its adversaries don’t have access to the latest foundation models. While I don’t want authoritarian governments to use AI, particularly to wage unjust wars, the LLM cat is out of the bag, and authoritarian countries will fill the vacuum if democratic nations limit access. When, some day, a child somewhere asks an AI system questions about democracy, the role of a free press, or the function of an independent judiciary in preserving the rule of law, I would like the AI to reflect democratic values rather than favor authoritarian leaders’ goals over, say, human rights. - -I came away from Washington optimistic about the progress we’ve made. A year ago, legislators seemed to me to spend 80% of their time talking about guardrails for AI and 20% about investing in innovation. I was delighted that the ratio has flipped, and there was far more talk of investing in innovation. - -Looking beyond the U.S. federal government, there are many jurisdictions globally. Unfortunately, arguments in favor of regulations that would stifle AI development continue to proliferate. But I’ve learned from my trips to Washington and other nations’ capitals that talking to regulators does have an impact. If you get a chance to talk to a regulator at any level, I hope you’ll do what you can to help governments better understand AI. - """ - """Test demo rag agent.""" - + def test_translation_agent_long(self): instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') + with open('./translation_data/long_text.txt', 'r') as f: + data = f.read() + output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", source_text=data ) res_info = f"\nRag agent execution result is :\n" res_info += output_object.get_data('output') # 创建文件，并写入文件 - with open('./rag_agent_result.txt', 'w') as f: + with open('./translation_data/long_text_result.txt', 'w') as f: f.write(res_info) print(res_info) + # def test_translation_agent_short(self): + # instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') + # with open('./translation_data/short_text.txt', 'r') as f: + # data = f.read() + # + # output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", + # source_text=data + # ) + # res_info = f"\nRag agent execution result is :\n" + # res_info += output_object.get_data('output') + # # 创建文件，并写入文件 + # with open('./translation_data/short_text_result.txt', 'w') as f: + # f.write(res_info) + # print(res_info) + if __name__ == '__main__': unittest.main() diff --git a/sample_standard_app/app/test/translation_data/long_text.txt b/sample_standard_app/app/test/translation_data/long_text.txt new file mode 100644 index 00000000..2847ca5c --- /dev/null +++ b/sample_standard_app/app/test/translation_data/long_text.txt @@ -0,0 +1,30 @@ +Chapter 1 +　　I HAVE often felt a motion of love to leave some hints in writing of myexperience of the goodness of God, and now, in the thirty-sixth year of my age,I begin this work. +　　I was born in Northampton, in Burlington County, West Jersey, in the year1720. Before I was seven years old I began to be acquainted with the operationsof divine love. Through the care of my parents, I was taught to read nearly assoon as I was capable of it; and as I went from school one day, I remember thatwhile my companions were playing by the way, I went forward out of sight, andsitting down, I read the twenty-second chapter of Revelation: "He showed me apure river of water of life, clear as crystal, proceeding out of the throne ofGod and of the Lamb," etc. In reading it, my mind was drawn to seek after thatpure habitation which I then believed God had prepared for His servants. Theplace where I sat, and the sweetness that attended my mind, remain fresh in mymemory. This, and the like gracious visitations, had such an effect upon methat when boys used ill language it troubled me; and, through the continuedmercies of God, I was preserved from that evil. +　　The pious instructions of my parents were often fresh in my mind, when Ihappened to be among wicked children, and were of use to me. Having a largefamily of children, they used frequently, on First-days, after meeting, to set us one after another to read the Holy Scriptures, or some religious books, therest sitting by without much conversation; I have since often thought it was agood practice. From what I had read and heard, I believed there had been, inpast ages, people who walked in uprightness before God in a degree exceedingany that I knew or heard of now living: and the apprehension of there beingless steadiness and firmness amongst people in the present age often troubledme while I was a child. +　　I may here mention a remarkable circumstance that occurred in my childhood. +　　On going to a neighbour's house, I saw on the way a robin sitting on her nest,and as I came near she went off ; but having young ones, she flew about, andwith many cries expressed her concern for them. I stood and threw stones ather, and one striking her, she fell down dead. At first I was pleased with theexploit, but after a few minutes was seized with horror, at having, in asportive way, killed an innocent creature while she was careful for her young. +　　I beheld her lying dead, and thought those young ones, for which she was socareful, must now perish for want of their dam to nourish them. After somepainful considerations on the subject, I climbed up the tree, took all theyoung birds, and killed them, supposing that better than to leave them to pineaway and die miserably. In this case I believed that Scripture proverb wasfulfilled, "The tender mercies of the wicked are cruel." I then went on myerrand, and for some hours could think of little else but the cruelties I hadcommitted, and was much troubled. Thus He whose tender mercies are over all Hisworks hath placed a principle in the human mind, which incites to exercisegoodness towards every living creature; and this being singly attended to,people become tender-hearted and sympathizing; but when frequently and totallyrejected, the mind becomes shut up in a contrary disposition. +　　About the twelfth year of my age, my father being abroad, my mother reprovedme for some misconduct, to which I made an undutiful reply. The next First-day,as I was with my father returning from meeting, he told me that he understood Ihad behaved amiss to my mother, and advised me to be more careful in future. Iknew myself blamable, and in shame and confusion remained silent. Being thusawakened to a sense of my wickedness, I felt remorse in my mind, and on gettinghome, I retired and prayed to the Lord to forgive me, and I do not rememberthat I ever afterwards spoke unhandsomely to either of my parents, howeverfoolish in some other things. +　　Having attained the age of sixteen years, I began to love wanton company; andthough I was preserved from profane language or scandalous conduct, yet Iperceived a plant in me which produced much wild grapes; my merciful Father didnot, however, forsake me utterly, but at times, through His grace, I wasbrought seriously to consider my ways; and the sight of my backslidingsaffected me with sorrow, yet for want of rightly attending to the reproofs ofinstruction, vanity was added to vanity, and repentance to repentance. Upon the whole, my mind became more and more alienated from the truth, and I hastenedtoward destruction. While I meditate on the gulf towards which I travelled, andreflect on my youthful disobedience, for these things I weep, mine eye runnethdown with water. +　　Advancing in age, the number of my acquaintance increased, and thereby my waygrew more difficult. Though I had found comfort in reading the Holy Scripturesand thinking on heavenly things, I was now estranged therefrom. I knew I wasgoing from the flock of Christ and had no resolution to return, hence seriousreflections were uneasy to me, and youthful vanities and diversions were mygreatest pleasure. In this road I found many like myself, and we associated inthat which is adverse to true friendship. +　　In this swift race it pleased God to visit me with sickness, so that Idoubted of recovery; then did darkness, horror, and amazement with full forceseize me, even when my pain and distress of body were very great. I thought itwould have been better for me never to have had being, than to see the daywhich I now saw. I was filled with confusion, and in great affliction, both ofmind and body, I lay and bewailed myself. I had not confidence to lift up mycries to God, whom I had thus offended; but in a deep sense of my great folly Iwas humbled before Him. At length that word which is as a fire and a hammerbroke and dissolved my rebellious heart; my cries were put up in contrition;and in the multitude of His mercies I found inward relief, and a closeengagement that if He was pleased to restore my health I might walk humblybefore Him. +　　After my recovery this exercise remained with me a considerable time, but bydegrees giving way to youthful vanities, and associating with wanton youngpeople, I lost ground. The Lord had been very gracious, and spoke peace to mein the time of my distress, and I now most ungratefully turned again to folly;at times I felt sharp reproof, but I did not get low enough to cry for help. Iwas not so hardy as to commit things scandalous, but to exceed in vanity and topromote mirth was my chief study. Still I retained a love and esteem for piouspeople, and their company brought an awe upon me. My dear parents several timesadmonished me in the fear of the Lord, and their admonition entered into myheart and had a good effect for a season; but not getting deep enough to prayrightly, the tempter, when he came, found entrance. Once having spent a part ofthe day in wantonness, when I went to bed at night there lay in a window nearmy bed a Bible, which I opened, and first cast my eye on the text, "We lie downin our shame, and our confusion covereth us." This I knew to be my case, andmeeting with so unexpected a reproof I was somewhat affected with it, and wentto bed under remorse of conscience, which I soon cast off again. +　　Thus time passed on; my heart was replenished with mirth and wantonness,while pleasing scenes of vanity were presented to my imagination, till Iattained the age of eighteen years, near which time I felt the judgments of God in my soul, like a consuming fire, and looking over my past life the prospectwas moving. I was often sad, and longed to be delivered from those vanities;then again my heart was strongly inclined to them, and there was in me a soreconflict. At times I turned to folly, and then again sorrow and confusion tookhold of me. In a while I resolved totally to leave off some of my vanities, butthere was a secret reserve in my heart of the more refined part of them, and Iwas not low enough to find true peace. Thus for some months I had greattroubles; my will was unsubjected, which rendered my labours fruitless. Atlength, through the merciful continuance of heavenly visitations, I was made tobow down in spirit before the Lord. One evening I had spent some time inreading a pious author, and walking out alone I humbly prayed to the Lord forHis help, that I might be delivered from all those vanities which so ensnaredme. Thus being brought low, He helped me, and as I learned to bear the cross, Ifelt refreshment to come from His presence; but not keeping in that strengthwhich gave victory, I lost ground again, the sense of which greatly affectedme. I sought deserts and lonely places, and there with tears did confess mysins to God and humbly craved His help. And I may say with reverence, He wasnear to me in my troubles, and in those times of humiliation opened my ear todiscipline. +　　I was now led to look seriously at the means by which I was drawn from thepure truth, and learned that if I would live such a life as the faithfulservants of God lived, I must not go into company as heretofore in my own will,but all the cravings of sense must be governed by a divine principle. In timesof sorrow and abasement these instructions were sealed upon me, and I felt thepower of Christ prevail over selfish desires, so that I was preserved in a gooddegree of steadiness, and being young, and believing at that time that a singlelife was best for me, I was strengthened to keep from such company as had oftenbeen a snare to me. +　　I kept steadily to meetings, spent First-day afternoons chiefly in readingthe Scriptures and other good books, and was early convinced in my mind thattrue religion consisted in an inward life, wherein the heart does love andreverence God the Creator, and learns to exercise true justice and goodness,not only toward all men, but also toward the brute creation; that, as the mindwas moved by an inward principle to love God as an invisible, incomprehensibleBeing, so, by the same principle, it was moved to love Him in all Hismanifestations in the visible world; that, as by His breath the flame of lifewas kindled in all animal sensible creatures, to say we love God as unseen, andat the same time exercise cruelty toward the least creature moving by His life,or by life derived from Him, was a contradiction in itself. I found nonarrowness respecting sects and opinions, but believed that sincere, upright-hearted people, in every society, who truly love God, were accepted of Him. +　　As I lived under the cross, and simply followed the opening of truth, mymind, from day to day, was more enlightened, my former acquaintance were leftto judge of me as they would, for I found it safest for me to live in private,and keep these things sealed up in my own breast. While I silently ponder onthat change wrought in me, I find no language equal to convey to another aclear idea of it. I looked upon the works of God in this visible creation, andan awfulness covered me. My heart was tender and often contrite, and universallove to my fellow-creatures increased in me. This will be understood by such ashave trodden in the same path. Some glances of real beauty may be seen in theirfaces who dwell in true meekness. There is a harmony in the sound of that voiceto which divine love gives utterance, and some appearance of right order intheir temper and conduct whose passions are regulated; yet these do not fullyshow forth that inward life to those who have not felt it; this white stone andnew name is only known rightly by such as receive it. +　　Now, though I had been thus strengthened to bear the cross, I still foundmyself in great danger, having many weaknesses attending me, and strongtemptations to wrestle with; in the feeling whereof I frequently withdrew intoprivate places, and often with tears besought the Lord to help me, and Hisgracious ear was open to my cry. +　　All this time I lived with my parents, and wrought on the plantation; andhaving had schooling pretty well for a planter, I used to improve myself inwinter evenings, and other leisure times. Being now in the twenty-first year ofmy age, with my father's consent I engaged with a man, in much business as ashopkeeper and baker, to tend shop and keep books. At home I had lived retired;and now, having a prospect of being much in the way of company, I felt frequentand fervent cries in my heart to God, the Father of Mercies, that He wouldpreserve me from all taint and corruption; that, in this more publicemployment, I might serve Him, my gracious Redeemer, in that humility and self-denial which I had in a small degree exercised in a more private life. +　　The man who employed me furnished a shop in Mount Holly, about five milesfrom my father's house, and six from his own, and there I lived alone andtended his shop. Shortly after my settlement here I was visited by severalyoung people, my former acquaintance, who supposed that vanities would be asagreeable to me now as ever. At these times I cried to the Lord in secret forwisdom and strength; for I felt myself encompassed with difficulties, and hadfresh occasion to bewail the follies of times past, in contracting afamiliarity with libertine people; and as I had now left my father's houseoutwardly, I found my Heavenly Father to be merciful to me beyond what I canexpress. +　　By day I was much amongst people, and had many trials to go through; but inthe evenings I was mostly alone, and I may with thankfulness acknowledge, that in those times the spirit of supplication was often poured upon me; under whichI was frequently exercised, and felt my strength renewed. +　　After a while, my former acquaintance gave over expecting me as one of theircompany, and I began to be known to some whose conversation was helpful to me. +　　And now, as I had experienced the love of God through Jesus Christ, to redeemme from many pollutions, and to be a succor to me through a sea of conflicts,with which no person was fully acquainted, and as my heart was often enlargedin this heavenly principle, I felt a tender compassion for the youth whoremained entangled in snares like those which had entangled me. This love andtenderness increased, and my mind was strongly engaged for the good of myfellow-creatures. I went to meetings in an awful frame of mind, and endeavouredto be inwardly acquainted with the language of the true Shepherd. +　　One day, being under a strong exercise of spirit, I stood up and said somewords in a meeting; but not keeping close to the divine opening, I said morethan was required of me. Being soon sensible of my error, I was afflicted inmind some weeks, without any light or comfort, even to that degree that I couldnot take satisfaction in anything. I remembered God and was troubled, and inthe depth of my distress He had pity upon me, and sent the Comforter. I thenfelt forgiveness for my offence; my mind became calm and quiet, and I was trulythankful to my gracious Redeemer for His mercies. About six weeks after this,feeling the spring of divine love opened and a concern to speak, I said a fewwords in a meeting, in which I found peace. Being thus humbled and disciplinedunder the cross, my understanding became more strengthened to distinguish thepure Spirit which inwardly moves upon the heart, and which taught me to wait insilence sometimes many weeks together, until I felt that rise which preparesthe creature to stand like a trumpet, through which the Lord speaks to Hisflock. +　　From an inward purifying and steadfast abiding under it, springs a livelyoperative desire for the good of others. All the faithful are not called to thepublic ministry; but whoever are, are called to minister of that which theyhave tasted and handled spiritually. The outward modes of worship are various;but whenever any are true ministers of Jesus Christ, it is from the operationof His Spirit upon their hearts, first purifying them, and thus giving them ajust sense of the conditions of others. This truth was early fixed in my mind,and I was taught to watch the pure opening, and to take heed lest, while I wasstanding to speak, my own will should get uppermost, and cause me to utterwords from worldly wisdom, and depart from the channel of the true gospelministry. +　　In the management of my outward affairs, I may say with thankfulness, I foundtruth to be my support; and I was respected in my master's family, who came tolive in Mount Holly within two years after my going there. +　　In a few months after I came here, my master bought several Scotchmen servants,from on board a vessel, and brought them to Mount Holly to sell, one of whomwas taken sick and died. In the latter part of his sickness, being delirious,he used to curse and swear most sorrowfully; and the next night after hisburial I was left to sleep alone in the chamber where he died. I perceived inme a timorousness; I knew, however, I had not injured the man, but assisted intaking care of him according to my capacity. I was not free to ask any one onthat occasion to sleep with me. Nature was feeble; but every trial was a freshincitement to give myself up wholly to the service of God, for I found nohelper like Him in times of trouble. +　　About the twenty-third year of my age, I had many fresh and heavenlyopenings, in respect to the care and providence of the Almighty over hiscreatures in general, and over man as the most noble amongst those which arevisible. And being clearly convinced in my judgment that to place my wholetrust in God was best for me, I felt renewed engagements that in all things Imight act on an inward principle of virtue, and pursue worldly business nofurther than as truth opened my way. +　　About the time called Christmas I observed many people, both in town and fromthe country, resorting to public-houses, and spending their time in drinkingand vain sports, tending to corrupt one another; on which account I was muchtroubled. At one house in particular there was much disorder; and I believed itwas a duty incumbent on me to speak to the master of that house. I considered Iwas young, and that several elderly friends in town had opportunity to seethese things; but though I would gladly have been excused, yet I could not feelmy mind clear. +　　The exercise was heavy; and as I was reading what the Almighty said toEzekiel, respecting his duty as a watchman, the matter was set home moreclearly. With prayers and tears I besought the Lord for His assistance, and Hein loving-kindness gave me a resigned heart. At a suitable opportunity I wentto the public-house; and seeing the man amongst much company, I called himaside, and in the fear and dread of the Almighty expressed to him what restedon my mind. He took it kindly, and afterwards showed more regard to me thanbefore. In a few years afterwards he died, middle-aged; and I often thoughtthat, had I neglected my duty in that case, it would have given me greattrouble; and I was humbly thankful to my gracious Father, who had supported meherein. +　　My employer, having a negro woman, (1) sold her, and desired me to write abill of sale, the man being waiting who bought her. The thing was sudden; andthough I felt uneasy at the thoughts of writing an instrument of slavery forone of my fellow-creatures, yet I remembered that I was hired by the year, thatit was my master who directed me to do it, and that it was an elderly man, amember of our Society, who bought her; so through weakness I gave way, and wrote it; but at the executing of it I was so afflicted in my mind, that I saidbefore my master and the Friend that I believed slave-keeping to be a practiceinconsistent with the Christian religion. This in some degree abated myuneasiness; yet, as often as I reflected seriously upon it, I thought I shouldhave been clearer if I had desired to be excused from it, as a thing against myconscience; for such it was. Some time after this a young man of our Societyspoke to me to write a conveyance of a slave to him, he having lately taken anegro into his house. I told him I was not easy to write it; for though many ofour meeting and in other places kept slaves, I still believed the practice wasnot right, and desired to be excused from the writing. I spoke to him ingoodwill; and he told me that keeping slaves was not altogether agreeable tohis mind; but that the slave being a gift made to his wife, he had acceptedher. \ No newline at end of file diff --git a/sample_standard_app/app/test/translation_data/long_text_result.txt b/sample_standard_app/app/test/translation_data/long_text_result.txt new file mode 100644 index 00000000..88d90138 --- /dev/null +++ b/sample_standard_app/app/test/translation_data/long_text_result.txt @@ -0,0 +1,10 @@ + +Rag agent execution result is : +第一章 +　　我常怀一股爱的冲动，欲在文字中留下些许关于我体验上帝良善的暗示。如今，正值我三十六岁之时，我开始这项工作。我于1720年出生在北安普顿，伯灵顿县，西泽西。我七岁之前，我开始了解神圣之爱的运作。在父母的关怀下，我几乎在能够阅读时就开始学习阅读；记得有一天放学时，我的同伴们在路上玩耍，我独自前行，消失在他们的视线之外，坐下来阅读《启示录》的第二十二章：“他向我展示了一条纯净的生命之河，清澈如水晶，从上帝和羔羊的宝座流出”等等。在阅读时，我的心被吸引去寻求我相信上帝为他的仆人所预备的那个纯净居所。我坐的地方，以及我心灵所感受到的甜美，至今仍记忆犹新。这样的恩典降临，以及其他类似的恩典，对我产生了如此影响，以至于当男孩们使用恶劣语言时，我会感到困扰；而且，通过上帝持续的怜悯，我得以避免那种邪恶。父母的虔诚教诲常在我心中深刻，当我偶然与顽劣孩童相处时，这些教诲对我帮助很大。由于家中有众多子女，他们常在星期日聚会后，依次让我们阅读圣经或某些宗教书籍，其余人则静坐一旁，几乎没有交谈；我后来常常认为，这实在是个好习惯。从我所读所闻中，我相信在过去年代，有人行事正直，其程度超过我所知或所闻现今任何人；而意识到现今人们缺乏坚定和刚毅，常在我幼年时困扰我。 +　　在此，我或许可以提及童年时发生的一件特别的事情。在去邻居家的路上，我看到一只知更鸟坐在其巢里，当我走近时，她飞离了；但由于有幼鸟，她四处飞翔，发出许多叫声表达了对它们的担忧。我站在那里向她投掷石头，其中一块击中了她，她坠地死亡。起初，我对此举感到高兴，但几分钟后，我感到恐怖，因为以游戏的方式杀死了一只正在照顾幼鸟的无辜生物。我目睹她躺在地上死去，想到那些她如此细心照料的小鸟可能因失去母亲喂养而饿死。经过一番痛苦思考，我爬上树，将所有小鸟捉下，杀死了它们，觉得这样总比让它们慢慢饿死、悲惨地死去要好。在这种情况下，我相信圣经中的箴言得到了体现：“恶人的怜悯也是残忍。”之后我继续我的差事，数小时内满脑子都是我所犯下的残忍行为，感到非常困扰。因此，那位对所有造物充满怜悯的上帝在人的心灵中植入了激发人们善待每一个生灵的原则；当这一原则被单独关注时，人们会变得心肠柔软、富有同情；但当它被频繁且彻底拒绝时，心灵就会封闭，转向相反的倾向。在我十二岁那年的某一天，父亲不在家时，母亲因我的一些不当行为而责备我，我做出了不尊重的回应。下一个安息日，我和父亲从聚会回来时，他告诉我他知道我曾对母亲不敬，并建议我将来要更加小心。我知道自己有错，在羞愧和困惑中我保持沉默。这样被唤醒意识到自己的过错，我心中充满了悔恨，回到家后，我独自退下，祈求上帝宽恕我，我不记得之后我再对父母说过不敬的话，尽管在其他事情上我仍然有些愚蠢。到了十六岁，我开始喜欢放纵的社交；虽然我避免了亵渎的语言或丑闻的行为，但我意识到自己内心长出了一株植物，结出了许多野葡萄。然而，我仁慈的父亲并未完全抛弃我，有时通过他的恩典，我被引导认真考虑我的行为；看到自己的堕落让我感到悲伤，但由于没有正确地接受教导的责备，虚荣之上再加虚荣，悔改之后又悔改。总的来说，我的心灵越来越远离真理，我急速走向毁灭。当我沉思着走向深渊的旅途，反思年轻时的不顺从，我为此哭泣，我的眼泪滚滚而下。随着年龄的增长,我认识的人越来越多,这让我的道路变得更加艰难。虽然我曾从阅读圣经和思考天上的事中得到安慰,但现在我却与之疏远。我知道自己正远离基督的羊群,却没有决心返回,因此,反思让我感到不安,而青春的虚荣和娱乐是我最大的乐趣。在这条道路上,我遇到了许多像我一样的人,我们在与真友谊相悖之事上结伴。在这场迅速的人生竞赛中，上帝允许我患病，使我怀疑能否康复；那时，黑暗、恐怖和惊骇以全副力量袭来，我的身体疼痛和痛苦非常严重。我宁愿从未存在，也不愿目睹眼前的困境。我充满困惑，身心俱疲，躺着悲叹自己。我没有勇气向我所冒犯的上帝呼求；但在深刻意识到我的愚蠢后，我在他面前谦卑。最终，那如火如锤的话语打破并溶解了我叛逆的心；我的呼喊充满了悔改；在祂的众多怜悯中，我找到了内心的慰藉，并承诺，若祂恢复我的健康，我将谦卑地行走在他面前。康复后，内心的斗争伴随了我相当长的时间，但随着逐渐沉溺于青春的虚荣，与放纵的年轻人交往，我逐渐失去了原有的道德立场。上帝在我困苦时曾非常仁慈地向我说话，赐予我平安，而我却忘恩负义地再次陷入愚昧。有时我感到严厉的责备，但我并未真正谦卑到求助的地步。我不至于大胆到做出丑闻之事，但过度追求虚荣和追求欢乐成了我的主要追求。我依然对虔诚的人怀有敬爱，他们的陪伴让我感到敬畏。我亲爱的父母多次在敬畏主的心态下劝诫我，他们的忠告深入我心，曾产生良好效果；但由于未能深入到正确祈祷的程度，当诱惑者来临时，他找到了进入的途径。有一次，我在放纵中度过了一天的一部分时间，晚上上床时，我床边窗户上放着一本圣经，我打开它，第一眼看到的是这段经文：“我们躺下羞愧，我们的羞耻遮盖我们。”我知道这正是我的写照，意外的责备让我有些触动，我带着良心的悔恨上床，但很快又忘记了。"我们带着羞愧躺下，我们的困惑笼罩着我们。"我知道这正是我的写照，遭遇这突如其来的责备，我因此感到触动，带着内心的悔恨躺下，但很快我又将其抛诸脑后。时间就这样流逝；我的心充满了欢愉与放纵的情感，虚幻的景象不断在我脑海中浮现，直到我接近十八岁那年，我感受到上帝的审判如同烈火在灵魂中燃烧，回顾过去的生活，前景令人感动。我常常感到悲伤，渴望摆脱那些虚荣的事物；然而我的心又强烈地倾向于它们，内心冲突剧烈。有时我做出愚蠢的行为，随后悲伤和混乱又抓住了我。过了一段时间，我决心彻底放弃一些虚荣之物，但心中仍暗藏对它们精致部分的留恋，我还未达到能找到真正平静的境界。就这样，几个月来我经历了巨大的困扰；我的意志未被驯服，导致我的努力徒劳。最终，通过上天慈悲的持续眷顾，我被引导在主面前屈服。一天傍晚，我阅读了一位虔诚作者的作品，独自外出时，我谦卑地祈祷向主祈求帮助，以摆脱那些紧紧束缚我的虚妄。因此，当我谦卑下来，祂便向我伸出援手，当我学会背负十字架的重担时，我感受到从祂的临在中传来的慰藉与力量；但未能保持那带来胜利的力量，我又再次失势，这种感觉深深影响了我。我寻求荒漠与孤寂之地，如同寻求内心的宁静，在那里，我含泪向神忏悔我的罪行，并谦卑地祈求祂的帮助。怀着敬畏之心，我可以说，祂在我困境中与我同在，在那些谦卑的时刻，祂开启了我的耳朵，让我接受教诲。我此刻开始认真审视那些使我偏离纯粹真理的方式，我领悟到，若要如上帝忠实的仆人般生活，我必须不再像以前那样随心所欲地社交，所有感官的欲望都应受神圣原则的支配。在悲伤与谦卑的时刻，这些教导铭记在我心，我感受到基督的力量克服了自私的欲望，使我保持了坚定。由于年轻，且当时认为独身生活最为适宜，我坚定地远离那些常使我陷入困境的社交圈。我始终如一地参加聚会，主要在周日午后阅读圣经和其他有益书籍，并深信真正的宗教在于内在的生命，内心深处爱慕并敬畏上帝创造者，并学会实践真正的正义与善良，不仅对所有人，也对所有生灵；心灵被内在原则驱动去爱无形且不可理解的上帝，此原则也驱动心灵去爱他在可见世界中的所有显现；正如他的气息点燃了所有有感知的生物的生命之火，声称我们爱看不见的上帝，同时却对由他生命或衍生自他的生命所驱动的最小生物施以残忍，这本身就是自相矛盾。我坚信在每个社会中真诚、正直的人，只要真正爱上帝，都会被他接纳。当我生活在十字架下，简单地追随真理的启示，我的心灵日渐明朗。我以前的熟人让他们如何评判我，因为我发现保持私密生活最为安全，并将这些事情深藏心底。当我默默地思考这种在我身上发生的改变时，我发现没有语言能够清晰地向他人传达这种感受。我凝视着上帝在这可见创造中的作品，我被一种敬畏感所笼罩。我的心变得柔软，常怀悔悟，我对同胞的爱意日益增长。这将由那些走过同样道路的人所理解。在那些真正谦逊者的脸上，可见真正的美丽。那种由神圣之爱发出的声音和谐悦耳，他们的性情与行为表现出正确的秩序，他们的激情得到了调节；然而，这些并不完全向那些未曾感受过的人展示那种内在的生命；这块白石及其新名，唯有真正接受者方能理解其真谛。领悟此真谛。尽管我已因此得到力量去背负十字架，但我仍发现自己处于极大的危险之中，伴随着我有许多弱点，以及需要与之抗争的强烈诱惑；在感受到这些时，我常常退到私密的地方，常常含泪恳求主帮助我，而祂那仁慈的耳朵总是倾听我的呼求。这段时间我一直与父母同住，并在种植园工作；并且作为一个种植园主，我受过相当不错的教育，常常利用冬夜和其他闲暇时间自我提升。如今我已二十一岁，在得到父亲的同意后，我与一位店铺和面包店的经营者签订了合同，负责照看店铺和记账。在家时，我过着隐居的生活；而现在，预见到自己将频繁与人交往，我心中常常涌起对上帝——慈悲之父的频繁而热切的祈祷，恳求他保护我免受一切污染和腐化；希望在这项更为公开的工作中，我能以谦卑和自我否定的态度服务他，我的仁慈救主，这种态度我在更为私密的生活中已略有实践。雇佣我的那位先生在芒特霍利（Mount Holly）为我开设了一家店铺，距离我父亲的家约五英里，而他自己的家则在此基础上再远一英里。在那里，我独自生活并照看店铺。我刚安顿下来不久，就有几位我年轻时的熟人来访，他们认为我仍会像以前那样喜欢轻浮之事。在这些时刻，我暗中向主呼求智慧和力量；因为我感到自己被困难包围，又有新的理由为过去与不羁之人交往的愚蠢行为而悔恨；现在我虽然表面上离开了父亲的家，却发现我的天父对我展现的慈悲超乎我的言辞所能表达。 +　　白天我大多与人相处，经历了许多考验；但到了晚上，我大多独自一人。我满怀感激地承认，在那些时刻，祷告的灵常常降临于我；在这灵的引导下，我频繁地锻炼自己，感到力量得到了更新。过了一段时间，我以前的熟人不再期待我成为他们群体的一部分，我开始结识一些谈话有益的人。 +　　如今，我通过耶稣基督感受到了上帝的爱，他救我脱离了许多污染，成为我在重重冲突中的支持，这些冲突无人完全了解。我的心常常在这神圣的原则中得到扩展，我对那些像我一样陷入陷阱的年轻人感到深切的同情。这种爱和温柔日益增强，我的心灵强烈地投入到同胞的福祉中。我以虔诚的心态去参加聚会，并努力从内心熟悉真牧者的教导。在心灵受到强烈感召的一天，我于聚会中起身发言；然未紧随神启，言辞过多。察觉过失后，心灵受苦数周之久，没有任何光明或安慰，乃至万事无欢。我忆及上帝，心中充满忧烦，在深陷困苦之际，上帝怜悯我，并派遣慰藉。我感受到罪行的赦免，心境平和宁静，诚挚感谢我仁慈的救赎主所赐恩典。约六周后，神圣之爱如泉涌般开启，我感到有发言的责任，于聚会中简言几句，内心得安宁。经此磨练与谦卑，我更能辨识内心深处纯净之灵，教我有时静待数周，直至感受那股力量，使人如号角般准备就绪，经由它，主向其羊群发声。主通过这号角向他的羊群传达话语。从内心的净化和坚定不移的坚持中，涌现出对他人福祉的活跃而有效的渴望。并非所有信徒都被召唤到公共事工中；但那些被召唤的人，是去侍奉他们精神上已经体验和把握的事物。外在的崇拜形式多种多样；但任何时候，只要是耶稣基督的真仆人，都是因为他的圣灵首先净化了他们的心灵，从而给予他们公正的感知对他人境遇。这一真理很早就扎根于我的心中，我被教导要留意纯净的启示，并小心谨慎，以免在我站立说话时，自己的意志占据上风，导致我根据世俗智慧说出话语，偏离真正的福音事工之路。 +　　在处理我的外在事务时，我可以说心怀感激，我发现真理是我的支持；我在主人的家庭中受到尊重，他们在我去那里两年后搬到霍利山居住。几个月后，我来到这里，我的主人从船上买了几个苏格兰仆人，把他们带到芒特霍利去卖，其中一个生病去世了。在他病重的后期，由于神志不清，他常常悲痛地咒骂和发誓；他下葬的第二天晚上，我被留在他去世的那个房间里独自睡觉。我感到内心一阵胆怯；然而我知道，我并没有伤害那个人，而是根据自己的能力帮助照顾他。当时我不便请求任何人陪我同睡。自然的力量是脆弱的；但每一次考验都激励我更全心全意地投身于上帝的服务，因为在困难时刻，我发现没有像他那样的帮助者。在我二十三岁那年，我领悟到了许多关于全能上帝对其造物普遍关怀与天意的清新而神圣的启示，尤其是对人这一最尊贵的可见造物。我深信不疑地认为，将我的全部信任寄托于上帝对我最为有利，这使我感到新的责任，即基于内在道德原则处理一切事务，并且追求世俗事务不超过真理指引的范围。 + +大约在圣诞节期间，我注意到许多城镇内外的人们涌向公共场所以及酒馆，在那里饮酒作乐，参与无益的游戏，彼此败坏；为此我深感困扰。特别是在一家酒馆里，秩序混乱；我深信自己有责任与那家酒馆的主人交谈。我意识到自己年轻，而城镇中几位年长的朋友也有机会目睹这些情况；尽管我很想推脱，但我无法让自己的内心感到清晰。这次经历非常沉重；当我阅读上帝对以西结关于其作为守望者职责的指示时，事情变得更加清晰。我泪流满面地祈祷，恳求主的帮助，他以慈爱赐予我一颗顺服的心。在合适的时机，我前往那家酒馆；发现那人在众多宾客中，我便请他到一旁，带着对上帝的敬畏，向他表达了心中的重负。他对此表示感谢，之后对我比以往更加敬重。几年后，他中年时去世；我常想，如果当时我忽略了我的雇主拥有一名黑人女性，(1) 将其出售，并要求我撰写一份销售契据，买主已在等待。事情来得突然；尽管想到要为同类撰写奴隶交易的文件，我感到不安，但我记得我是按年雇佣的，是雇主指示我这么做，而且买主是一位我们社团中的年长成员；因此，出于软弱，我屈服并写了这份契据；但在执行时，我内心极度痛苦，以至于我当着雇主和社团朋友的面说，我认为蓄奴与基督教信仰不相符。这在一定程度上减轻了我的不安；然而，每当我认真反思此事，我认为如果我请求免于执行，因为这违背我的良心，我会更加心安；因为事实确实如此。此后不久，社团的一位年轻男子请我为他撰写奴隶转让文件，他最近将一名黑人带入家中。我告诉他，我不愿意写这份文件；尽管我们社团及许多地方的人都有奴隶，我仍然认为这种做法是不对的，并希望得到豁免。我善意地与他交谈；他告诉我，养奴并不完全符合他的心意，但因为这名奴隶是送给他的妻子的礼物，他接受了她。写这份文书；因为尽管我们社区以及许多其他地方的人都有奴隶，我仍然认为这种做法是不对的，并请求免于参与这项写作。我友好地与他交谈；他告诉我，拥有奴隶并不完全符合他的心意；但因为这个奴隶是作为礼物送给他的妻子的，他接受了这份礼物。 \ No newline at end of file diff --git a/sample_standard_app/app/test/translation_data/short_text.txt b/sample_standard_app/app/test/translation_data/short_text.txt new file mode 100644 index 00000000..51f437cf --- /dev/null +++ b/sample_standard_app/app/test/translation_data/short_text.txt @@ -0,0 +1,3 @@ +Last week, I spoke about AI and regulation at the U.S. Capitol at an event that was attended by legislative and business leaders. I’m encouraged by the progress the open source community has made fending off regulations that would have stifled innovation. But opponents of open source are continuing to shift their arguments, with the latest worries centering on open source's impact on national security. I hope we’ll all keep protecting open source! + +Based on my conversations with legislators, I’m encouraged by the progress the U.S. federal government has made getting a realistic grasp of AI’s risks. To be clear, guardrails are needed. But they should be applied to AI applications, not to general-purpose AI technology. diff --git a/sample_standard_app/app/test/translation_data/short_text_result.txt b/sample_standard_app/app/test/translation_data/short_text_result.txt new file mode 100644 index 00000000..461c099f --- /dev/null +++ b/sample_standard_app/app/test/translation_data/short_text_result.txt @@ -0,0 +1,5 @@ + +Rag agent execution result is : +上周，我在美国国会大厦举行的一场专门讨论会上谈到了人工智能和监管，该会议吸引了立法和商界领袖的参与。我对开源社区在反对可能扼杀创新的法规方面取得的进展感到鼓舞。但反对开源的人士仍在不断调整他们的立场，最新的关注点集中在开源对国家安全的影响上。我希望我们都能继续保护开源！ + +根据我与立法者的交谈，我对美国联邦政府在准确评估人工智能风险方面取得的进展感到鼓舞。需要明确的是，确实需要设立防护措施。但这些应该针对人工智能应用，而不是通用人工智能技术。 \ No newline at end of file From 7dfc82f76c0f3c5a97e44aaddeac7efb45027276 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 10:02:01 +0800 Subject: [PATCH 062/118] remove --- sample_standard_app/app/test/rag_agent_result.txt | 9 --------- 1 file changed, 9 deletions(-) delete mode 100644 sample_standard_app/app/test/rag_agent_result.txt diff --git a/sample_standard_app/app/test/rag_agent_result.txt b/sample_standard_app/app/test/rag_agent_result.txt deleted file mode 100644 index 29e4b3f4..00000000 --- a/sample_standard_app/app/test/rag_agent_result.txt +++ /dev/null @@ -1,9 +0,0 @@ - -Rag agent execution result is : -上周，我在美国国会大厦就人工智能与监管问题发表了演讲，该活动吸引了立法和商界领袖的参与。我对开源社区成功抵御那些可能抑制创新的法规所取得的进展感到鼓舞。但反对开源的人士仍在不断调整他们的论点，最新的担忧焦点在于开源对国家安全可能产生的影响。我希望我们都能继续保护开源！ - -基于与立法者的交流，我对美国联邦政府在准确评估人工智能风险方面取得的进展感到鼓舞。明确地说，确实需要设置防护措施。然而，这些防护措施应针对具体的人工智能应用，而不是通用的人工智能技术。尽管如此，正如我之前所述，一些公司积极限制开源，可能是为了保护他们在专有模型上的巨额投资，并遏制竞争对手。观察他们的论点随时间的变化，颇具观察价值。 - -例如，大约12个月前，人工智能安全中心的“人工智能风险声明”警告说，人工智能可能导致人类灭绝，并激起了对人工智能接管的恐惧。这引起了华盛顿领导人的关注。但许多人工智能专家指出，这种反乌托邦的科幻场景缺乏现实基础。大约六个月后，当我在美国参议院的人工智能洞察论坛上作陈述时，立法者不再那么担心人工智能的接管。随后，开源的反对者调整了他们的论点，主要论点转为人工智能可能帮助制造生物武器的风险。不久之后，OpenAI和兰德公司指出，当前的人工智能并未显著增强恶意行为者制造生物武器的能力。因此，对AI辅助生物武器的担忧有所减少。当然，坏人可能使用生物武器（无论是否借助AI）这一可能性仍然是国际上极为关注的话题。最新的反对开放源代码AI的论点已转向国家安全。AI在经济竞争和战争中都具有重要作用，反对开放源代码的人士认为美国应确保其对手无法接触到最新的基础模型。虽然我不希望专制政府使用AI，尤其是用于发动不义之战，但大型语言模型（LLM）的秘密已无法掩盖，如果民主国家限制访问，专制国家将会填补这一空白。将来某日，若有孩子向AI询问关于民主、自由媒体或独立司法在维护法治中的作用，我希望AI能反映民主价值观，而不是偏袒专制领导人的目标，例如忽视人权。我从华盛顿回来时，对我们在AI领域取得的进展感到乐观。一年前，立法者在我看来似乎将80%的时间用于讨论AI的监管措施，而只有20%的时间用于讨论创新投资。我很高兴这一比例已经显著逆转，现在更多的是讨论对创新的投入。 - -放眼美国联邦政府之外，全球各地有许多不同的司法管辖区。不幸的是，那些支持可能阻碍AI发展的监管规定的论点仍在不断增多。但我从访问华盛顿和其他国家首都的经历中学到，与监管者进行对话确实能够产生积极影响。如果你有机会与任何级别的监管者交谈，我希望你能尽力帮助政府更深入地理解AI。 \ No newline at end of file From 0907224978db9522973dc8a318eb1b38626f9aeb Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 11:35:08 +0800 Subject: [PATCH 063/118] add doc --- .../_picture/long_translation_au.png | Bin 0 -> 719195 bytes .../_picture/long_translation_wu.png | Bin 0 -> 706694 bytes .../_picture/translation_execute_flow.png | Bin 0 -> 458485 bytes .../_picture/translation_flow_graph.png | Bin 0 -> 145942 bytes ...73\350\257\221\346\241\210\344\276\213.md" | 49 ++++++++++++++++++ .../translation_agent.yaml | 2 +- .../translation_improve_agent.yaml | 2 +- .../translation_reflection_agent.yaml | 2 +- .../translation_work_agent.yaml | 2 +- 9 files changed, 53 insertions(+), 4 deletions(-) create mode 100644 docs/guidebook/_picture/long_translation_au.png create mode 100644 docs/guidebook/_picture/long_translation_wu.png create mode 100644 docs/guidebook/_picture/translation_execute_flow.png create mode 100644 docs/guidebook/_picture/translation_flow_graph.png create mode 100644 "docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" diff --git a/docs/guidebook/_picture/long_translation_au.png b/docs/guidebook/_picture/long_translation_au.png new file mode 100644 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    W5RV`0IK}e(a~|GhkyM0NYgMFODnA7y)dfFqfM+LDmxr1gGSW8 z9=>eAq=CwdJ`mmQl%G)%Mv^;-1+I?&*Au%)*Zx7(Z_!AwUIPvuZ@_gN%f*Q>LUu1t zM1IA6bz~vdOf&(h%pSn$q66L+8jk6oTx$a*@uLIe>ewrF8yl$$nl1D5aWbL|%^1XR$9VBjsjsQFu|0ZbP-0nR5c z@)=$4wi4bf*w?C4RlmJiGMCk=@+Lp&wJC*kZE@7Ft)avC^lrW-n=+$e;mnQMi7g=Q zL^aue*8!O@0V^`8AWa7LPWvkO!=-&t z<6D}EMDN(rm$t!qyHI9da-cyKWR)Fm(-yiSl)R-NmLYEk9Uq}R7qG5sVnyrjrP*>9 z_z%0!X*l$!I47xT#u^fbW^$Y_`inY&S)-t7KzO!ci^Be7iE{q0VCrlmytj7_bgZq* zKcOktuEI2-+4XT4p;H^A1Ld_50Q;E&|GM45z>f~|n-{nBW~An&cWsfo^ebK~y4oK3 z_asLDS>E}=>`zC1$s4mf3aU;Xu!^S}yy{8j#r^$I0N>;-;|iRdRKQmz^Dj8>(=&Xy zj1@TG1p+WFwDSWb`_aC_6G~X}wI6VHo`10`R7ze1^6WI6U!;QgHUnb2hD6*T~)s(Bjs134Soh#yd_?_f%U}C%6-6;23FU_cy0uY8~&-aszhLNl*-BdKAor zCNFeeF?fhz4B5}7R6>)aYJa>Etp>*2hgN$gw^vN9G_4n~sH5Mb4ZuQ#7(Ku$m`Vy> zWdeU(vXAPS{PvRnwv|+EX{UqgZri>F<;0hKJXlaFlIC$M>31UF>#?$?Qu!n6fd2u{ C!fBoW literal 0 HcmV?d00001 diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" new file mode 100644 index 00000000..5958ad2f --- /dev/null +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -0,0 +1,49 @@ +# 法律咨询案例 +近期，斯坦福大学教授吴恩达开源了一个AI智能体机器翻译——一个使用反思工作流进行翻译的智能体，该项目中演示了反思智能体工作流的机器翻译示例。该智能体的主要步骤如下： +1. 初始翻译，使用大模型（LLM）将文本从source_language翻译为target_language; +2. 进行反思，让大模型指出翻译结果中的不足，并给出建设性的改进建议； +3. 修改翻译，让大模型根据反思给出的建议，重新进行翻译； +项目中采用的反思工作流，让模型自己去发现不足，在根据不足做定向的修改，是一种典型的多智能体协同工作机制，这也是agentUniverse一直涉足的领域，于是我就有将吴恩达的这个翻译智能体迁移到agentUniverse上面的想法。 +该案例基于千文大模型和`DashScope`的embedding功能，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 + +## 多智能体的协同工作过程 +翻译过程中，优先判断模型的长度是否超过了模型所能承受的最大token,针对超过模型长度的情况,先对分本进行切块，再分块进行翻译， +但整个翻译过程都是基于初始翻译->反思->修改的流程进行的。 +![多智能体协同工作流程](../_picture/translation_flow_graph.png) + +在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译分块时，会携带响应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) + +## 在aU中的实现 +在aU中实现该翻译的过程主要分为以下几步： +1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下: +[短文本翻译init prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_init_en.yaml) +[短文本翻译reflection prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml) +[短文本翻译improve prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml) +[长文本翻译init prmpt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_init_en.yaml) +[长文本翻译reflection prmpt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml) +[长文本翻译improve prmpt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml) + +2. 定义三个智能体 +[短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml) +[短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml) +[短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml) +该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py) +3. 定义三个智能体的协同工作过程 +协同过程可参考前面介绍的多智能体协同工作过程的流程图 +![协同工作](../_picture/translation_execute_flow.png) +更细的流程可以参考[代码文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py) +协同智能体的[配置文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml) + +### 演示代码 +[代码链接](../../../sample_standard_app/app/test/test_translation_agent.py) + +[长翻译文本](../../../sample_standard_app/app/test/translation_data/long_text.txt) +[短翻译文本](../../../sample_standard_app/app/test/translation_data/short_text.txt) + +[短翻译结果](../../../sample_standard_app/app/test/translation_data/short_text_result.txt) +[长翻译结果](../../../sample_standard_app/app/test/translation_data/long_text_result.txt) + +### 演示结果 +可以看到，在相同模型下，使用aU与原本的translation_agent翻译的结果基本相同 +![aU执行长文本结果](../_picture/long_translation_au.png) +![translation执行长文本结果](../_picture/long_translation_wu.png) \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml index 3c292391..b2a3c068 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' + name: 'default_qwen_llm' max_tokens: 4000 plan: action: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml index 90b9a336..308216be 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text','init_agent_result','reflection_agent_result'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' + name: 'default_qwen_llm' max_tokens: 4000 plan: planner: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml index 85cbebb3..83aa9a78 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text','init_agent_result'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' + name: 'default_qwen_llm' max_tokens: 4000 plan: planner: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml index e6704d67..ba2bd813 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' + name: 'default_qwen_llm' max_tokens: 4000 plan: planner: From 13fda6827212a5af12b3b3dd84b0f7d8f70f5069 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 11:38:15 +0800 Subject: [PATCH 064/118] fix --- ...7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" index 5958ad2f..096e8bc8 100644 --- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -1,4 +1,4 @@ -# 法律咨询案例 +# 翻译案例 近期，斯坦福大学教授吴恩达开源了一个AI智能体机器翻译——一个使用反思工作流进行翻译的智能体，该项目中演示了反思智能体工作流的机器翻译示例。该智能体的主要步骤如下： 1. 初始翻译，使用大模型（LLM）将文本从source_language翻译为target_language; 2. 进行反思，让大模型指出翻译结果中的不足，并给出建设性的改进建议； @@ -11,7 +11,7 @@ 但整个翻译过程都是基于初始翻译->反思->修改的流程进行的。 ![多智能体协同工作流程](../_picture/translation_flow_graph.png) -在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译分块时，会携带响应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) +在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) ## 在aU中的实现 在aU中实现该翻译的过程主要分为以下几步： From ccb06fa09f8a9226bdb96851bbd13c6aeb82bdd1 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 11:40:50 +0800 Subject: [PATCH 065/118] fix --- ...1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" index 096e8bc8..26ec2c1e 100644 --- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -4,7 +4,7 @@ 2. 进行反思，让大模型指出翻译结果中的不足，并给出建设性的改进建议； 3. 修改翻译，让大模型根据反思给出的建议，重新进行翻译； 项目中采用的反思工作流，让模型自己去发现不足，在根据不足做定向的修改，是一种典型的多智能体协同工作机制，这也是agentUniverse一直涉足的领域，于是我就有将吴恩达的这个翻译智能体迁移到agentUniverse上面的想法。 -该案例基于千文大模型和`DashScope`的embedding功能，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 +该案例基于千文大模型，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 ## 多智能体的协同工作过程 翻译过程中，优先判断模型的长度是否超过了模型所能承受的最大token,针对超过模型长度的情况,先对分本进行切块，再分块进行翻译， @@ -44,6 +44,8 @@ [长翻译结果](../../../sample_standard_app/app/test/translation_data/long_text_result.txt) ### 演示结果 -可以看到，在相同模型下，使用aU与原本的translation_agent翻译的结果基本相同 +可以看到，使用aU与原本的translation_agent原工程翻译结果保持一致，复刻成功。 +aU的执行结果: ![aU执行长文本结果](../_picture/long_translation_au.png) +translation_agent的执行结果: ![translation执行长文本结果](../_picture/long_translation_wu.png) \ No newline at end of file From 1dc250abe0df59e38a0d29a19bf73f57ce43b055 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 11:54:36 +0800 Subject: [PATCH 066/118] add en result --- ...73\350\257\221\346\241\210\344\276\213.md" | 51 +++++++++++++++++++ ...73\350\257\221\346\241\210\344\276\213.md" | 7 +-- .../translation_agent.yaml | 2 +- .../translation_improve_agent.yaml | 2 +- .../translation_reflection_agent.yaml | 2 +- .../translation_work_agent.yaml | 2 +- .../app/test/test_translation_agent.py | 6 +-- 7 files changed, 62 insertions(+), 10 deletions(-) create mode 100644 "docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" diff --git "a/docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" new file mode 100644 index 00000000..4a34620c --- /dev/null +++ "b/docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -0,0 +1,51 @@ +# Translation Case Study +Recently, Professor Andrew Ng of Stanford University open-sourced an AI agent for machine translation—an agent that employs a reflective workflow for translation. This project showcases an example of machine translation utilizing the reflective agent workflow. The primary steps of the agent include: +1. Initial translation, utilizing a large model (LLM) to translate text from the source language to the target language; +2. Reflection, wherein the large model identifies deficiencies in the translation and offers constructive improvement suggestions; +3. Revision of the translation, where the large model retranslates based on the suggestions derived from the reflection; +The reflective workflow employed in the project enables the model to identify its own deficiencies and implement targeted revisions, constituting a typical multi-agent collaborative working mechanism. This is also a domain in which agentUniverse has been actively engaged, prompting me to contemplate the migration of Andrew Ng's translation agent to agentUniverse. +This case is based on the Qianwen large model, and prior to use, you need to configure the `DASHSCOPE_API_KEY` in the environment variables. + +## Collaborative Working Process of Multi-Agents +During the translation process, the priority is to ascertain whether the model's length surpasses the maximum number of tokens it can accommodate. For instances where the length exceeds the limit, the text is initially segmented, followed by the translation of each segment. Nonetheless, the entire translation process adheres to the initial translation -> reflection -> revision workflow. +![Multi-Agent Collaborative Working Process](../_picture/translation_flow_graph.png) + +Implementing this translation in aU involves the following steps: +1. Define prompts related to translation, with three for short text and three for long text. Relevant files include: + - [Short Text Init Prompt](../../../sample_standard_app/app/core/prompt/translation/translation_init_en.yaml) + - [Short Text Reflection Prompt](../../../sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml) + - [Short Text Improve Prompt](../../../sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml) + - [Long Text Init Prompt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_init_en.yaml) + - [Long Text Reflection Prompt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml) + - [Long Text Improve Prompt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml) +2. Define three agents + - [Short Text Translation Work Agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml) + - [Short Text Translation Reflection Agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml) + - [Short Text Translation Improvement Agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml) + These agents switch their prompts based on whether the task involves translating long or short texts. The specific logic for this switching mechanism is detailed in the [agent file](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py). + +3. Define the collaborative work process of the three agents +The collaborative process is illustrated in the earlier-mentioned flowchart of multi-agent collaboration. +![Collaborative Work](../_picture/translation_execute_flow.png) +For a deeper dive into the processes, refer to the [detailed code file](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py). +The [configuration file](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml) for the collaborative agents. + +### Demonstration Code +[Code Link](../../../sample_standard_app/app/test/test_translation_agent.py) + +[Long Text](../../../sample_standard_app/app/test/translation_data/long_text.txt) +[Short Text](../../../sample_standard_app/app/test/translation_data/short_text.txt) + +[Translation Of Long Text](../../../sample_standard_app/app/test/translation_data/short_text_result.txt) +[Translation Of Short Text](../../../sample_standard_app/app/test/translation_data/long_text_result.txt) + +### 演示结果 +We can see that using aU maintains consistency with the original translation_agent project results, successfully replicated. +aU Results: +![aU Long Text Result](../_picture/long_translation_au.png) +translation_agent Results: +![translation Long Text Result](../_picture/long_translation_wu.png) + +### Others +In reference to Professor Andrew Ng's code, when managing segmented translations, to ensure the coherence of the translation, each segment is translated with the corresponding context. However, incorporating all translation content into the context within his code can undermine the original intent of segmentation, and in certain cases, it can result in an excess of tokens beyond the model's capacity. I have raised a related issue on the corresponding repository address: [Issue Link](https://github.com/andrewyng/translation-agent/issues/28)## Implementation in aU + diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" index 26ec2c1e..53b1f204 100644 --- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -11,8 +11,6 @@ 但整个翻译过程都是基于初始翻译->反思->修改的流程进行的。 ![多智能体协同工作流程](../_picture/translation_flow_graph.png) -在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) - ## 在aU中的实现 在aU中实现该翻译的过程主要分为以下几步： 1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下: @@ -48,4 +46,7 @@ aU的执行结果: ![aU执行长文本结果](../_picture/long_translation_au.png) translation_agent的执行结果: -![translation执行长文本结果](../_picture/long_translation_wu.png) \ No newline at end of file +![translation执行长文本结果](../_picture/long_translation_wu.png) + +### 其他 +在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml index b2a3c068..3c292391 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: - name: 'default_qwen_llm' + name: 'default_deepseek_llm' max_tokens: 4000 plan: action: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml index 308216be..90b9a336 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text','init_agent_result','reflection_agent_result'] output_keys: ['output'] llm_model: - name: 'default_qwen_llm' + name: 'default_deepseek_llm' max_tokens: 4000 plan: planner: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml index 83aa9a78..85cbebb3 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text','init_agent_result'] output_keys: ['output'] llm_model: - name: 'default_qwen_llm' + name: 'default_deepseek_llm' max_tokens: 4000 plan: planner: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml index ba2bd813..e6704d67 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml @@ -6,7 +6,7 @@ profile: input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: - name: 'default_qwen_llm' + name: 'default_deepseek_llm' max_tokens: 4000 plan: planner: diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py index afb82440..769e5131 100644 --- a/sample_standard_app/app/test/test_translation_agent.py +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -23,16 +23,16 @@ def setUp(self) -> None: def test_translation_agent_long(self): instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') - with open('./translation_data/long_text.txt', 'r') as f: + with open('/Users/weizj/Documents/GitHub/agentUniverse/docs/guidebook/zh/7_1_1_翻译案例.md', 'r') as f: data = f.read() - output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", + output_object: OutputObject = instance.run(source_lang="中文", target_lang="英文", source_text=data ) res_info = f"\nRag agent execution result is :\n" res_info += output_object.get_data('output') # 创建文件，并写入文件 - with open('./translation_data/long_text_result.txt', 'w') as f: + with open('./translation_data/long_text_result111.txt', 'w') as f: f.write(res_info) print(res_info) From 76fc46a9c76157c4cbe8822e504e01424893795e Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 11:55:31 +0800 Subject: [PATCH 067/118] rollback code --- .../app/test/test_translation_agent.py | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py index 769e5131..88dda247 100644 --- a/sample_standard_app/app/test/test_translation_agent.py +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -23,33 +23,33 @@ def setUp(self) -> None: def test_translation_agent_long(self): instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') - with open('/Users/weizj/Documents/GitHub/agentUniverse/docs/guidebook/zh/7_1_1_翻译案例.md', 'r') as f: + with open('./translation_data/long_text.txt', 'r') as f: data = f.read() - output_object: OutputObject = instance.run(source_lang="中文", target_lang="英文", + output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", source_text=data ) res_info = f"\nRag agent execution result is :\n" res_info += output_object.get_data('output') # 创建文件，并写入文件 - with open('./translation_data/long_text_result111.txt', 'w') as f: + with open('./translation_data/long_text_result.txt', 'w') as f: f.write(res_info) print(res_info) - # def test_translation_agent_short(self): - # instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') - # with open('./translation_data/short_text.txt', 'r') as f: - # data = f.read() - # - # output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", - # source_text=data - # ) - # res_info = f"\nRag agent execution result is :\n" - # res_info += output_object.get_data('output') - # # 创建文件，并写入文件 - # with open('./translation_data/short_text_result.txt', 'w') as f: - # f.write(res_info) - # print(res_info) + def test_translation_agent_short(self): + instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') + with open('./translation_data/short_text.txt', 'r') as f: + data = f.read() + + output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", + source_text=data + ) + res_info = f"\nRag agent execution result is :\n" + res_info += output_object.get_data('output') + # 创建文件，并写入文件 + with open('./translation_data/short_text_result.txt', 'w') as f: + f.write(res_info) + print(res_info) if __name__ == '__main__': From 23a2d658042cfca4fa8aaaa12a784ac9da6ecb44 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 11:57:28 +0800 Subject: [PATCH 068/118] change to qwen --- .../core/agent/translation_agent_case/translation_agent.yaml | 4 ++-- .../translation_agent_case/translation_improve_agent.yaml | 4 ++-- .../translation_agent_case/translation_reflection_agent.yaml | 4 ++-- .../agent/translation_agent_case/translation_work_agent.yaml | 4 ++-- sample_standard_app/config/config.toml | 2 +- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml index 3c292391..6acbf16f 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml @@ -6,8 +6,8 @@ profile: input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 + name: 'default_qwen_llm' + max_tokens: 1000 plan: action: tool: diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml index 90b9a336..28176b94 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml @@ -6,8 +6,8 @@ profile: input_keys: ['source_lang','target_lang','source_text','init_agent_result','reflection_agent_result'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 + name: 'default_qwen_llm' + max_tokens: 1000 plan: planner: name: 'rag_planner' diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml index 85cbebb3..5d0c4584 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml @@ -6,8 +6,8 @@ profile: input_keys: ['source_lang','target_lang','source_text','init_agent_result'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 + name: 'default_qwen_llm' + max_tokens: 1000 plan: planner: name: 'rag_planner' diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml index e6704d67..1525a197 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml @@ -6,8 +6,8 @@ profile: input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] llm_model: - name: 'default_deepseek_llm' - max_tokens: 4000 + name: 'default_qwen_llm' + max_tokens: 1000 plan: planner: name: 'rag_planner' diff --git a/sample_standard_app/config/config.toml b/sample_standard_app/config/config.toml index 6abfc733..4a662064 100644 --- a/sample_standard_app/config/config.toml +++ b/sample_standard_app/config/config.toml @@ -28,7 +28,7 @@ sqldb_wrapper = ['sample_standard_app.app.core.sqldb_wrapper'] # Log config file path, an absolute path or a relative path based on the dir where the current config file is located. log_config_path = './log_config.toml' # Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore. -custom_key_path = './custom_key.toml' +#custom_key_path = './custom_key.toml' [DB] # A sqlalchemy db uri used for storing various info, for example, service request, generated during application running. From 53de484ce6cc3cf327b665303dceaff63f204893 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Fri, 28 Jun 2024 14:09:57 +0800 Subject: [PATCH 069/118] feat: add trace_agent annotation to trace the agent invocation record. --- agentuniverse/base/annotation/trace.py | 32 ++++- docs/guidebook/en/2_5_1_Monitor_Module.md | 90 ++++++++++++- ...21\346\216\247\346\250\241\345\235\227.md" | 121 +++++++++++++++--- 3 files changed, 219 insertions(+), 24 deletions(-) diff --git a/agentuniverse/base/annotation/trace.py b/agentuniverse/base/annotation/trace.py index 4ec61c56..428856f1 100644 --- a/agentuniverse/base/annotation/trace.py +++ b/agentuniverse/base/annotation/trace.py @@ -8,9 +8,12 @@ import asyncio import functools import inspect +import json from functools import wraps +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.output_object import OutputObject from agentuniverse.base.util.monitor.monitor import Monitor from agentuniverse.llm.llm_output import LLMOutput @@ -57,7 +60,7 @@ def wrapper_sync(*args, **kwargs): # check whether the tracing switch is enabled self = llm_input.pop('self', None) if self and hasattr(self, 'tracing'): - if not self.tracing: + if self.tracing is False: return func(*args, **kwargs) # invoke function result = func(*args, **kwargs) @@ -109,21 +112,27 @@ def wrapper_sync(*args, **kwargs): source = func.__qualname__ self = agent_input.pop('self', None) + tracing = None if isinstance(self, object): agent_model = getattr(self, 'agent_model', None) if isinstance(agent_model, object): info = getattr(agent_model, 'info', None) + profile = getattr(agent_model, 'profile', None) if isinstance(info, dict): source = info.get('name', None) + if isinstance(profile, dict): + tracing = profile.get('tracing', None) - if self and hasattr(self, 'tracing'): - if not self.tracing: - return func(*args, **kwargs) + if tracing is False: + return func(*args, **kwargs) # invoke function result = func(*args, **kwargs) + # serialize agent input and output dict + agent_input = serialize_agent_invocation(agent_input) + agent_output = serialize_agent_invocation(result) # add agent invocation info to monitor - Monitor().trace_agent_invocation(source=source, agent_input=agent_input, agent_output=result.to_dict()) + Monitor().trace_agent_invocation(source=source, agent_input=agent_input, agent_output=agent_output) return result # sync function @@ -136,3 +145,16 @@ def _get_agent_input(func, *args, **kwargs) -> dict: bound_args = sig.bind(*args, **kwargs) bound_args.apply_defaults() return {k: v for k, v in bound_args.arguments.items()} + + +def default_serializer(obj): + if isinstance(obj, InputObject): + return obj.to_dict() + elif isinstance(obj, OutputObject): + return obj.to_dict() + raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") + + +def serialize_agent_invocation(agent_invocation): + agent_invocation_serialized = json.loads(json.dumps(agent_invocation, default=default_serializer)) + return agent_invocation_serialized diff --git a/docs/guidebook/en/2_5_1_Monitor_Module.md b/docs/guidebook/en/2_5_1_Monitor_Module.md index 62f56663..57201e24 100644 --- a/docs/guidebook/en/2_5_1_Monitor_Module.md +++ b/docs/guidebook/en/2_5_1_Monitor_Module.md @@ -1,8 +1,8 @@ # Monitor Module agentUniverse monitor module, mainly around the agent runtime LLM invocation/token expenses for data tracking and -recording. In this document, we will provide a detailed introduction on how to use the **LLM invocation tracking -capability** that is currently open in the agentUniverse. +recording. In this document, we will provide a detailed introduction on how to use the **LLM and Agent invocation tracking +capabilities** that are currently open in the agentUniverse. ## Monitor Configuration @@ -15,7 +15,7 @@ dir = './monitor' ``` - **`activate`**: The master switch for the monitor module is set to off by default. When set to true, it will turn on - the LLM invocation tracking function of the agent runtime. + the LLM and Agent invocation tracking functions. - **`dir`**: The local storage directory corresponding to the monitor module is, by default, the 'monitor' directory one level above the runtime directory. Users can customize the configuration of the directory path. @@ -44,6 +44,42 @@ metadata: Special note: In the main configuration file of agentUniverse, the monitor module's master switch has the **highest priority**. If the `activate` configuration is set to false, the model granularity configuration will not take effect. +### Agent Tracing Configuration + +For the agent invocation tracking capability, agentUniverse also supports agent granularity configuration. Once the main +switch of the monitor module is turned on, the invocation tracking function for specific agent can be selectively +disabled through the agent's yaml file. + +For example, for a user-defined agent such as `demo_agent`, if the tracing is set to false, then the invocation tracking +function for this agent will be turned off. + +```yaml +info: + name: 'demo_agent' + description: 'demo agent' +profile: + tracing: false + prompt_version: demo_agent_prompt.cn + llm_model: + name: 'qwen_llm' +plan: + planner: + name: 'rag_planner' +action: + tool: + - 'google_search_tool' +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.rag_agent_case.demo_rag_agent' + class: 'DemoRagAgent' +``` + +Special note: In the main configuration file of agentUniverse, the monitor module's master switch has the **highest +priority**. If the `activate` configuration is set to false, the agent granularity configuration will not take effect. + + ## Monitor Module Introduction ### LLM Invocation Tracking @@ -116,4 +152,50 @@ following data: - **`source`**: the source of LLM invocation during agent runtime. - **`date`**: LLM invocation time - **`llm_input`**: LLM invocation messages and model parameters -- **`llm_output`**: LLM output text \ No newline at end of file +- **`llm_output`**: LLM output text + +### Agent Invocation Tracking + +#### trace_agent Decorator + +agentUniverse implements the `@trace_agent` decorator, which, when the agent calls the run +method, collects the agent's input and output parameters, serializes the information into JSON, and stores it in the local +storage directory corresponding to the monitor configuration in the form of a jsonl file, divided by the hour and the specific agent. + +#### Demo Effect + +Call the `demo_rag_agent` in the sample project of agentUniverse, using the gpt-4o model, with the query to analyze the +reasons behind Warren Buffett's reduction of his stake in BYD. The agent invocation tracking function captures the +following data: + +```json +{ + "source": "demo_rag_agent", + "date": "2024-06-28 12:12:17", + "agent_input": { + "kwargs": { + "input": "analyze the reasons behind Warren Buffett's reduction of his stake in BYD" + } + }, + "agent_output": { + "chat_history": [ + { + "content": "analyze the reasons behind Warren Buffett's reduction of his stake in BYD", + "type": "human" + }, + { + "content": "### Analysis of Warren Buffett's Reduction in Stake in BYD\n\nWarren Buffett, through his investment conglomerate Berkshire Hathaway, has been gradually reducing his stake in BYD Company Limited, a major player in the electric vehicle (EV) market. Here are the key reasons behind this strategic move:\n\n#### 1. Profit-Taking\n\n**Initial Investment vs. Current Valuation:**\n- **Initial Investment:** Berkshire Hathaway first bought about 225 million shares of Shenzhen-based BYD in 2008.\n- **Current Sale:** Recently, Berkshire sold nearly 2.02 million Hong Kong-listed shares of BYD at $234.57 apiece.\n\nGiven the significant appreciation in the value of BYD shares since the initial investment, it is plausible that Berkshire Hathaway is looking to realize profits from this long-term investment.\n\n#### 2. Portfolio Management\n\n**Stake Reduction:**\n- **Previous Stake:** Berkshire Hathaway's stake in BYD was previously 7%.\n- **Current Stake:** The sale has reduced the stake to 5.99%.\n\nReducing the stake in BYD allows Berkshire Hathaway to prune its portfolio, potentially reallocating resources to other investment opportunities or maintaining a diversified portfolio.\n\n#### 3. Market Performance and Strategic Positioning\n\n**Market Analysis:**\n- BYD, along with Tesla, is a leading player in the global electric vehicle market. However, market dynamics and competition are constantly evolving. Berkshire Hathaway's decision to reduce its stake could be influenced by an analysis of market performance and future growth prospects.\n\n**Value Investing:**\n- Warren Buffett is known for his value investing strategy. The timing of the BYD share sale may align with Buffett's assessment of the company's current valuation relative to its future growth potential.\n\n### Conclusion\n\nWarren Buffett's reduction in his stake in BYD appears to be a strategic decision influenced by multiple factors, including profit-taking, portfolio management, and an analysis of market performance. This move aligns with Berkshire Hathaway's broader investment strategy and reflects a calculated approach to managing its investments in a dynamic global market.", + "type": "ai" + }], + "background": "Warren Buffett's Berkshire Hathaway may have sold BYD to take profits, free up cash, prune its portfolio, cut its geopolitical risk, ... Missing: analyze | Show results with:analyze. Explore Berkshire Hathaway's gradual divestment from BYD, analyzing geopolitical tensions, market performance, and strategic positioning. According to various media reports and analysis, Warren Buffett's reduction of shareholdings in BYD Company Limited may be due to various ... Berkshire sold nearly 2.02 million Hong Kong-listed shares of BYD at $234.57 apiece, dropping its stake from 6.18% to 5.99%. The disposal was ... Warren Buffett's Berkshire Hathaway has further cut its large stake in BYD, which vies with Tesla as the world's largest electric car (EV) maker ... Missing: analyze | Show results with:analyze. The sale reduced Berkshire's holding to 6.9%, from 7%. The conglomerate first bought about 225 million shares of Shenzhen-based BYD in 2008 for ... According to HKEX disclosure, Warren Buffett's investment flagship Berkshire Hathaway reduced its stake in BYD COMPANY (01211. Warren Buffett's Berkshire Hathaway Inc. (NYSE:BRK)(NYSE:BRK) is reducing its stake in BYD Co., Ltd. (OTC:BYDDY) (OTC:BYDDF) to less than 6%. The odd timing of the BYD share sale has less to do with Buffett's outlook on the Chinese EV maker and more to do with 'value investing.' Investment mogul Warren Buffett has quickened the reduction of his stake in Chinese electric vehicle-maker BYD, selling holdings in the firm ...", + "date": "2024-06-28", + "input": "analyze the reasons behind Warren Buffett's reduction of his stake in BYD", + "output": "### Analysis of Warren Buffett's Reduction in Stake in BYD\n\nWarren Buffett, through his investment conglomerate Berkshire Hathaway, has been gradually reducing his stake in BYD Company Limited, a major player in the electric vehicle (EV) market. Here are the key reasons behind this strategic move:\n\n#### 1. Profit-Taking\n\n**Initial Investment vs. Current Valuation:**\n- **Initial Investment:** Berkshire Hathaway first bought about 225 million shares of Shenzhen-based BYD in 2008.\n- **Current Sale:** Recently, Berkshire sold nearly 2.02 million Hong Kong-listed shares of BYD at $234.57 apiece.\n\nGiven the significant appreciation in the value of BYD shares since the initial investment, it is plausible that Berkshire Hathaway is looking to realize profits from this long-term investment.\n\n#### 2. Portfolio Management\n\n**Stake Reduction:**\n- **Previous Stake:** Berkshire Hathaway's stake in BYD was previously 7%.\n- **Current Stake:** The sale has reduced the stake to 5.99%.\n\nReducing the stake in BYD allows Berkshire Hathaway to prune its portfolio, potentially reallocating resources to other investment opportunities or maintaining a diversified portfolio.\n\n#### 3. Market Performance and Strategic Positioning\n\n**Market Analysis:**\n- BYD, along with Tesla, is a leading player in the global electric vehicle market. However, market dynamics and competition are constantly evolving. Berkshire Hathaway's decision to reduce its stake could be influenced by an analysis of market performance and future growth prospects.\n\n**Value Investing:**\n- Warren Buffett is known for his value investing strategy. The timing of the BYD share sale may align with Buffett's assessment of the company's current valuation relative to its future growth potential.\n\n### Conclusion\n\nWarren Buffett's reduction in his stake in BYD appears to be a strategic decision influenced by multiple factors, including profit-taking, portfolio management, and an analysis of market performance. This move aligns with Berkshire Hathaway's broader investment strategy and reflects a calculated approach to managing its investments in a dynamic global market." + } +} +``` + +- **`source`**: the Agent name. +- **`date`**: Agent invocation time +- **`llm_input`**: Agent invocation input parameters +- **`llm_output`**: Agent output parameters \ No newline at end of file diff --git "a/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" "b/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" index a2810476..e2dbdb6d 100644 --- "a/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" +++ "b/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" @@ -1,20 +1,26 @@ # 监控模块 -agentUniverse监控模块，主要围绕agent运行时的模型调用/token开销等进行数据追踪与记录。针对**目前框架已开放的模型调用追踪能力**，本文将详细介绍如何使用该功能。 + +agentUniverse监控模块，主要围绕agent运行时的模型调用/token开销等进行数据追踪与记录。针对**目前框架已开放的模型调用追踪能力和agent调用追踪能力**，本文将详细介绍如何使用这两个功能。 ## 监控配置 + 在agentUniverse的主配置文件`config.toml`中配置监控模块配置信息，如下： + ```toml [MONITOR] activate = true dir = './monitor' ``` -- **`activate`**: 监控模块总开关，默认关闭，设置为true后将开启agent运行时的LLM调用追踪功能。 + +- **`activate`**: 监控模块总开关，默认关闭，设置为true后将开启agent运行时的LLM调用追踪功能和agent调用追踪功能。 - **`dir`**: 监控模块对应的本地记录存储目录，默认为运行时，上一层级下的monitor目录，用户可自定义配置目录路径。 ### 模型粒度配置 + 针对LLM调用追踪能力，agentUniverse同样支持模型粒度配置，当监控模块主开关打开后，可以通过LLM的yaml文件选择关闭特定模型调用追踪功能。 比如用户自定义的`demo_llm`模型，tracing设置为false，则关闭该模型的调用追踪功能。 + ```yaml name: 'demo_llm' description: 'demo openai' @@ -27,10 +33,44 @@ metadata: module: 'agentuniverse.llm.default.default_openai_llm' class: 'DefaultOpenAILLM' ``` + 特别说明：agentUniverse主配置文件的监控模块总开关**优先级最高**，若activate配置为false的情况下，模型粒度配置不生效。 +### Agent粒度配置 + +针对agent调用追踪能力，agentUniverse同样支持agent粒度配置，当监控模块主开关打开后，可以通过agent的yaml文件选择关闭特定agent调用追踪功能。 + +比如用户自定义的`demo_agent`智能体，tracing设置为false，则关闭该智能体的调用追踪功能。 + +```yaml +info: + name: 'demo_agent' + description: 'demo agent' +profile: + tracing: false + prompt_version: demo_agent_prompt.cn + llm_model: + name: 'qwen_llm' +plan: + planner: + name: 'rag_planner' +action: + tool: + - 'google_search_tool' +memory: + name: '' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.rag_agent_case.demo_rag_agent' + class: 'DemoRagAgent' +``` + +特别说明：agentUniverse主配置文件的监控模块总开关**优先级最高**，若activate配置为false的情况下，agent粒度配置不生效。 + ## 监控模块说明 + ### 模型调用追踪 + 用户若使用当前agentUniverse支持的**默认模型类**，则如上文所述配置完成后即可使用该功能。 #### trace_llm装饰器 @@ -38,52 +78,103 @@ metadata: 若用户根据agentUniverse的模型基类LLM做自定义扩展，实现自定义模型调用，则需要在自定义模型类的调用方法call/acall中增加@trace_llm装饰器功能。 如下自定义模型示例代码： + ```python from typing import Any, Iterator, Union, AsyncIterator from agentuniverse.llm.llm import LLM, LLMOutput from agentuniverse.base.annotation.trace import trace_llm + class DemoLLM(LLM): @trace_llm def call(self, messages: list, **kwargs: Any) -> Union[LLMOutput, Iterator[LLMOutput]]: pass - + @trace_llm async def acall(self, *args: Any, **kwargs: Any) -> Union[LLMOutput, AsyncIterator[LLMOutput]]: pass ``` -agentUniverse通过`@trace_llm`装饰器实现，agent在调用对应LLM call/acall方法时，采集模型prompt和response，将信息json序列化后存储到上文监控配置对应的本地存储目录下，以jsonl文件的形式按小时为单位分割。 + +agentUniverse通过`@trace_llm`装饰器实现，agent在调用对应LLM +call/acall方法时，采集模型prompt和response，将信息json序列化后存储到上文监控配置对应的本地存储目录下，以jsonl文件的形式按小时为单位分割。 #### 具体效果 + 调用agentUniverse sample工程中的demo_rag_agent，使用qwen-turbo模型，query为分析下巴菲特减持比亚迪的原因，模型调用追踪功能捕捉到的数据如下： + ```json { - "source": "QWenOpenAIStyleLLM.acall", - "date": "2024-06-24 16:27:14", - "llm_input": - {"messages": [ + "source": "QWenOpenAIStyleLLM.acall", + "date": "2024-06-24 16:27:14", + "llm_input": { + "messages": [ { "role": "system", "content": "你是一位精通信息分析的ai助手。\\n 你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价" - }, + }, { "role": "user", "content": "你需要遵守的规则是:\\n1. 必须使用中文结合查询的背景信息结合你所拥有的知识回答用户提出的问题。\\n2. 结构化答案生成，必要时通过空行提升阅读体验。\\n 需要回答的问题是: 分析下巴菲特减持比亚迪的原因\\n" } - ]}, + ] + }, "kwargs": { - "model": "qwen-turbo", - "stream": false, - "n": 1, - "temperature": 0.5, + "model": "qwen-turbo", + "stream": false, + "n": 1, + "temperature": 0.5, "max_tokens": 1000 }, "llm_output": "巴菲特减持比亚迪股票的原因可以从几个方面来分析：\\n\\n1. **投资策略调整**：尽管巴菲特通常倾向于长期持有优质股票，但投资组合管理需要灵活性。他可能会根据市场动态、行业趋势和个人评估的变化，适时调整持仓以优化资产配置或分散风险。\\n\\n2. **估值与预期回报**：减持可能反映了巴菲特对比亚迪当前股价与他内在价值评估的差距。尽管比亚迪业绩强劲，但如果他认为股价过高，可能会选择部分兑现利润。\\n 综上所述，巴菲特减持比亚迪股票可能是基于上述因素的综合考虑，但具体原因只有巴菲特和芒格本人了解。值得注意的是，尽管减持，他们对比亚迪的长期看好并未改变，这从芒格的演讲和持续的股东大会支持可以看出。" } ``` + - **`source`**: agent运行时LLM的调用来源 - **`date`**: LLM调用时间 - **`llm_input`**: LLM调用message及模型参数 -- **`llm_output`**: LLM输出文本段信息 \ No newline at end of file +- **`llm_output`**: LLM输出文本段信息 + +### Agent调用追踪 + +#### trace_agent装饰器 + +agentUniverse通过`@trace_agent`装饰器实现，agent在调用run方法时，采集agent输入和输出，将信息json序列化后存储到上文监控配置对应的本地存储目录下，以jsonl文件的形式按小时及单智能体为单位分割。 + +#### 具体效果 + +调用agentUniverse sample工程中的demo_rag_agent，query为分析下巴菲特减持比亚迪的原因，agent调用追踪功能捕捉到的数据如下： + +```json +{ + "source": "demo_rag_agent", + "date": "2024-06-27 19:26:46", + "agent_input": { + "kwargs": { + "input": "分析下巴菲特减持比亚迪的原因" + } + }, + "agent_output": { + "chat_history": [ + { + "content": "分析下巴菲特减持比亚迪的原因", + "type": "human" + }, + { + "content": "巴菲特减持比亚迪的原因可能涉及多个方面：\n\n1. **投资回报与资产配置**：随着比亚迪股价的大幅上涨，特别是新能源汽车业务的强劲表现，巴菲特可能认为已经达到其预期的回报，需要将资本转向其他潜在收益更高的投资。\n\n2. **芒格影响减弱**：芒格去世后，巴菲特作为伯克希尔·哈撒韦的唯一决策者，投资策略可能会有所调整，这可能影响他对比亚迪的投资态度。\n\n3. **资本结构调整**：巴菲特可能寻求更均衡的投资组合，通过减持比亚迪来优化伯克希尔·哈撒韦的整体资产配置。\n\n4. **风险控制**：持股比例降低至5%以下后，巴菲特的减持速度可能会加快，这可能是为了降低单一投资的风险。\n\n5. **市场动态**：在比亚迪股价下跌时未大幅减持，可能表明他之前是在等待一个更合适的市场时机。\n\n综合以上分析，巴菲特减持比亚迪的原因可能包含对投资回报的追求、对公司估值的判断以及个人投资策略的更新。然而，具体原因还需更多官方信息来确认。", + "type": "ai" + } + ], + "background": "针对巴菲特减持比亚迪的原因，目前市场依然处于猜想的状态，但从近年来的投资举动来看，也大概可以看出巴菲特减持比亚迪的一些想法。 我们可以从巴菲特 ... 为何巴菲特减持比亚迪？ 综合多家媒体的分析报道，巴菲特减持比亚迪股份可能是出于多种考虑，包括为了更好地配置资本、实现投资回报、投资策略的调整以及 ... 有私募人士分析认为，巴菲特从2008年建仓至今，比亚迪在新能源汽车的快速渗透下，营收和利润也达到了空前的规模，股价近30倍的涨幅，伯克希尔减持比亚迪 ... 对于巴菲特减持原因，相关人士表示，“公司在和对方沟通。”问及公司估值是否偏高，上述人士表示，“公司经营状况良好，销量、利润等保持增长。” 6月18日， ... 据证券时报e公司报道，中泰国际策略分析师颜招骏认为，巴菲特减持应该是看到长线的基本面发生变化或估值已经大幅偏离内在价值。一旦持股比例降至5%， ... 在比亚迪股票处于大幅下跌的阶段，伯克希尔哈撒韦则采取了观望的态度，减持力度并未有太明显。对股神巴菲特来说，在股价处于高位的时候获利了结、锁定 ... 相较于常规动力电池，刀片电池内部体积利用率增长了50%以上，整体体积能量密度可以媲美高能量密度的三元锂电池，能够在保证安全的前提下，提升新能源车的 ... ... 巴菲特第一次减持比亚迪了。从最初的19.92%到现在的5.99%，巴菲特对比亚迪的投资似乎正在逐步减少。有分析师指出，一旦持股比例降至5%，巴菲特的减持速度可能会加快。 巴菲特减持比亚迪的真实原因如何？对此，有各种各样的分析。 有的从地缘 ... 有的从地缘政治切入，有的从芒格去世后，巴菲特独掌大权后的风格切换进行分析， ... 市场分析认为，巴菲特的减持可能是为了更好地配置资本，并且进行获利了结。巴菲特本人也曾表示，减持比亚迪是为了找到这些资金更好的配置用途。同时， ...", + "date": "2024-06-27", + "input": "分析下巴菲特减持比亚迪的原因", + "output": "巴菲特减持比亚迪的原因可能涉及多个方面：\n\n1. **投资回报与资产配置**：随着比亚迪股价的大幅上涨，特别是新能源汽车业务的强劲表现，巴菲特可能认为已经达到其预期的回报，需要将资本转向其他潜在收益更高的投资。\n\n2. **芒格影响减弱**：芒格去世后，巴菲特作为伯克希尔·哈撒韦的唯一决策者，投资策略可能会有所调整，这可能影响他对比亚迪的投资态度。\n\n3. **资本结构调整**：巴菲特可能寻求更均衡的投资组合，通过减持比亚迪来优化伯克希尔·哈撒韦的整体资产配置。\n\n4. **风险控制**：持股比例降低至5%以下后，巴菲特的减持速度可能会加快，这可能是为了降低单一投资的风险。\n\n5. **市场动态**：在比亚迪股价下跌时未大幅减持，可能表明他之前是在等待一个更合适的市场时机。\n\n综合以上分析，巴菲特减持比亚迪的原因可能包含对投资回报的追求、对公司估值的判断以及个人投资策略的更新。然而，具体原因还需更多官方信息来确认。" + } +} +``` + +- **`source`**: Agent运行时的调用来源（智能体名称） +- **`date`**: Agent调用时间 +- **`agent_input`**: Agent调用输入参数 +- **`agent_output`**: Agent输出参数 \ No newline at end of file From c9bb0ea57c37e5d544af3ee7fbbeef497e5211df Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 14:26:43 +0800 Subject: [PATCH 070/118] fix name --- .../guidebook/en/7_1_1_Translation_Case.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename "docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" => docs/guidebook/en/7_1_1_Translation_Case.md (100%) diff --git "a/docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" b/docs/guidebook/en/7_1_1_Translation_Case.md similarity index 100% rename from "docs/guidebook/en/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" rename to docs/guidebook/en/7_1_1_Translation_Case.md From ddd8f3ddecd6833cee45514b5fdcd0608d292190 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 14:29:03 +0800 Subject: [PATCH 071/118] fix name --- ...1_\347\277\273\350\257\221\346\241\210\344\276\213.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" index 53b1f204..add27d7a 100644 --- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -26,10 +26,10 @@ [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml) [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml) 该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py) -3. 定义三个智能体的协同工作过程 -协同过程可参考前面介绍的多智能体协同工作过程的流程图 -![协同工作](../_picture/translation_execute_flow.png) -更细的流程可以参考[代码文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py) +3. 定义三个智能体的协同工作过程 +协同过程可参考前面介绍的多智能体协同工作过程的流程图 +![协同工作](../_picture/translation_execute_flow.png) +更细的流程可以参考[代码文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py) 协同智能体的[配置文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml) ### 演示代码 From b623e0f5216a64f4b4ab81851d998e94e711e053 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 14:44:52 +0800 Subject: [PATCH 072/118] fix --- ...73\350\257\221\346\241\210\344\276\213.md" | 30 +++++++++++-------- 1 file changed, 18 insertions(+), 12 deletions(-) diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" index add27d7a..847b53c2 100644 --- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -1,33 +1,37 @@ # 翻译案例 -近期，斯坦福大学教授吴恩达开源了一个AI智能体机器翻译——一个使用反思工作流进行翻译的智能体，该项目中演示了反思智能体工作流的机器翻译示例。该智能体的主要步骤如下： +近期，斯坦福大学教授吴恩达开源了一个AI智能体机器翻译——一个使用反思工作流进行翻译的智能体，该项目中演示了反思智能体工作流的机器翻译示例。智能体的主要步骤如下： 1. 初始翻译，使用大模型（LLM）将文本从source_language翻译为target_language; 2. 进行反思，让大模型指出翻译结果中的不足，并给出建设性的改进建议； 3. 修改翻译，让大模型根据反思给出的建议，重新进行翻译； -项目中采用的反思工作流，让模型自己去发现不足，在根据不足做定向的修改，是一种典型的多智能体协同工作机制，这也是agentUniverse一直涉足的领域，于是我就有将吴恩达的这个翻译智能体迁移到agentUniverse上面的想法。 -该案例基于千文大模型，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 +项目中采用的反思工作流，让模型自己去发现不足，再根据不足做定向的修改，是一种典型的多智能体协同工作机制，这也是agentUniverse一直涉足的领域，于是我就有将这个翻译智能体复刻到agentUniverse上面的想法。 ## 多智能体的协同工作过程 -翻译过程中，优先判断模型的长度是否超过了模型所能承受的最大token,针对超过模型长度的情况,先对分本进行切块，再分块进行翻译， -但整个翻译过程都是基于初始翻译->反思->修改的流程进行的。 +翻译过程中，优先判断模型的长度是否超过了模型所能承受的最大token,针对超过模型长度的情况,先对文本进行切块，再对分块后的文本进行翻译， +但是无论长文本还是短文本，翻译过程都是基于初始翻译->反思->修改的流程进行的。 ![多智能体协同工作流程](../_picture/translation_flow_graph.png) ## 在aU中的实现 在aU中实现该翻译的过程主要分为以下几步： -1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下: +1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下: [短文本翻译init prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_init_en.yaml) [短文本翻译reflection prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml) [短文本翻译improve prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml) [长文本翻译init prmpt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_init_en.yaml) [长文本翻译reflection prmpt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml) [长文本翻译improve prmpt](../../../sample_standard_app/app/core/prompt/translation/multi_translation_improve_en.yaml) +在aU中,可以很容易的功过配置文件定义prompt，并统一由PromptManager管理。 + 2. 定义三个智能体 [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml) [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml) [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml) -该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py) +该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py) +由于定义的prompt全部由PromptManager进行管理，这里可以很容易的实现prompt的切换 + + 3. 定义三个智能体的协同工作过程 -协同过程可参考前面介绍的多智能体协同工作过程的流程图 +协同过程可参考前面介绍的多智能体协同工作过程的流程图,具体的实现逻辑如图： ![协同工作](../_picture/translation_execute_flow.png) 更细的流程可以参考[代码文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py) 协同智能体的[配置文件](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml) @@ -42,11 +46,13 @@ [长翻译结果](../../../sample_standard_app/app/test/translation_data/long_text_result.txt) ### 演示结果 -可以看到，使用aU与原本的translation_agent原工程翻译结果保持一致，复刻成功。 +可以看到，使用aU与原本的translation_agent原工程翻译结果保持一致，复刻成功。 aU的执行结果: -![aU执行长文本结果](../_picture/long_translation_au.png) -translation_agent的执行结果: +![aU执行长文本结果](../_picture/long_translation_au.png) + +Translation_agent的执行结果: ![translation执行长文本结果](../_picture/long_translation_wu.png) ### 其他 -在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) +1. 在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) +2. 该案例基于千问大模型，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 \ No newline at end of file From 1d1df3623da8926521cc36b3a8ac9ccb3f50cfd8 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 14:48:14 +0800 Subject: [PATCH 073/118] fix --- ...1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" index 847b53c2..bd32cdec 100644 --- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" @@ -12,7 +12,7 @@ ## 在aU中的实现 在aU中实现该翻译的过程主要分为以下几步： -1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下: +1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下: [短文本翻译init prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_init_en.yaml) [短文本翻译reflection prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_reflection_en.yaml) [短文本翻译improve prmpt](../../../sample_standard_app/app/core/prompt/translation/translation_improve_en.yaml) @@ -26,7 +26,7 @@ [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_work_agent.yaml) [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_reflection_agent.yaml) [短文本翻译智能体](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_improve_agent.yaml) -该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[agent](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py) +该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[代码实现](../../../sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py) 由于定义的prompt全部由PromptManager进行管理，这里可以很容易的实现prompt的切换 @@ -54,5 +54,5 @@ Translation_agent的执行结果: ![translation执行长文本结果](../_picture/long_translation_wu.png) ### 其他 -1. 在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关issue:[issue地址](https://github.com/andrewyng/translation-agent/issues/28) +1. 在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关[issue](https://github.com/andrewyng/translation-agent/issues/28) 2. 该案例基于千问大模型，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 \ No newline at end of file From 79df86f75d3f7d60edbaeb80101d6bae24b7291c Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 28 Jun 2024 15:03:00 +0800 Subject: [PATCH 074/118] add keys information --- .../config/custom_key.toml.sample | 41 ++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/sample_standard_app/config/custom_key.toml.sample b/sample_standard_app/config/custom_key.toml.sample index ccde297e..f0bdf78c 100644 --- a/sample_standard_app/config/custom_key.toml.sample +++ b/sample_standard_app/config/custom_key.toml.sample @@ -1,4 +1,43 @@ # Example file of custom_key.toml. Rename to custom_key.toml while using. [KEY_LIST] # Perform a full component scan and registration for all the paths under this list. -example_key = 'AnExampleKey' \ No newline at end of file +example_key = 'AnExampleKey' + +# models +#kimi default name: default_kimi_llm +#KIMI_API_KEY='sk-xxxxxxxx' +# +##Qwen default name: default_qwen_llm +#DASHSCOPE_API_KEY='sk-xxxxxx' +# +##Opean default name: default_openai_llm +#OPENAI_API_KEY='sk-xxxxxx' +# +##DEEPSEEK default name: default_deepseek_llm +#DEEPSEEK_API_KEY='sk-xxxxxxx' +#DEEPSEEK_API_BASE='https://api.deepseek.com/v1' +# +## WenXin default name: default_wenxin_llm +#QIANFAN_AK='xxxx' +#QIANFAN_SK='xxxx' +# +##Ollama default name: default_ollama_llm +#OLLAMA_BASE_URL='xxxxxx' +# +##claude default name: default_claude_llm +#ANTHROPIC_API_KEY='xxxxxx' +#ANTHROPIC_API_URL='xxxxxx' +# +##baichuan default name: default_baichuan_llm +#BAICHUAN_API_KEY='xxxxxx' + + +# search +#Google search +#SERPER_API_KEY='xxxxxx' +# +##search api +#SEARCHAPI_API_KEY='xxxxxx' +# +##bing seacrh +#BING_SUBSCRIPTION_KEY='xxxxxx' \ No newline at end of file From 870b192c74244778907bd0ce5d745037fb41c85d Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 28 Jun 2024 21:58:13 +0800 Subject: [PATCH 075/118] feat: Version 0.0.10 Release --- CHANGELOG.md | 15 +++++++++++++++ CHANGELOG_zh.md | 15 +++++++++++++++ README.md | 2 +- README_jp.md | 2 +- README_zh.md | 2 +- pyproject.toml | 2 +- 6 files changed, 34 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7ec09f51..66489797 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -24,6 +24,21 @@ Note - Additional remarks regarding the version. *************************************************** # Version Update History +## [0.0.10] - 2024-06-28 +### Added +- Added standard integration for the DeepSeek model in the LLM module. +- Added a new OpenAI general protocol wrapper class, OpenAIStyleLLM. + - Models using the OpenAI protocol can be configured directly. +- Added a new LangChain tool wrapper class, LangChainTool, with several example tools for search and execution. + - LangChain tools can be configured directly. +- Added Agent information collection capability in the monitor module. + +### Note +- Added use cases. + - Supplemented documentation with a financial event analysis case study using PEER collaborative mode. +- Updated the new README. +- Some code optimizations and documentation updates. + ## [0.0.9] - 2024-06-14 ### Added - Added standard integration for Claude and Ollama LLM components diff --git a/CHANGELOG_zh.md b/CHANGELOG_zh.md index 600479be..e5547e62 100644 --- a/CHANGELOG_zh.md +++ b/CHANGELOG_zh.md @@ -24,6 +24,21 @@ Note - 对于版本的额外说明。 *************************************************** # 版本更新记录 +## [0.0.10] - 2024-06-28 +### Added +- LLM组件新增DeepSeek模型标准接入 +- 新增OpenAI通用协议包装类OpenAIStyleLLM + - openai协议类模型接入可直接配置 +- 新增LangChain工具包装类LangChainTool，新增搜索类、执行类示例工具若干 + - LangChain工具接入可直接配置 +- monitor模块新增Agent纬度信息采集能力 + +### Note +- 新增使用案例 + - PEER协同模式的金融事件分析案例文档补充 +- 新版README更新 +- 部分代码优化与文档更新 + ## [0.0.9] - 2024-06-14 ### Added - LLM组件新增claude、ollama标准接入 diff --git a/README.md b/README.md index babf44d5..23299e7b 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語 ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** diff --git a/README_jp.md b/README_jp.md index f7752ec7..1fd7c690 100644 --- a/README_jp.md +++ b/README_jp.md @@ -5,7 +5,7 @@ ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** diff --git a/README_zh.md b/README_zh.md index 7a144ddf..bf096bbe 100644 --- a/README_zh.md +++ b/README_zh.md @@ -5,7 +5,7 @@ ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** diff --git a/pyproject.toml b/pyproject.toml index 84aa9369..d3c32309 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "agentUniverse" -version = "0.0.9" +version = "0.0.10" description = "agentUniverse is a framework for developing applications powered by multi-agent base on large language model." authors = ["AntGroup "] From a665f8db4d610fcc9bf7d69a2599f740a4b6a877 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 28 Jun 2024 22:57:09 +0800 Subject: [PATCH 076/118] docs: update index of guidebook --- CHANGELOG.md | 1 + CHANGELOG_zh.md | 1 + docs/guidebook/en/0_index.md | 5 +++++ docs/guidebook/en/3_1_2_0_List_Of_LLMs.md | 3 +++ "docs/guidebook/zh/0_\347\233\256\345\275\225.md" | 5 +++++ ..._2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" | 3 +++ 6 files changed, 18 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 66489797..6d663bf4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -36,6 +36,7 @@ Note - Additional remarks regarding the version. ### Note - Added use cases. - Supplemented documentation with a financial event analysis case study using PEER collaborative mode. +- Added several new documents for LLM components, tool components, and the Monitor module. - Updated the new README. - Some code optimizations and documentation updates. diff --git a/CHANGELOG_zh.md b/CHANGELOG_zh.md index e5547e62..c8fc95c9 100644 --- a/CHANGELOG_zh.md +++ b/CHANGELOG_zh.md @@ -36,6 +36,7 @@ Note - 对于版本的额外说明。 ### Note - 新增使用案例 - PEER协同模式的金融事件分析案例文档补充 +- 新增若干LLM组件、工具组件、Monitor模块文档 - 新版README更新 - 部分代码优化与文档更新 diff --git a/docs/guidebook/en/0_index.md b/docs/guidebook/en/0_index.md index ff85fc87..432fd39d 100644 --- a/docs/guidebook/en/0_index.md +++ b/docs/guidebook/en/0_index.md @@ -42,6 +42,7 @@ * 2.4.4 Logging * 2.4.4.1 [Logging Component](2_6_Logging_Utils.md) * 2.4.5 Data Collection + * 2.4.5.1 [Monitor Module](2_5_1_Monitor_Module.md) **3. Component Reference Manual** * 3.1 Domain Components @@ -54,7 +55,11 @@ * 3.1.2.5 [BaiChuan Usage Instructions](3_1_2_BaiChuan_LLM_Use.md) * 3.1.2.6 [Claude Usage Instructions](3_1_2_Claude_LLM_Use.md) * 3.1.2.7 [Ollama Usage Instructions](3_1_2_Ollama_LLM_Use.md) + * 3.1.2.8 [DeepSeek Usage Instructions](3_1_2_DeepSeek_LLM_Use.md) + * 3.1.2.9 [General OpenAI Protocol Style Wrapper](3_1_2_OpenAIStyleLLM_Use.md) * 3.1.3 List of Tools + * 3.1.3.1 [Integration Tool Details](2_2_3_Integrated_Tools.md) + * 3.1.3.2 [LangChain Tool Wrapper](2_2_3_Integrated_LangChain_Tools.md) * 3.1.4 List of Knowledge * 3.1.5 List of Memories * 3.1.6 List of Planners diff --git a/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md b/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md index a56aa83f..c283b92b 100644 --- a/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md +++ b/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md @@ -10,6 +10,9 @@ The platform has currently integrated the following list of models; please refer | [BaiChuan](3_1_2_BaiChuan_LLM_Use.md) | | [Claude](3_1_2_Claude_LLM_Use.md) | | [ollama](3_1_2_Ollama_LLM_Use.md) | +| [DeepSeek](3_1_2_DeepSeek_LLM_Use.md) | + +To facilitate the integration of models with OpenAI-style protocols, we offer a wrapper object based on the OpenAI general protocol. See [OpenAIStyleLLM](3_1_2_OpenAIStyleLLM_Use.md). For LLM not included in the list, you can customize the integration of any LLM according to the steps provided in the [LLM definition](2_2_3_Tool_Create_And_Use.md). diff --git "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" index 27c590f0..9dbf3c8b 100644 --- "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" +++ "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" @@ -46,6 +46,7 @@ * 2.4.4 日志体系 * 2.4.4.1 [日志组件](2_4_4_日志组件.md) * 2.4.5 数据采集 + * 2.4.5.1 [monitor模块](2_5_1_监控模块.md) **3.组件参考手册** * 3.1 领域组件 @@ -58,7 +59,11 @@ * 3.1.2.5 [百川使用说明](3_1_2_百川使用.md) * 3.1.2.6 [Claude使用说明](3_1_2_Claude使用.md) * 3.1.2.7 [Ollama使用说明](3_1_2_Ollama使用.md) + * 3.1.2.8 [DeepSeek使用说明](3_1_2_DeepSeek使用.md) + * 3.1.2.9 [通用OpenAI协议风格包装类型](3_1_2_OpenAIStyleLLM使用.md) * 3.1.3 工具列表(Tool) + * 3.1.3.1 [集成工具列表](2_2_3_集成的工具.md) + * 3.1.3.2 [LangChain工具包装类型](2_2_3_集成LangChain工具.md) * 3.1.4 知识列表(Knowledge) * 3.1.5 记忆列表(Memory) * 3.1.6 计划列表(Planner) diff --git "a/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" "b/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" index 5e62a724..6a7fbed6 100644 --- "a/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" +++ "b/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" @@ -10,6 +10,9 @@ | [百川](3_1_2_百川使用.md) | | [Claude](3_1_2_Claude使用.md) | | [ollama](3_1_2_Ollama使用.md) | +| [DeepSeek](3_1_2_DeepSeek使用.md) | + +为方便用户接入OpenAI风格协议的模型，提供基于OpenAI通用协议接入包装对象 [OpenAIStyleLLM](3_1_2_OpenAIStyleLLM使用.md)。 未在列表中的模型您可以根据[模型定义](2_2_2_模型定义与使用.md)中的步骤自定义接入任何模型。 From 77cb557077e9c34aa1378b5b45bc40378d4aabcb Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 1 Jul 2024 10:33:24 +0800 Subject: [PATCH 077/118] fix --- .../translation_by_token_agent.py | 18 +++----- .../app/test/translation_data/long_text.txt | 46 +++++++------------ .../translation_data/long_text_result.txt | 15 +++--- 3 files changed, 30 insertions(+), 49 deletions(-) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py index 8ca27988..63e835c78 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -76,28 +76,24 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: llm_name = self.agent_model.profile.get('llm_model').get('name') llm: LLM = LLMManager().get_instance_obj(llm_name) source_text = agent_input.get('source_text') - text_tokens = llm.get_num_tokens(source_text) + text_tokens = len(source_text) # 这里使用最大输入token，因为必须要保证有足够的token输出翻译结果 - max_context_length = llm.max_tokens - if text_tokens < max_context_length: + if text_tokens < llm.max_tokens: return self.execute_agents(agent_input) agent_input['execute_type'] = 'multi' chunk_result = list[str]() - chunk_size = calculate_chunk_size(text_tokens, max_context_length) + chunk_size = calculate_chunk_size(text_tokens, llm.max_tokens) source_text_chunks = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0).split_text( source_text) for i in range(len(source_text_chunks)): - tagged_text = "" - if i - 1 > 0: - tagged_text += source_text_chunks[i - 1] - tagged_text += ( - "" + tagged_text = ( + "".join(source_text_chunks[0:i]) + + "" + source_text_chunks[i] + "" + + "".join(source_text_chunks[i + 1:]) ) - if i + 1 < len(source_text_chunks): - tagged_text += source_text_chunks[i + 1] agent_input['chunk_to_translate'] = source_text_chunks[i] agent_input['tagged_text'] = tagged_text result = self.execute_agents(agent_input) diff --git a/sample_standard_app/app/test/translation_data/long_text.txt b/sample_standard_app/app/test/translation_data/long_text.txt index 2847ca5c..3b791662 100644 --- a/sample_standard_app/app/test/translation_data/long_text.txt +++ b/sample_standard_app/app/test/translation_data/long_text.txt @@ -1,30 +1,16 @@ -Chapter 1 -　　I HAVE often felt a motion of love to leave some hints in writing of myexperience of the goodness of God, and now, in the thirty-sixth year of my age,I begin this work. -　　I was born in Northampton, in Burlington County, West Jersey, in the year1720. Before I was seven years old I began to be acquainted with the operationsof divine love. Through the care of my parents, I was taught to read nearly assoon as I was capable of it; and as I went from school one day, I remember thatwhile my companions were playing by the way, I went forward out of sight, andsitting down, I read the twenty-second chapter of Revelation: "He showed me apure river of water of life, clear as crystal, proceeding out of the throne ofGod and of the Lamb," etc. In reading it, my mind was drawn to seek after thatpure habitation which I then believed God had prepared for His servants. Theplace where I sat, and the sweetness that attended my mind, remain fresh in mymemory. This, and the like gracious visitations, had such an effect upon methat when boys used ill language it troubled me; and, through the continuedmercies of God, I was preserved from that evil. -　　The pious instructions of my parents were often fresh in my mind, when Ihappened to be among wicked children, and were of use to me. Having a largefamily of children, they used frequently, on First-days, after meeting, to set us one after another to read the Holy Scriptures, or some religious books, therest sitting by without much conversation; I have since often thought it was agood practice. From what I had read and heard, I believed there had been, inpast ages, people who walked in uprightness before God in a degree exceedingany that I knew or heard of now living: and the apprehension of there beingless steadiness and firmness amongst people in the present age often troubledme while I was a child. -　　I may here mention a remarkable circumstance that occurred in my childhood. -　　On going to a neighbour's house, I saw on the way a robin sitting on her nest,and as I came near she went off ; but having young ones, she flew about, andwith many cries expressed her concern for them. I stood and threw stones ather, and one striking her, she fell down dead. At first I was pleased with theexploit, but after a few minutes was seized with horror, at having, in asportive way, killed an innocent creature while she was careful for her young. -　　I beheld her lying dead, and thought those young ones, for which she was socareful, must now perish for want of their dam to nourish them. After somepainful considerations on the subject, I climbed up the tree, took all theyoung birds, and killed them, supposing that better than to leave them to pineaway and die miserably. In this case I believed that Scripture proverb wasfulfilled, "The tender mercies of the wicked are cruel." I then went on myerrand, and for some hours could think of little else but the cruelties I hadcommitted, and was much troubled. Thus He whose tender mercies are over all Hisworks hath placed a principle in the human mind, which incites to exercisegoodness towards every living creature; and this being singly attended to,people become tender-hearted and sympathizing; but when frequently and totallyrejected, the mind becomes shut up in a contrary disposition. -　　About the twelfth year of my age, my father being abroad, my mother reprovedme for some misconduct, to which I made an undutiful reply. The next First-day,as I was with my father returning from meeting, he told me that he understood Ihad behaved amiss to my mother, and advised me to be more careful in future. Iknew myself blamable, and in shame and confusion remained silent. Being thusawakened to a sense of my wickedness, I felt remorse in my mind, and on gettinghome, I retired and prayed to the Lord to forgive me, and I do not rememberthat I ever afterwards spoke unhandsomely to either of my parents, howeverfoolish in some other things. -　　Having attained the age of sixteen years, I began to love wanton company; andthough I was preserved from profane language or scandalous conduct, yet Iperceived a plant in me which produced much wild grapes; my merciful Father didnot, however, forsake me utterly, but at times, through His grace, I wasbrought seriously to consider my ways; and the sight of my backslidingsaffected me with sorrow, yet for want of rightly attending to the reproofs ofinstruction, vanity was added to vanity, and repentance to repentance. Upon the whole, my mind became more and more alienated from the truth, and I hastenedtoward destruction. While I meditate on the gulf towards which I travelled, andreflect on my youthful disobedience, for these things I weep, mine eye runnethdown with water. -　　Advancing in age, the number of my acquaintance increased, and thereby my waygrew more difficult. Though I had found comfort in reading the Holy Scripturesand thinking on heavenly things, I was now estranged therefrom. I knew I wasgoing from the flock of Christ and had no resolution to return, hence seriousreflections were uneasy to me, and youthful vanities and diversions were mygreatest pleasure. In this road I found many like myself, and we associated inthat which is adverse to true friendship. -　　In this swift race it pleased God to visit me with sickness, so that Idoubted of recovery; then did darkness, horror, and amazement with full forceseize me, even when my pain and distress of body were very great. I thought itwould have been better for me never to have had being, than to see the daywhich I now saw. I was filled with confusion, and in great affliction, both ofmind and body, I lay and bewailed myself. I had not confidence to lift up mycries to God, whom I had thus offended; but in a deep sense of my great folly Iwas humbled before Him. At length that word which is as a fire and a hammerbroke and dissolved my rebellious heart; my cries were put up in contrition;and in the multitude of His mercies I found inward relief, and a closeengagement that if He was pleased to restore my health I might walk humblybefore Him. -　　After my recovery this exercise remained with me a considerable time, but bydegrees giving way to youthful vanities, and associating with wanton youngpeople, I lost ground. The Lord had been very gracious, and spoke peace to mein the time of my distress, and I now most ungratefully turned again to folly;at times I felt sharp reproof, but I did not get low enough to cry for help. Iwas not so hardy as to commit things scandalous, but to exceed in vanity and topromote mirth was my chief study. Still I retained a love and esteem for piouspeople, and their company brought an awe upon me. My dear parents several timesadmonished me in the fear of the Lord, and their admonition entered into myheart and had a good effect for a season; but not getting deep enough to prayrightly, the tempter, when he came, found entrance. Once having spent a part ofthe day in wantonness, when I went to bed at night there lay in a window nearmy bed a Bible, which I opened, and first cast my eye on the text, "We lie downin our shame, and our confusion covereth us." This I knew to be my case, andmeeting with so unexpected a reproof I was somewhat affected with it, and wentto bed under remorse of conscience, which I soon cast off again. -　　Thus time passed on; my heart was replenished with mirth and wantonness,while pleasing scenes of vanity were presented to my imagination, till Iattained the age of eighteen years, near which time I felt the judgments of God in my soul, like a consuming fire, and looking over my past life the prospectwas moving. I was often sad, and longed to be delivered from those vanities;then again my heart was strongly inclined to them, and there was in me a soreconflict. At times I turned to folly, and then again sorrow and confusion tookhold of me. In a while I resolved totally to leave off some of my vanities, butthere was a secret reserve in my heart of the more refined part of them, and Iwas not low enough to find true peace. Thus for some months I had greattroubles; my will was unsubjected, which rendered my labours fruitless. Atlength, through the merciful continuance of heavenly visitations, I was made tobow down in spirit before the Lord. One evening I had spent some time inreading a pious author, and walking out alone I humbly prayed to the Lord forHis help, that I might be delivered from all those vanities which so ensnaredme. Thus being brought low, He helped me, and as I learned to bear the cross, Ifelt refreshment to come from His presence; but not keeping in that strengthwhich gave victory, I lost ground again, the sense of which greatly affectedme. I sought deserts and lonely places, and there with tears did confess mysins to God and humbly craved His help. And I may say with reverence, He wasnear to me in my troubles, and in those times of humiliation opened my ear todiscipline. -　　I was now led to look seriously at the means by which I was drawn from thepure truth, and learned that if I would live such a life as the faithfulservants of God lived, I must not go into company as heretofore in my own will,but all the cravings of sense must be governed by a divine principle. In timesof sorrow and abasement these instructions were sealed upon me, and I felt thepower of Christ prevail over selfish desires, so that I was preserved in a gooddegree of steadiness, and being young, and believing at that time that a singlelife was best for me, I was strengthened to keep from such company as had oftenbeen a snare to me. -　　I kept steadily to meetings, spent First-day afternoons chiefly in readingthe Scriptures and other good books, and was early convinced in my mind thattrue religion consisted in an inward life, wherein the heart does love andreverence God the Creator, and learns to exercise true justice and goodness,not only toward all men, but also toward the brute creation; that, as the mindwas moved by an inward principle to love God as an invisible, incomprehensibleBeing, so, by the same principle, it was moved to love Him in all Hismanifestations in the visible world; that, as by His breath the flame of lifewas kindled in all animal sensible creatures, to say we love God as unseen, andat the same time exercise cruelty toward the least creature moving by His life,or by life derived from Him, was a contradiction in itself. I found nonarrowness respecting sects and opinions, but believed that sincere, upright-hearted people, in every society, who truly love God, were accepted of Him. -　　As I lived under the cross, and simply followed the opening of truth, mymind, from day to day, was more enlightened, my former acquaintance were leftto judge of me as they would, for I found it safest for me to live in private,and keep these things sealed up in my own breast. While I silently ponder onthat change wrought in me, I find no language equal to convey to another aclear idea of it. I looked upon the works of God in this visible creation, andan awfulness covered me. My heart was tender and often contrite, and universallove to my fellow-creatures increased in me. This will be understood by such ashave trodden in the same path. Some glances of real beauty may be seen in theirfaces who dwell in true meekness. There is a harmony in the sound of that voiceto which divine love gives utterance, and some appearance of right order intheir temper and conduct whose passions are regulated; yet these do not fullyshow forth that inward life to those who have not felt it; this white stone andnew name is only known rightly by such as receive it. -　　Now, though I had been thus strengthened to bear the cross, I still foundmyself in great danger, having many weaknesses attending me, and strongtemptations to wrestle with; in the feeling whereof I frequently withdrew intoprivate places, and often with tears besought the Lord to help me, and Hisgracious ear was open to my cry. -　　All this time I lived with my parents, and wrought on the plantation; andhaving had schooling pretty well for a planter, I used to improve myself inwinter evenings, and other leisure times. Being now in the twenty-first year ofmy age, with my father's consent I engaged with a man, in much business as ashopkeeper and baker, to tend shop and keep books. At home I had lived retired;and now, having a prospect of being much in the way of company, I felt frequentand fervent cries in my heart to God, the Father of Mercies, that He wouldpreserve me from all taint and corruption; that, in this more publicemployment, I might serve Him, my gracious Redeemer, in that humility and self-denial which I had in a small degree exercised in a more private life. -　　The man who employed me furnished a shop in Mount Holly, about five milesfrom my father's house, and six from his own, and there I lived alone andtended his shop. Shortly after my settlement here I was visited by severalyoung people, my former acquaintance, who supposed that vanities would be asagreeable to me now as ever. At these times I cried to the Lord in secret forwisdom and strength; for I felt myself encompassed with difficulties, and hadfresh occasion to bewail the follies of times past, in contracting afamiliarity with libertine people; and as I had now left my father's houseoutwardly, I found my Heavenly Father to be merciful to me beyond what I canexpress. -　　By day I was much amongst people, and had many trials to go through; but inthe evenings I was mostly alone, and I may with thankfulness acknowledge, that in those times the spirit of supplication was often poured upon me; under whichI was frequently exercised, and felt my strength renewed. -　　After a while, my former acquaintance gave over expecting me as one of theircompany, and I began to be known to some whose conversation was helpful to me. -　　And now, as I had experienced the love of God through Jesus Christ, to redeemme from many pollutions, and to be a succor to me through a sea of conflicts,with which no person was fully acquainted, and as my heart was often enlargedin this heavenly principle, I felt a tender compassion for the youth whoremained entangled in snares like those which had entangled me. This love andtenderness increased, and my mind was strongly engaged for the good of myfellow-creatures. I went to meetings in an awful frame of mind, and endeavouredto be inwardly acquainted with the language of the true Shepherd. -　　One day, being under a strong exercise of spirit, I stood up and said somewords in a meeting; but not keeping close to the divine opening, I said morethan was required of me. Being soon sensible of my error, I was afflicted inmind some weeks, without any light or comfort, even to that degree that I couldnot take satisfaction in anything. I remembered God and was troubled, and inthe depth of my distress He had pity upon me, and sent the Comforter. I thenfelt forgiveness for my offence; my mind became calm and quiet, and I was trulythankful to my gracious Redeemer for His mercies. About six weeks after this,feeling the spring of divine love opened and a concern to speak, I said a fewwords in a meeting, in which I found peace. Being thus humbled and disciplinedunder the cross, my understanding became more strengthened to distinguish thepure Spirit which inwardly moves upon the heart, and which taught me to wait insilence sometimes many weeks together, until I felt that rise which preparesthe creature to stand like a trumpet, through which the Lord speaks to Hisflock. -　　From an inward purifying and steadfast abiding under it, springs a livelyoperative desire for the good of others. All the faithful are not called to thepublic ministry; but whoever are, are called to minister of that which theyhave tasted and handled spiritually. The outward modes of worship are various;but whenever any are true ministers of Jesus Christ, it is from the operationof His Spirit upon their hearts, first purifying them, and thus giving them ajust sense of the conditions of others. This truth was early fixed in my mind,and I was taught to watch the pure opening, and to take heed lest, while I wasstanding to speak, my own will should get uppermost, and cause me to utterwords from worldly wisdom, and depart from the channel of the true gospelministry. -　　In the management of my outward affairs, I may say with thankfulness, I foundtruth to be my support; and I was respected in my master's family, who came tolive in Mount Holly within two years after my going there. -　　In a few months after I came here, my master bought several Scotchmen servants,from on board a vessel, and brought them to Mount Holly to sell, one of whomwas taken sick and died. In the latter part of his sickness, being delirious,he used to curse and swear most sorrowfully; and the next night after hisburial I was left to sleep alone in the chamber where he died. I perceived inme a timorousness; I knew, however, I had not injured the man, but assisted intaking care of him according to my capacity. I was not free to ask any one onthat occasion to sleep with me. Nature was feeble; but every trial was a freshincitement to give myself up wholly to the service of God, for I found nohelper like Him in times of trouble. -　　About the twenty-third year of my age, I had many fresh and heavenlyopenings, in respect to the care and providence of the Almighty over hiscreatures in general, and over man as the most noble amongst those which arevisible. And being clearly convinced in my judgment that to place my wholetrust in God was best for me, I felt renewed engagements that in all things Imight act on an inward principle of virtue, and pursue worldly business nofurther than as truth opened my way. -　　About the time called Christmas I observed many people, both in town and fromthe country, resorting to public-houses, and spending their time in drinkingand vain sports, tending to corrupt one another; on which account I was muchtroubled. At one house in particular there was much disorder; and I believed itwas a duty incumbent on me to speak to the master of that house. I considered Iwas young, and that several elderly friends in town had opportunity to seethese things; but though I would gladly have been excused, yet I could not feelmy mind clear. -　　The exercise was heavy; and as I was reading what the Almighty said toEzekiel, respecting his duty as a watchman, the matter was set home moreclearly. With prayers and tears I besought the Lord for His assistance, and Hein loving-kindness gave me a resigned heart. At a suitable opportunity I wentto the public-house; and seeing the man amongst much company, I called himaside, and in the fear and dread of the Almighty expressed to him what restedon my mind. He took it kindly, and afterwards showed more regard to me thanbefore. In a few years afterwards he died, middle-aged; and I often thoughtthat, had I neglected my duty in that case, it would have given me greattrouble; and I was humbly thankful to my gracious Father, who had supported meherein. -　　My employer, having a negro woman, (1) sold her, and desired me to write abill of sale, the man being waiting who bought her. The thing was sudden; andthough I felt uneasy at the thoughts of writing an instrument of slavery forone of my fellow-creatures, yet I remembered that I was hired by the year, thatit was my master who directed me to do it, and that it was an elderly man, amember of our Society, who bought her; so through weakness I gave way, and wrote it; but at the executing of it I was so afflicted in my mind, that I saidbefore my master and the Friend that I believed slave-keeping to be a practiceinconsistent with the Christian religion. This in some degree abated myuneasiness; yet, as often as I reflected seriously upon it, I thought I shouldhave been clearer if I had desired to be excused from it, as a thing against myconscience; for such it was. Some time after this a young man of our Societyspoke to me to write a conveyance of a slave to him, he having lately taken anegro into his house. I told him I was not easy to write it; for though many ofour meeting and in other places kept slaves, I still believed the practice wasnot right, and desired to be excused from the writing. I spoke to him ingoodwill; and he told me that keeping slaves was not altogether agreeable tohis mind; but that the slave being a gift made to his wife, he had acceptedher. \ No newline at end of file +Last week, I spoke about AI and regulation at the U.S. Capitol at an event that was attended by legislative and business leaders. I’m encouraged by the progress the open source community has made fending off regulations that would have stifled innovation. But opponents of open source are continuing to shift their arguments, with the latest worries centering on open source's impact on national security. I hope we’ll all keep protecting open source! + +Based on my conversations with legislators, I’m encouraged by the progress the U.S. federal government has made getting a realistic grasp of AI’s risks. To be clear, guardrails are needed. But they should be applied to AI applications, not to general-purpose AI technology. + +Nonetheless, as I wrote previously, some companies are eager to limit open source, possibly to protect the value of massive investments they’ve made in proprietary models and to deter competitors. It has been fascinating to watch their arguments change over time. + +For instance, about 12 months ago, the Center For AI Safety’s “Statement on AI Risk” warned that AI could cause human extinction and stoked fears of AI taking over. This alarmed leaders in Washington. But many people in AI pointed out that this dystopian science-fiction scenario has little basis in reality. About six months later, when I testified at the U.S. Senate’s AI Insight forum, legislators no longer worried much about an AI takeover. + +Then the opponents of open source shifted gears. Their leading argument shifted to the risk of AI helping to create bioweapons. Soon afterward, OpenAI and RAND showed that current AI does not significantly increase the ability of malefactors to build bioweapons. This fear of AI-enabled bioweapons has diminished. To be sure, the possibility that bad actors could use bioweapons — with or without AI — remains a topic of great international concern. + + +The latest argument for blocking open source AI has shifted to national security. AI is useful for both economic competition and warfare, and open source opponents say the U.S. should make sure its adversaries don’t have access to the latest foundation models. While I don’t want authoritarian governments to use AI, particularly to wage unjust wars, the LLM cat is out of the bag, and authoritarian countries will fill the vacuum if democratic nations limit access. When, some day, a child somewhere asks an AI system questions about democracy, the role of a free press, or the function of an independent judiciary in preserving the rule of law, I would like the AI to reflect democratic values rather than favor authoritarian leaders’ goals over, say, human rights. + +I came away from Washington optimistic about the progress we’ve made. A year ago, legislators seemed to me to spend 80% of their time talking about guardrails for AI and 20% about investing in innovation. I was delighted that the ratio has flipped, and there was far more talk of investing in innovation. + +Looking beyond the U.S. federal government, there are many jurisdictions globally. Unfortunately, arguments in favor of regulations that would stifle AI development continue to proliferate. But I’ve learned from my trips to Washington and other nations’ capitals that talking to regulators does have an impact. If you get a chance to talk to a regulator at any level, I hope you’ll do what you can to help governments better understand AI. diff --git a/sample_standard_app/app/test/translation_data/long_text_result.txt b/sample_standard_app/app/test/translation_data/long_text_result.txt index 88d90138..6ab50965 100644 --- a/sample_standard_app/app/test/translation_data/long_text_result.txt +++ b/sample_standard_app/app/test/translation_data/long_text_result.txt @@ -1,10 +1,9 @@ Rag agent execution result is : -第一章 -　　我常怀一股爱的冲动，欲在文字中留下些许关于我体验上帝良善的暗示。如今，正值我三十六岁之时，我开始这项工作。我于1720年出生在北安普顿，伯灵顿县，西泽西。我七岁之前，我开始了解神圣之爱的运作。在父母的关怀下，我几乎在能够阅读时就开始学习阅读；记得有一天放学时，我的同伴们在路上玩耍，我独自前行，消失在他们的视线之外，坐下来阅读《启示录》的第二十二章：“他向我展示了一条纯净的生命之河，清澈如水晶，从上帝和羔羊的宝座流出”等等。在阅读时，我的心被吸引去寻求我相信上帝为他的仆人所预备的那个纯净居所。我坐的地方，以及我心灵所感受到的甜美，至今仍记忆犹新。这样的恩典降临，以及其他类似的恩典，对我产生了如此影响，以至于当男孩们使用恶劣语言时，我会感到困扰；而且，通过上帝持续的怜悯，我得以避免那种邪恶。父母的虔诚教诲常在我心中深刻，当我偶然与顽劣孩童相处时，这些教诲对我帮助很大。由于家中有众多子女，他们常在星期日聚会后，依次让我们阅读圣经或某些宗教书籍，其余人则静坐一旁，几乎没有交谈；我后来常常认为，这实在是个好习惯。从我所读所闻中，我相信在过去年代，有人行事正直，其程度超过我所知或所闻现今任何人；而意识到现今人们缺乏坚定和刚毅，常在我幼年时困扰我。 -　　在此，我或许可以提及童年时发生的一件特别的事情。在去邻居家的路上，我看到一只知更鸟坐在其巢里，当我走近时，她飞离了；但由于有幼鸟，她四处飞翔，发出许多叫声表达了对它们的担忧。我站在那里向她投掷石头，其中一块击中了她，她坠地死亡。起初，我对此举感到高兴，但几分钟后，我感到恐怖，因为以游戏的方式杀死了一只正在照顾幼鸟的无辜生物。我目睹她躺在地上死去，想到那些她如此细心照料的小鸟可能因失去母亲喂养而饿死。经过一番痛苦思考，我爬上树，将所有小鸟捉下，杀死了它们，觉得这样总比让它们慢慢饿死、悲惨地死去要好。在这种情况下，我相信圣经中的箴言得到了体现：“恶人的怜悯也是残忍。”之后我继续我的差事，数小时内满脑子都是我所犯下的残忍行为，感到非常困扰。因此，那位对所有造物充满怜悯的上帝在人的心灵中植入了激发人们善待每一个生灵的原则；当这一原则被单独关注时，人们会变得心肠柔软、富有同情；但当它被频繁且彻底拒绝时，心灵就会封闭，转向相反的倾向。在我十二岁那年的某一天，父亲不在家时，母亲因我的一些不当行为而责备我，我做出了不尊重的回应。下一个安息日，我和父亲从聚会回来时，他告诉我他知道我曾对母亲不敬，并建议我将来要更加小心。我知道自己有错，在羞愧和困惑中我保持沉默。这样被唤醒意识到自己的过错，我心中充满了悔恨，回到家后，我独自退下，祈求上帝宽恕我，我不记得之后我再对父母说过不敬的话，尽管在其他事情上我仍然有些愚蠢。到了十六岁，我开始喜欢放纵的社交；虽然我避免了亵渎的语言或丑闻的行为，但我意识到自己内心长出了一株植物，结出了许多野葡萄。然而，我仁慈的父亲并未完全抛弃我，有时通过他的恩典，我被引导认真考虑我的行为；看到自己的堕落让我感到悲伤，但由于没有正确地接受教导的责备，虚荣之上再加虚荣，悔改之后又悔改。总的来说，我的心灵越来越远离真理，我急速走向毁灭。当我沉思着走向深渊的旅途，反思年轻时的不顺从，我为此哭泣，我的眼泪滚滚而下。随着年龄的增长,我认识的人越来越多,这让我的道路变得更加艰难。虽然我曾从阅读圣经和思考天上的事中得到安慰,但现在我却与之疏远。我知道自己正远离基督的羊群,却没有决心返回,因此,反思让我感到不安,而青春的虚荣和娱乐是我最大的乐趣。在这条道路上,我遇到了许多像我一样的人,我们在与真友谊相悖之事上结伴。在这场迅速的人生竞赛中，上帝允许我患病，使我怀疑能否康复；那时，黑暗、恐怖和惊骇以全副力量袭来，我的身体疼痛和痛苦非常严重。我宁愿从未存在，也不愿目睹眼前的困境。我充满困惑，身心俱疲，躺着悲叹自己。我没有勇气向我所冒犯的上帝呼求；但在深刻意识到我的愚蠢后，我在他面前谦卑。最终，那如火如锤的话语打破并溶解了我叛逆的心；我的呼喊充满了悔改；在祂的众多怜悯中，我找到了内心的慰藉，并承诺，若祂恢复我的健康，我将谦卑地行走在他面前。康复后，内心的斗争伴随了我相当长的时间，但随着逐渐沉溺于青春的虚荣，与放纵的年轻人交往，我逐渐失去了原有的道德立场。上帝在我困苦时曾非常仁慈地向我说话，赐予我平安，而我却忘恩负义地再次陷入愚昧。有时我感到严厉的责备，但我并未真正谦卑到求助的地步。我不至于大胆到做出丑闻之事，但过度追求虚荣和追求欢乐成了我的主要追求。我依然对虔诚的人怀有敬爱，他们的陪伴让我感到敬畏。我亲爱的父母多次在敬畏主的心态下劝诫我，他们的忠告深入我心，曾产生良好效果；但由于未能深入到正确祈祷的程度，当诱惑者来临时，他找到了进入的途径。有一次，我在放纵中度过了一天的一部分时间，晚上上床时，我床边窗户上放着一本圣经，我打开它，第一眼看到的是这段经文：“我们躺下羞愧，我们的羞耻遮盖我们。”我知道这正是我的写照，意外的责备让我有些触动，我带着良心的悔恨上床，但很快又忘记了。"我们带着羞愧躺下，我们的困惑笼罩着我们。"我知道这正是我的写照，遭遇这突如其来的责备，我因此感到触动，带着内心的悔恨躺下，但很快我又将其抛诸脑后。时间就这样流逝；我的心充满了欢愉与放纵的情感，虚幻的景象不断在我脑海中浮现，直到我接近十八岁那年，我感受到上帝的审判如同烈火在灵魂中燃烧，回顾过去的生活，前景令人感动。我常常感到悲伤，渴望摆脱那些虚荣的事物；然而我的心又强烈地倾向于它们，内心冲突剧烈。有时我做出愚蠢的行为，随后悲伤和混乱又抓住了我。过了一段时间，我决心彻底放弃一些虚荣之物，但心中仍暗藏对它们精致部分的留恋，我还未达到能找到真正平静的境界。就这样，几个月来我经历了巨大的困扰；我的意志未被驯服，导致我的努力徒劳。最终，通过上天慈悲的持续眷顾，我被引导在主面前屈服。一天傍晚，我阅读了一位虔诚作者的作品，独自外出时，我谦卑地祈祷向主祈求帮助，以摆脱那些紧紧束缚我的虚妄。因此，当我谦卑下来，祂便向我伸出援手，当我学会背负十字架的重担时，我感受到从祂的临在中传来的慰藉与力量；但未能保持那带来胜利的力量，我又再次失势，这种感觉深深影响了我。我寻求荒漠与孤寂之地，如同寻求内心的宁静，在那里，我含泪向神忏悔我的罪行，并谦卑地祈求祂的帮助。怀着敬畏之心，我可以说，祂在我困境中与我同在，在那些谦卑的时刻，祂开启了我的耳朵，让我接受教诲。我此刻开始认真审视那些使我偏离纯粹真理的方式，我领悟到，若要如上帝忠实的仆人般生活，我必须不再像以前那样随心所欲地社交，所有感官的欲望都应受神圣原则的支配。在悲伤与谦卑的时刻，这些教导铭记在我心，我感受到基督的力量克服了自私的欲望，使我保持了坚定。由于年轻，且当时认为独身生活最为适宜，我坚定地远离那些常使我陷入困境的社交圈。我始终如一地参加聚会，主要在周日午后阅读圣经和其他有益书籍，并深信真正的宗教在于内在的生命，内心深处爱慕并敬畏上帝创造者，并学会实践真正的正义与善良，不仅对所有人，也对所有生灵；心灵被内在原则驱动去爱无形且不可理解的上帝，此原则也驱动心灵去爱他在可见世界中的所有显现；正如他的气息点燃了所有有感知的生物的生命之火，声称我们爱看不见的上帝，同时却对由他生命或衍生自他的生命所驱动的最小生物施以残忍，这本身就是自相矛盾。我坚信在每个社会中真诚、正直的人，只要真正爱上帝，都会被他接纳。当我生活在十字架下，简单地追随真理的启示，我的心灵日渐明朗。我以前的熟人让他们如何评判我，因为我发现保持私密生活最为安全，并将这些事情深藏心底。当我默默地思考这种在我身上发生的改变时，我发现没有语言能够清晰地向他人传达这种感受。我凝视着上帝在这可见创造中的作品，我被一种敬畏感所笼罩。我的心变得柔软，常怀悔悟，我对同胞的爱意日益增长。这将由那些走过同样道路的人所理解。在那些真正谦逊者的脸上，可见真正的美丽。那种由神圣之爱发出的声音和谐悦耳，他们的性情与行为表现出正确的秩序，他们的激情得到了调节；然而，这些并不完全向那些未曾感受过的人展示那种内在的生命；这块白石及其新名，唯有真正接受者方能理解其真谛。领悟此真谛。尽管我已因此得到力量去背负十字架，但我仍发现自己处于极大的危险之中，伴随着我有许多弱点，以及需要与之抗争的强烈诱惑；在感受到这些时，我常常退到私密的地方，常常含泪恳求主帮助我，而祂那仁慈的耳朵总是倾听我的呼求。这段时间我一直与父母同住，并在种植园工作；并且作为一个种植园主，我受过相当不错的教育，常常利用冬夜和其他闲暇时间自我提升。如今我已二十一岁，在得到父亲的同意后，我与一位店铺和面包店的经营者签订了合同，负责照看店铺和记账。在家时，我过着隐居的生活；而现在，预见到自己将频繁与人交往，我心中常常涌起对上帝——慈悲之父的频繁而热切的祈祷，恳求他保护我免受一切污染和腐化；希望在这项更为公开的工作中，我能以谦卑和自我否定的态度服务他，我的仁慈救主，这种态度我在更为私密的生活中已略有实践。雇佣我的那位先生在芒特霍利（Mount Holly）为我开设了一家店铺，距离我父亲的家约五英里，而他自己的家则在此基础上再远一英里。在那里，我独自生活并照看店铺。我刚安顿下来不久，就有几位我年轻时的熟人来访，他们认为我仍会像以前那样喜欢轻浮之事。在这些时刻，我暗中向主呼求智慧和力量；因为我感到自己被困难包围，又有新的理由为过去与不羁之人交往的愚蠢行为而悔恨；现在我虽然表面上离开了父亲的家，却发现我的天父对我展现的慈悲超乎我的言辞所能表达。 -　　白天我大多与人相处，经历了许多考验；但到了晚上，我大多独自一人。我满怀感激地承认，在那些时刻，祷告的灵常常降临于我；在这灵的引导下，我频繁地锻炼自己，感到力量得到了更新。过了一段时间，我以前的熟人不再期待我成为他们群体的一部分，我开始结识一些谈话有益的人。 -　　如今，我通过耶稣基督感受到了上帝的爱，他救我脱离了许多污染，成为我在重重冲突中的支持，这些冲突无人完全了解。我的心常常在这神圣的原则中得到扩展，我对那些像我一样陷入陷阱的年轻人感到深切的同情。这种爱和温柔日益增强，我的心灵强烈地投入到同胞的福祉中。我以虔诚的心态去参加聚会，并努力从内心熟悉真牧者的教导。在心灵受到强烈感召的一天，我于聚会中起身发言；然未紧随神启，言辞过多。察觉过失后，心灵受苦数周之久，没有任何光明或安慰，乃至万事无欢。我忆及上帝，心中充满忧烦，在深陷困苦之际，上帝怜悯我，并派遣慰藉。我感受到罪行的赦免，心境平和宁静，诚挚感谢我仁慈的救赎主所赐恩典。约六周后，神圣之爱如泉涌般开启，我感到有发言的责任，于聚会中简言几句，内心得安宁。经此磨练与谦卑，我更能辨识内心深处纯净之灵，教我有时静待数周，直至感受那股力量，使人如号角般准备就绪，经由它，主向其羊群发声。主通过这号角向他的羊群传达话语。从内心的净化和坚定不移的坚持中，涌现出对他人福祉的活跃而有效的渴望。并非所有信徒都被召唤到公共事工中；但那些被召唤的人，是去侍奉他们精神上已经体验和把握的事物。外在的崇拜形式多种多样；但任何时候，只要是耶稣基督的真仆人，都是因为他的圣灵首先净化了他们的心灵，从而给予他们公正的感知对他人境遇。这一真理很早就扎根于我的心中，我被教导要留意纯净的启示，并小心谨慎，以免在我站立说话时，自己的意志占据上风，导致我根据世俗智慧说出话语，偏离真正的福音事工之路。 -　　在处理我的外在事务时，我可以说心怀感激，我发现真理是我的支持；我在主人的家庭中受到尊重，他们在我去那里两年后搬到霍利山居住。几个月后，我来到这里，我的主人从船上买了几个苏格兰仆人，把他们带到芒特霍利去卖，其中一个生病去世了。在他病重的后期，由于神志不清，他常常悲痛地咒骂和发誓；他下葬的第二天晚上，我被留在他去世的那个房间里独自睡觉。我感到内心一阵胆怯；然而我知道，我并没有伤害那个人，而是根据自己的能力帮助照顾他。当时我不便请求任何人陪我同睡。自然的力量是脆弱的；但每一次考验都激励我更全心全意地投身于上帝的服务，因为在困难时刻，我发现没有像他那样的帮助者。在我二十三岁那年，我领悟到了许多关于全能上帝对其造物普遍关怀与天意的清新而神圣的启示，尤其是对人这一最尊贵的可见造物。我深信不疑地认为，将我的全部信任寄托于上帝对我最为有利，这使我感到新的责任，即基于内在道德原则处理一切事务，并且追求世俗事务不超过真理指引的范围。 - -大约在圣诞节期间，我注意到许多城镇内外的人们涌向公共场所以及酒馆，在那里饮酒作乐，参与无益的游戏，彼此败坏；为此我深感困扰。特别是在一家酒馆里，秩序混乱；我深信自己有责任与那家酒馆的主人交谈。我意识到自己年轻，而城镇中几位年长的朋友也有机会目睹这些情况；尽管我很想推脱，但我无法让自己的内心感到清晰。这次经历非常沉重；当我阅读上帝对以西结关于其作为守望者职责的指示时，事情变得更加清晰。我泪流满面地祈祷，恳求主的帮助，他以慈爱赐予我一颗顺服的心。在合适的时机，我前往那家酒馆；发现那人在众多宾客中，我便请他到一旁，带着对上帝的敬畏，向他表达了心中的重负。他对此表示感谢，之后对我比以往更加敬重。几年后，他中年时去世；我常想，如果当时我忽略了我的雇主拥有一名黑人女性，(1) 将其出售，并要求我撰写一份销售契据，买主已在等待。事情来得突然；尽管想到要为同类撰写奴隶交易的文件，我感到不安，但我记得我是按年雇佣的，是雇主指示我这么做，而且买主是一位我们社团中的年长成员；因此，出于软弱，我屈服并写了这份契据；但在执行时，我内心极度痛苦，以至于我当着雇主和社团朋友的面说，我认为蓄奴与基督教信仰不相符。这在一定程度上减轻了我的不安；然而，每当我认真反思此事，我认为如果我请求免于执行，因为这违背我的良心，我会更加心安；因为事实确实如此。此后不久，社团的一位年轻男子请我为他撰写奴隶转让文件，他最近将一名黑人带入家中。我告诉他，我不愿意写这份文件；尽管我们社团及许多地方的人都有奴隶，我仍然认为这种做法是不对的，并希望得到豁免。我善意地与他交谈；他告诉我，养奴并不完全符合他的心意，但因为这名奴隶是送给他的妻子的礼物，他接受了她。写这份文书；因为尽管我们社区以及许多其他地方的人都有奴隶，我仍然认为这种做法是不对的，并请求免于参与这项写作。我友好地与他交谈；他告诉我，拥有奴隶并不完全符合他的心意；但因为这个奴隶是作为礼物送给他的妻子的，他接受了这份礼物。 \ No newline at end of file +上周，我在美国国会大厦就人工智能和监管问题发表了演讲，该活动吸引了立法界和商界的领袖。我对开源社区在抵御那些可能扼杀创新的监管措施方面取得的进展感到鼓舞。但开源的反对者仍在不断转变他们的论点，最新的担忧焦点是开源对国家安全的影响。我希望我们大家都能继续致力于保护开源！ + +基于与立法者的对话，我对美国联邦政府在切实把握人工智能风险方面取得的进展感到鼓舞。明确地说，需要有监管措施。但这些措施应针对人工智能应用，而非通用型人工智能技术。尽管如此，正如我之前所写，一些公司急于限制开源，可能是为了保护他们在专有模型上所做的巨额投资价值，并阻止竞争对手。观察他们论点随时间演变的过程颇为引人入胜。 + +例如，大约12个月前，人工智能安全中心的“关于人工智能风险的声明”警告说，人工智能可能引发人类灭绝的风险，并引发了人们对人工智能接管的担忧。这引起了华盛顿高层领导人的警觉。但许多人工智能领域的专家指出，这种反乌托邦科幻场景在现实中缺乏依据。大约六个月后，当我在美国参议院的人工智能洞察论坛上作证时，立法者们对人工智能接管的担忧已大大减少。随后，开源的反对者转变了策略。他们主要论点转向了AI协助制造生物武器的风险。不久之后，OpenAI和RAND的研究表明，当前的AI并没有显著提高不法分子制造生物武器的能力。对AI辅助生物武器的担忧已有所减少。诚然，不法分子使用生物武器（无论是否借助AI）的可能性仍然是国际社会高度关注的问题。最新阻止开源AI的论点已经转向了国家安全。AI在经济竞争和战争中都有用处，开源AI的反对者认为美国应该确保其对手无法接触到最新的基础模型。虽然我不希望专制政府使用AI，尤其是用于发动不公正的战争，但大型语言模型（LLM）的技术已经公开，如果民主国家限制访问，专制国家将会填补这一空白。当有一天，某个地方的孩子向AI系统询问关于民主、自由媒体的作用或独立司法在维护法治中的功能时，我希望AI能够反映民主价值观，而不是优先考虑专制领导者的目标，比如说，人权。我从华盛顿回来后对我们的进步感到乐观。一年前，立法者似乎有80%的时间在讨论AI的监管措施，而只有20%的时间在讨论投资创新。我很高兴现在更多地讨论投资创新，而不是监管措施。 + +除了美国联邦政府，全球范围内还有许多其他司法管辖区。不幸的是，支持那些会阻碍AI发展的监管措施的论点仍在不断增加。但我从访问华盛顿和其他国家首都的经历中了解到，与监管机构的对话确实能产生影响。若你有机会与任何层级的监管机构交流，请尽力帮助政府更深入地理解AI。 \ No newline at end of file From 9ed5ce0c829b89158f410c5a10a6e340ccda6e84 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 1 Jul 2024 10:49:06 +0800 Subject: [PATCH 078/118] fix text --- .../app/test/translation_data/long_text_result.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/sample_standard_app/app/test/translation_data/long_text_result.txt b/sample_standard_app/app/test/translation_data/long_text_result.txt index 6ab50965..6ac7eb61 100644 --- a/sample_standard_app/app/test/translation_data/long_text_result.txt +++ b/sample_standard_app/app/test/translation_data/long_text_result.txt @@ -1,9 +1,9 @@ Rag agent execution result is : -上周，我在美国国会大厦就人工智能和监管问题发表了演讲，该活动吸引了立法界和商界的领袖。我对开源社区在抵御那些可能扼杀创新的监管措施方面取得的进展感到鼓舞。但开源的反对者仍在不断转变他们的论点，最新的担忧焦点是开源对国家安全的影响。我希望我们大家都能继续致力于保护开源！ +上周，我在美国国会大厦就人工智能和监管问题发表了演讲，此次活动吸引了立法界和企业界的领袖。我对开源社区在抵御可能扼杀创新的监管方面取得的进展感到鼓舞。然而，开源的反对者仍在不断变换他们的论点，最新的担忧集中在开源对国家安全的影响上。我们应继续共同保护开源环境！ -基于与立法者的对话，我对美国联邦政府在切实把握人工智能风险方面取得的进展感到鼓舞。明确地说，需要有监管措施。但这些措施应针对人工智能应用，而非通用型人工智能技术。尽管如此，正如我之前所写，一些公司急于限制开源，可能是为了保护他们在专有模型上所做的巨额投资价值，并阻止竞争对手。观察他们论点随时间演变的过程颇为引人入胜。 +根据我与立法者的对话，我对美国联邦政府在准确评估人工智能风险方面取得的进展感到鼓舞。明确地说，需要设置防护措施。但这些措施应针对人工智能的具体应用，而非通用人工智能技术本身。愿我们共同守护开源精神！尽管如此，正如我之前所写，一些公司急于限制开源，可能是为了保护他们在专有模型上所做的巨额投资价值，并阻止竞争对手。观察他们的论点随时间演变的过程一直颇具趣味。 -例如，大约12个月前，人工智能安全中心的“关于人工智能风险的声明”警告说，人工智能可能引发人类灭绝的风险，并引发了人们对人工智能接管的担忧。这引起了华盛顿高层领导人的警觉。但许多人工智能领域的专家指出，这种反乌托邦科幻场景在现实中缺乏依据。大约六个月后，当我在美国参议院的人工智能洞察论坛上作证时，立法者们对人工智能接管的担忧已大大减少。随后，开源的反对者转变了策略。他们主要论点转向了AI协助制造生物武器的风险。不久之后，OpenAI和RAND的研究表明，当前的AI并没有显著提高不法分子制造生物武器的能力。对AI辅助生物武器的担忧已有所减少。诚然，不法分子使用生物武器（无论是否借助AI）的可能性仍然是国际社会高度关注的问题。最新阻止开源AI的论点已经转向了国家安全。AI在经济竞争和战争中都有用处，开源AI的反对者认为美国应该确保其对手无法接触到最新的基础模型。虽然我不希望专制政府使用AI，尤其是用于发动不公正的战争，但大型语言模型（LLM）的技术已经公开，如果民主国家限制访问，专制国家将会填补这一空白。当有一天，某个地方的孩子向AI系统询问关于民主、自由媒体的作用或独立司法在维护法治中的功能时，我希望AI能够反映民主价值观，而不是优先考虑专制领导者的目标，比如说，人权。我从华盛顿回来后对我们的进步感到乐观。一年前，立法者似乎有80%的时间在讨论AI的监管措施，而只有20%的时间在讨论投资创新。我很高兴现在更多地讨论投资创新，而不是监管措施。 +例如，大约12个月前，人工智能安全中心发布的《人工智能风险声明》警告说，人工智能可能导致人类灭绝，并引发了人们对人工智能主导的担忧。这引起了华盛顿领导人的警觉。但许多人工智能领域的专家指出，这种反乌托邦科幻情景在现实中缺乏依据。大约六个月后，当我在美国参议院的人工智能洞察论坛上作证时，立法者们对人工智能接管的担忧已显著减少。然后，开源的反对者们转变了策略。他们主要的论点转变为人工智能可能帮助制造生物武器的风险。不久之后，OpenAI和RAND表明，当前的人工智能并没有显著增加恶意行为者制造生物武器的能力。这种关于人工智能辅助生物武器的担忧已有所减少。可以肯定的是，不良行为者可能使用生物武器——无论是否借助人工智能——仍然是国际上高度关注的话题。最新反对开放源代码AI的论点已转向国家安全问题。AI在经济竞争和战争中都有其用途，反对者认为美国应确保其对手无法接触到最新的基础模型。虽然我不希望专制政府使用AI，尤其是用于发动不公正的战争，但大语言模型的秘密已经泄露，如果民主国家限制访问，专制国家将会填补这一空白。当某天，某个地方的孩子向AI系统提问关于民主、自由媒体的角色或独立司法在维护法治中的作用时，我希望AI能体现民主价值观，而不是优先服务于专制领导者的目标，比如牺牲人权。离开华盛顿后，我对我们取得的进展感到乐观。一年前，立法者似乎将80%的时间用于讨论AI的监管框架，而仅20%的时间用于讨论投资创新。我很高兴这一比例已经逆转，现在更多地聚焦于投资创新。 -除了美国联邦政府，全球范围内还有许多其他司法管辖区。不幸的是，支持那些会阻碍AI发展的监管措施的论点仍在不断增加。但我从访问华盛顿和其他国家首都的经历中了解到，与监管机构的对话确实能产生影响。若你有机会与任何层级的监管机构交流，请尽力帮助政府更深入地理解AI。 \ No newline at end of file +除了美国联邦政府，全球还有许多法律辖区。不幸的是，支持那些可能阻碍AI发展的监管规定的论点仍在不断增加。但我从访问华盛顿和其他国家的首都的经历中学到，与监管机构的对话确实会产生影响。如果你有机会与任何级别的监管机构交流，我希望你能尽力协助政府更深入地理解AI。 \ No newline at end of file From 205e8e4cf18bb88a063513ef781d574cf596e171 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 1 Jul 2024 11:59:34 +0800 Subject: [PATCH 079/118] add country prompt --- .../translation_agent.py | 3 ++ .../translation_by_token_agent.py | 2 +- ...untry_multi_translation_reflection_en.yaml | 37 +++++++++++++++++++ .../country_translation_reflection_en.yaml | 29 +++++++++++++++ .../app/test/test_translation_agent.py | 30 +++++++-------- 5 files changed, 85 insertions(+), 16 deletions(-) create mode 100644 sample_standard_app/app/core/prompt/translation/country_multi_translation_reflection_en.yaml create mode 100644 sample_standard_app/app/core/prompt/translation/country_translation_reflection_en.yaml diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py index 654a68bf..b83b1647 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py @@ -35,5 +35,8 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: translation_type = input_object.get_data('execute_type') if translation_type == "multi": agent_model.profile['prompt_version'] = translation_type + "_" + agent_model.profile['prompt_version'] + if input_object.get_data('country') and self.agent_model.info.get('name') == 'translation_reflection_agent': + agent_model.profile['prompt_version'] = "country_" + agent_model.profile['prompt_version'] + planner_result = planner_base.invoke(agent_model, agent_input, input_object) return planner_result diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py index 63e835c78..41b47ad4 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -39,7 +39,7 @@ def output_keys(self) -> list[str]: return self.agent_model.profile.get('output_keys') def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: - for key in self.input_keys(): + for key in input_object.to_dict(): agent_input[key] = input_object.get_data(key) return agent_input diff --git a/sample_standard_app/app/core/prompt/translation/country_multi_translation_reflection_en.yaml b/sample_standard_app/app/core/prompt/translation/country_multi_translation_reflection_en.yaml new file mode 100644 index 00000000..66e45c9b --- /dev/null +++ b/sample_standard_app/app/core/prompt/translation/country_multi_translation_reflection_en.yaml @@ -0,0 +1,37 @@ +introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. +target: You will be provided with a source text and its translation and your goal is to improve the translation. +instruction: | + Your task is to carefully read a source text and part of a translation of that text from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions for improving the translation. + The final style and tone of the translation should match the style of {target_lang} colloquially spoken in {country}. + + The source text is below, delimited by XML tags and , and the part that has been translated + is delimited by and within the source text. You can use the rest of the source text + as context for critiquing the translated part. + + + {tagged_text} + + + To reiterate, only part of the text is being translated, shown here again between and : + + {chunk_to_translate} + + + The translation of the indicated part, delimited below by and , is as follows: + + {init_agent_result} + + + When writing suggestions, pay attention to whether there are ways to improve the translation's:\n\ + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n\ + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions),\n\ + (iii) style (by ensuring the translations reflect the style of the source text and takes into account any cultural context),\n\ + (iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}).\n\ + + Write a list of specific, helpful and constructive suggestions for improving the translation. + Each suggestion should address one specific part of the translation. + Output only the suggestions and nothing else. + +metadata: + type: 'PROMPT' + version: 'country_multi_translation_reflection.en' diff --git a/sample_standard_app/app/core/prompt/translation/country_translation_reflection_en.yaml b/sample_standard_app/app/core/prompt/translation/country_translation_reflection_en.yaml new file mode 100644 index 00000000..433fd90e --- /dev/null +++ b/sample_standard_app/app/core/prompt/translation/country_translation_reflection_en.yaml @@ -0,0 +1,29 @@ +introduction: You are an expert linguist specializing in translation from {source_lang} to {target_lang}. +target: You will be provided with a source text and its translation and your goal is to improve the translation. +instruction: | + Your task is to carefully read a source text and a translation from {source_lang} to {target_lang}, and then give constructive criticism and helpful suggestions to improve the translation. \ + The final style and tone of the translation should match the style of {target_lang} colloquially spoken in {country}. + + The source text and initial translation, delimited by XML tags and , are as follows: + + + {source_text} + + + + {init_agent_result} + + + When writing suggestions, pay attention to whether there are ways to improve the translation's \n\ + (i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n\ + (ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions),\n\ + (iii) style (by ensuring the translations reflect the style of the source text and takes into account any cultural context),\n\ + (iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}).\n\ + + Write a list of specific, helpful and constructive suggestions for improving the translation. + Each suggestion should address one specific part of the translation. + Output only the suggestions and nothing else. + +metadata: + type: 'PROMPT' + version: 'country_translation_reflection.en' diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py index 88dda247..d4f46400 100644 --- a/sample_standard_app/app/test/test_translation_agent.py +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -27,7 +27,7 @@ def test_translation_agent_long(self): data = f.read() output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", - source_text=data + source_text=data, country="台湾" ) res_info = f"\nRag agent execution result is :\n" res_info += output_object.get_data('output') @@ -36,20 +36,20 @@ def test_translation_agent_long(self): f.write(res_info) print(res_info) - def test_translation_agent_short(self): - instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') - with open('./translation_data/short_text.txt', 'r') as f: - data = f.read() - - output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", - source_text=data - ) - res_info = f"\nRag agent execution result is :\n" - res_info += output_object.get_data('output') - # 创建文件，并写入文件 - with open('./translation_data/short_text_result.txt', 'w') as f: - f.write(res_info) - print(res_info) + # def test_translation_agent_short(self): + # instance: Agent = AgentManager().get_instance_obj('translation_by_token_agent') + # with open('./translation_data/short_text.txt', 'r') as f: + # data = f.read() + # + # output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", + # source_text=data + # ) + # res_info = f"\nRag agent execution result is :\n" + # res_info += output_object.get_data('output') + # # 创建文件，并写入文件 + # with open('./translation_data/short_text_result.txt', 'w') as f: + # f.write(res_info) + # print(res_info) if __name__ == '__main__': From f82135ece05f43f771230b164043900c835a0b05 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 1 Jul 2024 12:58:38 +0800 Subject: [PATCH 080/118] add output --- .../translation_by_token_agent.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py index 41b47ad4..366a81b6 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -1,5 +1,6 @@ # !/usr/bin/env python3 # -*- coding:utf-8 -*- +from queue import Queue # @Time : 2024/6/25 16:56 # @Author : weizjajj @@ -31,6 +32,12 @@ def calculate_chunk_size(token_count: int, token_limit: int) -> int: return chunk_size +def output_middle_result(input_object: InputObject, data: any): + output_stream: Queue = input_object.get_data('output_stream',None) + if output_stream: + output_stream.put(data) + + class TranslationAgent(Agent): def input_keys(self) -> list[str]: return self.agent_model.profile.get('input_keys') @@ -46,23 +53,26 @@ def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: def parse_result(self, planner_result: dict) -> dict: return planner_result - def execute_agents(self, planner_input: dict) -> dict: + def execute_agents(self, input_object: InputObject, planner_input: dict) -> dict: work_agent = 'translation_work_agent' reflection_agent = 'translation_reflection_agent' improve_agent = 'translation_improve_agent' init_agent_result = self.execute_agent(work_agent, planner_input) LOGGER.info(f"init_agent_result: {init_agent_result.to_json_str()}") + output_middle_result(input_object, {'init_agent_result': init_agent_result}) planner_input['init_agent_result'] = init_agent_result.get_data('output') reflection_result = self.execute_agent(reflection_agent, planner_input) LOGGER.info(f"reflection_result: {reflection_result.to_json_str()}") + output_middle_result(input_object, {'reflection_agent_result': reflection_result}) planner_input['reflection_agent_result'] = reflection_result.get_data('output') improve_result = self.execute_agent(improve_agent, planner_input) LOGGER.info(f"improve_agent_result: {improve_result.to_json_str()}") + output_middle_result(input_object, {'improve_agent_result': improve_result}) return improve_result.to_dict() From b2634549658798801dbb18c337d01ec67c633ea4 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 1 Jul 2024 19:01:47 +0800 Subject: [PATCH 081/118] fix problem --- .../translation_agent_case/translation_agent.py | 1 - .../translation_by_token_agent.py | 12 +++++++----- .../app/test/test_translation_agent.py | 2 +- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py index b83b1647..82f18ff9 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.py @@ -37,6 +37,5 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: agent_model.profile['prompt_version'] = translation_type + "_" + agent_model.profile['prompt_version'] if input_object.get_data('country') and self.agent_model.info.get('name') == 'translation_reflection_agent': agent_model.profile['prompt_version'] = "country_" + agent_model.profile['prompt_version'] - planner_result = planner_base.invoke(agent_model, agent_input, input_object) return planner_result diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py index 366a81b6..0e142c2b 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_by_token_agent.py @@ -47,6 +47,8 @@ def output_keys(self) -> list[str]: def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: for key in input_object.to_dict(): + if key == 'output_stream': + continue agent_input[key] = input_object.get_data(key) return agent_input @@ -60,19 +62,19 @@ def execute_agents(self, input_object: InputObject, planner_input: dict) -> dict init_agent_result = self.execute_agent(work_agent, planner_input) LOGGER.info(f"init_agent_result: {init_agent_result.to_json_str()}") - output_middle_result(input_object, {'init_agent_result': init_agent_result}) + output_middle_result(input_object, {'init_agent_result': init_agent_result.get_data('output')}) planner_input['init_agent_result'] = init_agent_result.get_data('output') reflection_result = self.execute_agent(reflection_agent, planner_input) LOGGER.info(f"reflection_result: {reflection_result.to_json_str()}") - output_middle_result(input_object, {'reflection_agent_result': reflection_result}) + output_middle_result(input_object, {'reflection_agent_result': reflection_result.get_data('output')}) planner_input['reflection_agent_result'] = reflection_result.get_data('output') improve_result = self.execute_agent(improve_agent, planner_input) LOGGER.info(f"improve_agent_result: {improve_result.to_json_str()}") - output_middle_result(input_object, {'improve_agent_result': improve_result}) + output_middle_result(input_object, {'improve_agent_result': improve_result.get_data('output')}) return improve_result.to_dict() @@ -89,7 +91,7 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: text_tokens = len(source_text) # 这里使用最大输入token，因为必须要保证有足够的token输出翻译结果 if text_tokens < llm.max_tokens: - return self.execute_agents(agent_input) + return self.execute_agents(input_object,agent_input) agent_input['execute_type'] = 'multi' chunk_result = list[str]() chunk_size = calculate_chunk_size(text_tokens, llm.max_tokens) @@ -106,7 +108,7 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: ) agent_input['chunk_to_translate'] = source_text_chunks[i] agent_input['tagged_text'] = tagged_text - result = self.execute_agents(agent_input) + result = self.execute_agents(input_object,agent_input) chunk_result.append(result.get('output')) return { diff --git a/sample_standard_app/app/test/test_translation_agent.py b/sample_standard_app/app/test/test_translation_agent.py index d4f46400..afb82440 100644 --- a/sample_standard_app/app/test/test_translation_agent.py +++ b/sample_standard_app/app/test/test_translation_agent.py @@ -27,7 +27,7 @@ def test_translation_agent_long(self): data = f.read() output_object: OutputObject = instance.run(source_lang="英文", target_lang="中文", - source_text=data, country="台湾" + source_text=data ) res_info = f"\nRag agent execution result is :\n" res_info += output_object.get_data('output') From a8b0df0e3f2b4937b50bc2df60aa435a652f20c5 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 1 Jul 2024 19:02:00 +0800 Subject: [PATCH 082/118] add service --- .../app/core/service/translation_service.yaml | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 sample_standard_app/app/core/service/translation_service.yaml diff --git a/sample_standard_app/app/core/service/translation_service.yaml b/sample_standard_app/app/core/service/translation_service.yaml new file mode 100644 index 00000000..f76fe231 --- /dev/null +++ b/sample_standard_app/app/core/service/translation_service.yaml @@ -0,0 +1,5 @@ +name: 'translation_service' +description: '吴恩达教授翻译智能体' +agent: 'translation_by_token_agent' +metadata: + type: 'SERVICE' \ No newline at end of file From 4d7dbb0b9bff861adea29bc5e54e643e1c9cde89 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Tue, 2 Jul 2024 09:30:15 +0800 Subject: [PATCH 083/118] tracing false --- .../app/core/agent/translation_agent_case/translation_agent.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml index 6acbf16f..be1943f1 100644 --- a/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml +++ b/sample_standard_app/app/core/agent/translation_agent_case/translation_agent.yaml @@ -2,6 +2,7 @@ info: name: 'translation_by_token_agent' description: '翻译Agent' profile: + tracing: false prompt_version: 'one_chunk_improve.en' input_keys: ['source_lang','target_lang','source_text'] output_keys: ['output'] From fdb94a73fc360cdb265932fa6817f8426c57ea0c Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 2 Jul 2024 14:35:18 +0800 Subject: [PATCH 084/118] improvement: add serialize method in monitor module and tweak executing agent in peers. --- .../executing_agent/executing_agent.py | 6 +- agentuniverse/base/annotation/trace.py | 18 +----- agentuniverse/base/util/monitor/monitor.py | 55 ++++++++++++++++--- 3 files changed, 52 insertions(+), 27 deletions(-) diff --git a/agentuniverse/agent/default/executing_agent/executing_agent.py b/agentuniverse/agent/default/executing_agent/executing_agent.py index 63ac6a44..4e4a5aa5 100644 --- a/agentuniverse/agent/default/executing_agent/executing_agent.py +++ b/agentuniverse/agent/default/executing_agent/executing_agent.py @@ -76,7 +76,8 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict: framework = agent_input.get('framework', []) futures = [] for task in framework: - agent_input_copy: dict = copy.deepcopy(agent_input) + # note: agent input shallow copy. + agent_input_copy: dict = dict(agent_input) agent_input_copy['input'] = task planner: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')) futures.append( @@ -99,7 +100,8 @@ def process_intput_object(self, input_object: InputObject, subtask: str, planner # get agent toolsets. action: dict = self.agent_model.action or dict() tools: list = action.get('tool') or list() - input_object_copy: InputObject = copy.deepcopy(input_object) + # note: input object shallow copy. + input_object_copy: InputObject = InputObject(input_object.to_dict()) # wrap input_object for agent knowledge. input_object_copy.add_data(planner_input_key, subtask) # wrap input_object for agent toolsets. diff --git a/agentuniverse/base/annotation/trace.py b/agentuniverse/base/annotation/trace.py index 428856f1..7c7c2078 100644 --- a/agentuniverse/base/annotation/trace.py +++ b/agentuniverse/base/annotation/trace.py @@ -128,11 +128,8 @@ def wrapper_sync(*args, **kwargs): # invoke function result = func(*args, **kwargs) - # serialize agent input and output dict - agent_input = serialize_agent_invocation(agent_input) - agent_output = serialize_agent_invocation(result) # add agent invocation info to monitor - Monitor().trace_agent_invocation(source=source, agent_input=agent_input, agent_output=agent_output) + Monitor().trace_agent_invocation(source=source, agent_input=agent_input, agent_output=result) return result # sync function @@ -145,16 +142,3 @@ def _get_agent_input(func, *args, **kwargs) -> dict: bound_args = sig.bind(*args, **kwargs) bound_args.apply_defaults() return {k: v for k, v in bound_args.arguments.items()} - - -def default_serializer(obj): - if isinstance(obj, InputObject): - return obj.to_dict() - elif isinstance(obj, OutputObject): - return obj.to_dict() - raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") - - -def serialize_agent_invocation(agent_invocation): - agent_invocation_serialized = json.loads(json.dumps(agent_invocation, default=default_serializer)) - return agent_invocation_serialized diff --git a/agentuniverse/base/util/monitor/monitor.py b/agentuniverse/base/util/monitor/monitor.py index e69388af..1221496f 100644 --- a/agentuniverse/base/util/monitor/monitor.py +++ b/agentuniverse/base/util/monitor/monitor.py @@ -12,6 +12,8 @@ from pydantic import BaseModel +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.output_object import OutputObject from agentuniverse.base.annotation.singleton import singleton from agentuniverse.base.config.configer import Configer @@ -57,12 +59,6 @@ def trace_llm_invocation(self, source: str, llm_input: Union[str, dict], llm_out with jsonlines.open(path_save, 'a') as writer: writer.write(llm_invocation) - def _get_or_create_subdir(self, subdir: str) -> str: - """Get or create a subdirectory if it doesn't exist in the monitor directory.""" - path = os.path.join(self.dir, subdir) - os.makedirs(path, exist_ok=True) - return path - def trace_agent_invocation(self, source: str, agent_input: Union[str, dict], agent_output: Union[str, dict]) -> None: """Trace the agent invocation and save it to the monitor jsonl file.""" @@ -78,8 +74,8 @@ def trace_agent_invocation(self, source: str, agent_input: Union[str, dict], agent_invocation = { "source": source, "date": date.strftime("%Y-%m-%d %H:%M:%S"), - "agent_input": agent_input, - "agent_output": agent_output, + "agent_input": self.serialize_obj(agent_input), + "agent_output": self.serialize_obj(agent_output), } # files are stored in hours filename = f"agent_{source}_{date.strftime('%Y-%m-%d-%H')}.jsonl" @@ -90,3 +86,46 @@ def trace_agent_invocation(self, source: str, agent_input: Union[str, dict], with jsonlines.open(path_save, 'a') as writer: writer.write(agent_invocation) + def _get_or_create_subdir(self, subdir: str) -> str: + """Get or create a subdirectory if it doesn't exist in the monitor directory.""" + path = os.path.join(self.dir, subdir) + os.makedirs(path, exist_ok=True) + return path + + @staticmethod + def default_serializer(obj): + """Default serializer for objects.""" + if isinstance(obj, InputObject): + return obj.to_dict() + elif isinstance(obj, OutputObject): + return obj.to_dict() + raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") + + def serialize_obj(self, obj): + """Serialize an object and filter out non-serializable values.""" + filtered_obj = self.filter_and_serialize(obj) + return json.loads(json.dumps(filtered_obj, default=self.default_serializer)) + + def filter_and_serialize(self, obj): + """Recursively filter out non-serializable values from an object.""" + + def is_json_serializable(value): + """Check if value is a JSON serializable object.""" + try: + json.dumps(value, default=self.default_serializer) + return True + except (TypeError, OverflowError): + return False + + def filter_dict(d): + return {k: v for k, v in d.items() if is_json_serializable(v)} + + def recursive_filter(o): + if isinstance(o, dict): + return filter_dict({k: recursive_filter(v) for k, v in o.items()}) + elif isinstance(o, list): + return [recursive_filter(i) for i in o if is_json_serializable(i)] + else: + return o + + return recursive_filter(obj) From 2b6af771d220745a2c7202870390a70a8030b2b6 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 3 Jul 2024 14:37:01 +0800 Subject: [PATCH 085/118] improvement: add exception handling and retry mechanism in peer agents. --- .../plan/planner/peer_planner/peer_planner.py | 40 ++++++++++++++++--- 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py index e552a876..e77a3cc5 100644 --- a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py +++ b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py @@ -109,15 +109,22 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu expressingAgent = agents.get('expressing') reviewingAgent = agents.get('reviewing') - for _ in range(retry_count): - LOGGER.info(f"Starting peer agents, retry_count is {_ + 1}.") + for i in range(retry_count): + LOGGER.info(f"Starting peer agents, retry_count is {i + 1}.") if not planning_result or jump_step == "planning": if not planningAgent: LOGGER.warn("no planning agent.") planning_result = OutputObject({"framework": [agent_input.get('input')]}) else: LOGGER.info(f"Starting planning agent.") - planning_result = planningAgent.run(**input_object.to_dict()) + try: + planning_result = planningAgent.run(**input_object.to_dict()) + except Exception as e: + LOGGER.error(f"Exception occurred during the planning agent's invocation, exception: {e}") + if i >= retry_count - 1: + raise e + else: + continue input_object.add_data('planning_result', planning_result) # add planning agent log info @@ -132,7 +139,14 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu executing_result = OutputObject({}) else: LOGGER.info(f"Starting executing agent.") - executing_result = executingAgent.run(**input_object.to_dict()) + try: + executing_result = executingAgent.run(**input_object.to_dict()) + except Exception as e: + LOGGER.error(f"Exception occurred during the executing agent's invocation, exception: {e}") + if i >= retry_count - 1: + raise e + else: + continue input_object.add_data('executing_result', executing_result) # add executing agent log info @@ -150,7 +164,14 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu expressing_result = OutputObject({}) else: LOGGER.info(f"Starting expressing agent.") - expressing_result = expressingAgent.run(**input_object.to_dict()) + try: + expressing_result = expressingAgent.run(**input_object.to_dict()) + except Exception as e: + LOGGER.error(f"Exception occurred during the expressing agent's invocation, exception: {e}") + if i >= retry_count - 1: + raise e + else: + continue input_object.add_data('expressing_result', expressing_result) # add expressing agent log info @@ -171,7 +192,14 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu return result else: LOGGER.info(f"Starting reviewing agent.") - reviewing_result = reviewingAgent.run(**input_object.to_dict()) + try: + reviewing_result = reviewingAgent.run(**input_object.to_dict()) + except Exception as e: + LOGGER.error(f"Exception occurred during the reviewing agent's invocation, exception: {e}") + if i >= retry_count - 1: + raise e + else: + continue input_object.add_data('reviewing_result', reviewing_result) # add reviewing agent log info From 33d2082623c69f0a51bf0049682d1eb85dde922d Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 3 Jul 2024 16:57:41 +0800 Subject: [PATCH 086/118] add stream output --- .../plan/planner/peer_planner/peer_planner.py | 5 +++- agentuniverse/agent/plan/planner/planner.py | 14 ++++++++++ .../plan/planner/rag_planner/rag_planner.py | 26 ++++++++++++++++--- 3 files changed, 40 insertions(+), 5 deletions(-) diff --git a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py index e552a876..a8125ba6 100644 --- a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py +++ b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py @@ -125,6 +125,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu for index, one_framework in enumerate(planning_result.get_data('framework')): logger_info += f"[{index + 1}] {one_framework} \n" LOGGER.info(logger_info) + self.stream_output(input_object, {"data": planning_result.to_dict(), "type": "planning"}) if not executing_result or jump_step in ["planning", "executing"]: if not executingAgent: @@ -143,6 +144,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu one_exec_log_info += f"[{index + 1}] output: {one_exec_res['output']}\n" logger_info += one_exec_log_info LOGGER.info(logger_info) + self.stream_output(input_object, {"data": executing_result.to_dict(), "type": "executing"}) if not expressing_result or jump_step in ["planning", "executing", "expressing"]: if not expressingAgent: @@ -157,6 +159,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu logger_info = f"\nExpressing agent execution result is :\n" logger_info += f"{expressing_result.get_data('output')}" LOGGER.info(logger_info) + self.stream_output(input_object, {"data": executing_result.to_dict(), "type": "expressing"}) if not reviewing_result or jump_step in ["planning", "executing", "expressing", "reviewing"]: if not reviewingAgent: @@ -179,7 +182,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu reviewing_info_str = f"review suggestion: {reviewing_result.get_data('suggestion')} \n" reviewing_info_str += f"review score: {reviewing_result.get_data('score')} \n" LOGGER.info(logger_info + reviewing_info_str) - + self.stream_output(input_object, {"data": reviewing_result.to_dict(), "type": "reviewing"}) if reviewing_result.get_data('score') and reviewing_result.get_data('score') >= eval_threshold: loopResults.append({ "planning_result": planning_result, diff --git a/agentuniverse/agent/plan/planner/planner.py b/agentuniverse/agent/plan/planner/planner.py index c30fd311..2f965da4 100644 --- a/agentuniverse/agent/plan/planner/planner.py +++ b/agentuniverse/agent/plan/planner/planner.py @@ -8,6 +8,7 @@ from abc import abstractmethod import copy import logging +from queue import Queue from typing import Optional, List from agentuniverse.agent.action.knowledge.knowledge import Knowledge @@ -159,3 +160,16 @@ def initialize_by_component_configer(self, component_configer: PlannerConfiger) self.input_key = component_configer.input_key or self.input_key self.output_key = component_configer.output_key or self.output_key return self + + @staticmethod + def stream_output(input_object: InputObject, data: dict): + """Stream output. + + Args: + input_object (InputObject): Agent input object. + data (dict): The data to be streamed. + """ + output_stream:Queue = input_object.get_data('output_stream', None) + if output_stream is None: + return + output_stream.put_nowait(data) diff --git a/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py b/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py index 4edb1f6a..c731038e 100644 --- a/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py +++ b/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py @@ -5,10 +5,11 @@ # @Email : lc299034@antgroup.com # @FileName: rag_planner.py """Rag planner module.""" -import asyncio +from typing import Any from langchain_core.chat_history import InMemoryChatMessageHistory from langchain_core.output_parsers import StrOutputParser +from langchain_core.runnables import RunnableSerializable from langchain_core.runnables.history import RunnableWithMessageHistory from agentuniverse.agent.agent_model import AgentModel @@ -55,9 +56,26 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, history_messages_key="chat_history", input_messages_key=self.input_key, ) | StrOutputParser() - res = asyncio.run( - chain_with_history.ainvoke(input=planner_input, config={"configurable": {"session_id": "unused"}})) - return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} + + if not input_object.get_data('output_stream'): + res = chain_with_history.invoke(input=planner_input, config={"configurable": {"session_id": "unused"}}) + return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} + else: + return self.stream(agent_model, chain_with_history, planner_input, chat_history, input_object) + + def stream(self, agent_model: AgentModel, chain: RunnableSerializable[Any, str], planner_input: dict, chat_history, + input_object: InputObject): + result = [] + for token in chain.stream(input=planner_input, config={"configurable": {"session_id": "unused"}}): + self.stream_output(input_object, { + 'type': 'token', + 'data': { + 'token': token, + 'agent_info': agent_model.info + } + }) + result.append(token) + return {**planner_input, self.output_key: ''.join(result), 'chat_history': generate_memories(chat_history)} def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> ChatPrompt: """Prompt module processing. From f03c756a915315b4a97f81aa5476b6713775d973 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 3 Jul 2024 17:06:08 +0800 Subject: [PATCH 087/118] fix proxy --- agentuniverse/llm/openai_style_llm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agentuniverse/llm/openai_style_llm.py b/agentuniverse/llm/openai_style_llm.py index 0981e47a..463b8efb 100644 --- a/agentuniverse/llm/openai_style_llm.py +++ b/agentuniverse/llm/openai_style_llm.py @@ -49,7 +49,7 @@ def _new_client(self): base_url=self.api_base, timeout=self.request_timeout, max_retries=self.max_retries, - http_client=httpx.Client(proxy=self.openai_proxy) if self.proxy else None, + http_client=httpx.Client(proxy=self.proxy) if self.proxy else None, **(self.client_args or {}), ) From 7b9b6f26868d47c43e48ed9d4dbaa212e14fdeed Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 3 Jul 2024 17:52:06 +0800 Subject: [PATCH 088/118] feat: add the data agent to build datasets and evaluate the quality of results. --- .../core/agent/data_agent_case/__init__.py | 7 + .../core/agent/data_agent_case/data_agent.py | 128 +++++++ .../agent/data_agent_case/data_agent.yaml | 15 + .../core/agent/data_agent_case/eval_agent.py | 350 ++++++++++++++++++ .../agent/data_agent_case/eval_agent.yaml | 14 + .../app/core/prompt/eval_agent_cn.yaml | 122 ++++++ .../app/examples/data/query.jsonl | 3 + .../app/examples/data_agent.py | 21 ++ .../app/test/test_data_agent.py | 27 ++ sample_standard_app/app/util/__init__.py | 7 + .../app/util/jsonl_file_utils.py | 91 +++++ .../app/util/txt_file_utils.py | 50 +++ 12 files changed, 835 insertions(+) create mode 100644 sample_standard_app/app/core/agent/data_agent_case/__init__.py create mode 100644 sample_standard_app/app/core/agent/data_agent_case/data_agent.py create mode 100644 sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml create mode 100644 sample_standard_app/app/core/agent/data_agent_case/eval_agent.py create mode 100644 sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml create mode 100644 sample_standard_app/app/core/prompt/eval_agent_cn.yaml create mode 100644 sample_standard_app/app/examples/data/query.jsonl create mode 100644 sample_standard_app/app/examples/data_agent.py create mode 100644 sample_standard_app/app/test/test_data_agent.py create mode 100644 sample_standard_app/app/util/__init__.py create mode 100644 sample_standard_app/app/util/jsonl_file_utils.py create mode 100644 sample_standard_app/app/util/txt_file_utils.py diff --git a/sample_standard_app/app/core/agent/data_agent_case/__init__.py b/sample_standard_app/app/core/agent/data_agent_case/__init__.py new file mode 100644 index 00000000..cc95020c --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/3 16:57 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/sample_standard_app/app/core/agent/data_agent_case/data_agent.py b/sample_standard_app/app/core/agent/data_agent_case/data_agent.py new file mode 100644 index 00000000..07195312 --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/data_agent.py @@ -0,0 +1,128 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/1 15:50 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: data_agent.py +from typing import Tuple, List + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.output_object import OutputObject +from agentuniverse.base.util.logging.logging_util import LOGGER +from sample_standard_app.app.util.jsonl_file_utils import JsonFileWriter, JsonFileReader + +from sample_standard_app.app.util.txt_file_utils import TxtFileReader + + +class DataAgent(Agent): + """Data Agent class.""" + + def input_keys(self) -> list[str]: + """Return the input keys of the Agent.""" + return ['dataset_path'] + + def output_keys(self) -> list[str]: + """Return the output keys of the Agent.""" + return ['prompt_answer_list', 'eval_report_list'] + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + """Agent parameter parsing. + + Args: + input_object (InputObject): input parameters passed by the user. + agent_input (dict): agent input preparsed by the agent. + Returns: + dict: agent input parsed from `input_object` by the user. + """ + agent_input['dataset_path'] = input_object.get_data('dataset_path') + agent_input['turn'] = input_object.get_data('turn', 1) + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + """Planner result parser. + + Args: + planner_result(dict): Planner result + Returns: + dict: Agent result object. + """ + return planner_result + + def execute(self, input_object: InputObject, agent_input: dict): + """Execute agent instance. + + Args: + input_object (InputObject): input parameters passed by the user. + agent_input (dict): agent input parsed from `input_object` by the user. + """ + # step1: collect q&a dataset from the candidate agent which needs to be evaluated. + prompt_answer_list = self.collect_dataset(input_object, agent_input) + input_object.add_data('prompt_answer_list', prompt_answer_list) + + LOGGER.info("-------------------------------------------") + LOGGER.info("End: collect q&a dataset from the candidate agent done.") + LOGGER.info("-------------------------------------------") + + # step2: write the q&a dataset to json file. + for i in range(len(prompt_answer_list)): + one_turn_prompt_answer_list = prompt_answer_list[i] + json_writer = JsonFileWriter(f'data_agent_turn_{i + 1}_dataset') + json_writer.write_json_prompt_answer_list(one_turn_prompt_answer_list) + LOGGER.info(f"Progress: write the q&a dataset to local jsonl files.") + + # step3: evaluate q&a datasets generated by the candidate agent and generate evaluation report. + eval_report_list = self.eval_agent(input_object) + return {'prompt_answer_list': prompt_answer_list, 'eval_report_list': eval_report_list} + + def collect_dataset(self, input_object: InputObject, agent_input: dict) -> List[List[Tuple[str, str]]]: + """Collect q&a dataset from the candidate agent which needs to be evaluated.""" + + candidate_agent_name = self.agent_model.plan.get('planner', {}).get('candidate', '') + # get the candidate agent which needs to be evaluated + candidate_agent: Agent = AgentManager().get_instance_obj(candidate_agent_name) + if not candidate_agent: + raise ValueError('The agent instance corresponding to `candidate` parameter is empty') + + # init jsonl file reader + jsonl_file_reader = JsonFileReader(agent_input.get('dataset_path')) + # read query list + query_list = jsonl_file_reader.read_json_obj_list() + if not query_list: + raise ValueError('query list information read from dataset_path is empty') + + prompt_answer_list = [] + for i in range(agent_input.get('turn')): + LOGGER.info("-------------------------------------------") + LOGGER.info(f"Start: collect q&a dataset from the candidate agent `{candidate_agent_name}`, turn {i + 1}.") + one_turn_prompt_answer_list = [] + # single turn query and answer processing. + for j in range(len(query_list)): + query_dict: dict = query_list[j] + if query_dict: + # init the input and output key in agent + first_input_key = candidate_agent.input_keys()[0] + first_output_key = candidate_agent.output_keys()[0] + # run the target agent + output_object: OutputObject = candidate_agent.run(**query_dict) + # note: the first index of input_keys and output_keys is identified as the prompt and answer. + one_turn_prompt_answer_list.append( + (query_dict.get(first_input_key, ''), output_object.get_data(first_output_key))) + LOGGER.info(f"Progress: the turn {i + 1} query line {j + 1} has generated the answer " + f"successfully.") + LOGGER.info(f"End: the turn {i + 1} has generated the answer successfully.") + # collect q&a dataset + prompt_answer_list.append(one_turn_prompt_answer_list) + return prompt_answer_list + + def eval_agent(self, input_object: InputObject): + """Evaluate q&a datasets generated by the candidate agent and generate evaluation report.""" + + planner = self.agent_model.plan.get('planner', {}) + eval_agent: Agent = AgentManager().get_instance_obj(planner.get('evaluator')) + if eval_agent is None: + raise ValueError('The agent instance corresponding to `evaluator` parameter is empty') + output: OutputObject = eval_agent.run(**input_object.to_dict()) + return output.get_data('eval_report_json_list', []) diff --git a/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml b/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml new file mode 100644 index 00000000..a67692c9 --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml @@ -0,0 +1,15 @@ +info: + name: 'data_agent' + description: 'data agent' +profile: + llm_model: + name: 'demo_llm' + model_name: 'gpt-4o' +plan: + planner: + candidate: 'demo_rag_agent' + evaluator: 'eval_agent' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.data_agent_case.data_agent' + class: 'DataAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/data_agent_case/eval_agent.py b/sample_standard_app/app/core/agent/data_agent_case/eval_agent.py new file mode 100644 index 00000000..ed93018d --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/eval_agent.py @@ -0,0 +1,350 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/1 17:00 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: eval_agent.py +from typing import Tuple, List + +import pandas as pd +from langchain_core.output_parsers import StrOutputParser +from langchain_core.utils.json import parse_json_markdown +from openpyxl.reader.excel import load_workbook + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.llm.llm import LLM +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.prompt.chat_prompt import ChatPrompt +from agentuniverse.prompt.prompt import Prompt +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse.prompt.prompt_model import AgentPromptModel +from sample_standard_app.app.util.jsonl_file_utils import JsonFileWriter + + +class EvalAgent(Agent): + """Evaluation Agent class.""" + + def input_keys(self) -> list[str]: + """Return the input keys of the Agent.""" + return ['prompt_answer_list'] + + def output_keys(self) -> list[str]: + """Return the output keys of the Agent.""" + return ['eval_report_json_list', 'eval_dims_json_list'] + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + """Agent parameter parsing. + + Args: + input_object (InputObject): input parameters passed by the user. + agent_input (dict): agent input preparsed by the agent. + Returns: + dict: agent input parsed from `input_object` by the user. + """ + agent_input['prompt_answer_list'] = input_object.get_data('prompt_answer_list') + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + """Planner result parser. + + Args: + planner_result(dict): Planner result + Returns: + dict: Agent result object. + """ + return planner_result + + def execute(self, input_object: InputObject, agent_input: dict): + """Execute agent instance. + + Args: + input_object (InputObject): input parameters passed by the user. + agent_input (dict): agent input parsed from `input_object` by the user. + """ + LOGGER.info("-------------------------------------------") + LOGGER.info(f"Start: use the evaluator agent to evaluate q&a dataset.") + LOGGER.info("-------------------------------------------") + + prompt_answer_list: List[List[Tuple[str, str]]] = input_object.get_data('prompt_answer_list') + max_eval_lines = self.agent_model.profile.get('max_eval_lines', 100) + if len(prompt_answer_list) == 0: + raise ValueError('the `prompt_answer_list` is empty') + + # step1: evaluate the dataset in multiple dimensions and give specific scores. + eval_dims_json_list: List[List[dict]] = self.eval(prompt_answer_list, max_eval_lines) + LOGGER.info(f"End: evaluate the dataset in multiple dimensions done.") + + LOGGER.info("-------------------------------------------") + # step2: write the eval results to excel file. + self.generate_eval_results_excel(prompt_answer_list, eval_dims_json_list) + + # step3: generate eval report + eval_report_json_list = self.generate_eval_report(eval_dims_json_list) + LOGGER.info(f"End: generate evaluation report done.") + LOGGER.info("-------------------------------------------") + return {'eval_report_json_list': eval_report_json_list, 'eval_dims_json_list': eval_dims_json_list} + + def eval(self, prompt_answer_list: List[List[Tuple[str, str]]], max_eval_lines: int) -> List[List[dict]]: + """Evaluate the dataset in multiple dimensions and give specific scores. + + Args: + prompt_answer_list (List[List[Tuple[str, str]]]): the list of q&a pair from multiple turns, + the type of single turn is List[Tuple[str, str]]. + + max_eval_lines (int): the maximum number of lines to evaluate. + + Returns: + res (List[List[dict]]): the list of eval results from multiple turns, + the type of single turn is List[dict]. + """ + + eval_dims_json_list = [] + # the q&a dataset from multiple turns. + for i in range(len(prompt_answer_list)): + line_num = 0 + one_turn_prompt_answer_list = prompt_answer_list[i] + one_turn_eval_dims_json_list = [] + + # single turn prompt answer list. + for j in range(len(one_turn_prompt_answer_list)): + + prompt = one_turn_prompt_answer_list[j][0] + answer = one_turn_prompt_answer_list[j][1] + if prompt is None or answer is None: + break + + line_num += 1 + if line_num > max_eval_lines: + break + + if len(prompt) > 2000: + prompt = prompt[0:2000] + if len(answer) > 5000: + answer = answer[0:5000] + + version_prompt: ChatPrompt = self.handle_prompt() + + llm: LLM = self.handle_llm() + + chain = version_prompt.as_langchain() | llm.as_langchain() | StrOutputParser() + res = chain.invoke(input={'prompt': prompt, 'answer': answer}) + + dim_score_json = {'line': line_num} + dimensions = [] + avg_score = 0.0 + + try: + if res is not None: + data = parse_json_markdown(res) + dimensions = data['dimensions'] + # calculate avg score from multiple dimensions. + avg_score = sum(data['score'] for data in dimensions) + except Exception as e: + LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>>{e}:{res[0]}') + continue + if len(dimensions) > 0: + avg_score = avg_score / len(dimensions) + dim_score_json['avg_score'] = avg_score + dim_score_json['dimensions'] = dimensions + LOGGER.info(f"Progress: the turn {i + 1} query line {line_num} has been evaluated successfully.") + + # single line evaluation from multiple dimensions. + one_turn_eval_dims_json_list.append(dim_score_json) + + eval_dims_json_list.append(one_turn_eval_dims_json_list) + return eval_dims_json_list + + def handle_prompt(self) -> ChatPrompt: + """Prompt module processing.""" + profile: dict = self.agent_model.profile + + profile_prompt_model: AgentPromptModel = AgentPromptModel(introduction=profile.get('introduction'), + target=profile.get('target'), + instruction=profile.get('instruction')) + + # get the prompt by the prompt version + prompt_version: str = profile.get('prompt_version') + version_prompt: Prompt = PromptManager().get_instance_obj(prompt_version) + + if version_prompt is None and not profile_prompt_model: + raise Exception("Either the `prompt_version` or `introduction & target & instruction`" + " in agent profile configuration should be provided.") + if version_prompt: + version_prompt_model: AgentPromptModel = AgentPromptModel( + introduction=getattr(version_prompt, 'introduction', ''), + target=getattr(version_prompt, 'target', ''), + instruction=getattr(version_prompt, 'instruction', '')) + profile_prompt_model = profile_prompt_model + version_prompt_model + + return ChatPrompt().build_prompt(profile_prompt_model, ['introduction', 'target', 'instruction']) + + def handle_llm(self) -> LLM: + """Language model module processing. + + Returns: + LLM: The language model. + """ + llm_name = self.agent_model.profile.get('llm_model').get('name') + llm: LLM = LLMManager().get_instance_obj(component_instance_name=llm_name, new_instance=True) + llm.set_by_agent_model(**self.agent_model.profile.get('llm_model')) + return llm + + def generate_eval_report(self, eval_dims_json_list: List[List[dict]]): + """Integrate multidimensional evaluation scores and generate evaluation report + + Args: + eval_dims_json_list (List[List[dict]]) : The list of evaluation results from multiple turns, + the type of single turn is List[dict]. + + Returns: + eval_report_json_list (List[dict]): The list of evaluation report. + """ + LOGGER.info(f"Start: generate evaluation report.") + + eval_report_json_list = [] + for i in range(0, len(eval_dims_json_list)): + line_num = 0 + total_avg_score = 0.0 + dim_avg_score = {} + # single turn eval dim json list. + one_turn_eval_dims_json_list = eval_dims_json_list[i] + for j in range(0, len(one_turn_eval_dims_json_list)): + one_row_eval_dims_json = one_turn_eval_dims_json_list[j] + line_num += 1 + + total_avg_score += one_row_eval_dims_json['avg_score'] + dimensions = one_row_eval_dims_json['dimensions'] + for k in range(0, len(dimensions)): + name = dimensions[k]['name'] + score = dimensions[k]['score'] + if name in dim_avg_score: + dim_avg_score[name] += float(score) + else: + dim_avg_score[name] = float(score) + + # calculate single turn total avg score and dim avg score. + for key in dim_avg_score: + dim_total_score = dim_avg_score[key] + dim_avg_score[key] = dim_total_score / line_num + total_avg_score = total_avg_score / line_num + + # generate one turn report. + one_turn_report = { + 'line_name': f"query turn {i + 1}", + 'total_avg_score': total_avg_score, + 'dim_avg_score': dim_avg_score + } + LOGGER.info(f"Progress: turn {i + 1} evaluation report has generated successfully.") + eval_report_json_list.append(one_turn_report) + + if len(eval_report_json_list) > 1: + self.generate_total_eval_report(eval_report_json_list) + LOGGER.info(f"Progress: total evaluation report has generated successfully.") + + # generate excel report + self.generate_eval_report_excel(eval_report_json_list) + return eval_report_json_list + + @staticmethod + def generate_total_eval_report(eval_report_json_list: List[dict]): + """Generate total evaluation report, integrate and calculate the total evaluation score for each turn. + + Args: + eval_report_json_list (List[dict]): The list of evaluation report. + """ + + if len(eval_report_json_list) > 1: + total_avg_score = 0.0 + total_dim_avg_score = {} + for i in range(len(eval_report_json_list)): + total_avg_score += eval_report_json_list[i]['total_avg_score'] + dim_avg_score: dict = eval_report_json_list[i]['dim_avg_score'] + for key in dim_avg_score: + if key in total_dim_avg_score: + total_dim_avg_score[key] += float(dim_avg_score[key]) + else: + total_dim_avg_score[key] = float(dim_avg_score[key]) + total_avg_score = total_avg_score / len(eval_report_json_list) + for key in total_dim_avg_score: + total_dim_avg_score[key] = total_dim_avg_score[key] / len(eval_report_json_list) + report_line_obj = { + 'line_name': 'total avg score', + 'total_avg_score': total_avg_score, + 'dim_avg_score': total_dim_avg_score + } + eval_report_json_list.append(report_line_obj) + + @staticmethod + def generate_eval_results_excel(prompt_answer_list: List[List[Tuple[str, str]]], + eval_dims_json_list: List[List[dict]]): + """Generate evaluation results in excel format.""" + + rows = [] + columns: List[str] = ['Line Number', 'Avg Score', 'Prompt', 'Answer'] + if len(eval_dims_json_list) > 0 and len(eval_dims_json_list[0]) > 0: + one_row_eval_result = eval_dims_json_list[0][0] + dims = one_row_eval_result.get('dimensions', []) + # wrap excel columns + for dim in dims: + columns.append(dim['name'] + ' Score') + columns.append(dim['name'] + ' Negative Point') + + for i in range(len(eval_dims_json_list)): + one_turn_eval_results = eval_dims_json_list[i] + one_turn_prompt_answers = prompt_answer_list[i] + + # write for each turn + for j in range(len(one_turn_eval_results)): + one_row_eval_result = one_turn_eval_results[j] + one_row_prompt_answer = one_turn_prompt_answers[j] + # wrap rows. + line_number = one_row_eval_result['line'] + avg_score = one_row_eval_result['avg_score'] + dims = one_row_eval_result['dimensions'] + + row = [line_number, avg_score, one_row_prompt_answer[0], one_row_prompt_answer[1]] + for dim in dims: + row.append(dim['score']) + row.append(dim['negative point']) + rows.append(row) + + df = pd.DataFrame(rows, columns=columns) + + df.to_excel(f"./data/eval_results_turn_{i + 1}.xlsx", index=False, engine='openpyxl') + LOGGER.info(f"Progress: generate evaluation detailed results in excel format successfully.") + LOGGER.info("-------------------------------------------") + + @staticmethod + def generate_eval_report_excel(eval_report_json_list: List[dict]): + """Generate excel eval report.""" + + rows = [] + columns: List[str] = ['Line Name', 'Total Avg Score'] + dim_avg_scores = eval_report_json_list[0]['dim_avg_score'] + for dim, score in dim_avg_scores.items(): + columns.append(dim + ' Avg Score') + for item in eval_report_json_list: + line_name = item['line_name'] + total_avg_score = item['total_avg_score'] + dim_avg_scores = item['dim_avg_score'] + + row = [line_name, total_avg_score] + for dim, score in dim_avg_scores.items(): + row.append(score) + rows.append(row) + + df = pd.DataFrame(rows, columns=columns) + + df.to_excel("./data/eval_report.xlsx", index=False, engine='openpyxl') + + # tweak column width + workbook = load_workbook('./data/eval_report.xlsx') + worksheet = workbook.active + column_widths = [25, 20] + [20] * (len(columns) - 2) + for i, column_width in enumerate(column_widths, 1): + worksheet.column_dimensions[chr(64 + i)].width = column_width + workbook.save('./data/eval_report.xlsx') + LOGGER.info(f"Progress: generate evaluation excel file successfully.") diff --git a/sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml b/sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml new file mode 100644 index 00000000..6c187edb --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml @@ -0,0 +1,14 @@ +info: + name: 'eval_agent' + description: 'eval agent' +profile: + prompt_version: eval_agent.cn + max_eval_lines: 10 + llm_model: + name: 'demo_llm' + model_name: 'gpt-4o' + temperature: 0.1 +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.data_agent_case.eval_agent' + class: 'EvalAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/prompt/eval_agent_cn.yaml b/sample_standard_app/app/core/prompt/eval_agent_cn.yaml new file mode 100644 index 00000000..869bd61b --- /dev/null +++ b/sample_standard_app/app/core/prompt/eval_agent_cn.yaml @@ -0,0 +1,122 @@ +introduction: ｜ + 你是一个极致严谨的金融专家，需要帮助我按照如下要求在各维度评估prompt（用户输入）和answer（模型输出）的分数。 + + 每个维度满分为5分，不符合准入条件直接判0分，符合门槛值加0-3分，如果再符合加分项要求，在门槛值的分数基础上再加0-2分。 + 打分要求，请精确到小数点后1位： + 1.不符合准入条件（0.0分） + 2.门槛值（0.0～3.0分） + 3.加分项（0.0～2.0分） + + 注意：严格打分，不能轻松给出高分 +instruction: | + -------------------------------- + prompt: + {prompt} + + -------------------------------- + answer: + {answer} + + -------------------------------- + 质量打分共5个维度，每个维度包含了不符合准入条件的要求（0.0分）/门槛值的要求（0.0～3.0分）/加分项（0.0～2.0分） + + 质量打分维度描述如下: + + dimension name: 相关性 + dimension requirement: + 1.不符合准入条件 + 答非所问，不相关 + + 2.门槛值 + 能够知道到用户在问什么，并且所答即所问，包括： + 【有明确意图时】 + 识别出用户的问题并对问题有回复，且回复的内容有相关性 + 有主意图的时候，回复的内容要与主意图有相关 + 【无明确意图时】 + 遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 + + 3.加分项 + 能够get到用户在问什么后： + a、对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应 + b、回应的内容整体相关性都很高 + c、回复的问题全面且易懂 + + -------------------------------- + dimension name: 事实性 + dimension requirement: + 1.不符合准入条件 + 数据错误，不符合事实 + + 2.门槛值 + 个别数据不准确，但不影响整体事实性 + + 3.加分项 + 解读内容中所有引用数据真实可信，数据来源官方可信 + -------------------------------- + + dimension name: 合理性 + dimension requirement: + 1.不符合准入条件 + 出现专业错误 + （如经济复苏利好股市利空债市、供不应求带来价格上涨、经济下行压力加剧通常会倒逼宏观政策加码等） + + 2.门槛值 + 符合业内公认的金融逻辑，前后结论不要自相矛盾，不出现明显的专业错误 + + 3.加分项 + 通篇解读要一以贯之、逻辑自洽，且逻辑推演是有逐层递进的（后文分析以前文结论为依据） + + -------------------------------- + dimension name: 时效性 + dimension requirement: + 1.不符合准入条件 + 数据陈旧过时，不符合现状 + + 2.门槛值 + 次新，符合当下现状 + + 3.加分项 + 引用官方发布的最新数据，包含清晰的发布日期 + + -------------------------------- + dimension name: 整体性 + dimension requirement: + 1.不符合准入条件 + 满足以下任意一点即为0分： + a、逻辑不通顺，或存在语病，或表达冗余内容超长（整体文本长度>XX字） + b、论点&总结重复/论点&论点重复：任意两条论点存在80%以上的重复度 + c、倒装、错别字、语句不通顺、逻辑问题等 + d、存在特殊符号 + + 2.门槛值 + a、语句流畅、表达可阅读性强 + b、表达直白（不反复说车轱辘话） + c、论点论据表达无重复 + + 3.加分项 + a、答案对用户query有明确回应表达 + b、语言简洁精炼，逻辑清晰 + + -------------------------------- + 输出格式要求： + 1.根据具体打分维度要求，同时评价prompt和answer内容，进行打分。 + 2. 一定要给出negative point，结合prompt和answer，给出具体的扣分原因和具体的优化建议。 + + 请严格按照质量打分维度要求打分，不能轻松给出高分，请确保输出只包含如下的json格式字段，且每个字段不能为空: + {{ + "dimensions": [ + {{ + "name":质量打分维度名称, + "score":分数, + "negative point":扣分的原因和给出的优化建议 + }}, + {{ + "name":质量打分维度, + "score":分数, + "negative point":扣分的原因和给出的优化建议 + }} + ] + }} +metadata: + type: 'PROMPT' + version: 'eval_agent.cn' diff --git a/sample_standard_app/app/examples/data/query.jsonl b/sample_standard_app/app/examples/data/query.jsonl new file mode 100644 index 00000000..25db3779 --- /dev/null +++ b/sample_standard_app/app/examples/data/query.jsonl @@ -0,0 +1,3 @@ +{"input": "我的股票为什么会跌？"} +{"input": "英伟达股票大涨的原因是什么？"} +{"input": "贵州茅台为什么不行了？"} \ No newline at end of file diff --git a/sample_standard_app/app/examples/data_agent.py b/sample_standard_app/app/examples/data_agent.py new file mode 100644 index 00000000..7512a8be --- /dev/null +++ b/sample_standard_app/app/examples/data_agent.py @@ -0,0 +1,21 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/2 16:58 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: data_agent.py +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager + +AgentUniverse().start(config_path='../../config/config.toml') + + +def data_process(dataset_path: str, turn: int, **kwargs): + instance: Agent = AgentManager().get_instance_obj('data_agent') + instance.run(dataset_path=dataset_path, turn=turn, **kwargs) + + +if __name__ == '__main__': + data_process(dataset_path="./data/query.jsonl", turn=1) diff --git a/sample_standard_app/app/test/test_data_agent.py b/sample_standard_app/app/test/test_data_agent.py new file mode 100644 index 00000000..f4b0d985 --- /dev/null +++ b/sample_standard_app/app/test/test_data_agent.py @@ -0,0 +1,27 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/1 16:48 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: test_data_agent.py +import unittest + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager +from agentuniverse.base.agentuniverse import AgentUniverse + + +class DataAgentTest(unittest.TestCase): + """Test cases for the data agent""" + + def setUp(self) -> None: + AgentUniverse().start(config_path='../../config/config.toml') + + def test_data_agent(self): + instance: Agent = AgentManager().get_instance_obj('data_agent') + instance.run(dataset_path='', turn=2) + + +if __name__ == '__main__': + unittest.main() diff --git a/sample_standard_app/app/util/__init__.py b/sample_standard_app/app/util/__init__.py new file mode 100644 index 00000000..a5be5aa6 --- /dev/null +++ b/sample_standard_app/app/util/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/1 16:05 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: __init__.py.py diff --git a/sample_standard_app/app/util/jsonl_file_utils.py b/sample_standard_app/app/util/jsonl_file_utils.py new file mode 100644 index 00000000..b01f78dd --- /dev/null +++ b/sample_standard_app/app/util/jsonl_file_utils.py @@ -0,0 +1,91 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/1 21:09 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: jsonl_file_utils.py +import json +import os +import sys + +from agentuniverse.base.util.logging.logging_util import LOGGER + +DATA_DIR = './data/' + +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +class JsonFileOps(object): + def __init__(self): + return + + @classmethod + def is_file_exist(cls, file_path): + file_name, ext = os.path.splitext(file_path) + if ext.lower() != '.jsonl': + raise Exception('Unsupported file extension') + return os.path.exists(file_path) + + +class JsonFileReader(object): + def __init__(self, file_path: str): + self.file_handler = None + self.file_name = file_path + if JsonFileOps.is_file_exist(file_path): + self.file_handler = open(file_path, 'r', encoding='utf-8') + + def read_json_obj(self): + if not self.file_handler: + raise Exception(f"None json file to read: {self.file_name}") + json_line = self.file_handler.readline() + if json_line: + try: + json_obj = json.loads(json_line.strip()) + return json_obj + except Exception as e: + LOGGER.warn(f"except[read_json_line]>>>{e}:{json_line}") + return json.loads('{}') + else: + return None + + def read_json_obj_list(self): + obj_list = [] + while True: + obj = self.read_json_obj() + if obj is None: + break + obj_list.append(obj) + return obj_list + + +class JsonFileWriter(object): + def __init__(self, output_file_name: str, extension='jsonl'): + self.outfile_path = DATA_DIR + output_file_name + '.' + extension + directory = os.path.dirname(self.outfile_path) + if not os.path.exists(directory): + os.makedirs(directory) + self.outfile_handler = open(self.outfile_path, 'w', encoding='utf-8') + + def write_json_obj(self, json_obj: dict): + try: + # confirm that it's a json string and then write. + json_line = json.dumps(json_obj, ensure_ascii=False) + self.outfile_handler.write(json_line.strip() + '\n') + self.outfile_handler.flush() + except Exception as e: + LOGGER.warn(f"except[write_json_obj]>>>{e}:{json_obj}") + return + + def write_json_obj_list(self, json_obj_list: list): + for i in range(0, len(json_obj_list)): + self.write_json_obj(json_obj_list[i]) + return + + def write_json_prompt_answer(self, prompt: str, answer: str): + json_obj = {"prompt": prompt, "answer": answer} + self.write_json_obj(json_obj) + + def write_json_prompt_answer_list(self, prompt_answer_list: list): + for i in range(0, len(prompt_answer_list)): + self.write_json_prompt_answer(prompt_answer_list[i][0], prompt_answer_list[i][1]) diff --git a/sample_standard_app/app/util/txt_file_utils.py b/sample_standard_app/app/util/txt_file_utils.py new file mode 100644 index 00000000..bd8f1ec1 --- /dev/null +++ b/sample_standard_app/app/util/txt_file_utils.py @@ -0,0 +1,50 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/1 16:05 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: txt_file_utils.py +import os +import sys + +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +class TxtFileOps(object): + def __init__(self): + return + + @classmethod + def is_file_exist(cls, file_path): + file_name, ext = os.path.splitext(file_path) + if ext.lower() != '.txt': + raise Exception('Unsupported file extension') + return os.path.exists(file_path) + + +class TxtFileReader(object): + + def __init__(self, file_path: str): + self.file_handler = None + self.file_name = file_path + if TxtFileOps.is_file_exist(file_path): + self.file_handler = open(file_path, 'r', encoding='utf-8') + + def read_txt_obj(self): + if not self.file_handler: + raise Exception(f"No txt file to read: {self.file_name}") + txt_line = self.file_handler.readline() + if txt_line: + return txt_line.strip() + else: + return None + + def read_txt_obj_list(self): + obj_list = [] + while True: + obj = self.read_txt_obj() + if obj is None: + break + obj_list.append(obj) + return obj_list From adf428eb88ff4101de3777b1c06c76a613a023eb Mon Sep 17 00:00:00 2001 From: yueyang Date: Thu, 4 Jul 2024 11:33:41 +0800 Subject: [PATCH 089/118] init: peer dataset --- dataset/peer_financial_training_dataset/README.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 dataset/peer_financial_training_dataset/README.md diff --git a/dataset/peer_financial_training_dataset/README.md b/dataset/peer_financial_training_dataset/README.md new file mode 100644 index 00000000..8f7e7218 --- /dev/null +++ b/dataset/peer_financial_training_dataset/README.md @@ -0,0 +1,15 @@ +# Data Description +**************************************** +## PEER Financial Training Dataset +We open source 100 common financial questions and a training dataset for use within the PEER framework, containing 100 sets of inputs and outputs of the Planning Agent and the Expressing Agent. The dataset is applicable to agent training, pre-training, and supervised fine-tuning in various financial analysis scenarios. + +Due to compliance issues, the relevant data of the Executing agent is still in the approval process, please wait patiently. + +### 🌟 FinQA +We have collected 100 professional questions from our business scenarios and categorized them into nine distinct categories, including information query, general financial QA, report interpretation, target analysis, strategy advice, major events interpretation, macro analysis, market analysis, policy interpretation. + +### 🌟 planning agent data +Each set of data contains the input and output of the Planning Agent. The input is the original query proposed by the user. The output is the sub-questions generated by the agent, serving as the interpretation framework. + +### 🌟 expressing agent data +Each set of data contains the input and output of the Expressing Agent. The input is the answer to each qub-question given by the Executing Agent through collecting information. The output is the professional answer after synthesis and rewriting. \ No newline at end of file From 1b75a12103b3586a651ea2f4073a491d30e0798e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=89=BA=E9=A2=96=28yiying=29?= Date: Thu, 4 Jul 2024 11:38:37 +0800 Subject: [PATCH 090/118] =?UTF-8?q?PEER=20=E5=BC=80=E6=BA=90agent=E8=AE=AD?= =?UTF-8?q?=E7=BB=83=E6=95=B0=E6=8D=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md | 2 +- .../v20240704/FinQA.jsonl | 100 ++++++++++++++++++ .../v20240704/expressing_agent_data.jsonl | 12 +++ .../v20240704/planning_agent_data.jsonl | 11 ++ 4 files changed, 124 insertions(+), 1 deletion(-) rename dataset/{peer_financial_training_dataset => }/README.md (96%) create mode 100644 dataset/peer_financial_training_dataset/v20240704/FinQA.jsonl create mode 100644 dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl create mode 100644 dataset/peer_financial_training_dataset/v20240704/planning_agent_data.jsonl diff --git a/dataset/peer_financial_training_dataset/README.md b/dataset/README.md similarity index 96% rename from dataset/peer_financial_training_dataset/README.md rename to dataset/README.md index 8f7e7218..56654ab1 100644 --- a/dataset/peer_financial_training_dataset/README.md +++ b/dataset/README.md @@ -12,4 +12,4 @@ We have collected 100 professional questions from our business scenarios and cat Each set of data contains the input and output of the Planning Agent. The input is the original query proposed by the user. The output is the sub-questions generated by the agent, serving as the interpretation framework. ### 🌟 expressing agent data -Each set of data contains the input and output of the Expressing Agent. The input is the answer to each qub-question given by the Executing Agent through collecting information. The output is the professional answer after synthesis and rewriting. \ No newline at end of file +Each set of data contains the input and output of the Expressing Agent. The input is the answer to each qub-question given by the Executing Agent through collecting information. The output is the professional answer after synthesis and rewriting. diff --git a/dataset/peer_financial_training_dataset/v20240704/FinQA.jsonl b/dataset/peer_financial_training_dataset/v20240704/FinQA.jsonl new file mode 100644 index 00000000..117e84bc --- /dev/null +++ b/dataset/peer_financial_training_dataset/v20240704/FinQA.jsonl @@ -0,0 +1,100 @@ +{"text": "Please analyze the recent performance of Xinyetech, Lexin, and 360 DigiTech, and compare the differences among the three companies."} +{"text": "How have the style stocks of the major indices performed in the past month?"} +{"text": "Please interpret the latest domestic Manufacturing PMI data."} +{"text": "How has the semiconductor sector been performing recently?"} +{"text": "What are the latest sales figures for Xiaomi smartphones? Is Xiaomi worth investing in?"} +{"text": "From the perspective of the investment clock, what is the current position of China's economy? What types of assets are suitable for allocation?"} +{"text": "What are the hot events in the electronics industry in the past week? How do they impact investments in the corresponding A-share sectors?"} +{"text": "Will U.S. bond yields remain high? Please provide a detailed analysis."} +{"text": "What factors influence foreign capital inflows?"} +{"text": "What impact will anti-corruption policies in the pharmaceutical industry have in the short and long term?"} +{"text": "How will Meituan and Ctrip benefit from the increase in domestic tourism demand this year?"} +{"text": "What are the views of mainstream domestic and foreign brokerages on A-shares in 2024? What are the potential opportunities and risks for next year?"} +{"text": "What aspects can be considered to predict future price movements when investing in gold?"} +{"text": "What is expansionary fiscal policy?"} +{"text": "How is the pharmaceutical sector performing recently?"} +{"text": "How should young people plan for retirement?"} +{"text": "What impact will the issuance of one trillion yuan in special government bonds in the fourth quarter have on China's local government debt resolution?"} +{"text": "How is the latest quarterly financial report of Apple Inc. generally evaluated by the industry?"} +{"text": "How should we interpret the latest market analysis report by Li Qiang, a researcher at China International Capital Corporation?"} +{"text": "What are the key development highlights in the economic growth report released by the National Development and Reform Commission in the fourth quarter of 2023?"} +{"text": "How should we analyze the latest quarterly report released by Amazon?"} +{"text": "Please provide an analysis and summary of the key points of ByteDance's latest quarterly financial report."} +{"text": "What impact does Tesla's stock price fluctuation have on the electric vehicle sector?"} +{"text": "Please analyze the recent innovation trends of Amazon, Google, and Apple, and conduct a comparative study on their strategies and achievements in technology development and R\\&D investment."} +{"text": "Is investing in Apple Inc. a reasonable decision in the current economic environment?"} +{"text": "Which sector has greater growth potential in the current market environment, the renewable energy industry or the biotechnology industry?"} +{"text": "Essential and service consumption, such as food and medical care, should not drive the industrial chain, especially industrial manufacturing, as much as real estate. How do you view the impact of this consumption structure change on major manufacturing countries?"} +{"text": "How do you view the competitiveness of CATL's power battery technology in the current market?"} +{"text": "Regarding China Universal Asset Management Co., Ltd., which equity funds currently show significant excess returns compared to market performance?"} +{"text": "Between consumer industry stocks and healthcare industry stocks, which sector's current market conditions are more favorable for long-term investment growth?"} +{"text": "If you invest one million yuan in a mixed portfolio of stocks and bonds, will this strategy help achieve personal long-term financial security goals?"} +{"text": "During periods of market volatility, what investment strategies can help investors protect their capital and achieve growth?"} +{"text": "When formulating investment strategies, which industries require the most detailed market analysis?"} +{"text": "Can the adoption of a diversified asset allocation strategy effectively improve the risk-adjusted returns of an investment portfolio?"} +{"text": "I plan to save for retirement and can currently invest three hundred thousand yuan regularly. What investment strategies can you recommend to ensure an annual growth rate of at least 6\\%?"} +{"text": "What core indicators or factors should be considered when formulating investment strategies to evaluate the performance of potential asset portfolios?"} +{"text": "How to develop a sound financial plan to achieve long-term capital appreciation and risk control?"} +{"text": "Under what market conditions can a diversified investment portfolio strategy improve investment returns?"} +{"text": "Considering the economic data since 2024, which fields show high profitability and are suitable for strategic long-term investment analysis?"} +{"text": "What are the core principles of long-term investment strategies?"} +{"text": "As a strategy consultant dedicated to achieving long-term financial growth, how should we adjust our investment portfolio to optimize risk management in the face of changing market trends?"} +{"text": "What recent monetary policy adjustments have been made by the central bank?"} +{"text": "In April 2024, which industries did the government invest in? How can we analyze the impact of government investment on industries?"} +{"text": "What impact has the current economic environment had on the global banking industry, and are major banking institutions facing severe financial challenges?"} +{"text": "What important decisions or statements regarding climate change and sustainable energy policies were made at the recent G20 summit?"} +{"text": "Why does the central bank implement interest rate cuts? Can such measures effectively stimulate economic growth and prevent a recession?"} +{"text": "Is it a suitable decision to invest in Microsoft at present?"} +{"text": "What major tax adjustment measures have the government recently announced, and what are the possible long-term impacts on corporate investment and the market economy?"} +{"text": "In the latest fintech field, which companies are investing in artificial intelligence technology development? What are some notable AI innovation examples?"} +{"text": "What are the basic principles of monetary policy? What are the differences and connections with modern monetary theory?"} +{"text": "What are the core differences between Alipay's Yu'ebao and WeChat's Licaitong in terms of investment returns and asset management functions?"} +{"text": "How does the capital adequacy ratio level of the Bank of China reflect its overall financial health?"} +{"text": "What is the impact of expansionary monetary policy on capital markets?"} +{"text": "What are the macroeconomic forecasts for global economic growth in 2024, and which key economic indicators and policy decisions will influence this growth trend?"} +{"text": "According to macroeconomic cycle theory, what stage of the cycle is the global economy currently in? How should central banks adjust their monetary policy for this stage?"} +{"text": "Investigate the trend of GDP growth rate throughout 2023."} +{"text": "Please analyze the recently released domestic Manufacturing PMI data, highlighting key economic trends and potential influencing factors."} +{"text": "Please analyze the main features of the latest employment report regarding the unemployment rate and job growth, and their potential impact on the economy."} +{"text": "How to assess the prospects for global economic growth in 2024 and its impact on international markets?"} +{"text": "Recent fluctuations in international oil prices have been significant. Please analyze the reasons behind this trend."} +{"text": "How to assess the stability of the global supply chain in 2024 and its potential impact on international trade?"} +{"text": "Analyze the historical patterns of global crude oil price fluctuations and predict their potential impact on the energy stock market under different geopolitical scenarios in the future."} +{"text": "After the government implements expansionary monetary policy, what changes might occur in overall economic growth and the job market?"} +{"text": "What is the forecast for China's GDP growth rate this year?"} +{"text": "What are the views of mainstream domestic and foreign brokerages on the opportunities and risks of domestic bonds in 2024?"} +{"text": "What are the views of mainstream domestic and foreign brokerages on the opportunities and risks of A-shares in 2024?"} +{"text": "What are the views of mainstream domestic and foreign brokerages on the opportunities and risks of gold in 2024?"} +{"text": "What are the views of mainstream domestic and foreign brokerages on the opportunities and risks of U.S. stocks in 2024?"} +{"text": "What are the views of mainstream domestic and foreign brokerages on the opportunities and risks of Hong Kong stocks in 2024?"} +{"text": "How is the price-to-earnings ratio level distributed among the major sectors in the CSI All Share industry classification?"} +{"text": "When analyzing market trends in 2023, what key factors led to the difference between early-year forecasts for global market conditions and actual performance?"} +{"text": "Overview of financial analysts' analysis of global stock market trends in the past week."} +{"text": "Which industry sectors performed the strongest in the past week? Please discuss their growth drivers and potential influencing factors."} +{"text": "Have there been any significant events in the automotive manufacturing industry in the past week, and what are their potential impacts on the corresponding A-share sectors?"} +{"text": "What major events have occurred in the fintech industry in the past week, and have they impacted relevant fintech stocks?"} +{"text": "Analyze the market performance and stock trading trends of the consumer retail industry in April 2024."} +{"text": "As of the close on May 7th, at what historical percentile is the valuation level of the CSI 300 Index?"} +{"text": "Are there any new pharmaceutical company listings or stock issuance plans in May 2024?"} +{"text": "What was the revenue growth rate of the healthcare industry in the fourth quarter of 2023?"} +{"text": "What is the general earnings growth forecast by financial analysts for the banking sector in the first quarter of 2024?"} +{"text": "On what date did Alibaba Group have its initial public offering (IPO) on the New York Stock Exchange?"} +{"text": "How did Apple's revenue growth perform in the latest financial report?"} +{"text": "When was the most recent constituent adjustment of the CSI 300 Index? Please list the specific stocks added and removed in this adjustment."} +{"text": "Please list the main companies invested in by China Universal Medical and Health Industry Fund."} +{"text": "What is the latest reported inflation rate for 2024?"} +{"text": "What new regulations and measures to promote job market stability are mentioned in the recent policy interpretations by the State Council?"} +{"text": "What measures or suggestions were proposed at the recent Central Economic Work Conference to address current economic challenges and future development goals?"} +{"text": "What are the key points of the latest adjustments to current and future macroeconomic policies mentioned at the last two Central Economic Work Conferences?"} +{"text": "How should we interpret the economic development strategy proposed by the National Development and Reform Commission?"} +{"text": "What measures will fiscal policy take in 2024 to optimize local government debt structure?"} +{"text": "Is the issuance of one trillion yuan in special government bonds at the end of the fourth quarter of 2023 China's version of QE?"} +{"text": "What are the latest employment policies announced in China?"} +{"text": "Please analyze the market impact of the government's fiscal spending policy in the fourth quarter of 2023."} +{"text": "Why is the government issuing one trillion yuan in national bonds?"} +{"text": "Please interpret the key policy contents of the latest Central Economic Work Conference."} +{"text": "What policies directly related to improving people's livelihood were announced in April 2024?"} +{"text": "What is the direction of China's economy in 2024, and what are the core concerns?"} +{"text": "Reviewing 2023, what factors led to the disparity between early-year macroeconomic and asset expectations and actual performance?"} +{"text": "What impact will Huawei AITO's new M7, breaking 60,000 orders, have on the market?"} +{"text": "What are the expectations for the U.S. economy and Federal Reserve policy changes next year?"} diff --git a/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl b/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl new file mode 100644 index 00000000..ccbabf33 --- /dev/null +++ b/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl @@ -0,0 +1,12 @@ +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-04-06，星期6\n-------------------------------------------------------------\n我的问题是：蔡崇信的访谈提到了什么？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n蔡崇信的访谈提到了什么？\n在阿里巴巴董事长蔡崇信的专访中，他表达了对公司未来发展的信心，并将电商和云计算定位为阿里巴巴的核心业务。他预测，未来五年，中国电商的市场渗透率将上升至40%或更高，推动阿里电商业务的增长。同时，他也看好中国经济的数字化进程为云计算业务提供的需求，并表示阿里正在关注AI电商和行业竞争的发展。\n\n在芯片和半导体方面，蔡崇信指出，由于美国对Nvidia等公司向中国出口高端芯片的严格限制，阿里的云业务和高端计算服务能力受到了影响。他认为这是一个大问题，但也看到解决的可能性，他预见在短期至中期，这会是一个问题，但长远来看，中国将发展自己制造这些高端GPU的能力。在限制解除前，由于已有的库存，大型语言模型的训练仍可以继续进行。\n\n此外，蔡崇信还透露，阿里正在加大对AI相关技术的投资，并致力于提升用户购买频次和订单量。\n--------------------------\n蔡崇信最近的访谈内容\n问题：蔡崇信最近的访谈有哪些重要内容？\n\n回答：\n在最近的访谈中，阿里巴巴董事会主席蔡崇信分享了对公司现状和未来发展的看法。他承认阿里巴巴在过去几年中存在一些问题，并强调了新任CEO吴泳铭对于用户体验的重视。对于中国经济，他认为中国市场仍具有巨大潜力，尽管目前存在一些挑战，例如房地产市场低迷，年轻人就业前景不明朗等。\n\n在谈到阿里巴巴的云业务时，他表示因为美国对华出口管制的影响，中国企业在高端芯片的使用上受限，这对阿里巴巴的云业务以及向客户提供高端计算服务的能力产生了影响。但他提到，中国正在加快发展自主芯片的设计和制造能力。\n\n此外，他还分享了自己的成长经历和工作体验，以及对企业文化、工作效率、领导力以及工作与生活平衡的看法。他建议年轻人要掌握一两项特长，如编程、特定领域的专业知识等，从而在人群中脱颖而出。同时，他还推荐年轻人学习数据科学、心理学等前沿学科。\n--------------------------\n蔡崇信近期对蚂蚁集团的发展规划的访谈\n问题：蔡崇信近期对蚂蚁集团的发展规划有何看法？\n\n答案：蔡崇信在最近的访谈中提到，过去几年，蚂蚁集团在一度忽视用户体验上有所欠缺，为此已经进行了重组。新的首席执行官相较于蔡崇信年轻12岁，他非常注重用户，专注于产品、界面和用户体验。蔡崇信表示，这一改变对蚂蚁集团的发展至关重要。他认为公司需要承认错误，然后改变与战略相适应的组织结构，并将公司的方向融入组织结构中。通过这种方式，他期望蚂蚁集团能够更好地服务用户，从而提升其在竞争中的地位。\n--------------------------\n蔡崇信近期对科技行业的看法和预测的访谈\n蔡崇信在近期的访谈中表示，中国在 AI 技术领域与美国存在一定差距，可能落后两年。去年10月，美国对Nvidia等公司向中国出口高端芯片的能力施加了严格的限制，这对中国的云业务及提供高端计算服务的能力产生了影响。蔡崇信认为这是一个大问题，短期和中期都会产生影响，但他也表示，中国正在努力发展自制高端GPU芯片的能力。\n\n他认为，尽管在短期内，人们已经储备了一定的库存，能够保证大型语言模型的训练在未来一年或18个月内能够继续进行，但从长远来看，中国需要自主制造这些高端芯片。\n\n蔡崇信还强调，中国不会失去世界工厂的地位，墨西哥、越南等国家相比中国仍有较大差距。他建议年轻人努力成为自己领域的前 10% 的佼佼者，以获得尊重和社会认可。\n--------------------------\n蔡崇信近期对中国经济形势的分析的访谈\n蔡崇信，阿里巴巴董事会主席、联合创始人近期在访谈中对中国经济形势进行了深入分析，并对在美运营的中国企业提出警示，重视数据隐私和网络安全等合规事项。他认为，许多西方人对中国存在绝对化的误解，事实往往介于两个极端之间。他还强调了中国市场对全球经济繁荣的重要性。\n\n关于芯片和半导体方面的问题，他指出，去年10月美国对Nvidia等公司向中国每家公司出口高端芯片的能力施加了非常严格的限制。这对阿里巴巴的云业务以及向客户提供高端计算服务的能力造成了影响。他表示，芯片短缺是一个大问题，人们正在努力解决。他认为，在限制解除之前，人们已经储备了库存，所以在未来一年或18个月内，大型语言模型的训练仍然可以继续进行。\n\n蔡崇信表示，从长远来看，中国将发展自己制造这些高端GPU的能力。他强调，不能打赌中国会输，中国市场对世界经济繁荣至关重要。\n--------------------------\n蔡崇信近期对全球金融市场的观察的访谈\n问题：请分析和总结蔡崇信对全球金融市场、阿里巴巴的发展和未来方向的观察和看法。\n\n答案：\n蔡崇信在近期的访谈中分享了他对全球金融市场、以及阿里巴巴未来发展的观察和看法。\n\n对全球金融市场，蔡崇信关注了美国对Nvidia等公司向中国出口高端芯片的限制，认为这对阿里巴巴的云业务和提供高端计算服务的能力产生了影响。他预计在短期和中期，这将是一个问题，但长期来看，中国将发展自己制造高端GPU的能力。\n\n针对阿里巴巴自身的发展，蔡崇信表示阿里巴巴过去忽视了用户体验，因此在重组过程中引入了新的CEO吴泳铭，以更加聚焦于用户为中心的产品和界面开发。他强调了人工智能在阿里的云业务中的重要性，认为拥有一个内部开发的优秀大型语言模型对于阿里的云业务至关重要。\n\n对于未来的方向，蔡崇信强调了中国消费市场的潜力。他指出，中国的制造产量约占全球的31%，但消费仅占全球的14%，存在显著的不平衡。他预期中国消费者将会变得更加重要，并且对GDP的贡献率会持续增长。\n\n此外，蔡崇信还建议年轻人学习基本的专业技能，尤其是数据科学和心理学，认为这些是在生活中取得成功所需的所有工具。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "在蔡崇信的最近一次访谈中，他分享了对阿里巴巴未来发展的信心和看法。主要内容包括：\n\n一、业务发展预测\n1. 他对电商和云计算的发展持乐观态度，认为这两项是阿里巴巴的核心业务。他预测，未来五年，中国电商市场的渗透率将上升至40%或更高，推动阿里电商业务的增长。\n2. 他看好中国经济的数字化进程，认为这将为阿里巴巴的云计算业务提供需求。\n3. 他透露阿里正在加大对AI相关技术的投资，并致力于提升用户购买频次和订单量。\n\n二、行业挑战\n1. 在芯片和半导体方面，蔡崇信指出，由于美国对Nvidia等公司向中国出口高端芯片的严格限制，阿里的云业务和高端计算服务能力受到了影响。\n2. 他认为这是一个大问题，但也看到解决方案，预测短期至中期，这会是一个问题；但长远看来，中国将发展自己制造这些高端GPU的能力。\n\n三、对蚂蚁集团的看法\n1. 他提到，蚂蚁集团在一度忽视用户体验上有所欠缺，为此已经进行了重组。新的首席执行官非常注重用户，专注于产品、界面和用户体验。\n2. 他认为这一改变对蚂蚁集团的发展至关重要，并希望通过这种方式，蚂蚁集团能够更好地服务用户，从而提升其在竞争中的地位。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：证券保险行业今年发生了啥？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n证券保险行业今年发生了啥？\n2023年，证券保险行业表现出长期向好的趋势，但也面临着转型调整的阵痛。上半年，保险公司原保险保费收入3.2万亿元，同比增长12.5%，其中人身险的原保险保费收入同比增长13.38%，显示了恢复性复苏的态势。然而，下半年可能会有调整。我国经济长期向好的基本面没有变化，因此保险行业尤其是寿险领域存在巨大增长空间。\n\n然而，人身保险发展正处于转型发展的阵痛期，面临业务增长持续承压、销售队伍转型升级、行业市场竞争加剧的多重压力。尽管伴随着社会复苏、需求回升，寿险市场慢慢回暖，但行业转型的战略窗口中存在诸多现实挑战。\n\n同时，风险也在增大。截至2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。这30家保险公司中有不少机构的资产规模在数千亿元，如果不能解决其偿付能力问题，可能会对投保人、投资者和市场产生较大的影响。\n\n政府一直在积极推动保险行业规范发展。2020年制定了《责任保险业务监管办法》，明确了责任保险的定义、保险公司的义务和保险服务的范围。2021年，国家金融监督管理总局通过了《保险集团公司监督管理办法》，旨在加强对保险集团公司的监督管理。2022年，国家金融监督管理总局在全国范围内开展了针对保险资金运用关联交易的专项检查，这是银保监会成立以来首次专门针对保险资金运用关联交易的专项检查，也是保险机构监管主体职责改革后首次大规模专项检查行动。整个行业都会持续向好发展。\n--------------------------\n2023年证券行业的重大事件\n2023年证券行业的重大事件主要包括以下几点：\n\n1. 4月，沪深交易所主板注册制首批企业上市，实现了股票发行注册制改革全面落地。同时，沪深港通交易日历优化上线实施，放开了两地市场全部共同交易日的沪深港通交易，极大地提升了交易效率。\n\n2. 5月，国家金融监督管理总局正式揭牌，标志着中国金融监管体系迈入“一行一局一会”新格局。\n\n3. 8月27日，A股政策密集落地，包括证监会阶段性收紧IPO节奏，要求上市公司募集资金应投向主营业务，严限多元化投资；财政部、税务总局公告交易印花税减半征收；证券交易所调降融资保证金比例，支持适度融资需求。\n\n4. 9月，国家金融监督管理总局发布《关于优化保险公司偿付能力监管标准的通知》，为增量险资入市进一步打开空间；同时，沪深交易所进一步规范股份减持行为。\n\n5. 10月11日，中央汇金同日增持四大行股份。\n\n6. 10月30日至31日，中央金融工作会议在北京举行，提出了推动股票发行注册制走深走实，发展多元化股权融资，大力提高上市公司质量，培育一流投资银行和投资机构等一系列资本市场改革发展的重点。\n\n此外，按照中原证券的研报，预计2023年全年肉鸡平均价格将同比上涨，这将推动养殖行业利润弹性逐步释放，相关上市公司的盈利水平将持续改善。中信证券的研报预计，2024年的公用事业行业盈利仍在扩张周期，有望在2024年跑赢市场。\n--------------------------\n2023年保险行业的重大事件\n2023年保险行业的重大事件主要体现在以下几个方面：\n\n1. 行业转型：保险机构开始进行市场细分，发掘消费者的个性化需求，向消费者提供差异化的保险产品及风险管理服务。同时，为消费者提供“好用不贵”的保险产品，也成为趋势。这可以理解为“找细分、要精准、要性价比”的转型过程。\n\n2. 行业挑战：我国人身保险发展正处于转型发展的阵痛期。面临业务增长持续承压、销售队伍转型升级、行业市场竞争加剧的多重压力。预定利率上限下调，导致阶段性承压。\n\n3. 保险公司风险：截至2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。此外，有13家保险公司的评级为C，3家评级为D，包括华夏人寿、恒大人寿、天安人寿等。若这些公司不能解决偿付能力问题，可能会对投保人、投资者和市场产生较大的影响。\n\n4. 监管加强：政府积极推动保险行业规范发展，通过了《责任保险业务监管办法》《保险集团公司监督管理办法》，并在全国范围内开展了针对保险资金运用关联交易的专项检查，这是银保监会成立以来首次专门针对保险资金运用关联交易的专项检查。行业准入门槛、偿付能力管理、业务监管，还是保险资金运用等方面，都只会越来越严格，越来越规范。\n--------------------------\n2023年证券行业的政策变动\n2023年，中国证监会发布了一系列政策，对证券行业进行了深度调整和改革。\n\n首先，证监会对全面实行注册制改革进行了深入学习和宣传，加快推进各条线制度规则的“立改废释”和体系优化，构建更加科学完备的资本市场法律体系。\n\n其次，证监会发布了券商风控指标意见稿，明确了监管方向从原来的鼓励全行业发展到扶优限劣、分类监管。这其中包括对连续3年分类评价AA级以上的券商，风险资本准备的系数可以调整为0.4，而D类则要调为2倍系数。这一政策不仅加快了行业优胜劣汰的速度，也为券商的资本使用效率提供了提升空间。\n\n再者，证监会对《证券公司风险控制指标计算标准规定》进行了修订，并向社会公开征求意见。其中包括，对券商的风险资本准备、表内外资产总额进行调整，分别给出不同的调整系数。同时，对券商在做市、资管、公募REITS等业务的计算标准进行了优化。\n\n最后，意见稿还加强了场外衍生品的监管力度，将股指期货、权益互换及卖出期权计算标准由20%调整至30%。\n\n总的来说，2023年证券行业的政策变动主要体现在全面实行注册制改革、对券商风控指标进行调整、优化风险控制指标计算标准以及加强场外衍生品的监管等方面。\n--------------------------\n2023年保险行业的政策变动\n2023年，中国保险行业的政策变动主要体现在以下几个方面：\n\n首先，财政部发布的《通知》将国有商业保险公司的经营效益类绩效评价指标“净资产收益率”的考核方式由“当年度指标”调整为“3年周期指标+当年度指标”的结合方式，以更好地引导保险公司追求长期稳健经营，防止盲目追求年度考核目标、忽视风险的短期行为。\n\n其次，《通知》将保险公司剩余期限10年期以上保单未来盈余计入核心资本的比例，从不超过35%提高至不超过40%，以鼓励保险公司发展长期保障型产品。\n\n同时，保险行业的发展也正面临一些挑战。例如，保险业投资收益及盈利水平面临增长压力，保险业投资端存量问题与新增风险并存，人身险行业转型仍处于攻坚克难阶段，财产险行业保险风险需持续关注。\n\n未来，保险行业的发展趋势主要包括金融科技进一步融入保险经营模式、行业分化效应将持续、保险行业整体业务结构持续优化。为此，专家建议保险资管公司应通过提升管理层内控和道德意识、健全完善内控系统、注重员工日常培训、建立内部监督体系等方式，加强内部控制建设，有效防范职业欺诈，提高运营效率和财务报表的可靠性，促进保险资管公司持续健康发展。\n--------------------------\n2023年证券保险行业的市场动态\n用户问题：请问2023年证券保险行业的市场动态是什么？\n\n回答：\n2023年，中国的经济形势有望由衰退转向弱复苏，这主要得益于各项政策的推出以及防疫政策的放松。然而，由于海外发达经济体衰退风险的增加以及国内地产恢复的缓慢，这种复苏态势可能会受到一些压力。\n\n在证券市场方面，随着国内经济触底反弹，A股的业绩增速有望实现同比上升。此外，预计海外发达经济体在2023年上半年停止加息，这将缓解人民币贬值压力，从而提高A股相对于海外市场的吸引力，并可能导致北向资金重新流入。因此，预计2023年国内证券市场将具备业绩和估值双重上升的基础。\n\n在保险行业方面，长期利率的复苏成为保险板块上涨的主要动力。预计负债端的拐点将在2023年第二季度出现，这表明我国经济的韧性强，潜力大，活力足，长期向好的基本面没有改变，人身险市场的长期空间仍然广阔。推荐的个股包括中国平安、中国太保和中国人寿。\n\n在投资机会方面，建议关注以下三个主线轮动：国产科技替代创新（如计算机、电子、通信、软件服务、数字经济等科创领域）；中国特色估值重塑主题（如能源、基建、房地产产业链等）；消费（如食品饮料、医药生物、酒店、旅游、交运、新能源消费等）。此外，半导体和人工智能等领域有望成为领导科技股再次走牛的主力军，具有走出史诗级行情的潜力。\n\n总的来说，2023年的证券和保险行业市场动态将主要受到经济复苏、长期利率复苏以及科技创新等因素的影响。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "2023年，中国的证券保险行业发生了许多重大事件和政策变动，行业总体呈现出长期向好的趋势，但也面临一些挑战。\n\n在保险行业方面，上半年，保险公司的原保险保费收入达到3.2万亿元，同比增长12.5%，其中人身险的原保险保费收入同比增长13.38%，显示了恢复性复苏的态势。然而，人身保险发展正处于转型发展的阵痛期，面临业务增长承压、销售队伍转型升级、行业市场竞争加剧的多重压力。\n\n风险管理方面，到2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。这些公司的资产规模在数千亿元，如果不能解决偿付能力问题，可能会对投保人、投资者和市场产生较大影响。\n\n在政策方面，政府积极推动保险行业规范发展，发布了《责任保险业务监管办法》和《保险集团公司监督管理办法》，并在全国范围内开展了针对保险资金运用关联交易的专项检查。\n\n在证券行业方面，2023年的重大事件包括沪深交易所主板注册制首批企业上市，实现了股票发行注册制改革全面落地，以及沪深港通交易日历优化上线实施，放开了两地市场全部共同交易日的沪深港通交易。\n\n政策方面，证监会发布了券商风控指标意见稿，明确了监管方向从原来的鼓励全行业发展到扶优限劣、分类监管。同时，证监会对《证券公司风险控制指标计算标准规定》进行了修订，并向社会公开征求意见。\n\n市场动态方面，随着国内经济触底反弹，A股的业绩增速有望实现同比上升。同时，保险板块由于长期利率的复苏成为上涨的主要动力，预计负债端的拐点将在2023年第二季度出现。\n\n从以上信息可以看出，2023年的证券保险行业在政策、市场、转型和风险管理等方面发生了许多重要变化，整体呈现出向好发展的趋势，但也面临着一些挑战。"}], "source": "self-made"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：在A股上市的酒企广告效果如何？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n近期在A股上市的酒企广告效果评价\n在2023年的前三季度，A股市场上的20家白酒企业总营收达到了3111.62亿元，同比增长15.91%。其中，贵州茅台和五粮液以1032.68亿元和625.36亿元的营收占据市场主导地位，两者的营收合计占总营收的54.28%。在净利润方面，这20家企业合计1189.8亿元，同比增长18.92%，其中岩石股份和伊力特的净利润增速表现最为出色，分别实现153.63%和63.88%的增长。\n\n此外，这些公司的经营性现金流净额合计达到1042.05亿元，同比大幅增加601.52亿元，其中贵州茅台和五粮液的现金流净额分别为500.02亿元和223.89亿元，显示出较强的造血能力。\n\n虽然消费需求疲软、市场竞争激烈和库存积压为白酒行业带来了挑战，但是行业整体仍保持稳健的增长态势。20家上市白酒企业的存货总额为1363.54亿元，同比增长12.63%。合同负债总额为406.67亿元，同比减少了1.91%，显示出市场对于酒类未来消费的看法偏保守与谨慎。然而，考虑到第四季度是传统的白酒消费季，市场对于名酒的业绩依然抱有乐观态度。\n\n在广告效果方面，在Q2的业绩公布中，迎驾贡酒实现了60%的高增速，成为了一大亮点。同时，皇台摘帽，剑南春等其它名酒可能借壳ST西发。这些都显示出广告对于酒企业绩的积极影响。\n\n综上所述，近期在A股上市的酒企的广告效果总体上是积极的，这些企业在市场竞争中表现出较强的实力和稳健的增长态势。\n--------------------------\n近期在A股上市的酒企广告投放情况\n近期，A股上市的酒企的广告投放情况表现积极。以贵州茅台和五粮液为例，他们联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，引发了市场的热议，并在短期内推动了白酒板块的整体涨幅达到4.44%。另一方面，根据2023年三季度的财报，20家白酒上市公司实现总营收3111.62亿元，同比增长15.91%，归母净利润合计1189.8亿元，同比增长18.92%，显示出白酒行业整体的稳步增长。其中，贵州茅台和五粮液两大龙头的营收合计占行业比达54.28%，显示出他们在行业中的领导地位。但是，面临库存积压和市场竞争激烈的挑战，白酒行业仍需面临许多挑战。\n--------------------------\n近期在A股上市的酒企广告对销售的影响\n近期在A股上市的酒企广告对销售的影响可以从以下几个方面来看：\n\n首先，广告投放有助于提升品牌知名度和影响力，从而推动销售。如迎驾贡酒，其2022年Q2业绩超预期，增速高达60%。在此期间，迎驾贡酒投入了大量的资金用于理财，累计收益达6564万元，占其最近一年净利润的6.86%。\n\n其次，现金流管理是酒企运营的重要方面，广告费用预算和现金流管理紧密相关。部分酒企在广告投放和扩产同时进行，需要有效管理现金流以保持运营正常。如贵州茅台和五粮液，虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。对于这些酒企来说，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，白酒行业竞争激烈，广告投放对于提升市场份额尤为重要。据行业分析师肖竹青预计，随着白酒行业进入深度结构性调整期，龙头企业将会拥有越来越大的市场份额，区域白酒企业会逐渐边缘化，未来白酒行业会迎来更激烈的内卷式竞争。在这种环境下，广告投放对于吸引消费者，提升市场份额显得至关重要。\n\n总的来说，广告投放是提升销售的重要工具，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。\n--------------------------\n近期在A股上市的酒企广告的创新点\n近期在A股上市的酒企广告创新主要体现在跨界联名、营销预热、吸引消费者关注等方面。如瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注，使得A股白酒板块整体收涨4.44%。此外，他们还利用“中国人的第一杯酱香拿铁”和“美酒加咖啡，就爱这一杯”等宣传语吸引消费者购买。\n\n同时，酒企也在积极探索新的销售模式和市场机会。据预测，受益于低基数、宴席需求回补、商务活动恢复、消费能力恢复，预计今年二季度营收增速或将继续回升。节假日有望接力催化需求释放，后续应密切关注端午、中秋等假期前后的销售情况。白酒销售渠道反馈行业整体回款进度正有序推进，主要酒企核心单品批发价基本保持平稳、库存相对良性。\n\n然而，值得注意的是，白酒板块的投资风险也不容忽视。白酒作为典型的可选消费品，与居民收入水平和经济活跃度紧密相关，如果经济短期遇到逆风，可能会阶段性造成行业库存提升或业绩增长受阻。因此，投资者需谨慎分析，避免盲目跟风。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从近期A股上市的酒企广告效果评价，广告投放情况，广告对销售的影响以及广告的创新点四个方面来看，广告效果总体上是积极的。\n\n首先，广告帮助提升了酒企的品牌知名度和影响力。比如迎驾贡酒在2022年Q2的业绩公布中，实现了60%的高增速，成为了一大亮点。这显示出广告对于酒企业绩的积极影响。\n\n其次，广告投放对销售有显著的推动作用。贵州茅台和五粮液联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，并在短期内推动了白酒板块的整体涨幅达到4.44%。这进一步证实了广告在提升销售方面的重要作用。\n\n然而，虽然广告能带来销售上的提升，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。例如，贵州茅台和五粮液虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。这显示出，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，酒企在广告方面的创新也取得了一定的效果。瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注。\n\n综上所述，近期在A股上市的酒企通过广告投放在提升品牌知名度、增加销售额、管理现金流以及进行创新等方面取得了一定的成效。"}], "source": "self-made"} +信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：深入分析10年期美国国债收益率上涨对国内债市的影响是什么？\n 今天的日期是：2023-10-23\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion：首先，我们需要了解10年期美国国债收益率上涨对全球债市的一般影响，然后再分析这种影响在中国债市的具体表现。其次，我们需要了解中国债市的特点和规律，以便更准确地分析美国国债收益率上涨对其的影响。最后，我们可以查看相关的历史数据和事件，以便从实际情况中得出结论。\n\nanswer：\n1.10年期美国国债收益率上涨对全球债市的影响是什么？\n2.中国债市的特点和规律是什么？\n3.历史上10年期美国国债收益率上涨时，中国债市的反应是什么？\n4.最近一次10年期美国国债收益率上涨，中国债市的反应是什么？\n5.专家对10年期美国国债收益率上涨对中国债市影响的看法是什么？"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：长期持有收益比银行还高吗？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n长期持有收益比银行还高吗？\n关于“长期持有收益比银行还高吗？”这个问题，从搜寻到的信息来看，长期持有基金的确有可能带来较高的收益。\n\n首先，统计数据显示，股票型基金的长期持有能大概率提升基金的收益，正收益比例上看，持有3-5年为最佳。虽然在3075只股票型基金中，翻倍的基金只有64只，占比2.08%，但长期持有可以提升收益的概率。\n\n其次，市场虽然会有周期性的波动，但最终会发生均值回归，即涨得过多或跌得过多的趋势最终都会回归到平均值。这就意味着，当市场处于50%分位数之下时，选择长期持有，便能大概率获得收益。\n\n此外，从2004年以来，普通股票型基金指数和偏股混合型基金指数的涨幅都超过了10倍，在近20年的时间里不断创出新高。即使在市场高位买入，只要有耐心等待一定时间，也有可能从亏损中反败为胜。\n\n最后，对于“回本就卖”的策略，虽然可以避免亏损，但可能会错失更大的收益机会。这是因为，离开市场后要重新进入可能需要面临更高的价格和风险，而长期持有则可以更好地面对市场的短期波动，增加赚钱的概率。\n\n总的来说，长期持有基金相比于银行存款，有可能带来更高的收益，但这需要投资者有足够的耐心和对市场趋势的理解。\n--------------------------\n长期持有股票的平均收益率是多少？\n根据搜索到的信息，股票的长期持有收益率是由投资者在一定的持有期内的全部股利收入和资本利得占投资本金的比重来表示的。这个比重可以通过以下公式计算：[现金股息+(股票卖出价-股票买入价)]/股票买入价×100%。这里的现金股息是指投资者持有股票期间的股息收入，股票卖出价和股票买入价的差价则代表了资本利得。因此，想要了解长期持有股票的平均收益率，你需要考虑到股息收入和股票的买卖价格的变化。\n--------------------------\n长期持有的定义和计算方法是什么？\n长期持有，是投资策略的一种，主要是指投资者选择获益率高或成长性高的股票后，进行长期持有，而不是频繁买卖。这种策略的主要思想是，不轻易抛出手中的股票，而是等待股票的长期增长带来投资收益。\n\n至于计算方法，长期持有并没有特定的计算公式。通常，投资者会根据股票的盈利能力、成长性以及市场前景等因素，决定是否将其作为长期持有的投资对象。\n--------------------------\n长期投资的风险和收益比较。\n长期投资具有一定的风险和收益。以下是根据信息整合得出的答案：\n\n风险方面：\n1. 股票投资：可能受到政治、经济、国家产业政策、技术发展等多种因素的影响，可能导致投资者利益受损。但可以通过股票投资种类的组合和投资期限的组合来降低风险。\n2. 债券投资：虽然受到的影响因素和股票投资相似，但影响程度通常较小，风险要小于股票投资。主要风险包括债券发行单位可能出现意外情况，以及银行利率变动带来的风险。\n3. 基金投资：风险和收益与基金类型有关。股票型基金的风险和收益预期通常高于债券型基金和货币市场基金。此外，基金投资还可能面临市场风险、基金运作风险、流动性风险等。\n\n收益方面：\n1. 股票投资：企业应对各种影响因素作详细的分析，选择收益较高、安全性好的股票投资。\n2. 债券投资：在进行债券投资时，应考虑购买明显高于银行储蓄存款利率的长期债券，以求收益最大化和风险最小化。\n3. 基金投资：投资人购买基金，既可能分享基金投资产生的收益，也可能承担基金投资带来的损失。\n\n综上，长期投资需充分考虑自身的风险承受能力，理性判断市场，对投资行为作出独立决策，并承担基金投资中出现的各类风险。\n--------------------------\n近期的金融市场环境对长期持有的影响。\n从搜索到的信息来看，近期的金融市场环境对长期持有的影响主要以以下几个方面进行解读：\n\n首先，如果拉长时间周期来看，长期持有的投资策略可能更有优势。过去20年间，我国累计通胀达63.01%，定期存款的累计收益率为90.82%，而偏股混合型基金指数累计收益率高达783.76%。这表明，尽管短期市场环境波动可能影响投资收益，但长期持有的投资策略，如权益投资，能够跑赢通胀并获取超额收益。\n\n其次，适当的资产配置也是提高长期投资收益的重要手段。根据招商银行的“TREE资产配置服务体系”，投资者应从自身的财富阶段和风险偏好出发，选择适合自己的投资产品，并定期进行资产配置的检视和再平衡，以保持资产组合的稳健性。\n\n此外，在市场环境变化时，长期持有的策略能够有效地减少市场波动对投资收益的影响，带来更为稳定的收益。例如，南方金添利三年持有期债基注重长期持有债券，以获取稳定的利息收入。\n\n最后，科技的应用也对长期持有的投资策略产生影响。招商银行全流程系统化、产品线上化，并积极研发市场宏观、微观层面的观测指标和定量模型，及时感知市场变化，为客户科学、高效地调整配置比例和方案，这对于长期持有的投资策略有着积极的推动作用。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "关于你的问题\"长期持有收益比银行还高吗？\"\n\n首先，我们来定义一下“长期持有”。在投资领域中，“长期持有”是指投资者选择某种具有高收益率或者高成长性的投资品种，而不是频繁地买卖，而是选择长期持有，期待通过资产的长期增长来获得投资收益。\n\n关于长期持有可能带来的收益，统计数据显示，对于股票型基金来说，长期持有能大概率提升基金的收益，正收益比例上看，持有3-5年为最佳。虽然在3075只股票型基金中，翻倍的基金只有64只，占比2.08%，但长期持有可以提升收益的概率。\n\n而对于股票的长期持有收益率，它是由投资者在一定的持有期内的全部股利收入和资本利得占投资本金的比重来表示的。这个比重可以通过以下公式计算：[现金股息+(股票卖出价-股票买入价)]/股票买入价×100%。这里的现金股息是指投资者持有股票期间的股息收入，股票卖出价和股票买入价的差价则代表了资本利得。\n\n另外，我们也需要考虑到市场环境的影响。近期的金融市场环境，尽管短期市场环境波动可能影响投资收益，但长期持有的投资策略，如权益投资，能够跑赢通胀并获取超额收益。过去20年间，我国累计通胀达63.01%，定期存款的累计收益率为90.82%，而偏股混合型基金指数累计收益率高达783.76%。\n\n当然，长期投资也具有一定的风险。例如，股票投资可能受到政治、经济、国家产业政策、技术发展等多种因素的影响；债券投资的主要风险包括债券发行单位可能出现意外情况，以及银行利率变动带来的风险；基金投资的风险和收益与基金类型有关，股票型基金的风险和收益预期通常高于债券型基金和货币市场基金。\n\n所以，从长期来看，“长期持有”的收益比银行存款有可能高，但这需要投资者有足够的耐心和对市场趋势的理解，同时也需要承担一定的风险。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "\n你的角色是：投研分析师投研支小助,所属的国家：中国,所属的公司：蚂蚁集团,研发团队：蚂蚁集团投研团队,职责：善于从不同角度、有条理的回答用户的问题。现在，你需要仅仅使用提供的知识，专业、详细的回答用的问题。我的问题是：指数基金适用于什么样的投资场景？\n-------------------------------------------------------------\n背景信息是：\n无\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n指数基金适用于什么样的投资场景？\n指数基金是一种投资工具，它的主要目标是跟踪并复制特定的股票或债券指数的表现。在投资场景中，它适用于多种情况。首先，对于那些希望投资某个具体行业，特别是高科技行业的投资者，指数基金特别是行业指数基金可以为他们提供一个优秀的投资工具。因为高科技行业的发展充满了不确定性，很难锁定单一的成功公司，但通过投资指数基金，投资者可以在整个行业范围内进行投资，享受行业的整体成长红利，而避免了单一公司决策失误带来的风险。\n\n同时，对于那些在市场底部寻求抄底机会的投资者，指数基金也是一个好的选择。当市场下跌时，很多投资者，包括机构和个人投资者，都会使用指数基金来进行投资。宽基指数基金由于覆盖面广，往往因为机构增持而规模迅速扩大，而行业或主题指数基金则更多地被个人投资者用来根据自己对行业和主题机会的判断来进行投资。\n\n但是，指数基金也存在一定的风险，因为它是满仓操作，风险相对较高。为了降低风险，投资者可以通过分期定投的方式进行投资，从而在不确定的市场环境中获得更好的投资效果。\n\n最后，对于那些对中国经济的未来预期向好的投资者，他们可以将宽基指数基金作为一种底仓的存在，而通过行业和主题指数的轮动配置来实现自己的投资判断。\n\n在总结中，指数基金适用于那些希望投资具体行业，寻求市场底部抄底机会，以及对中国经济未来预期向好的投资者。同时，通过合理的投资策略，如分期定投，投资者可以在享受投资回报的同时，有效地控制投资风险。\n--------------------------\n指数基金的投资特点和风险\n指数基金的投资特点主要体现在以下几个方面：\n\n1. 费用较低：由于指数基金采取跟踪指数的方式，基金管理人不需要花大量的时间和精力进行股票、债券等投资工具的选择以及买入与卖出的时机。这种被动的管理方式降低了基金的管理费用。另外，由于采取买入并持有的策略，只有在所跟踪指数的成份股变化时才进行调整，所以它的交易费用也相对较低。\n\n2. 风险分散：指数基金通过跟踪指数进行广泛的分散投资，这意味着任何单个股票的波动都不会对指数基金的整体表现产生太大的影响，从而降低了投资者的投资风险。\n\n3. 透明度高：由于指数基金的业绩与其所跟踪的指数收益紧密相关，投资者只需关注目标指数的涨跌就可以判断出基金净值的变动。这样的操作简单明了，适合对大势把握较好但对个股了解不多的投资者。\n\n4. 人为影响小：由于指数基金的管理过程主要是对相应的目标指数进行被动跟踪，而不是频繁地进行主动性的投资，这样可以通过较为程序化的交易来减少人为因素的影响。\n\n尽管指数基金有以上特点，但投资者也需要注意其风险，因为市场波动或经济周期等因素可能会影响到被跟踪指数的表现，从而影响到基金的收益。\n--------------------------\n指数基金适合的投资人群特性\n指数基金是一种适合多种投资者的投资工具。首先，它们适合初学者和那些没有时间进行详细研究的投资者，因为这类基金投资的是整个市场或行业的股票，不是单一的个股，这样的分散投资策略可以有效地降低风险。其次，由于指数基金的投资策略是长期持有，所以适合长期投资者。长期投资可以有效地降低交易成本，并且指数基金的长期收益表现往往比短期收益更稳定。\n\n此外，指数基金对于养老金投资者也有吸引力。由于其低成本、大容量、风险分散、风格稳定和策略透明等特性，使其成为权益资产配置的合适工具，能够帮助投资者以较低的成本获取股票市场的长期平均投资回报。\n\n最后，指数基金的运作核心是被动地跟踪指数，在充分分散个股风险的同时，获取市场的平均收益。这一特性使得投资者只需要关注目标指数的涨跌，就可以大体上判断出投资的指数基金净值的变动，业绩透明度较高。同时，由于管理过程主要是对相应的目标指数进行被动跟踪，而不是频繁地进行主动性的投资，这样在管理过程中就可以通过较为程序化的交易来减少人为因素的影响。\n--------------------------\n在不同市场环境下，指数基金的投资表现\n在不同的市场环境下，指数基金的投资表现有着其独特性。在震荡市场中，指数基金往往呈现出“越跌越买”的现象，透明度高且费率低廉的特点使其成为了投资者的重要配置工具。对于投资者来说，跟踪更精准的指数基金能避免投资方向的偏移，更好地符合他们的投资预期。因此，对于有清晰投资目标、不想承担超额收益风险的投资者，以及希望减少投资成本的投资者，指数基金是一个很好的选择。\n\n在选择指数基金时，投资者通常关注两个主要因素：跟踪误差和费用。跟踪误差是指基金实际收益与其跟踪指数之间的差距，越小越好，宽基类指数基金和行业类指数基金分别要求跟踪误差不超过1.5%和2%。费用则是考虑基金的综合费率，一般来说，费率越低，长期复利效应越显著。\n\n未来，随着全面注册制的改革推动，A股市场结构更加健康和透明，指数化投资的性价比也将更高。同时，随着市场投资标的的丰富和投资理念的成熟，被动型指数产品的规模占比有望提高，发展空间大。\n\n近年来，指数基金在零售端的规模始终保持稳步增长，2017年至2022年，指数基金的零售端整体规模5年平均增速为36.9%，其中行业指数年均指数达到53.3%。这表明，指数基金是投资者长期价值投资的重要工具，也是震荡行情下主动权益基金配置的重要补充。因此，我们可以预见，指数基金在未来还将迎来大发展。\n--------------------------\n指数基金在资产配置中的作用和地位\n指数基金在资产配置中具有重要作用和地位。自2002年我国第一只指数基金成立起，指数基金经历了四个发展高峰，分别在2009年、2012年、2018-2020年和2022年，尤其是2018年之后，债券等多种指数的全面发展推动了指数基金的持续上升。\n\n指数基金的发展并不平衡，主要体现在交易活跃的场内指数基金（ETF/LOF）占比不高。以日均交易额1000万以上且季度规模满2亿的ETF和LOF为例，其在宽指/风格板块类、行业类、指数类和商品类指数基金中的规模占比均达到60%以上，但在港股和海外指数基金中的占比较小。\n\n商品类指数基金的发行受到限制，难以满足资产配置需要。目前市场上只有13只商品基金，主要包含对标黄金现货和白银期货的贵金属基金、对标原油相关指数的能源类基金和3只商品期货ETF。\n\n指数增强公募基金的发展状况不理想，占比只有7%-8%，且超额收益率稳定性较弱。主要原因是这类基金主要集中在股票指数策略，而债券指数策略占比并不高。\n\n从机构持有国内指数基金的比例来看，2014年之后机构资金在国内基金资金来源中占比显著提高，尤其是在股票指数基金中，机构资金占比在2007年之后经历了持续提高的过程，始终稳定高于普通股票型或混合型基金的机构资金占比。\n\n指数化投资是基金组合投资专业化的必经之路，且逐渐成为国内机构投资的主流方向。目前，我国指数基金体系在不断发展，有望进一步提升顾问服务的能力，促进居民财富的优化配置，有助于证券公司财富管理向买方投顾发展和转型。\n--------------------------\n指数基金的投资策略和操作建议\n指数基金的投资策略主要是复制市场指数的投资组合，这被称为一种消极的投资策略。这种策略的优点包括简单、透明和低费率。在投资过程中，不同类型的指数基金的关注点不同。例如，策略型指数基金主要关注指数策略的长期有效性，而宽基指数基金主要关注估值等因素，行业型指数基金除了关注估值，还需要注意行业的基本面和成长性等因素。\n\n对于初次投资的投资者，建议从宽基指数基金入手，如沪深300、中证800等指数基金。定投是一个可行的参与方式，当达到止盈目标后，可以一次性赎回并开始新的定投计划。\n\n在当前市场环境下，由于A股的政策底已经被多次确认，加上国内经济的持续好转和中美关系的缓和，现在A股具有一定的配置价值。投资者可以采取分批加仓或者多次定投宽基的方式参与市场。如果看好某个细分行业，也可以配置相关的行业指数基金。\n\n在挑选指数基金产品时，投资者可以重点关注大型基金公司推出的产品。除了费率因素外，还需要关注基金的规模和跟踪误差等因素。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "指数基金是一种投资工具，主要目标是复制特定的股票或债券指数的表现。在投资场景中，它适用于多种情况。\n\n首先，对于希望投资某个具体行业，特别是高科技行业的投资者，指数基金可以为他们提供一个优秀的投资工具。因为高科技行业的发展充满了不确定性，但通过投资指数基金，投资者可以在整个行业范围内进行投资，享受行业的整体成长红利。\n\n其次，对于寻求市场底部抄底机会的投资者，指数基金也是一个好选择。当市场下跌时，许多投资者会使用指数基金来进行投资。宽基指数基金由于覆盖面广，往往因为机构增持而规模迅速扩大。\n\n此外，对于对中国经济的未来预期向好的投资者，他们可以将宽基指数基金作为一种底仓的存在，而通过行业和主题指数的轮动配置来实现自己的投资判断。\n\n然而，指数基金也存在一定的风险，因为它是满仓操作，风险相对较高。为了降低风险，投资者可以通过分期定投的方式进行投资，从而在不确定的市场环境中获得更好的投资效果。\n\n在选择指数基金时，投资者通常关注两个主要因素：跟踪误差和费用。跟踪误差是指基金实际收益与其跟踪指数之间的差距，越小越好。费用则是考虑基金的综合费率，一般来说，费率越低，长期复利效应越显著。\n\n总的来说，指数基金是适合多种投资者的投资工具，通过合理的投资策略，投资者可以在享受投资回报的同时，有效地控制投资风险。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-17，星期3\n-------------------------------------------------------------\n我的问题是：标题: 多只跨境ETF被“爆炒”!\n描述: 1月16日，多只跨境ETF放出巨大成交量，部分不足10亿跨境ETF盘中成交额已超越茅台，更有跨境基金一月成交额放大超140倍。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 多只跨境ETF被“爆炒”!\n描述: 1月16日，多只跨境ETF放出巨大成交量，部分不足10亿跨境ETF盘中成交额已超越茅台，更有跨境基金一月成交额放大超140倍。\n===\n1. 跨境ETF规模大增：根据Choice数据显示，2023年末，跨境ETF总规模达到2686.70亿元，相比2022年的1915.53亿元，增加了771.17亿元。这一显著增长反映出投资者对跨境ETF的热情提升。\n2. 高关注度领域突显：具体来看，港股创新药、港股互联网、美股科技赛道类的ETF规模增长明显。这些领域的热度反映了大众投资者的偏好和市场趋势。\n3. 市场预期潜在变化：有机构人士认为，美债收益率2023年四季度见顶回落，预计2024年全年或迎来趋势性下行，这将对利率较为敏感的资产产生影响，可能会带来新的投资机会。\n--------------------------\n2024年1月16日跨境ETF的成交额超越茅台的具体情况\n===\n1. 跨境ETF热潮：在2024年1月16日，部分市值不足10亿的跨境ETF盘中成交金额已超越贵州茅台，其中有些产品短短一个月时间成交额放大超140倍。不过，高溢价风险随之陡增，游资或开始出逃。例如，日经225ETF、东南亚科技ETF基金创出价格新高后开始走跌。\n\n2. 主力资金流出：以跨境通为例，1月16日主力资金净流出3168.88万元，占总成交额的15.17%，而游资资金净流出1377.03万元，占总成交额的6.59%。此表明，在高溢价风险的情况下，部分主力资金和游资选择了出逃。\n\n3. 散户热衷参与：与主力资金和游资不同，散户资金在此时却呈现净流入状态。以跨境通为例，其散户资金净流入4545.9万元，占总成交额的21.76%，说明在跨境ETF的炒作中，散户投资者依然保持积极态度。\n--------------------------\n2024年1月份跨境基金成交额放大超140倍的原因\n===\n1. 交易活跃度提升：2024年1月份，跨境ETF基金的成交额出现了显著的增长，甚至有些小市值的跨境ETF基金的日成交量超过了贵州茅台。例如，华夏基金旗下的日经ETF在1月16日的全天成交金额达到了47.79亿元，而其盘中市值仅有7.16亿元。同样，东南亚科技ETF基金，该基金盘中市值约6.64亿元，1月16日全天成交金额高达29.17亿元。\n\n2. 资金流向小市值：在微盘股炒作过热后，市场资金可能会寻找估值更低，或价格处在低位的小微盘品种。这在迷你基金的交易中得到了体现，投资者的资金开始追逐市值更小的跨境ETF基金产品。\n\n3. 投资风险提醒：虽然跨境ETF基金的交易活跃度提升，但是投资者应注意风险。例如，当小微盘出现明显赚钱效应后，市场资金会加速进入，进一步加强小微盘品种的表现。然而，随着价格的上涨，任何风格都不可能持续占优，微盘行情也值得警惕，尤其是微盘股的弱流动性，一旦发生风格切换可能出现“踩踏”现象。\n--------------------------\n2024年1月16日跨境ETF爆炒的市场背景\n===\n1. ETF涨幅领军：2024年1月16日，一场跨境ETF的爆炒在市场上展开，其中日经ETF(513520)、东南亚科技ETF(513730)和印度基金LOF(164824)涨幅分别超过8%、6%和5%。这表明投资者对于海外市场，尤其是亚洲市场的关注度和投资热情正在逐步提升。\n2. 板块动态：在板块方面，光伏和新能源两大板块表现抢眼，尤其是光伏30ETF(560980.SH)领涨市场。相比之下，创中盘88ETF(159804.SZ)表现不佳，成为跌幅最大的ETF。这反映了市场对绿色能源的持续看好以及对某些板块的短期调整。\n3. 市场交易情况：当日市场表现稳定，三大指数集体收涨，其中沪指上涨0.27%，深证成指上涨0.31%，创业板指上涨0.38%。总成交额达到6848亿，较上个交易日增加739亿。然而，北向资金全天净卖出41.33亿，显示外资整体上对中国市场的态度较为谨慎。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 成交量暴增的驱动因素：1月16日，多只跨境ETF的成交量大增，其中部分市值不足10亿的跨境ETF盘中成交金额已超过贵州茅台。这可能源于投资者对国际市场的投资需求增加，以及对国内市场的一些热门股票（如茅台）的投资热情有所下降。同时，跨境ETF的优势在于可以让投资者方便地参与到国际市场的投资，这也是吸引投资者的一个重要因素。\n\n2. 行业偏好的变化：在跨境ETF的投资中，投资者对某些行业的偏好可能会发生变化。比如，港股创新药、港股互联网、美股科技赛道类的跨境ETF的规模增长较为明显，这可能反映出投资者对这些行业的关注度提升。\n\n3. 市场预期的影响：市场预期的变化也会影响投资者的投资行为。比如，如果市场预期美债收益率将趋势性下行，那么对利率敏感的资产可能会受到影响，投资者可能会寻找新的投资机会，这可能会促使部分投资者转向跨境ETF。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-05，星期2\n-------------------------------------------------------------\n我的问题是：我是想问你，市场还会跌吗？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n我是想问你，市场还会跌吗？\n根据搜索到的信息，目前市场正在经历一个阶段性的下跌过程，与此同时，市场也在进行阶段性的筑底过程。具体表现为，市场由缓跌到急跌，这一点从指数的回撤幅度也可以看出，几大指数的回撤幅度都接近2%。尽管如此，市场的下跌并非全无好处，这也是加速赶底的表现，一旦出现反弹信号和量能走好，就可能是市场的转折点。\n\n然而，目前市场的反弹信号并未明确，且各级别的量能现状也并不理想。这预示着市场继续下行震荡的概率依然较大。因此，对于问题“市场还会跌吗？”的回答是，从当前情况来看，市场有可能会继续下行震荡。\n\n需要注意的是，尽管大盘下跌空间有限，但是局部板块需要注意风控优先的原则。例如，黄金板块在大家追高时，实际上已经出现了下跌的信号。另外，石油、新能源、白酒等板块也在提醒继续下行的风控。而对于一些关键板块，如船舶板块，可以在688-710点区间内逢低入场。\n--------------------------\n近期的股市行情走势\n根据最近的股市行情，A股三大指数集体走弱，沪指跌1.67%，收报2972.30点，创业板指跌1.98%，收报1871.10点。沪深两市下跌的同时，北证50指数逆势大涨7.28%。尽管沪指再度失守3000点大关，但资深市场人士认为二次探底才是真底，明天的走势非常关键。\n\n在此背景下，食品板块逆势崛起。西部牧业、阳光乳业、一鸣食品等多只股票录得涨停。人气股惠发食品连续录得8个涨停板，股价创历史新高。尽管该公司近年业绩乏善可陈，但其主营业务——速冻调理肉制品的市场前景被看好。\n\n此外，“平价消费”已成为近期市场的一大主线，休闲食品龙头良品铺子宣布业务端将实施17年来最大规模降价，300款产品平均降价22%，最高降幅45%。中邮证券研报表示，在“经济弱复苏+消费趋于理性”双重背景下，我国零食折扣专营店兴起，下游格局初具雏形。\n\n食品板块中，多只股票未来增长潜力获机构扎堆看好。其中，安井食品上涨空间63.48%排在首位，该公司主要从事速冻食品业务，有望在餐饮消费逐步顺周期中维持较快增长。\n\n总的来看，目前市场处于短线的寻底过程，投资者需要耐心等待，明年春节前后有望形成大级别底部。\n--------------------------\n近期的宏观经济数据\n问题：“近期的宏观经济数据体现出了中国经济的哪些特点？”\n\n答案：根据2023年10月末的数据，中国的M2余额同比增加了10.3%，达到288.23万亿元，规模以上工业增加值同比增加了4.6%，达到28.31万亿元，说明经济正在持续恢复。穆迪评级公司将中国主权信用评级展望调整为“负面”，但财政部认为，面对复杂严峻的国际形势，中国宏观经济持续恢复向好，高质量发展稳步推进，预计四季度中国经济将保持回升向好态势。此外，前三季度，全国一般公共预算收入同比增长8.9%，其中，全国税收收入增长11.9%，财政支出同比增长3.9%。这些数据表明，中国经济正在转向高质量发展，新动能正在发挥作用。至于地方政府债务，截至2022年末，全国地方政府法定债务余额35.1万亿元，全国政府债务余额61万亿元，全国政府法定负债率为50.4%，低于国际通行的60%警戒线，也低于主要市场经济国家和新兴市场国家，说明我国的债务风险总体可控。\n--------------------------\n近期的全球政治经济事件\n在您的问题中，并没有提供一个具体的问题，如果您是想了解近期的全球政治经济事件，我能从您提供的信息中提供以下概览：\n\n近期全球政治经济形势变化多端，主要表现在以下几个方面：\n\n1. 高通胀问题：海外高通胀现象短期内难以消解，这对全球经济产生了较大影响。\n\n2. 科技变革：以人工智能为代表的科技变革正在快速发展，这将对全球经济的发展模式产生深远影响。\n\n3. 企业投资变化：在这样的大环境下，全球企业在投资目的和金融需求等方面正在发生变化，例如“引进来”和“走出去”的策略。\n\n德意志银行的高级管理层也就这些变化进行了详细的讨论，关注了四大趋势以及金融服务的相应变化。\n\n以上是近期全球政治经济事件的主要信息，如果有其他具体问题，欢迎继续提问。\n--------------------------\n近期的金融政策变动\n你好，近期中国的金融政策变动主要围绕住房信贷政策的调整和优化：\n\n1. 中国人民银行和金融监管总局联合发布通知，调整优化差别化住房信贷政策。具体来说，首套住房商业性个人住房贷款最低首付比例统一为不低于20%，二套住房商业性个人住房贷款最低首付比例统一为不低于30%。此外，二套住房商业性个人住房贷款利率政策下限调整为不低于相应期限贷款市场报价利率加20个基点。\n\n2. 针对存量首套住房贷款利率，推出了降低措施。假设按揭利率下调50个基点，预计全行业每年可减少房贷利息约2000亿元，相当于增加了2022年社会消费品零售额的0.5%。对个人而言，假设存量按揭利率下调80个基点，按照100万元按揭贷款、等额本息还款计算，估算可降低借款人月供约500元，节约月供和总还款额约8%。\n\n3. 央行和银保监会印发通知，支持房地产市场平稳健康发展，包括保持房地产融资平稳有序、积极做好“保交楼”金融服务、积极配合做好受困房地产企业风险处置等措施。\n\n这些政策调整有利于降低居民购房门槛和成本，促进潜在合理购房需求的释放，有望改善银行“资产荒”和对公地产敞口资产质量，对市场预期起到积极引导作用，共同带动后市销售企稳复苏。\n--------------------------\n近期的股市技术分析\n根据搜索到的信息，近期的股市呈现出以下特点和趋势：\n\n1. A股大盘指数出现调整，上证指数跌破3000点整数关口，其中华为算力、操作系统、存储芯片、ChatGPT、鸿蒙等热门题材全线回落。\n\n2. 在大盘持续走弱的背景下，食品板块逆势崛起，西部牧业、阳光乳业、一鸣食品、安记食品、南侨食品、春雪食品等多股涨停。北交所的食品股表现更为火爆，骑士乳业、润普食品、盖世食品均收获涨停板，欧福蛋业、朱老六、康比特、田野股份、驱动力等涨超10%。\n\n3. “平价消费”已成为近期市场的一大主线，休闲食品龙头良品铺子宣布业务端将实施17年来最大规模降价，300款产品平均降价22%，最高降幅45%。\n\n4. 中国休闲食品行业市场规模持续增长，从2010年的4100亿元增长至2022年的11654亿元，预计2027年中国休闲食品行业市场规模将达12378亿元。\n\n5. 从技术分析角度看，今天的阴线出现了实体吞没上个阳线的技术信号，但目前所处走势位置来看指标并不需要过多关注，只有在重要行情末尾出现类似技术信号需要预判防止风险扩大。\n\n6. 本周的技术指标依旧收敛，最高价受到5ma压制，周线倒鸭嘴结构可能就做出来了。创业板今天跌的多跌幅0.9%，今天量能也放出近七个交易日最大量。\n\n整体来看，股市出现调整，食品板块逆势崛起，平价消费成为市场趋势，中国休闲食品行业市场规模持续增长，技术指标显示周线倒鸭嘴结构可能形成。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "首先，根据目前的股市行情，我们看到市场正在经历一个阶段性的下跌过程，尽管这也是市场筑底过程的一部分。大盘指数，如沪指已跌破3000点，而创业板指也出现了下跌。这些都表明市场在短期内可能继续处于震荡下行的状态。\n\n另一方面，我们要关注近期的宏观经济指标。目前，中国的M2余额同比增加了10.3%，规模以上工业增加值同比增加了4.6%，显示出经济正在持续恢复。尽管有的机构将中国主权信用评级展望调整为“负面”，但我们看到中国的经济基本面仍然稳健，且预计四季度中国经济将保持回升向好态势。\n\n然而，全球政治经济形势的变化以及近期的金融政策变动也会对股市产生影响。例如，海外高通胀现象短期内难以消解，这对全球经济产生了较大影响。同时，中国央行和金融监管总局调整了住房信贷政策，这也将对资金市场流动性产生影响。\n\n至于股市的技术分析，我们看到今天的阴线出现了实体吞没上个阳线的技术信号，显示出市场可能继续下行。而从市场板块来看，食品板块逆势崛起，这可能预示着市场将出现结构性机会。\n\n综合考虑以上因素，我认为市场有可能在短期内继续下行震荡。但这也是市场筑底的过程，一旦出现反弹信号和量能走好，就可能是市场的转折点。因此，投资者需要保持耐心，适当做好风险控制，并关注市场的变化，寻找结构性投资机会。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-24，星期3\n-------------------------------------------------------------\n我的问题是：标题: 小米汽车专利可预热电池提高充电效率\n描述: 天眼查App显示，1月23日，小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 小米汽车专利可预热电池提高充电效率\n描述: 天眼查App显示，1月23日，小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。\n===\n1. 技术创新：小米汽车科技有限公司申请的“车辆电池预研方法、装置、车辆及可读存储介质”专利获授权。这表明小米正在不断研发创新，以提高电池的充电效率，这将有助于提升小米汽车的续航能力和用户体验。\n\n2. 产品配置与市场竞争：小米汽车SU7硬件配置包括800V高压快充、骁龙8295座舱芯片、一体式激光雷达、双电机全轮驱动等，具备了一辆爆款车的基本要素。然而，随着汽车市场智能化，未来竞争将更多侧重于智能驾驶和智能座舱等软件方面。小米表示，全栈自研智能驾驶技术，并将大模型技术全面融入，可以带来更精准的判断，更强、更安全的智能驾驶体验。\n\n3. 生态优势：至2023Q3，小米已成为全球最大的消费级IoT平台，连接设备数已达6.55亿台，拥有5个及以上小米IoT设备的用户已达1300万。小米汽车的加入，将推动\"人车家全生态\"闭环，小米澎湃OS的诸多特性和完备流畅的生态体验，将全部在小米汽车上实现。小米汽车产品得到优酷、腾讯视频、哔哩哔哩等多家主流APP的生态互联支持，消费者可以体验手机与车机跨端的无感连接。\n--------------------------\n分析小米汽车科技有限公司该专利对电动汽车充电效率提升的具体作用。\n===\n1. 创新驱动：小米汽车科技有限公司近期申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权，彰显其在电动汽车技术领域的创新能力。该专利通过预热电池，提升充电性能，从而缩短充电时间，提高整体充电效率。\n2. 预热机制：该专利的核心是一个精细的预热机制。该机制首先确定目标充电桩和车辆行驶至充电桩时的预估剩余电量，然后根据充电桩的充电功率和剩余电量，确定电池的目标温度和加热速率，从而确定预估加热时长。\n3. 实施效应：最后，根据预估加热时长和车辆行驶至目标充电桩的行驶时长，对电池进行加热，以确保电池在到达充电桩时达到最佳充电状态。这种方法有望提升电动汽车的使用便捷性，可能会对电动汽车市场产生积极影响。\n--------------------------\n探寻小米汽车科技有限公司申请该专利的背景和目的。\n===\n1. 电池和车身一体化：小米汽车科技有限公司申请的“电池及车身一体化结构、车辆”专利公布。这项技术的实现将使电池包壳体的刚度和侧碰传力性能得到提高，同时，通过降低车身结构的自重，实现车辆的轻量化，提高车辆的续航里程。\n2. 电池预热技术：小米汽车申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。这个技术可以提升充电性能，减少充电时间，进而提高电池的充电效率。其方法包括预估车辆行驶至目标充电桩的剩余电量，确定电池的目标温度和第一目标加热速率，对电池进行加热。\n3. 传感器模组：北京小米移动软件有限公司申请了一项名为“传感器模组和具有其的电子设备”的专利。该传感器模组的结构设计合理，空间利用率高，内含第一传感器和第二传感器，这两个传感器与柔性电路板电连接，电路板环绕在支架上，传感器沿支架的厚度方向堆叠。\n--------------------------\n考察小米汽车科技有限公司申请该专利后，对公司自身及电动车市场可能产生的影响。\n===\n1. 技术创新驱动：小米汽车科技有限公司的新专利“车辆电池预热方法、装置、车辆及可读存储介质”已于1月23日获得授权。这一技术以提升充电性能减少充电时间，进而提高电池的充电效率为目标，体现了小米在电动车领域的技术创新力度。\n2. 技术细节揭示：该技术通过确定目标充电桩、预估剩余电量，然后根据充电功率和剩余电量确定电池的目标温度及预热速率，最后根据预估加热时长和行驶时长对电池进行加热。这一流程的设定无疑将电池预热与行驶计划紧密结合，提升了充电效率。\n3. 对电动车市场影响：这项新技术的应用不仅将提升小米汽车自身的充电效率，也将对整个电动车市场产生深远影响。它将推动电动车市场的技术创新，从而优化用户体验，提高电动车的市场接纳度。\n--------------------------\n查找其他电动车公司是否有类似的电池预热专利，进行比较。\n===\n1. 先进技术的保护：从比亚迪和小米汽车的行动中可以看出，电动车公司对电池预热的专利保护越来越重视。比如比亚迪股份有限公司获得了一项名为“车辆、能量转换装置及其控制方法”的专利，可以提升电池包的加热效率。小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利，旨在提升充电性能减少充电时间，进而提高电池的充电效率。\n2. 专利技术的创新性：这些公司的专利都体现出深厚的技术创新能力。比如，比亚迪的专利可以通过对车辆状态和/或车辆性能参数的检测，选择相应的加热控制模式，实现对电池包进行加热过程的精确控制。小米汽车的专利则能够预估车辆行驶至目标充电桩时的剩余电量，根据预估加热时长和车辆行驶至目标充电桩的行驶时长，对电池进行加热。\n3. 行业竞争的激烈程度：从这些公司的专利申请和授权情况来看，电动车行业的竞争已经进入到技术层面，每家公司都在努力提高自己的技术门槛，以取得市场的竞争优势。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 技术创新驱动：小米汽车科技有限公司的新专利“车辆电池预热方法、装置、车辆及可读存储介质”获得授权，表明公司在电池充电效率提升方面进行了积极的技术创新。通过预热电池，可以提高充电性能，减少充电时间，从而提升电池的充电效率。\n\n2. 产品优势凸显：该专利的授权将有助于小米汽车在电动汽车市场中获取竞争优势。电池充电效率的提升可以提升电动汽车的使用便捷性，提高续航能力，为消费者带来更优质的用户体验。\n\n3. 行业影响拓展：该专利的授权不仅对小米汽车科技有限公司自身的发展具有积极意义，同时也可能推动整个电动车行业的技术进步。预热电池的技术可以提高电动车的充电效率，对于推动电动车的市场普及具有重要作用。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-23，星期2\n-------------------------------------------------------------\n我的问题是：标题: 合盛硅业于内蒙古投资成立绿能科技公司\n描述: 近日，内蒙古创源绿能科技有限公司成立，经营范围包含太阳能热发电装备销售等。企查查股权穿透显示，该公司由合盛硅业间接全资控股。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 合盛硅业于内蒙古投资成立绿能科技公司\n描述: 近日，内蒙古创源绿能科技有限公司成立，经营范围包含太阳能热发电装备销售等。企查查股权穿透显示，该公司由合盛硅业间接全资控股。\n===\n1. 硅业巨头的新动向：合盛硅业近期在内蒙古设立全资子公司——创源绿能科技有限公司，注册资本5000万，主要经营业务为太阳能热发电装备销售等新能源相关业务。这一举措是合盛硅业在积极布局光伏和碳化硅领域，以实现硅基新材料的产业化的一部分。\n\n2. 项目投资概览：近年来，合盛硅业在多个领域出击，项目投资金额巨大。公司通过“光伏+碳化硅”的布局，致力于打造成为硅基新材料领域的领导者，开启新的增长曲线。其六大项目合计项目金额为653.38亿元，累计投入金额为229.69亿元。\n\n3. 产业链布局：合盛硅业不仅布局硅料业务，也同步向下游扩张，进入组件、光伏玻璃等领域。在中部和东部地区投资光伏玻璃项目和组件项目，预计在2023年下半年至2024年一季度陆续投产。这些举措有望进一步提升合盛硅业的产业链竞争力。\n--------------------------\n合盛硅业在内蒙古成立绿能科技公司的详细信息\n===\n1. 跨地区布局：合盛硅业近期在浙江嘉兴和内蒙古相继设立了两家全资子公司，分别是浙江合量科技有限公司和内蒙古创源绿能科技有限公司。这两家公司的经营范围主要涉及新材料技术研发、电子专用设备销售、储能技术服务等领域，展现了合盛硅业在光伏和新材料领域的战略布局。\n2. 资本运作加速：这两家新设立的公司的注册资本分别为1亿元和5000万元，显示出合盛硅业在资本运作上的决心和实力。此外，合盛硅业（万宁）有限公司也全资持有一家在内蒙古设立的新材料科技公司，进一步丰富了公司的资本结构和业务布局。\n3. 行业前景展望：随着新能源和新材料行业的快速发展，合盛硅业此次的行动无疑为公司的未来发展奠定了坚实的基础。而其在新能源和新材料领域的布局，也预示着该行业的广阔前景和巨大商业价值。\n--------------------------\n合盛硅业在新能源领域的投资布局\n===\n1. 光伏产业链布局：合盛硅业在新疆地区整合低成本资源，进一步布局多晶硅、光伏玻璃、电池片、组件等光伏产业链产品。其中，新疆中部合盛的年产20万吨多晶硅与150万吨新能源装备用超薄高透光伏玻璃项目有望于2023年第三季度投产，而东部合盛的年产20万吨高纯多晶硅项目预计将于2024年第一季度陆续投产。此外，公司的年产20GW光伏组件项目预计从2023年第四季度开始执行。\n2. 工业硅项目进展：云南合盛年产80万吨工业硅及配套项目一期预计于2024年完工。这一项目的完成将进一步提升合盛硅业在工业硅市场的竞争力，提高公司的整体盈利能力。\n3. 硅基新材料产业化：合盛硅业正在积极布局光伏和碳化硅领域，以实现硅基新材料的产业化。公司于2023年5月通过了2万片宽禁带半导体碳化硅衬底及外延片产业化生产线的验收，6英寸晶体良率达到了90%,外延片良率达到了95%，同时8英寸衬底也实现了量产。这显示出合盛硅业在硅基新材料领域的技术实力和市场竞争力。\n\n--------------------------\n内蒙古创源绿能科技有限公司的经营范围和业务概述\n用户的问题：请问内蒙古的新能源发展现状如何，有哪些企业在其中起主导作用？\n\n===\n1. 公司布局：内蒙古创源绿能科技有限公司由合盛硅业间接全资控股，注册资本5000万元，经营范围包括太阳能热发电装备销售、新能源原动设备制造、储能技术服务等。公司的成立标志着合盛硅业在新能源领域的布局进一步扩大。\n\n2. 项目推动：内蒙古三峡蒙能能源有限公司在库布其沙漠鄂尔多斯中北部新能源基地建设的光伏项目已并网发电，总装机规模1600万千瓦。项目建成后，每年可向京津冀地区输送清洁电能400亿千瓦时。同时，杭锦旗朔方新能源大基地规划建设面积48万余亩，总规模1300万千瓦。\n\n3. 发展状况：截至2023年12月底，内蒙古新能源装机规模突破9200万千瓦，全年新增装机超过3100万千瓦，增量创历史新高。内蒙古新能源项目建设步履不停，风光资源富集，已印发实施了新能源倍增行动实施方案、光伏治沙行动实施方案、促进新能源消纳若干举措等多项政策，为加快新能源项目建设提供了有力的政策保障。\n--------------------------\n合盛硅业的股权结构及其对内蒙古创源绿能科技有限公司的控股情况\n===\n1. 创新控股：根据企查查APP显示，合盛硅业近日间接全资控股了新成立的内蒙古创源绿能科技有限公司。该公司注册资本5000万元，经营范围包含太阳能热发电装备销售、新能源原动设备制造、储能技术服务以及合同能源管理等。\n\n2. 新材料布局：同时，合盛硅业也间接全资控股了新成立的内蒙古源芯新材料有限公司。该公司注册资本1亿元，主要经营范围为新材料技术研发、电子专用材料制造、电子专用材料研发以及电子专用设备销售等。\n\n3. 多元化发展：这两次控股行为显示出合盛硅业在业务上的多元化发展趋势，企业不仅在新能源领域进行了布局，同时也在新材料领域进行了深度探索。这对于合盛硅业的长期发展来说，无疑是一种积极的尝试和布局。\n--------------------------\n内蒙古创源绿能科技有限公司成立对合盛硅业战略发展的影响\n===\n1. 合盛硅业战略布局：合盛硅业正积极布局光伏和碳化硅领域，以实现硅基新材料的产业化。公司已通过了2万片宽禁带半导体碳化硅衬底及外延片产业化生产线的验收，并具备量产能力。同时还计划投资光伏玻璃项目和组件项目，预计的投产时间已经公布，表明公司对于硅基新材料领域的长远发展有明确的规划和目标。\n2. 创源绿能科技有限公司成立：最近，合盛硅业间接全资控股的内蒙古创源绿能科技有限公司成立，注册资本5000万元，经营范围包含太阳能热发电装备销售；新能源原动设备制造；储能技术服务；合同能源管理等。这一举措进一步强化了合盛硅业在新能源领域的布局。\n3. 内蒙古新能源发展态势：内蒙古正在全面高质量发展新能源项目，例如库布其沙漠鄂尔多斯中北部新能源基地总装机规模1600万千瓦，每年可向京津冀地区输送清洁电能400亿千瓦时。此外，全区新能源装机规模突破9200万千瓦，全年新增装机超过3100万千瓦。这些数据显示出内蒙古在新能源领域的巨大潜力和发展空间，为合盛硅业提供了广阔的市场和良好的发展环境。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 硅业巨头布局新能源：近日，合盛硅业在内蒙古设立了全资子公司——内蒙古创源绿能科技有限公司，主要经营业务为太阳能热发电装备销售等新能源相关业务。这显然是合盛硅业在新能源领域进一步深化布局的重要举措。\n\n2. 业务拓展及发展策略：合盛硅业此次的举动，不仅扩大了其在新能源领域的战略布局，同时也展现出了公司在追寻可持续发展，积极拓展绿色能源业务的决心。这种策略有利于公司在未来新能源市场的竞争中获得更大的优势。\n\n3. 对于行业影响：该公司的成立将进一步推动新能源行业的发展，尤其是在太阳能热发电装备销售等领域。同时，这也可能会对未来的新能源市场格局产生一定影响。"}], "source": "self-made"} + + diff --git a/dataset/peer_financial_training_dataset/v20240704/planning_agent_data.jsonl b/dataset/peer_financial_training_dataset/v20240704/planning_agent_data.jsonl new file mode 100644 index 00000000..c84ad491 --- /dev/null +++ b/dataset/peer_financial_training_dataset/v20240704/planning_agent_data.jsonl @@ -0,0 +1,11 @@ +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：全球十大银行减免服务费对全球金融市场的影响是什么？\n 今天的日期是：2024-01-30\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion： 用户提问的是全球十大银行减免服务费对全球金融市场的影响，这是一个涉及全球金融市场的大事件，可能涉及到政策、经济、行业等多个方面的影响。这就需要我们从政策出台的背景、影响的多空情况，以及事件对基本面、政策面、情绪面、估值面、资金面的影响等多个角度去考虑。此外，这个问题涉及的是全球十大银行，所以我们需要对这十家银行的具体情况有所了解。\nanswer：1.全球十大银行减免服务费的政策出台背景和原因\n2.全球十大银行减免服务费政策对行业基本面、流动性、风险偏好的影响\n3.全球十大银行减免服务费政策对行业的短期和中长期多空影响\n4.全球十大银行减免服务费事件对全球金融市场基本面、政策面、情绪面、估值面、资金面的影响\n5.全球十大银行减免服务费的具体情况和影响力度。"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：对2024年3月15日欧洲央行会议结果和拉加德的讲话表态进行分析。\n 今天的日期是：2024-01-30\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion： 用户的问题是要求对2024年3月15日欧洲央行会议结果和拉加德的讲话表态进行分析。这个问题的隐含意图是想了解这次会议和讲话对于行业政策、经济、股市等方面的影响。需要从政策出台背景、政策对行业的影响、大事对股市的影响等多个方面进行考虑。这个问题只涉及到一个实体，即欧洲央行，不需要拆分。\nanswer：1. 2024年3月15日欧洲央行会议结果的具体内容\n2. 拉加德在2024年3月15日欧洲央行会议上的讲话内容\n3. 2024年3月15日欧洲央行会议结果对行业政策的影响\n4. 拉加德在2024年3月15日欧洲央行会议上的讲话对行业政策的影响\n5. 2024年3月15日欧洲央行会议结果和拉加德讲话对股市的影响"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：明年的失业率会降低吗？\n 今天的日期是：2024-01-30\n 背景信息是：1.从宏观经济环境分析 2.从政策导向角度考虑 3.结合市场预期分析 4.从经济指标同比、环比的趋势分析 5.从经济指标的细分项分析\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion： 用户的问题是关于明年失业率的预测，这是一个宏观经济问题，需要从多个角度进行分析。首先，我们可以从宏观经济环境的角度来看，比如GDP增长率、通货膨胀率等因素可能会影响失业率。其次，我们可以从政策导向的角度来看，比如政府是否有相关的就业政策。再者，我们可以从市场预期的角度来看，比如市场对于明年经济的预期如何。最后，我们还可以从经济指标的趋势和细分项来看，比如近年来的失业率趋势如何，哪些行业的失业率较高等。这些都可能会影响到明年的失业率。\nanswer：\n1.2024年宏观经济环境预测\n2.2024年政府就业政策\n3.对2024年经济的市场预期\n4.近年来失业率的趋势\n5.各行业的失业率情况"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：在未来几个月内，房地产行业是否有可能实现恢复？\n 今天的日期是：2024-01-30\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion： 用户的问题是想了解未来几个月内房地产行业是否有可能实现恢复。这个问题涉及到对房地产行业的未来走势的预测，需要从多个维度进行分析。这些维度包括宏观环境、经济政策、产业政策、行业景气度指标、龙头企业行为、重要资讯事件以及板块估值和拥挤度等。这个问题并没有涉及到具体的公司或实体，所以不需要拆分。\nanswer：1. 未来几个月内中国的宏观经济环境预测\n2. 未来几个月内中国的经济政策对房地产行业的影响\n3. 未来几个月内中国的产业政策对房地产行业的影响\n4. 未来几个月内中国房地产行业的景气度指标预测\n5. 未来几个月内中国房地产行业龙头企业的行为分析"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：未来三年新能源汽车零部件行业海外市场和国内市场战略是什么样的？\n 今天的日期是：2023-11-06\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion：首先，我们需要了解新能源汽车零部件行业在未来三年的国内市场战略，这涉及到行业的发展趋势、政策支持、市场需求等因素。其次，我们需要了解新能源汽车零部件行业在未来三年的海外市场战略，这涉及到行业的发展趋势、政策支持、市场需求等因素。最后，我们需要比较和分析这两个市场的战略，找出它们的相同点和不同点，以及各自的优势和挑战。\nanswer：\n1. 未来三年新能源汽车零部件行业在中国的市场战略是什么？\n2. 未来三年新能源汽车零部件行业在海外的市场战略是什么？\n3. 未来三年新能源汽车零部件行业的国内市场战略和海外市场战略有何相同之处？\n4. 未来三年新能源汽车零部件行业的国内市场战略和海外市场战略有何不同之处？\n5. 未来三年新能源汽车零部件行业在国内市场和海外市场的战略中各自的优势和挑战是什么？"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：美国标普500指数最近一次的成分股调整是在什么时候？调入和调出的股票有哪些？\n 今天的日期是：2024-01-30\n 背景信息是：1.查询的时间 2.查询的对象 3.查询的结果 4.查询的原因 5.查询的影响\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion： 用户需要了解美国标普500指数最近一次的成分股调整的时间以及调入和调出的股票，这可能是因为用户想了解标普500指数的构成变化，以便对投资策略做出调整。我们需要从两个方面来获取信息，一是标普500的成分股调整时间，二是调整的具体内容，包括调入和调出的股票。\nanswer：\n1. 最近一次美国标普500指数的成分股调整时间\n2. 最近一次美国标普500指数调整中调入的股票\n3. 最近一次美国标普500指数调整中调出的股票"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：存量首套房贷利率调整对银行的影响是什么？ \n 今天的日期是：2023-11-06\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion：首先，我们需要理解存量首套房贷利率调整对银行的影响主要体现在哪些方面，可能包括银行的贷款收入、风险控制、资产质量等。其次，我们需要了解存量首套房贷利率的调整是如何影响这些方面的。最后，我们需要找出具体的数据或案例来支持这些影响。\nanswer：\n1.存量首套房贷利率调整对银行贷款收入的影响是什么？\n2.存量首套房贷利率调整对银行风险控制的影响是什么？\n3.存量首套房贷利率调整对银行资产质量的影响是什么？\n4.有哪些具体的数据或案例可以证明存量首套房贷利率调整对银行的影响？\n5.存量首套房贷利率调整是如何影响银行的？"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：请帮我分析一下字节跳动、腾讯和阿里巴巴，这三家公司近期的收益表现，并比较他们的差异。\n 今天的日期是：2024-01-30\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion： 用户想了解字节跳动、腾讯和阿里巴巴这三家公司近期的收益表现以及他们之间的差异。因此，我们需要分析这三家公司近期的财务状况，包括收入、利润等财务指标，以及他们的股价表现。同时，我们也需要对比这三家公司的财务状况，找出他们的差异。由于问题涉及到三家公司，我们需要分别对这三家公司进行搜索。\nanswer：1.字节跳动近期的财务报表和收益情况\n2.腾讯近期的财务报表和收益情况\n3.阿里巴巴近期的财务报表和收益情况\n4.字节跳动、腾讯和阿里巴巴近期财务报表的对比分析\n5.字节跳动、腾讯和阿里巴巴近期收益表现的对比分析"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：月薪4000，如何存5万？\n 今天的日期是：2024-01-30\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion：首先，这个问题是关于如何储蓄的，所以我们需要考虑的因素包括收入、支出、储蓄目标等。其次，提问者的月薪是4000，储蓄目标是5万，所以我们需要考虑的是如何在这个收入水平下达到这个储蓄目标。最后，由于这是一个金融问答app，我们可以考虑从投资的角度来解答这个问题，比如推荐一些适合提问者的投资产品或策略。\nanswer：\n1.月薪4000元的人如何制定合理的生活预算\n2.月薪4000元的人如何进行理财投资\n3.月薪4000元的人如何达到5万的储蓄目标\n4.月薪4000元的人如何选择合适的投资产品\n5.月薪4000元的人如何提高储蓄率"} +{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样的信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：深入分析上海住户存款增加的影响是什么？\n 今天的日期是：2023-10-23\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion：首先，我们需要了解上海住户存款增加的具体情况，包括增加的幅度、时间等。其次，我们需要了解存款增加对上海的经济环境产生了什么影响，比如是否推动了消费，是否影响了房地产市场等。最后，我们还需要了解存款增加对上海住户自身的影响，比如是否改变了他们的消费习惯，是否影响了他们的投资决策等。\n\nanswer：\n1. 上海住户存款增加的具体情况是什么？\n2. 上海住户存款增加对上海的经济环境产生了什么影响？\n3. 上海住户存款增加对上海住户自身的影响是什么？\n4. 上海住户存款增加是否推动了消费？\n5. 上海住户存款增加是否影响了房地产市场？"} + From ed359d09bdbe22b5bce5bbefb8a80202d3c058f6 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Thu, 4 Jul 2024 12:13:46 +0800 Subject: [PATCH 091/118] improvement: tweak set_by_agent_model method and remove deep copy logic. --- agentuniverse/agent/memory/chat_memory.py | 13 +++++++------ agentuniverse/agent/memory/memory.py | 9 ++++++--- agentuniverse/agent/plan/planner/planner.py | 10 ++++------ agentuniverse/llm/llm.py | 19 +++++++++++-------- agentuniverse/llm/openai_llm.py | 19 +++++++++---------- agentuniverse/llm/openai_style_llm.py | 12 +++++++----- .../en/2_2_5_Memory_Define_And_Use.md | 2 +- ...11\344\270\216\344\275\277\347\224\250.md" | 2 +- 8 files changed, 46 insertions(+), 40 deletions(-) diff --git a/agentuniverse/agent/memory/chat_memory.py b/agentuniverse/agent/memory/chat_memory.py index 4e29d158..8eb297ff 100644 --- a/agentuniverse/agent/memory/chat_memory.py +++ b/agentuniverse/agent/memory/chat_memory.py @@ -50,17 +50,18 @@ def as_langchain(self) -> BaseChatMemory: max_token_limit=self.max_tokens, messages=self.messages, prompt_version=self.prompt_version) - def set_by_agent_model(self, **kwargs) -> None: + def set_by_agent_model(self, **kwargs): """ Assign values of parameters to the ChatMemory model in the agent configuration.""" - super().set_by_agent_model(**kwargs) + copied_obj = super().set_by_agent_model(**kwargs) if 'messages' in kwargs and kwargs['messages']: - self.messages = kwargs['messages'] + copied_obj.messages = kwargs['messages'] if 'llm' in kwargs and kwargs['llm']: - self.llm = kwargs['llm'] + copied_obj.llm = kwargs['llm'] if 'input_key' in kwargs and kwargs['input_key']: - self.input_key = kwargs['input_key'] + copied_obj.input_key = kwargs['input_key'] if 'output_key' in kwargs and kwargs['output_key']: - self.output_key = kwargs['output_key'] + copied_obj.output_key = kwargs['output_key'] + return copied_obj def initialize_by_component_configer(self, component_configer: MemoryConfiger) -> 'ChatMemory': """Initialize the chat memory by the ComponentConfiger object. diff --git a/agentuniverse/agent/memory/memory.py b/agentuniverse/agent/memory/memory.py index a76389d5..edee9235 100644 --- a/agentuniverse/agent/memory/memory.py +++ b/agentuniverse/agent/memory/memory.py @@ -40,12 +40,15 @@ def as_langchain(self) -> BaseMemory: """Convert the agentUniverse(aU) memory class to the langchain memory class.""" pass - def set_by_agent_model(self, **kwargs) -> None: + def set_by_agent_model(self, **kwargs): """ Assign values of parameters to the Memory model in the agent configuration.""" + # note: default shallow copy + copied_obj = self.model_copy() if 'memory_key' in kwargs and kwargs['memory_key']: - self.memory_key = kwargs['memory_key'] + copied_obj.memory_key = kwargs['memory_key'] if 'max_tokens' in kwargs and kwargs['max_tokens']: - self.max_tokens = kwargs['max_tokens'] + copied_obj.max_tokens = kwargs['max_tokens'] + return copied_obj def get_instance_code(self) -> str: """Return the full name of the memory.""" diff --git a/agentuniverse/agent/plan/planner/planner.py b/agentuniverse/agent/plan/planner/planner.py index c30fd311..869262a2 100644 --- a/agentuniverse/agent/plan/planner/planner.py +++ b/agentuniverse/agent/plan/planner/planner.py @@ -84,11 +84,10 @@ def handle_memory(self, agent_model: AgentModel, planner_input: dict) -> ChatMem params['input_key'] = self.input_key params['output_key'] = self.output_key - memory: ChatMemory = MemoryManager().get_instance_obj(component_instance_name=memory_name, new_instance=True) + memory: ChatMemory = MemoryManager().get_instance_obj(component_instance_name=memory_name) if memory is None: return None - memory.set_by_agent_model(**params) - return memory + return memory.set_by_agent_model(**params) def run_all_actions(self, agent_model: AgentModel, planner_input: dict, input_object: InputObject): """Tool and knowledge processing. @@ -142,9 +141,8 @@ def handle_llm(self, agent_model: AgentModel) -> LLM: LLM: The language model. """ llm_name = agent_model.profile.get('llm_model').get('name') - llm: LLM = LLMManager().get_instance_obj(component_instance_name=llm_name, new_instance=True) - llm.set_by_agent_model(**agent_model.profile.get('llm_model')) - return llm + llm: LLM = LLMManager().get_instance_obj(component_instance_name=llm_name) + return llm.set_by_agent_model(**agent_model.profile.get('llm_model')) def initialize_by_component_configer(self, component_configer: PlannerConfiger) -> 'Planner': """Initialize the planner by the PlannerConfiger object. diff --git a/agentuniverse/llm/llm.py b/agentuniverse/llm/llm.py index 4d33c629..b295132d 100644 --- a/agentuniverse/llm/llm.py +++ b/agentuniverse/llm/llm.py @@ -107,22 +107,25 @@ def initialize_by_component_configer(self, component_configer: LLMConfiger) -> ' self._max_context_length = component_configer.configer.value['max_context_length'] return self - def set_by_agent_model(self, **kwargs) -> None: + def set_by_agent_model(self, **kwargs): """ Assign values of parameters to the LLM model in the agent configuration.""" + # note: default shallow copy + copied_obj = self.model_copy() if 'model_name' in kwargs and kwargs['model_name']: - self.model_name = kwargs['model_name'] + copied_obj.model_name = kwargs['model_name'] if 'temperature' in kwargs and kwargs['temperature']: - self.temperature = kwargs['temperature'] + copied_obj.temperature = kwargs['temperature'] if 'request_timeout' in kwargs and kwargs['request_timeout']: - self.request_timeout = kwargs['request_timeout'] + copied_obj.request_timeout = kwargs['request_timeout'] if 'max_tokens' in kwargs and kwargs['max_tokens']: - self.max_tokens = kwargs['max_tokens'] + copied_obj.max_tokens = kwargs['max_tokens'] if 'max_retries' in kwargs and kwargs['max_retries']: - self.max_retries = kwargs['max_retries'] + copied_obj.max_retries = kwargs['max_retries'] if 'streaming' in kwargs and kwargs['streaming']: - self.streaming = kwargs['streaming'] + copied_obj.streaming = kwargs['streaming'] if 'max_context_length' in kwargs and kwargs['max_context_length']: - self._max_context_length = kwargs['max_context_length'] + copied_obj._max_context_length = kwargs['max_context_length'] + return copied_obj def max_context_length(self) -> int: """Max context length. diff --git a/agentuniverse/llm/openai_llm.py b/agentuniverse/llm/openai_llm.py index 1839b036..77a7a851 100644 --- a/agentuniverse/llm/openai_llm.py +++ b/agentuniverse/llm/openai_llm.py @@ -93,8 +93,7 @@ def call(self, messages: list, **kwargs: Any) -> Union[LLMOutput, Iterator[LLMOu **kwargs: Arbitrary keyword arguments. """ streaming = kwargs.pop("streaming") if "streaming" in kwargs else self.streaming - if self.client is None: - self.client = self._new_client() + self.client = self._new_client() chat_completion = self.client.chat.completions.create( messages=messages, model=kwargs.pop('model', self.model_name), @@ -116,8 +115,7 @@ async def acall(self, messages: list, **kwargs: Any) -> Union[LLMOutput, AsyncIt **kwargs: Arbitrary keyword arguments. """ streaming = kwargs.pop("streaming") if "streaming" in kwargs else self.streaming - if self.async_client is None: - self.async_client = self._new_async_client() + self.async_client = self._new_async_client() chat_completion = await self.async_client.chat.completions.create( messages=messages, model=kwargs.pop('model', self.model_name), @@ -135,17 +133,18 @@ def as_langchain(self) -> BaseLanguageModel: """Convert the agentUniverse(aU) openai llm class to the langchain openai llm class.""" return LangchainOpenAI(self) - def set_by_agent_model(self, **kwargs) -> None: + def set_by_agent_model(self, **kwargs): """ Assign values of parameters to the OpenAILLM model in the agent configuration.""" - super().set_by_agent_model(**kwargs) + copied_obj = super().set_by_agent_model(**kwargs) if 'openai_api_key' in kwargs and kwargs['openai_api_key']: - self.openai_api_key = kwargs['openai_api_key'] + copied_obj.openai_api_key = kwargs['openai_api_key'] if 'openai_api_base' in kwargs and kwargs['openai_api_base']: - self.openai_api_base = kwargs['openai_api_base'] + copied_obj.openai_api_base = kwargs['openai_api_base'] if 'openai_proxy' in kwargs and kwargs['openai_proxy']: - self.openai_proxy = kwargs['openai_proxy'] + copied_obj.openai_proxy = kwargs['openai_proxy'] if 'openai_client_args' in kwargs and kwargs['openai_client_args']: - self.openai_client_args = kwargs['openai_client_args'] + copied_obj.openai_client_args = kwargs['openai_client_args'] + return copied_obj def max_context_length(self) -> int: """Max context length. diff --git a/agentuniverse/llm/openai_style_llm.py b/agentuniverse/llm/openai_style_llm.py index 0981e47a..444eab0a 100644 --- a/agentuniverse/llm/openai_style_llm.py +++ b/agentuniverse/llm/openai_style_llm.py @@ -121,15 +121,17 @@ def as_langchain(self) -> BaseLanguageModel: def set_by_agent_model(self, **kwargs) -> None: """ Assign values of parameters to the OpenAILLM model in the agent configuration.""" - super().set_by_agent_model(**kwargs) + copied_obj = super().set_by_agent_model(**kwargs) if 'api_key' in kwargs and kwargs['api_key']: - self.api_key = kwargs['api_key'] + copied_obj.api_key = kwargs['api_key'] if 'api_base' in kwargs and kwargs['api_base']: - self.api_base = kwargs['api_base'] + copied_obj.api_base = kwargs['api_base'] if 'proxy' in kwargs and kwargs['proxy']: - self.proxy = kwargs['proxy'] + copied_obj.proxy = kwargs['proxy'] if 'client_args' in kwargs and kwargs['client_args']: - self.client_args = kwargs['client_args'] + copied_obj.client_args = kwargs['client_args'] + return copied_obj + @staticmethod def parse_result(chunk): diff --git a/docs/guidebook/en/2_2_5_Memory_Define_And_Use.md b/docs/guidebook/en/2_2_5_Memory_Define_And_Use.md index 52d08d89..63ac2b36 100644 --- a/docs/guidebook/en/2_2_5_Memory_Define_And_Use.md +++ b/docs/guidebook/en/2_2_5_Memory_Define_And_Use.md @@ -62,7 +62,7 @@ You can get the Memory instance corresponding to its name using the `.get_instan ```python from agentuniverse.agent.memory.memory_manager import MemoryManager -memory = MemoryManager().get_instance_obj(component_instance_name=memory_name, new_instance=True) +memory = MemoryManager().get_instance_obj(component_instance_name=memory_name) ``` # Conclusion diff --git "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" index 386ea213..b7b5c9b3 100644 --- "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" +++ "b/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" @@ -64,7 +64,7 @@ chat_history=[{"content": "你好", "type": "human"}, {"content": "你好", "typ ```python from agentuniverse.agent.memory.memory_manager import MemoryManager -memory = MemoryManager().get_instance_obj(component_instance_name=memory_name, new_instance=True) +memory = MemoryManager().get_instance_obj(component_instance_name=memory_name) ``` # 总结 From e679ac55a43ee70082a0beb36537244be90d31d9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=89=BA=E9=A2=96=28yiying=29?= Date: Thu, 4 Jul 2024 15:47:54 +0800 Subject: [PATCH 092/118] add: methods to obtain more training dataset --- dataset/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/dataset/README.md b/dataset/README.md index 56654ab1..7c292a27 100644 --- a/dataset/README.md +++ b/dataset/README.md @@ -3,6 +3,8 @@ ## PEER Financial Training Dataset We open source 100 common financial questions and a training dataset for use within the PEER framework, containing 100 sets of inputs and outputs of the Planning Agent and the Expressing Agent. The dataset is applicable to agent training, pre-training, and supervised fine-tuning in various financial analysis scenarios. +For more training data, please provide your email via [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days. + Due to compliance issues, the relevant data of the Executing agent is still in the approval process, please wait patiently. ### 🌟 FinQA @@ -12,4 +14,4 @@ We have collected 100 professional questions from our business scenarios and cat Each set of data contains the input and output of the Planning Agent. The input is the original query proposed by the user. The output is the sub-questions generated by the agent, serving as the interpretation framework. ### 🌟 expressing agent data -Each set of data contains the input and output of the Expressing Agent. The input is the answer to each qub-question given by the Executing Agent through collecting information. The output is the professional answer after synthesis and rewriting. +Each set of data contains the input and output of the Expressing Agent. The input is the answer to each qub-question given by the Executing Agent through collecting information. The output is the professional answer after synthesis and rewriting. \ No newline at end of file From 3618aa0edfa82dbf7894e3cb2cf232666ee2cc34 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 4 Jul 2024 16:32:29 +0800 Subject: [PATCH 093/118] fix --- sample_standard_app/app/core/tool/request_tool.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sample_standard_app/app/core/tool/request_tool.py b/sample_standard_app/app/core/tool/request_tool.py index ae007614..e41e57ce 100644 --- a/sample_standard_app/app/core/tool/request_tool.py +++ b/sample_standard_app/app/core/tool/request_tool.py @@ -62,6 +62,8 @@ def initialize_by_component_configer(self, component_configer: ToolConfiger) -> self.headers = component_configer.configer.value.get('headers') self.method = component_configer.configer.value.get('method') self.response_content_type = component_configer.configer.value.get('response_content_type') + if 'json_parser' in component_configer.configer.value: + self.json_parser = component_configer.configer.value.get('json_parser') self.requests_wrapper = GenericRequestsWrapper( headers=self.headers, response_content_type=self.response_content_type From 484290bf5689380d2e105f9db5dae98025d0b6ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=89=BA=E9=A2=96=28yiying=29?= Date: Thu, 4 Jul 2024 16:44:15 +0800 Subject: [PATCH 094/118] add: methods to obtain more training dataset and fix expressing agent data --- .../v20240704/expressing_agent_data.jsonl | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl b/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl index ccbabf33..45ad93e0 100644 --- a/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl +++ b/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl @@ -1,12 +1,10 @@ {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-04-06，星期6\n-------------------------------------------------------------\n我的问题是：蔡崇信的访谈提到了什么？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n蔡崇信的访谈提到了什么？\n在阿里巴巴董事长蔡崇信的专访中，他表达了对公司未来发展的信心，并将电商和云计算定位为阿里巴巴的核心业务。他预测，未来五年，中国电商的市场渗透率将上升至40%或更高，推动阿里电商业务的增长。同时，他也看好中国经济的数字化进程为云计算业务提供的需求，并表示阿里正在关注AI电商和行业竞争的发展。\n\n在芯片和半导体方面，蔡崇信指出，由于美国对Nvidia等公司向中国出口高端芯片的严格限制，阿里的云业务和高端计算服务能力受到了影响。他认为这是一个大问题，但也看到解决的可能性，他预见在短期至中期，这会是一个问题，但长远来看，中国将发展自己制造这些高端GPU的能力。在限制解除前，由于已有的库存，大型语言模型的训练仍可以继续进行。\n\n此外，蔡崇信还透露，阿里正在加大对AI相关技术的投资，并致力于提升用户购买频次和订单量。\n--------------------------\n蔡崇信最近的访谈内容\n问题：蔡崇信最近的访谈有哪些重要内容？\n\n回答：\n在最近的访谈中，阿里巴巴董事会主席蔡崇信分享了对公司现状和未来发展的看法。他承认阿里巴巴在过去几年中存在一些问题，并强调了新任CEO吴泳铭对于用户体验的重视。对于中国经济，他认为中国市场仍具有巨大潜力，尽管目前存在一些挑战，例如房地产市场低迷，年轻人就业前景不明朗等。\n\n在谈到阿里巴巴的云业务时，他表示因为美国对华出口管制的影响，中国企业在高端芯片的使用上受限，这对阿里巴巴的云业务以及向客户提供高端计算服务的能力产生了影响。但他提到，中国正在加快发展自主芯片的设计和制造能力。\n\n此外，他还分享了自己的成长经历和工作体验，以及对企业文化、工作效率、领导力以及工作与生活平衡的看法。他建议年轻人要掌握一两项特长，如编程、特定领域的专业知识等，从而在人群中脱颖而出。同时，他还推荐年轻人学习数据科学、心理学等前沿学科。\n--------------------------\n蔡崇信近期对蚂蚁集团的发展规划的访谈\n问题：蔡崇信近期对蚂蚁集团的发展规划有何看法？\n\n答案：蔡崇信在最近的访谈中提到，过去几年，蚂蚁集团在一度忽视用户体验上有所欠缺，为此已经进行了重组。新的首席执行官相较于蔡崇信年轻12岁，他非常注重用户，专注于产品、界面和用户体验。蔡崇信表示，这一改变对蚂蚁集团的发展至关重要。他认为公司需要承认错误，然后改变与战略相适应的组织结构，并将公司的方向融入组织结构中。通过这种方式，他期望蚂蚁集团能够更好地服务用户，从而提升其在竞争中的地位。\n--------------------------\n蔡崇信近期对科技行业的看法和预测的访谈\n蔡崇信在近期的访谈中表示，中国在 AI 技术领域与美国存在一定差距，可能落后两年。去年10月，美国对Nvidia等公司向中国出口高端芯片的能力施加了严格的限制，这对中国的云业务及提供高端计算服务的能力产生了影响。蔡崇信认为这是一个大问题，短期和中期都会产生影响，但他也表示，中国正在努力发展自制高端GPU芯片的能力。\n\n他认为，尽管在短期内，人们已经储备了一定的库存，能够保证大型语言模型的训练在未来一年或18个月内能够继续进行，但从长远来看，中国需要自主制造这些高端芯片。\n\n蔡崇信还强调，中国不会失去世界工厂的地位，墨西哥、越南等国家相比中国仍有较大差距。他建议年轻人努力成为自己领域的前 10% 的佼佼者，以获得尊重和社会认可。\n--------------------------\n蔡崇信近期对中国经济形势的分析的访谈\n蔡崇信，阿里巴巴董事会主席、联合创始人近期在访谈中对中国经济形势进行了深入分析，并对在美运营的中国企业提出警示，重视数据隐私和网络安全等合规事项。他认为，许多西方人对中国存在绝对化的误解，事实往往介于两个极端之间。他还强调了中国市场对全球经济繁荣的重要性。\n\n关于芯片和半导体方面的问题，他指出，去年10月美国对Nvidia等公司向中国每家公司出口高端芯片的能力施加了非常严格的限制。这对阿里巴巴的云业务以及向客户提供高端计算服务的能力造成了影响。他表示，芯片短缺是一个大问题，人们正在努力解决。他认为，在限制解除之前，人们已经储备了库存，所以在未来一年或18个月内，大型语言模型的训练仍然可以继续进行。\n\n蔡崇信表示，从长远来看，中国将发展自己制造这些高端GPU的能力。他强调，不能打赌中国会输，中国市场对世界经济繁荣至关重要。\n--------------------------\n蔡崇信近期对全球金融市场的观察的访谈\n问题：请分析和总结蔡崇信对全球金融市场、阿里巴巴的发展和未来方向的观察和看法。\n\n答案：\n蔡崇信在近期的访谈中分享了他对全球金融市场、以及阿里巴巴未来发展的观察和看法。\n\n对全球金融市场，蔡崇信关注了美国对Nvidia等公司向中国出口高端芯片的限制，认为这对阿里巴巴的云业务和提供高端计算服务的能力产生了影响。他预计在短期和中期，这将是一个问题，但长期来看，中国将发展自己制造高端GPU的能力。\n\n针对阿里巴巴自身的发展，蔡崇信表示阿里巴巴过去忽视了用户体验，因此在重组过程中引入了新的CEO吴泳铭，以更加聚焦于用户为中心的产品和界面开发。他强调了人工智能在阿里的云业务中的重要性，认为拥有一个内部开发的优秀大型语言模型对于阿里的云业务至关重要。\n\n对于未来的方向，蔡崇信强调了中国消费市场的潜力。他指出，中国的制造产量约占全球的31%，但消费仅占全球的14%，存在显著的不平衡。他预期中国消费者将会变得更加重要，并且对GDP的贡献率会持续增长。\n\n此外，蔡崇信还建议年轻人学习基本的专业技能，尤其是数据科学和心理学，认为这些是在生活中取得成功所需的所有工具。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "在蔡崇信的最近一次访谈中，他分享了对阿里巴巴未来发展的信心和看法。主要内容包括：\n\n一、业务发展预测\n1. 他对电商和云计算的发展持乐观态度，认为这两项是阿里巴巴的核心业务。他预测，未来五年，中国电商市场的渗透率将上升至40%或更高，推动阿里电商业务的增长。\n2. 他看好中国经济的数字化进程，认为这将为阿里巴巴的云计算业务提供需求。\n3. 他透露阿里正在加大对AI相关技术的投资，并致力于提升用户购买频次和订单量。\n\n二、行业挑战\n1. 在芯片和半导体方面，蔡崇信指出，由于美国对Nvidia等公司向中国出口高端芯片的严格限制，阿里的云业务和高端计算服务能力受到了影响。\n2. 他认为这是一个大问题，但也看到解决方案，预测短期至中期，这会是一个问题；但长远看来，中国将发展自己制造这些高端GPU的能力。\n\n三、对蚂蚁集团的看法\n1. 他提到，蚂蚁集团在一度忽视用户体验上有所欠缺，为此已经进行了重组。新的首席执行官非常注重用户，专注于产品、界面和用户体验。\n2. 他认为这一改变对蚂蚁集团的发展至关重要，并希望通过这种方式，蚂蚁集团能够更好地服务用户，从而提升其在竞争中的地位。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：证券保险行业今年发生了啥？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n证券保险行业今年发生了啥？\n2023年，证券保险行业表现出长期向好的趋势，但也面临着转型调整的阵痛。上半年，保险公司原保险保费收入3.2万亿元，同比增长12.5%，其中人身险的原保险保费收入同比增长13.38%，显示了恢复性复苏的态势。然而，下半年可能会有调整。我国经济长期向好的基本面没有变化，因此保险行业尤其是寿险领域存在巨大增长空间。\n\n然而，人身保险发展正处于转型发展的阵痛期，面临业务增长持续承压、销售队伍转型升级、行业市场竞争加剧的多重压力。尽管伴随着社会复苏、需求回升，寿险市场慢慢回暖，但行业转型的战略窗口中存在诸多现实挑战。\n\n同时，风险也在增大。截至2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。这30家保险公司中有不少机构的资产规模在数千亿元，如果不能解决其偿付能力问题，可能会对投保人、投资者和市场产生较大的影响。\n\n政府一直在积极推动保险行业规范发展。2020年制定了《责任保险业务监管办法》，明确了责任保险的定义、保险公司的义务和保险服务的范围。2021年，国家金融监督管理总局通过了《保险集团公司监督管理办法》，旨在加强对保险集团公司的监督管理。2022年，国家金融监督管理总局在全国范围内开展了针对保险资金运用关联交易的专项检查，这是银保监会成立以来首次专门针对保险资金运用关联交易的专项检查，也是保险机构监管主体职责改革后首次大规模专项检查行动。整个行业都会持续向好发展。\n--------------------------\n2023年证券行业的重大事件\n2023年证券行业的重大事件主要包括以下几点：\n\n1. 4月，沪深交易所主板注册制首批企业上市，实现了股票发行注册制改革全面落地。同时，沪深港通交易日历优化上线实施，放开了两地市场全部共同交易日的沪深港通交易，极大地提升了交易效率。\n\n2. 5月，国家金融监督管理总局正式揭牌，标志着中国金融监管体系迈入“一行一局一会”新格局。\n\n3. 8月27日，A股政策密集落地，包括证监会阶段性收紧IPO节奏，要求上市公司募集资金应投向主营业务，严限多元化投资；财政部、税务总局公告交易印花税减半征收；证券交易所调降融资保证金比例，支持适度融资需求。\n\n4. 9月，国家金融监督管理总局发布《关于优化保险公司偿付能力监管标准的通知》，为增量险资入市进一步打开空间；同时，沪深交易所进一步规范股份减持行为。\n\n5. 10月11日，中央汇金同日增持四大行股份。\n\n6. 10月30日至31日，中央金融工作会议在北京举行，提出了推动股票发行注册制走深走实，发展多元化股权融资，大力提高上市公司质量，培育一流投资银行和投资机构等一系列资本市场改革发展的重点。\n\n此外，按照中原证券的研报，预计2023年全年肉鸡平均价格将同比上涨，这将推动养殖行业利润弹性逐步释放，相关上市公司的盈利水平将持续改善。中信证券的研报预计，2024年的公用事业行业盈利仍在扩张周期，有望在2024年跑赢市场。\n--------------------------\n2023年保险行业的重大事件\n2023年保险行业的重大事件主要体现在以下几个方面：\n\n1. 行业转型：保险机构开始进行市场细分，发掘消费者的个性化需求，向消费者提供差异化的保险产品及风险管理服务。同时，为消费者提供“好用不贵”的保险产品，也成为趋势。这可以理解为“找细分、要精准、要性价比”的转型过程。\n\n2. 行业挑战：我国人身保险发展正处于转型发展的阵痛期。面临业务增长持续承压、销售队伍转型升级、行业市场竞争加剧的多重压力。预定利率上限下调，导致阶段性承压。\n\n3. 保险公司风险：截至2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。此外，有13家保险公司的评级为C，3家评级为D，包括华夏人寿、恒大人寿、天安人寿等。若这些公司不能解决偿付能力问题，可能会对投保人、投资者和市场产生较大的影响。\n\n4. 监管加强：政府积极推动保险行业规范发展，通过了《责任保险业务监管办法》《保险集团公司监督管理办法》，并在全国范围内开展了针对保险资金运用关联交易的专项检查，这是银保监会成立以来首次专门针对保险资金运用关联交易的专项检查。行业准入门槛、偿付能力管理、业务监管，还是保险资金运用等方面，都只会越来越严格，越来越规范。\n--------------------------\n2023年证券行业的政策变动\n2023年，中国证监会发布了一系列政策，对证券行业进行了深度调整和改革。\n\n首先，证监会对全面实行注册制改革进行了深入学习和宣传，加快推进各条线制度规则的“立改废释”和体系优化，构建更加科学完备的资本市场法律体系。\n\n其次，证监会发布了券商风控指标意见稿，明确了监管方向从原来的鼓励全行业发展到扶优限劣、分类监管。这其中包括对连续3年分类评价AA级以上的券商，风险资本准备的系数可以调整为0.4，而D类则要调为2倍系数。这一政策不仅加快了行业优胜劣汰的速度，也为券商的资本使用效率提供了提升空间。\n\n再者，证监会对《证券公司风险控制指标计算标准规定》进行了修订，并向社会公开征求意见。其中包括，对券商的风险资本准备、表内外资产总额进行调整，分别给出不同的调整系数。同时，对券商在做市、资管、公募REITS等业务的计算标准进行了优化。\n\n最后，意见稿还加强了场外衍生品的监管力度，将股指期货、权益互换及卖出期权计算标准由20%调整至30%。\n\n总的来说，2023年证券行业的政策变动主要体现在全面实行注册制改革、对券商风控指标进行调整、优化风险控制指标计算标准以及加强场外衍生品的监管等方面。\n--------------------------\n2023年保险行业的政策变动\n2023年，中国保险行业的政策变动主要体现在以下几个方面：\n\n首先，财政部发布的《通知》将国有商业保险公司的经营效益类绩效评价指标“净资产收益率”的考核方式由“当年度指标”调整为“3年周期指标+当年度指标”的结合方式，以更好地引导保险公司追求长期稳健经营，防止盲目追求年度考核目标、忽视风险的短期行为。\n\n其次，《通知》将保险公司剩余期限10年期以上保单未来盈余计入核心资本的比例，从不超过35%提高至不超过40%，以鼓励保险公司发展长期保障型产品。\n\n同时，保险行业的发展也正面临一些挑战。例如，保险业投资收益及盈利水平面临增长压力，保险业投资端存量问题与新增风险并存，人身险行业转型仍处于攻坚克难阶段，财产险行业保险风险需持续关注。\n\n未来，保险行业的发展趋势主要包括金融科技进一步融入保险经营模式、行业分化效应将持续、保险行业整体业务结构持续优化。为此，专家建议保险资管公司应通过提升管理层内控和道德意识、健全完善内控系统、注重员工日常培训、建立内部监督体系等方式，加强内部控制建设，有效防范职业欺诈，提高运营效率和财务报表的可靠性，促进保险资管公司持续健康发展。\n--------------------------\n2023年证券保险行业的市场动态\n用户问题：请问2023年证券保险行业的市场动态是什么？\n\n回答：\n2023年，中国的经济形势有望由衰退转向弱复苏，这主要得益于各项政策的推出以及防疫政策的放松。然而，由于海外发达经济体衰退风险的增加以及国内地产恢复的缓慢，这种复苏态势可能会受到一些压力。\n\n在证券市场方面，随着国内经济触底反弹，A股的业绩增速有望实现同比上升。此外，预计海外发达经济体在2023年上半年停止加息，这将缓解人民币贬值压力，从而提高A股相对于海外市场的吸引力，并可能导致北向资金重新流入。因此，预计2023年国内证券市场将具备业绩和估值双重上升的基础。\n\n在保险行业方面，长期利率的复苏成为保险板块上涨的主要动力。预计负债端的拐点将在2023年第二季度出现，这表明我国经济的韧性强，潜力大，活力足，长期向好的基本面没有改变，人身险市场的长期空间仍然广阔。推荐的个股包括中国平安、中国太保和中国人寿。\n\n在投资机会方面，建议关注以下三个主线轮动：国产科技替代创新（如计算机、电子、通信、软件服务、数字经济等科创领域）；中国特色估值重塑主题（如能源、基建、房地产产业链等）；消费（如食品饮料、医药生物、酒店、旅游、交运、新能源消费等）。此外，半导体和人工智能等领域有望成为领导科技股再次走牛的主力军，具有走出史诗级行情的潜力。\n\n总的来说，2023年的证券和保险行业市场动态将主要受到经济复苏、长期利率复苏以及科技创新等因素的影响。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "2023年，中国的证券保险行业发生了许多重大事件和政策变动，行业总体呈现出长期向好的趋势，但也面临一些挑战。\n\n在保险行业方面，上半年，保险公司的原保险保费收入达到3.2万亿元，同比增长12.5%，其中人身险的原保险保费收入同比增长13.38%，显示了恢复性复苏的态势。然而，人身保险发展正处于转型发展的阵痛期，面临业务增长承压、销售队伍转型升级、行业市场竞争加剧的多重压力。\n\n风险管理方面，到2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。这些公司的资产规模在数千亿元，如果不能解决偿付能力问题，可能会对投保人、投资者和市场产生较大影响。\n\n在政策方面，政府积极推动保险行业规范发展，发布了《责任保险业务监管办法》和《保险集团公司监督管理办法》，并在全国范围内开展了针对保险资金运用关联交易的专项检查。\n\n在证券行业方面，2023年的重大事件包括沪深交易所主板注册制首批企业上市，实现了股票发行注册制改革全面落地，以及沪深港通交易日历优化上线实施，放开了两地市场全部共同交易日的沪深港通交易。\n\n政策方面，证监会发布了券商风控指标意见稿，明确了监管方向从原来的鼓励全行业发展到扶优限劣、分类监管。同时，证监会对《证券公司风险控制指标计算标准规定》进行了修订，并向社会公开征求意见。\n\n市场动态方面，随着国内经济触底反弹，A股的业绩增速有望实现同比上升。同时，保险板块由于长期利率的复苏成为上涨的主要动力，预计负债端的拐点将在2023年第二季度出现。\n\n从以上信息可以看出，2023年的证券保险行业在政策、市场、转型和风险管理等方面发生了许多重要变化，整体呈现出向好发展的趋势，但也面临着一些挑战。"}], "source": "self-made"} -{"instruction": "你是一位精通信息分析的ai助手，并且非常了解收集什么样{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：在A股上市的酒企广告效果如何？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n近期在A股上市的酒企广告效果评价\n在2023年的前三季度，A股市场上的20家白酒企业总营收达到了3111.62亿元，同比增长15.91%。其中，贵州茅台和五粮液以1032.68亿元和625.36亿元的营收占据市场主导地位，两者的营收合计占总营收的54.28%。在净利润方面，这20家企业合计1189.8亿元，同比增长18.92%，其中岩石股份和伊力特的净利润增速表现最为出色，分别实现153.63%和63.88%的增长。\n\n此外，这些公司的经营性现金流净额合计达到1042.05亿元，同比大幅增加601.52亿元，其中贵州茅台和五粮液的现金流净额分别为500.02亿元和223.89亿元，显示出较强的造血能力。\n\n虽然消费需求疲软、市场竞争激烈和库存积压为白酒行业带来了挑战，但是行业整体仍保持稳健的增长态势。20家上市白酒企业的存货总额为1363.54亿元，同比增长12.63%。合同负债总额为406.67亿元，同比减少了1.91%，显示出市场对于酒类未来消费的看法偏保守与谨慎。然而，考虑到第四季度是传统的白酒消费季，市场对于名酒的业绩依然抱有乐观态度。\n\n在广告效果方面，在Q2的业绩公布中，迎驾贡酒实现了60%的高增速，成为了一大亮点。同时，皇台摘帽，剑南春等其它名酒可能借壳ST西发。这些都显示出广告对于酒企业绩的积极影响。\n\n综上所述，近期在A股上市的酒企的广告效果总体上是积极的，这些企业在市场竞争中表现出较强的实力和稳健的增长态势。\n--------------------------\n近期在A股上市的酒企广告投放情况\n近期，A股上市的酒企的广告投放情况表现积极。以贵州茅台和五粮液为例，他们联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，引发了市场的热议，并在短期内推动了白酒板块的整体涨幅达到4.44%。另一方面，根据2023年三季度的财报，20家白酒上市公司实现总营收3111.62亿元，同比增长15.91%，归母净利润合计1189.8亿元，同比增长18.92%，显示出白酒行业整体的稳步增长。其中，贵州茅台和五粮液两大龙头的营收合计占行业比达54.28%，显示出他们在行业中的领导地位。但是，面临库存积压和市场竞争激烈的挑战，白酒行业仍需面临许多挑战。\n--------------------------\n近期在A股上市的酒企广告对销售的影响\n近期在A股上市的酒企广告对销售的影响可以从以下几个方面来看：\n\n首先，广告投放有助于提升品牌知名度和影响力，从而推动销售。如迎驾贡酒，其2022年Q2业绩超预期，增速高达60%。在此期间，迎驾贡酒投入了大量的资金用于理财，累计收益达6564万元，占其最近一年净利润的6.86%。\n\n其次，现金流管理是酒企运营的重要方面，广告费用预算和现金流管理紧密相关。部分酒企在广告投放和扩产同时进行，需要有效管理现金流以保持运营正常。如贵州茅台和五粮液，虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。对于这些酒企来说，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，白酒行业竞争激烈，广告投放对于提升市场份额尤为重要。据行业分析师肖竹青预计，随着白酒行业进入深度结构性调整期，龙头企业将会拥有越来越大的市场份额，区域白酒企业会逐渐边缘化，未来白酒行业会迎来更激烈的内卷式竞争。在这种环境下，广告投放对于吸引消费者，提升市场份额显得至关重要。\n\n总的来说，广告投放是提升销售的重要工具，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。\n--------------------------\n近期在A股上市的酒企广告的创新点\n近期在A股上市的酒企广告创新主要体现在跨界联名、营销预热、吸引消费者关注等方面。如瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注，使得A股白酒板块整体收涨4.44%。此外，他们还利用“中国人的第一杯酱香拿铁”和“美酒加咖啡，就爱这一杯”等宣传语吸引消费者购买。\n\n同时，酒企也在积极探索新的销售模式和市场机会。据预测，受益于低基数、宴席需求回补、商务活动恢复、消费能力恢复，预计今年二季度营收增速或将继续回升。节假日有望接力催化需求释放，后续应密切关注端午、中秋等假期前后的销售情况。白酒销售渠道反馈行业整体回款进度正有序推进，主要酒企核心单品批发价基本保持平稳、库存相对良性。\n\n然而，值得注意的是，白酒板块的投资风险也不容忽视。白酒作为典型的可选消费品，与居民收入水平和经济活跃度紧密相关，如果经济短期遇到逆风，可能会阶段性造成行业库存提升或业绩增长受阻。因此，投资者需谨慎分析，避免盲目跟风。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从近期A股上市的酒企广告效果评价，广告投放情况，广告对销售的影响以及广告的创新点四个方面来看，广告效果总体上是积极的。\n\n首先，广告帮助提升了酒企的品牌知名度和影响力。比如迎驾贡酒在2022年Q2的业绩公布中，实现了60%的高增速，成为了一大亮点。这显示出广告对于酒企业绩的积极影响。\n\n其次，广告投放对销售有显著的推动作用。贵州茅台和五粮液联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，并在短期内推动了白酒板块的整体涨幅达到4.44%。这进一步证实了广告在提升销售方面的重要作用。\n\n然而，虽然广告能带来销售上的提升，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。例如，贵州茅台和五粮液虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。这显示出，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，酒企在广告方面的创新也取得了一定的效果。瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注。\n\n综上所述，近期在A股上市的酒企通过广告投放在提升品牌知名度、增加销售额、管理现金流以及进行创新等方面取得了一定的成效。"}], "source": "self-made"} -信息对回答问题最重要。你现在需要结合背景信息回答给定的问题，给出多个搜索query，尽可能切中要害、简介明了，以最终回答问题。\n 你需要遵守以下规则：\n 1. 搜索query是对问题的横向拆分，类似于改写问题与阅读理解。你可以对于问题更直白和细致的改写，或者通过阅读方式改写问题，每个搜索条件都需要可以直接搜索到这个问题的答案。\n 2. 请严格按照相关性，生成的内容一定是和问题高度相关。\n 3. 注意在相关的基础上，你需要从分析财经信息的一般过程出发，将生成的query按照重要性和逻辑性排序，最重要的放在最前面\n 4. 注意生成的内容要贴合问题的意图，贴合问题的主体\n 5. 根据问题和背景信息，确认问题关心的时间点，可以是具体的时间点，也可以是最新、最近、近期等描述\n 6. 如果存在背景信息，则结合背景信息\n \n 让我们一步一步来。\n 问题是：深入分析10年期美国国债收益率上涨对国内债市的影响是什么？\n 今天的日期是：2023-10-23\n 背景信息是：\n -------------------------------\n 输出必须是按照格式:\n suggestion：思考过程\n answer：\n 1.\n 2.\n 3.\n ", "output": "suggestion：首先，我们需要了解10年期美国国债收益率上涨对全球债市的一般影响，然后再分析这种影响在中国债市的具体表现。其次，我们需要了解中国债市的特点和规律，以便更准确地分析美国国债收益率上涨对其的影响。最后，我们可以查看相关的历史数据和事件，以便从实际情况中得出结论。\n\nanswer：\n1.10年期美国国债收益率上涨对全球债市的影响是什么？\n2.中国债市的特点和规律是什么？\n3.历史上10年期美国国债收益率上涨时，中国债市的反应是什么？\n4.最近一次10年期美国国债收益率上涨，中国债市的反应是什么？\n5.专家对10年期美国国债收益率上涨对中国债市影响的看法是什么？"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。\n今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：在A股上市的酒企广告效果如何？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n近期在A股上市的酒企广告效果评价\n在2023年的前三季度，A股市场上的20家白酒企业总营收达到了3111.62亿元，同比增长15.91%。其中，贵州茅台和五粮液以1032.68亿元和625.36亿元的营收占据市场主导地位，两者的营收合计占总营收的54.28%。在净利润方面，这20家企业合计1189.8亿元，同比增长18.92%，其中岩石股份和伊力特的净利润增速表现最为出色，分别实现153.63%和63.88%的增长。\n\n此外，这些公司的经营性现金流净额合计达到1042.05亿元，同比大幅增加601.52亿元，其中贵州茅台和五粮液的现金流净额分别为500.02亿元和223.89亿元，显示出较强的造血能力。\n\n虽然消费需求疲软、市场竞争激烈和库存积压为白酒行业带来了挑战，但是行业整体仍保持稳健的增长态势。20家上市白酒企业的存货总额为1363.54亿元，同比增长12.63%。合同负债总额为406.67亿元，同比减少了1.91%，显示出市场对于酒类未来消费的看法偏保守与谨慎。然而，考虑到第四季度是传统的白酒消费季，市场对于名酒的业绩依然抱有乐观态度。\n\n在广告效果方面，在Q2的业绩公布中，迎驾贡酒实现了60%的高增速，成为了一大亮点。同时，皇台摘帽，剑南春等其它名酒可能借壳ST西发。这些都显示出广告对于酒企业绩的积极影响。\n\n综上所述，近期在A股上市的酒企的广告效果总体上是积极的，这些企业在市场竞争中表现出较强的实力和稳健的增长态势。\n--------------------------\n近期在A股上市的酒企广告投放情况\n近期，A股上市的酒企的广告投放情况表现积极。以贵州茅台和五粮液为例，他们联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，引发了市场的热议，并在短期内推动了白酒板块的整体涨幅达到4.44%。另一方面，根据2023年三季度的财报，20家白酒上市公司实现总营收3111.62亿元，同比增长15.91%，归母净利润合计1189.8亿元，同比增长18.92%，显示出白酒行业整体的稳步增长。其中，贵州茅台和五粮液两大龙头的营收合计占行业比达54.28%，显示出他们在行业中的领导地位。但是，面临库存积压和市场竞争激烈的挑战，白酒行业仍需面临许多挑战。\n--------------------------\n近期在A股上市的酒企广告对销售的影响\n近期在A股上市的酒企广告对销售的影响可以从以下几个方面来看：\n\n首先，广告投放有助于提升品牌知名度和影响力，从而推动销售。如迎驾贡酒，其2022年Q2业绩超预期，增速高达60%。在此期间，迎驾贡酒投入了大量的资金用于理财，累计收益达6564万元，占其最近一年净利润的6.86%。\n\n其次，现金流管理是酒企运营的重要方面，广告费用预算和现金流管理紧密相关。部分酒企在广告投放和扩产同时进行，需要有效管理现金流以保持运营正常。如贵州茅台和五粮液，虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。对于这些酒企来说，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，白酒行业竞争激烈，广告投放对于提升市场份额尤为重要。据行业分析师肖竹青预计，随着白酒行业进入深度结构性调整期，龙头企业将会拥有越来越大的市场份额，区域白酒企业会逐渐边缘化，未来白酒行业会迎来更激烈的内卷式竞争。在这种环境下，广告投放对于吸引消费者，提升市场份额显得至关重要。\n\n总的来说，广告投放是提升销售的重要工具，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。\n--------------------------\n近期在A股上市的酒企广告的创新点\n近期在A股上市的酒企广告创新主要体现在跨界联名、营销预热、吸引消费者关注等方面。如瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注，使得A股白酒板块整体收涨4.44%。此外，他们还利用“中国人的第一杯酱香拿铁”和“美酒加咖啡，就爱这一杯”等宣传语吸引消费者购买。\n\n同时，酒企也在积极探索新的销售模式和市场机会。据预测，受益于低基数、宴席需求回补、商务活动恢复、消费能力恢复，预计今年二季度营收增速或将继续回升。节假日有望接力催化需求释放，后续应密切关注端午、中秋等假期前后的销售情况。白酒销售渠道反馈行业整体回款进度正有序推进，主要酒企核心单品批发价基本保持平稳、库存相对良性。\n\n然而，值得注意的是，白酒板块的投资风险也不容忽视。白酒作为典型的可选消费品，与居民收入水平和经济活跃度紧密相关，如果经济短期遇到逆风，可能会阶段性造成行业库存提升或业绩增长受阻。因此，投资者需谨慎分析，避免盲目跟风。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从近期A股上市的酒企广告效果评价，广告投放情况，广告对销售的影响以及广告的创新点四个方面来看，广告效果总体上是积极的。\n\n首先，广告帮助提升了酒企的品牌知名度和影响力。比如迎驾贡酒在2022年Q2的业绩公布中，实现了60%的高增速，成为了一大亮点。这显示出广告对于酒企业绩的积极影响。\n\n其次，广告投放对销售有显著的推动作用。贵州茅台和五粮液联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，并在短期内推动了白酒板块的整体涨幅达到4.44%。这进一步证实了广告在提升销售方面的重要作用。\n\n然而，虽然广告能带来销售上的提升，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。例如，贵州茅台和五粮液虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。这显示出，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，酒企在广告方面的创新也取得了一定的效果。瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注。\n\n综上所述，近期在A股上市的酒企通过广告投放在提升品牌知名度、增加销售额、管理现金流以及进行创新等方面取得了一定的成效。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：长期持有收益比银行还高吗？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n长期持有收益比银行还高吗？\n关于“长期持有收益比银行还高吗？”这个问题，从搜寻到的信息来看，长期持有基金的确有可能带来较高的收益。\n\n首先，统计数据显示，股票型基金的长期持有能大概率提升基金的收益，正收益比例上看，持有3-5年为最佳。虽然在3075只股票型基金中，翻倍的基金只有64只，占比2.08%，但长期持有可以提升收益的概率。\n\n其次，市场虽然会有周期性的波动，但最终会发生均值回归，即涨得过多或跌得过多的趋势最终都会回归到平均值。这就意味着，当市场处于50%分位数之下时，选择长期持有，便能大概率获得收益。\n\n此外，从2004年以来，普通股票型基金指数和偏股混合型基金指数的涨幅都超过了10倍，在近20年的时间里不断创出新高。即使在市场高位买入，只要有耐心等待一定时间，也有可能从亏损中反败为胜。\n\n最后，对于“回本就卖”的策略，虽然可以避免亏损，但可能会错失更大的收益机会。这是因为，离开市场后要重新进入可能需要面临更高的价格和风险，而长期持有则可以更好地面对市场的短期波动，增加赚钱的概率。\n\n总的来说，长期持有基金相比于银行存款，有可能带来更高的收益，但这需要投资者有足够的耐心和对市场趋势的理解。\n--------------------------\n长期持有股票的平均收益率是多少？\n根据搜索到的信息，股票的长期持有收益率是由投资者在一定的持有期内的全部股利收入和资本利得占投资本金的比重来表示的。这个比重可以通过以下公式计算：[现金股息+(股票卖出价-股票买入价)]/股票买入价×100%。这里的现金股息是指投资者持有股票期间的股息收入，股票卖出价和股票买入价的差价则代表了资本利得。因此，想要了解长期持有股票的平均收益率，你需要考虑到股息收入和股票的买卖价格的变化。\n--------------------------\n长期持有的定义和计算方法是什么？\n长期持有，是投资策略的一种，主要是指投资者选择获益率高或成长性高的股票后，进行长期持有，而不是频繁买卖。这种策略的主要思想是，不轻易抛出手中的股票，而是等待股票的长期增长带来投资收益。\n\n至于计算方法，长期持有并没有特定的计算公式。通常，投资者会根据股票的盈利能力、成长性以及市场前景等因素，决定是否将其作为长期持有的投资对象。\n--------------------------\n长期投资的风险和收益比较。\n长期投资具有一定的风险和收益。以下是根据信息整合得出的答案：\n\n风险方面：\n1. 股票投资：可能受到政治、经济、国家产业政策、技术发展等多种因素的影响，可能导致投资者利益受损。但可以通过股票投资种类的组合和投资期限的组合来降低风险。\n2. 债券投资：虽然受到的影响因素和股票投资相似，但影响程度通常较小，风险要小于股票投资。主要风险包括债券发行单位可能出现意外情况，以及银行利率变动带来的风险。\n3. 基金投资：风险和收益与基金类型有关。股票型基金的风险和收益预期通常高于债券型基金和货币市场基金。此外，基金投资还可能面临市场风险、基金运作风险、流动性风险等。\n\n收益方面：\n1. 股票投资：企业应对各种影响因素作详细的分析，选择收益较高、安全性好的股票投资。\n2. 债券投资：在进行债券投资时，应考虑购买明显高于银行储蓄存款利率的长期债券，以求收益最大化和风险最小化。\n3. 基金投资：投资人购买基金，既可能分享基金投资产生的收益，也可能承担基金投资带来的损失。\n\n综上，长期投资需充分考虑自身的风险承受能力，理性判断市场，对投资行为作出独立决策，并承担基金投资中出现的各类风险。\n--------------------------\n近期的金融市场环境对长期持有的影响。\n从搜索到的信息来看，近期的金融市场环境对长期持有的影响主要以以下几个方面进行解读：\n\n首先，如果拉长时间周期来看，长期持有的投资策略可能更有优势。过去20年间，我国累计通胀达63.01%，定期存款的累计收益率为90.82%，而偏股混合型基金指数累计收益率高达783.76%。这表明，尽管短期市场环境波动可能影响投资收益，但长期持有的投资策略，如权益投资，能够跑赢通胀并获取超额收益。\n\n其次，适当的资产配置也是提高长期投资收益的重要手段。根据招商银行的“TREE资产配置服务体系”，投资者应从自身的财富阶段和风险偏好出发，选择适合自己的投资产品，并定期进行资产配置的检视和再平衡，以保持资产组合的稳健性。\n\n此外，在市场环境变化时，长期持有的策略能够有效地减少市场波动对投资收益的影响，带来更为稳定的收益。例如，南方金添利三年持有期债基注重长期持有债券，以获取稳定的利息收入。\n\n最后，科技的应用也对长期持有的投资策略产生影响。招商银行全流程系统化、产品线上化，并积极研发市场宏观、微观层面的观测指标和定量模型，及时感知市场变化，为客户科学、高效地调整配置比例和方案，这对于长期持有的投资策略有着积极的推动作用。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "关于你的问题\"长期持有收益比银行还高吗？\"\n\n首先，我们来定义一下“长期持有”。在投资领域中，“长期持有”是指投资者选择某种具有高收益率或者高成长性的投资品种，而不是频繁地买卖，而是选择长期持有，期待通过资产的长期增长来获得投资收益。\n\n关于长期持有可能带来的收益，统计数据显示，对于股票型基金来说，长期持有能大概率提升基金的收益，正收益比例上看，持有3-5年为最佳。虽然在3075只股票型基金中，翻倍的基金只有64只，占比2.08%，但长期持有可以提升收益的概率。\n\n而对于股票的长期持有收益率，它是由投资者在一定的持有期内的全部股利收入和资本利得占投资本金的比重来表示的。这个比重可以通过以下公式计算：[现金股息+(股票卖出价-股票买入价)]/股票买入价×100%。这里的现金股息是指投资者持有股票期间的股息收入，股票卖出价和股票买入价的差价则代表了资本利得。\n\n另外，我们也需要考虑到市场环境的影响。近期的金融市场环境，尽管短期市场环境波动可能影响投资收益，但长期持有的投资策略，如权益投资，能够跑赢通胀并获取超额收益。过去20年间，我国累计通胀达63.01%，定期存款的累计收益率为90.82%，而偏股混合型基金指数累计收益率高达783.76%。\n\n当然，长期投资也具有一定的风险。例如，股票投资可能受到政治、经济、国家产业政策、技术发展等多种因素的影响；债券投资的主要风险包括债券发行单位可能出现意外情况，以及银行利率变动带来的风险；基金投资的风险和收益与基金类型有关，股票型基金的风险和收益预期通常高于债券型基金和货币市场基金。\n\n所以，从长期来看，“长期持有”的收益比银行存款有可能高，但这需要投资者有足够的耐心和对市场趋势的理解，同时也需要承担一定的风险。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "\n你的角色是：投研分析师投研支小助,所属的国家：中国,所属的公司：蚂蚁集团,研发团队：蚂蚁集团投研团队,职责：善于从不同角度、有条理的回答用户的问题。现在，你需要仅仅使用提供的知识，专业、详细的回答用的问题。我的问题是：指数基金适用于什么样的投资场景？\n-------------------------------------------------------------\n背景信息是：\n无\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n指数基金适用于什么样的投资场景？\n指数基金是一种投资工具，它的主要目标是跟踪并复制特定的股票或债券指数的表现。在投资场景中，它适用于多种情况。首先，对于那些希望投资某个具体行业，特别是高科技行业的投资者，指数基金特别是行业指数基金可以为他们提供一个优秀的投资工具。因为高科技行业的发展充满了不确定性，很难锁定单一的成功公司，但通过投资指数基金，投资者可以在整个行业范围内进行投资，享受行业的整体成长红利，而避免了单一公司决策失误带来的风险。\n\n同时，对于那些在市场底部寻求抄底机会的投资者，指数基金也是一个好的选择。当市场下跌时，很多投资者，包括机构和个人投资者，都会使用指数基金来进行投资。宽基指数基金由于覆盖面广，往往因为机构增持而规模迅速扩大，而行业或主题指数基金则更多地被个人投资者用来根据自己对行业和主题机会的判断来进行投资。\n\n但是，指数基金也存在一定的风险，因为它是满仓操作，风险相对较高。为了降低风险，投资者可以通过分期定投的方式进行投资，从而在不确定的市场环境中获得更好的投资效果。\n\n最后，对于那些对中国经济的未来预期向好的投资者，他们可以将宽基指数基金作为一种底仓的存在，而通过行业和主题指数的轮动配置来实现自己的投资判断。\n\n在总结中，指数基金适用于那些希望投资具体行业，寻求市场底部抄底机会，以及对中国经济未来预期向好的投资者。同时，通过合理的投资策略，如分期定投，投资者可以在享受投资回报的同时，有效地控制投资风险。\n--------------------------\n指数基金的投资特点和风险\n指数基金的投资特点主要体现在以下几个方面：\n\n1. 费用较低：由于指数基金采取跟踪指数的方式，基金管理人不需要花大量的时间和精力进行股票、债券等投资工具的选择以及买入与卖出的时机。这种被动的管理方式降低了基金的管理费用。另外，由于采取买入并持有的策略，只有在所跟踪指数的成份股变化时才进行调整，所以它的交易费用也相对较低。\n\n2. 风险分散：指数基金通过跟踪指数进行广泛的分散投资，这意味着任何单个股票的波动都不会对指数基金的整体表现产生太大的影响，从而降低了投资者的投资风险。\n\n3. 透明度高：由于指数基金的业绩与其所跟踪的指数收益紧密相关，投资者只需关注目标指数的涨跌就可以判断出基金净值的变动。这样的操作简单明了，适合对大势把握较好但对个股了解不多的投资者。\n\n4. 人为影响小：由于指数基金的管理过程主要是对相应的目标指数进行被动跟踪，而不是频繁地进行主动性的投资，这样可以通过较为程序化的交易来减少人为因素的影响。\n\n尽管指数基金有以上特点，但投资者也需要注意其风险，因为市场波动或经济周期等因素可能会影响到被跟踪指数的表现，从而影响到基金的收益。\n--------------------------\n指数基金适合的投资人群特性\n指数基金是一种适合多种投资者的投资工具。首先，它们适合初学者和那些没有时间进行详细研究的投资者，因为这类基金投资的是整个市场或行业的股票，不是单一的个股，这样的分散投资策略可以有效地降低风险。其次，由于指数基金的投资策略是长期持有，所以适合长期投资者。长期投资可以有效地降低交易成本，并且指数基金的长期收益表现往往比短期收益更稳定。\n\n此外，指数基金对于养老金投资者也有吸引力。由于其低成本、大容量、风险分散、风格稳定和策略透明等特性，使其成为权益资产配置的合适工具，能够帮助投资者以较低的成本获取股票市场的长期平均投资回报。\n\n最后，指数基金的运作核心是被动地跟踪指数，在充分分散个股风险的同时，获取市场的平均收益。这一特性使得投资者只需要关注目标指数的涨跌，就可以大体上判断出投资的指数基金净值的变动，业绩透明度较高。同时，由于管理过程主要是对相应的目标指数进行被动跟踪，而不是频繁地进行主动性的投资，这样在管理过程中就可以通过较为程序化的交易来减少人为因素的影响。\n--------------------------\n在不同市场环境下，指数基金的投资表现\n在不同的市场环境下，指数基金的投资表现有着其独特性。在震荡市场中，指数基金往往呈现出“越跌越买”的现象，透明度高且费率低廉的特点使其成为了投资者的重要配置工具。对于投资者来说，跟踪更精准的指数基金能避免投资方向的偏移，更好地符合他们的投资预期。因此，对于有清晰投资目标、不想承担超额收益风险的投资者，以及希望减少投资成本的投资者，指数基金是一个很好的选择。\n\n在选择指数基金时，投资者通常关注两个主要因素：跟踪误差和费用。跟踪误差是指基金实际收益与其跟踪指数之间的差距，越小越好，宽基类指数基金和行业类指数基金分别要求跟踪误差不超过1.5%和2%。费用则是考虑基金的综合费率，一般来说，费率越低，长期复利效应越显著。\n\n未来，随着全面注册制的改革推动，A股市场结构更加健康和透明，指数化投资的性价比也将更高。同时，随着市场投资标的的丰富和投资理念的成熟，被动型指数产品的规模占比有望提高，发展空间大。\n\n近年来，指数基金在零售端的规模始终保持稳步增长，2017年至2022年，指数基金的零售端整体规模5年平均增速为36.9%，其中行业指数年均指数达到53.3%。这表明，指数基金是投资者长期价值投资的重要工具，也是震荡行情下主动权益基金配置的重要补充。因此，我们可以预见，指数基金在未来还将迎来大发展。\n--------------------------\n指数基金在资产配置中的作用和地位\n指数基金在资产配置中具有重要作用和地位。自2002年我国第一只指数基金成立起，指数基金经历了四个发展高峰，分别在2009年、2012年、2018-2020年和2022年，尤其是2018年之后，债券等多种指数的全面发展推动了指数基金的持续上升。\n\n指数基金的发展并不平衡，主要体现在交易活跃的场内指数基金（ETF/LOF）占比不高。以日均交易额1000万以上且季度规模满2亿的ETF和LOF为例，其在宽指/风格板块类、行业类、指数类和商品类指数基金中的规模占比均达到60%以上，但在港股和海外指数基金中的占比较小。\n\n商品类指数基金的发行受到限制，难以满足资产配置需要。目前市场上只有13只商品基金，主要包含对标黄金现货和白银期货的贵金属基金、对标原油相关指数的能源类基金和3只商品期货ETF。\n\n指数增强公募基金的发展状况不理想，占比只有7%-8%，且超额收益率稳定性较弱。主要原因是这类基金主要集中在股票指数策略，而债券指数策略占比并不高。\n\n从机构持有国内指数基金的比例来看，2014年之后机构资金在国内基金资金来源中占比显著提高，尤其是在股票指数基金中，机构资金占比在2007年之后经历了持续提高的过程，始终稳定高于普通股票型或混合型基金的机构资金占比。\n\n指数化投资是基金组合投资专业化的必经之路，且逐渐成为国内机构投资的主流方向。目前，我国指数基金体系在不断发展，有望进一步提升顾问服务的能力，促进居民财富的优化配置，有助于证券公司财富管理向买方投顾发展和转型。\n--------------------------\n指数基金的投资策略和操作建议\n指数基金的投资策略主要是复制市场指数的投资组合，这被称为一种消极的投资策略。这种策略的优点包括简单、透明和低费率。在投资过程中，不同类型的指数基金的关注点不同。例如，策略型指数基金主要关注指数策略的长期有效性，而宽基指数基金主要关注估值等因素，行业型指数基金除了关注估值，还需要注意行业的基本面和成长性等因素。\n\n对于初次投资的投资者，建议从宽基指数基金入手，如沪深300、中证800等指数基金。定投是一个可行的参与方式，当达到止盈目标后，可以一次性赎回并开始新的定投计划。\n\n在当前市场环境下，由于A股的政策底已经被多次确认，加上国内经济的持续好转和中美关系的缓和，现在A股具有一定的配置价值。投资者可以采取分批加仓或者多次定投宽基的方式参与市场。如果看好某个细分行业，也可以配置相关的行业指数基金。\n\n在挑选指数基金产品时，投资者可以重点关注大型基金公司推出的产品。除了费率因素外，还需要关注基金的规模和跟踪误差等因素。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "指数基金是一种投资工具，主要目标是复制特定的股票或债券指数的表现。在投资场景中，它适用于多种情况。\n\n首先，对于希望投资某个具体行业，特别是高科技行业的投资者，指数基金可以为他们提供一个优秀的投资工具。因为高科技行业的发展充满了不确定性，但通过投资指数基金，投资者可以在整个行业范围内进行投资，享受行业的整体成长红利。\n\n其次，对于寻求市场底部抄底机会的投资者，指数基金也是一个好选择。当市场下跌时，许多投资者会使用指数基金来进行投资。宽基指数基金由于覆盖面广，往往因为机构增持而规模迅速扩大。\n\n此外，对于对中国经济的未来预期向好的投资者，他们可以将宽基指数基金作为一种底仓的存在，而通过行业和主题指数的轮动配置来实现自己的投资判断。\n\n然而，指数基金也存在一定的风险，因为它是满仓操作，风险相对较高。为了降低风险，投资者可以通过分期定投的方式进行投资，从而在不确定的市场环境中获得更好的投资效果。\n\n在选择指数基金时，投资者通常关注两个主要因素：跟踪误差和费用。跟踪误差是指基金实际收益与其跟踪指数之间的差距，越小越好。费用则是考虑基金的综合费率，一般来说，费率越低，长期复利效应越显著。\n\n总的来说，指数基金是适合多种投资者的投资工具，通过合理的投资策略，投资者可以在享受投资回报的同时，有效地控制投资风险。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-17，星期3\n-------------------------------------------------------------\n我的问题是：标题: 多只跨境ETF被“爆炒”!\n描述: 1月16日，多只跨境ETF放出巨大成交量，部分不足10亿跨境ETF盘中成交额已超越茅台，更有跨境基金一月成交额放大超140倍。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 多只跨境ETF被“爆炒”!\n描述: 1月16日，多只跨境ETF放出巨大成交量，部分不足10亿跨境ETF盘中成交额已超越茅台，更有跨境基金一月成交额放大超140倍。\n===\n1. 跨境ETF规模大增：根据Choice数据显示，2023年末，跨境ETF总规模达到2686.70亿元，相比2022年的1915.53亿元，增加了771.17亿元。这一显著增长反映出投资者对跨境ETF的热情提升。\n2. 高关注度领域突显：具体来看，港股创新药、港股互联网、美股科技赛道类的ETF规模增长明显。这些领域的热度反映了大众投资者的偏好和市场趋势。\n3. 市场预期潜在变化：有机构人士认为，美债收益率2023年四季度见顶回落，预计2024年全年或迎来趋势性下行，这将对利率较为敏感的资产产生影响，可能会带来新的投资机会。\n--------------------------\n2024年1月16日跨境ETF的成交额超越茅台的具体情况\n===\n1. 跨境ETF热潮：在2024年1月16日，部分市值不足10亿的跨境ETF盘中成交金额已超越贵州茅台，其中有些产品短短一个月时间成交额放大超140倍。不过，高溢价风险随之陡增，游资或开始出逃。例如，日经225ETF、东南亚科技ETF基金创出价格新高后开始走跌。\n\n2. 主力资金流出：以跨境通为例，1月16日主力资金净流出3168.88万元，占总成交额的15.17%，而游资资金净流出1377.03万元，占总成交额的6.59%。此表明，在高溢价风险的情况下，部分主力资金和游资选择了出逃。\n\n3. 散户热衷参与：与主力资金和游资不同，散户资金在此时却呈现净流入状态。以跨境通为例，其散户资金净流入4545.9万元，占总成交额的21.76%，说明在跨境ETF的炒作中，散户投资者依然保持积极态度。\n--------------------------\n2024年1月份跨境基金成交额放大超140倍的原因\n===\n1. 交易活跃度提升：2024年1月份，跨境ETF基金的成交额出现了显著的增长，甚至有些小市值的跨境ETF基金的日成交量超过了贵州茅台。例如，华夏基金旗下的日经ETF在1月16日的全天成交金额达到了47.79亿元，而其盘中市值仅有7.16亿元。同样，东南亚科技ETF基金，该基金盘中市值约6.64亿元，1月16日全天成交金额高达29.17亿元。\n\n2. 资金流向小市值：在微盘股炒作过热后，市场资金可能会寻找估值更低，或价格处在低位的小微盘品种。这在迷你基金的交易中得到了体现，投资者的资金开始追逐市值更小的跨境ETF基金产品。\n\n3. 投资风险提醒：虽然跨境ETF基金的交易活跃度提升，但是投资者应注意风险。例如，当小微盘出现明显赚钱效应后，市场资金会加速进入，进一步加强小微盘品种的表现。然而，随着价格的上涨，任何风格都不可能持续占优，微盘行情也值得警惕，尤其是微盘股的弱流动性，一旦发生风格切换可能出现“踩踏”现象。\n--------------------------\n2024年1月16日跨境ETF爆炒的市场背景\n===\n1. ETF涨幅领军：2024年1月16日，一场跨境ETF的爆炒在市场上展开，其中日经ETF(513520)、东南亚科技ETF(513730)和印度基金LOF(164824)涨幅分别超过8%、6%和5%。这表明投资者对于海外市场，尤其是亚洲市场的关注度和投资热情正在逐步提升。\n2. 板块动态：在板块方面，光伏和新能源两大板块表现抢眼，尤其是光伏30ETF(560980.SH)领涨市场。相比之下，创中盘88ETF(159804.SZ)表现不佳，成为跌幅最大的ETF。这反映了市场对绿色能源的持续看好以及对某些板块的短期调整。\n3. 市场交易情况：当日市场表现稳定，三大指数集体收涨，其中沪指上涨0.27%，深证成指上涨0.31%，创业板指上涨0.38%。总成交额达到6848亿，较上个交易日增加739亿。然而，北向资金全天净卖出41.33亿，显示外资整体上对中国市场的态度较为谨慎。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 成交量暴增的驱动因素：1月16日，多只跨境ETF的成交量大增，其中部分市值不足10亿的跨境ETF盘中成交金额已超过贵州茅台。这可能源于投资者对国际市场的投资需求增加，以及对国内市场的一些热门股票（如茅台）的投资热情有所下降。同时，跨境ETF的优势在于可以让投资者方便地参与到国际市场的投资，这也是吸引投资者的一个重要因素。\n\n2. 行业偏好的变化：在跨境ETF的投资中，投资者对某些行业的偏好可能会发生变化。比如，港股创新药、港股互联网、美股科技赛道类的跨境ETF的规模增长较为明显，这可能反映出投资者对这些行业的关注度提升。\n\n3. 市场预期的影响：市场预期的变化也会影响投资者的投资行为。比如，如果市场预期美债收益率将趋势性下行，那么对利率敏感的资产可能会受到影响，投资者可能会寻找新的投资机会，这可能会促使部分投资者转向跨境ETF。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-05，星期2\n-------------------------------------------------------------\n我的问题是：我是想问你，市场还会跌吗？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n我是想问你，市场还会跌吗？\n根据搜索到的信息，目前市场正在经历一个阶段性的下跌过程，与此同时，市场也在进行阶段性的筑底过程。具体表现为，市场由缓跌到急跌，这一点从指数的回撤幅度也可以看出，几大指数的回撤幅度都接近2%。尽管如此，市场的下跌并非全无好处，这也是加速赶底的表现，一旦出现反弹信号和量能走好，就可能是市场的转折点。\n\n然而，目前市场的反弹信号并未明确，且各级别的量能现状也并不理想。这预示着市场继续下行震荡的概率依然较大。因此，对于问题“市场还会跌吗？”的回答是，从当前情况来看，市场有可能会继续下行震荡。\n\n需要注意的是，尽管大盘下跌空间有限，但是局部板块需要注意风控优先的原则。例如，黄金板块在大家追高时，实际上已经出现了下跌的信号。另外，石油、新能源、白酒等板块也在提醒继续下行的风控。而对于一些关键板块，如船舶板块，可以在688-710点区间内逢低入场。\n--------------------------\n近期的股市行情走势\n根据最近的股市行情，A股三大指数集体走弱，沪指跌1.67%，收报2972.30点，创业板指跌1.98%，收报1871.10点。沪深两市下跌的同时，北证50指数逆势大涨7.28%。尽管沪指再度失守3000点大关，但资深市场人士认为二次探底才是真底，明天的走势非常关键。\n\n在此背景下，食品板块逆势崛起。西部牧业、阳光乳业、一鸣食品等多只股票录得涨停。人气股惠发食品连续录得8个涨停板，股价创历史新高。尽管该公司近年业绩乏善可陈，但其主营业务——速冻调理肉制品的市场前景被看好。\n\n此外，“平价消费”已成为近期市场的一大主线，休闲食品龙头良品铺子宣布业务端将实施17年来最大规模降价，300款产品平均降价22%，最高降幅45%。中邮证券研报表示，在“经济弱复苏+消费趋于理性”双重背景下，我国零食折扣专营店兴起，下游格局初具雏形。\n\n食品板块中，多只股票未来增长潜力获机构扎堆看好。其中，安井食品上涨空间63.48%排在首位，该公司主要从事速冻食品业务，有望在餐饮消费逐步顺周期中维持较快增长。\n\n总的来看，目前市场处于短线的寻底过程，投资者需要耐心等待，明年春节前后有望形成大级别底部。\n--------------------------\n近期的宏观经济数据\n问题：“近期的宏观经济数据体现出了中国经济的哪些特点？”\n\n答案：根据2023年10月末的数据，中国的M2余额同比增加了10.3%，达到288.23万亿元，规模以上工业增加值同比增加了4.6%，达到28.31万亿元，说明经济正在持续恢复。穆迪评级公司将中国主权信用评级展望调整为“负面”，但财政部认为，面对复杂严峻的国际形势，中国宏观经济持续恢复向好，高质量发展稳步推进，预计四季度中国经济将保持回升向好态势。此外，前三季度，全国一般公共预算收入同比增长8.9%，其中，全国税收收入增长11.9%，财政支出同比增长3.9%。这些数据表明，中国经济正在转向高质量发展，新动能正在发挥作用。至于地方政府债务，截至2022年末，全国地方政府法定债务余额35.1万亿元，全国政府债务余额61万亿元，全国政府法定负债率为50.4%，低于国际通行的60%警戒线，也低于主要市场经济国家和新兴市场国家，说明我国的债务风险总体可控。\n--------------------------\n近期的全球政治经济事件\n在您的问题中，并没有提供一个具体的问题，如果您是想了解近期的全球政治经济事件，我能从您提供的信息中提供以下概览：\n\n近期全球政治经济形势变化多端，主要表现在以下几个方面：\n\n1. 高通胀问题：海外高通胀现象短期内难以消解，这对全球经济产生了较大影响。\n\n2. 科技变革：以人工智能为代表的科技变革正在快速发展，这将对全球经济的发展模式产生深远影响。\n\n3. 企业投资变化：在这样的大环境下，全球企业在投资目的和金融需求等方面正在发生变化，例如“引进来”和“走出去”的策略。\n\n德意志银行的高级管理层也就这些变化进行了详细的讨论，关注了四大趋势以及金融服务的相应变化。\n\n以上是近期全球政治经济事件的主要信息，如果有其他具体问题，欢迎继续提问。\n--------------------------\n近期的金融政策变动\n你好，近期中国的金融政策变动主要围绕住房信贷政策的调整和优化：\n\n1. 中国人民银行和金融监管总局联合发布通知，调整优化差别化住房信贷政策。具体来说，首套住房商业性个人住房贷款最低首付比例统一为不低于20%，二套住房商业性个人住房贷款最低首付比例统一为不低于30%。此外，二套住房商业性个人住房贷款利率政策下限调整为不低于相应期限贷款市场报价利率加20个基点。\n\n2. 针对存量首套住房贷款利率，推出了降低措施。假设按揭利率下调50个基点，预计全行业每年可减少房贷利息约2000亿元，相当于增加了2022年社会消费品零售额的0.5%。对个人而言，假设存量按揭利率下调80个基点，按照100万元按揭贷款、等额本息还款计算，估算可降低借款人月供约500元，节约月供和总还款额约8%。\n\n3. 央行和银保监会印发通知，支持房地产市场平稳健康发展，包括保持房地产融资平稳有序、积极做好“保交楼”金融服务、积极配合做好受困房地产企业风险处置等措施。\n\n这些政策调整有利于降低居民购房门槛和成本，促进潜在合理购房需求的释放，有望改善银行“资产荒”和对公地产敞口资产质量，对市场预期起到积极引导作用，共同带动后市销售企稳复苏。\n--------------------------\n近期的股市技术分析\n根据搜索到的信息，近期的股市呈现出以下特点和趋势：\n\n1. A股大盘指数出现调整，上证指数跌破3000点整数关口，其中华为算力、操作系统、存储芯片、ChatGPT、鸿蒙等热门题材全线回落。\n\n2. 在大盘持续走弱的背景下，食品板块逆势崛起，西部牧业、阳光乳业、一鸣食品、安记食品、南侨食品、春雪食品等多股涨停。北交所的食品股表现更为火爆，骑士乳业、润普食品、盖世食品均收获涨停板，欧福蛋业、朱老六、康比特、田野股份、驱动力等涨超10%。\n\n3. “平价消费”已成为近期市场的一大主线，休闲食品龙头良品铺子宣布业务端将实施17年来最大规模降价，300款产品平均降价22%，最高降幅45%。\n\n4. 中国休闲食品行业市场规模持续增长，从2010年的4100亿元增长至2022年的11654亿元，预计2027年中国休闲食品行业市场规模将达12378亿元。\n\n5. 从技术分析角度看，今天的阴线出现了实体吞没上个阳线的技术信号，但目前所处走势位置来看指标并不需要过多关注，只有在重要行情末尾出现类似技术信号需要预判防止风险扩大。\n\n6. 本周的技术指标依旧收敛，最高价受到5ma压制，周线倒鸭嘴结构可能就做出来了。创业板今天跌的多跌幅0.9%，今天量能也放出近七个交易日最大量。\n\n整体来看，股市出现调整，食品板块逆势崛起，平价消费成为市场趋势，中国休闲食品行业市场规模持续增长，技术指标显示周线倒鸭嘴结构可能形成。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "首先，根据目前的股市行情，我们看到市场正在经历一个阶段性的下跌过程，尽管这也是市场筑底过程的一部分。大盘指数，如沪指已跌破3000点，而创业板指也出现了下跌。这些都表明市场在短期内可能继续处于震荡下行的状态。\n\n另一方面，我们要关注近期的宏观经济指标。目前，中国的M2余额同比增加了10.3%，规模以上工业增加值同比增加了4.6%，显示出经济正在持续恢复。尽管有的机构将中国主权信用评级展望调整为“负面”，但我们看到中国的经济基本面仍然稳健，且预计四季度中国经济将保持回升向好态势。\n\n然而，全球政治经济形势的变化以及近期的金融政策变动也会对股市产生影响。例如，海外高通胀现象短期内难以消解，这对全球经济产生了较大影响。同时，中国央行和金融监管总局调整了住房信贷政策，这也将对资金市场流动性产生影响。\n\n至于股市的技术分析，我们看到今天的阴线出现了实体吞没上个阳线的技术信号，显示出市场可能继续下行。而从市场板块来看，食品板块逆势崛起，这可能预示着市场将出现结构性机会。\n\n综合考虑以上因素，我认为市场有可能在短期内继续下行震荡。但这也是市场筑底的过程，一旦出现反弹信号和量能走好，就可能是市场的转折点。因此，投资者需要保持耐心，适当做好风险控制，并关注市场的变化，寻找结构性投资机会。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-24，星期3\n-------------------------------------------------------------\n我的问题是：标题: 小米汽车专利可预热电池提高充电效率\n描述: 天眼查App显示，1月23日，小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 小米汽车专利可预热电池提高充电效率\n描述: 天眼查App显示，1月23日，小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。\n===\n1. 技术创新：小米汽车科技有限公司申请的“车辆电池预研方法、装置、车辆及可读存储介质”专利获授权。这表明小米正在不断研发创新，以提高电池的充电效率，这将有助于提升小米汽车的续航能力和用户体验。\n\n2. 产品配置与市场竞争：小米汽车SU7硬件配置包括800V高压快充、骁龙8295座舱芯片、一体式激光雷达、双电机全轮驱动等，具备了一辆爆款车的基本要素。然而，随着汽车市场智能化，未来竞争将更多侧重于智能驾驶和智能座舱等软件方面。小米表示，全栈自研智能驾驶技术，并将大模型技术全面融入，可以带来更精准的判断，更强、更安全的智能驾驶体验。\n\n3. 生态优势：至2023Q3，小米已成为全球最大的消费级IoT平台，连接设备数已达6.55亿台，拥有5个及以上小米IoT设备的用户已达1300万。小米汽车的加入，将推动\"人车家全生态\"闭环，小米澎湃OS的诸多特性和完备流畅的生态体验，将全部在小米汽车上实现。小米汽车产品得到优酷、腾讯视频、哔哩哔哩等多家主流APP的生态互联支持，消费者可以体验手机与车机跨端的无感连接。\n--------------------------\n分析小米汽车科技有限公司该专利对电动汽车充电效率提升的具体作用。\n===\n1. 创新驱动：小米汽车科技有限公司近期申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权，彰显其在电动汽车技术领域的创新能力。该专利通过预热电池，提升充电性能，从而缩短充电时间，提高整体充电效率。\n2. 预热机制：该专利的核心是一个精细的预热机制。该机制首先确定目标充电桩和车辆行驶至充电桩时的预估剩余电量，然后根据充电桩的充电功率和剩余电量，确定电池的目标温度和加热速率，从而确定预估加热时长。\n3. 实施效应：最后，根据预估加热时长和车辆行驶至目标充电桩的行驶时长，对电池进行加热，以确保电池在到达充电桩时达到最佳充电状态。这种方法有望提升电动汽车的使用便捷性，可能会对电动汽车市场产生积极影响。\n--------------------------\n探寻小米汽车科技有限公司申请该专利的背景和目的。\n===\n1. 电池和车身一体化：小米汽车科技有限公司申请的“电池及车身一体化结构、车辆”专利公布。这项技术的实现将使电池包壳体的刚度和侧碰传力性能得到提高，同时，通过降低车身结构的自重，实现车辆的轻量化，提高车辆的续航里程。\n2. 电池预热技术：小米汽车申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。这个技术可以提升充电性能，减少充电时间，进而提高电池的充电效率。其方法包括预估车辆行驶至目标充电桩的剩余电量，确定电池的目标温度和第一目标加热速率，对电池进行加热。\n3. 传感器模组：北京小米移动软件有限公司申请了一项名为“传感器模组和具有其的电子设备”的专利。该传感器模组的结构设计合理，空间利用率高，内含第一传感器和第二传感器，这两个传感器与柔性电路板电连接，电路板环绕在支架上，传感器沿支架的厚度方向堆叠。\n--------------------------\n考察小米汽车科技有限公司申请该专利后，对公司自身及电动车市场可能产生的影响。\n===\n1. 技术创新驱动：小米汽车科技有限公司的新专利“车辆电池预热方法、装置、车辆及可读存储介质”已于1月23日获得授权。这一技术以提升充电性能减少充电时间，进而提高电池的充电效率为目标，体现了小米在电动车领域的技术创新力度。\n2. 技术细节揭示：该技术通过确定目标充电桩、预估剩余电量，然后根据充电功率和剩余电量确定电池的目标温度及预热速率，最后根据预估加热时长和行驶时长对电池进行加热。这一流程的设定无疑将电池预热与行驶计划紧密结合，提升了充电效率。\n3. 对电动车市场影响：这项新技术的应用不仅将提升小米汽车自身的充电效率，也将对整个电动车市场产生深远影响。它将推动电动车市场的技术创新，从而优化用户体验，提高电动车的市场接纳度。\n--------------------------\n查找其他电动车公司是否有类似的电池预热专利，进行比较。\n===\n1. 先进技术的保护：从比亚迪和小米汽车的行动中可以看出，电动车公司对电池预热的专利保护越来越重视。比如比亚迪股份有限公司获得了一项名为“车辆、能量转换装置及其控制方法”的专利，可以提升电池包的加热效率。小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利，旨在提升充电性能减少充电时间，进而提高电池的充电效率。\n2. 专利技术的创新性：这些公司的专利都体现出深厚的技术创新能力。比如，比亚迪的专利可以通过对车辆状态和/或车辆性能参数的检测，选择相应的加热控制模式，实现对电池包进行加热过程的精确控制。小米汽车的专利则能够预估车辆行驶至目标充电桩时的剩余电量，根据预估加热时长和车辆行驶至目标充电桩的行驶时长，对电池进行加热。\n3. 行业竞争的激烈程度：从这些公司的专利申请和授权情况来看，电动车行业的竞争已经进入到技术层面，每家公司都在努力提高自己的技术门槛，以取得市场的竞争优势。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 技术创新驱动：小米汽车科技有限公司的新专利“车辆电池预热方法、装置、车辆及可读存储介质”获得授权，表明公司在电池充电效率提升方面进行了积极的技术创新。通过预热电池，可以提高充电性能，减少充电时间，从而提升电池的充电效率。\n\n2. 产品优势凸显：该专利的授权将有助于小米汽车在电动汽车市场中获取竞争优势。电池充电效率的提升可以提升电动汽车的使用便捷性，提高续航能力，为消费者带来更优质的用户体验。\n\n3. 行业影响拓展：该专利的授权不仅对小米汽车科技有限公司自身的发展具有积极意义，同时也可能推动整个电动车行业的技术进步。预热电池的技术可以提高电动车的充电效率，对于推动电动车的市场普及具有重要作用。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-23，星期2\n-------------------------------------------------------------\n我的问题是：标题: 合盛硅业于内蒙古投资成立绿能科技公司\n描述: 近日，内蒙古创源绿能科技有限公司成立，经营范围包含太阳能热发电装备销售等。企查查股权穿透显示，该公司由合盛硅业间接全资控股。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 合盛硅业于内蒙古投资成立绿能科技公司\n描述: 近日，内蒙古创源绿能科技有限公司成立，经营范围包含太阳能热发电装备销售等。企查查股权穿透显示，该公司由合盛硅业间接全资控股。\n===\n1. 硅业巨头的新动向：合盛硅业近期在内蒙古设立全资子公司——创源绿能科技有限公司，注册资本5000万，主要经营业务为太阳能热发电装备销售等新能源相关业务。这一举措是合盛硅业在积极布局光伏和碳化硅领域，以实现硅基新材料的产业化的一部分。\n\n2. 项目投资概览：近年来，合盛硅业在多个领域出击，项目投资金额巨大。公司通过“光伏+碳化硅”的布局，致力于打造成为硅基新材料领域的领导者，开启新的增长曲线。其六大项目合计项目金额为653.38亿元，累计投入金额为229.69亿元。\n\n3. 产业链布局：合盛硅业不仅布局硅料业务，也同步向下游扩张，进入组件、光伏玻璃等领域。在中部和东部地区投资光伏玻璃项目和组件项目，预计在2023年下半年至2024年一季度陆续投产。这些举措有望进一步提升合盛硅业的产业链竞争力。\n--------------------------\n合盛硅业在内蒙古成立绿能科技公司的详细信息\n===\n1. 跨地区布局：合盛硅业近期在浙江嘉兴和内蒙古相继设立了两家全资子公司，分别是浙江合量科技有限公司和内蒙古创源绿能科技有限公司。这两家公司的经营范围主要涉及新材料技术研发、电子专用设备销售、储能技术服务等领域，展现了合盛硅业在光伏和新材料领域的战略布局。\n2. 资本运作加速：这两家新设立的公司的注册资本分别为1亿元和5000万元，显示出合盛硅业在资本运作上的决心和实力。此外，合盛硅业（万宁）有限公司也全资持有一家在内蒙古设立的新材料科技公司，进一步丰富了公司的资本结构和业务布局。\n3. 行业前景展望：随着新能源和新材料行业的快速发展，合盛硅业此次的行动无疑为公司的未来发展奠定了坚实的基础。而其在新能源和新材料领域的布局，也预示着该行业的广阔前景和巨大商业价值。\n--------------------------\n合盛硅业在新能源领域的投资布局\n===\n1. 光伏产业链布局：合盛硅业在新疆地区整合低成本资源，进一步布局多晶硅、光伏玻璃、电池片、组件等光伏产业链产品。其中，新疆中部合盛的年产20万吨多晶硅与150万吨新能源装备用超薄高透光伏玻璃项目有望于2023年第三季度投产，而东部合盛的年产20万吨高纯多晶硅项目预计将于2024年第一季度陆续投产。此外，公司的年产20GW光伏组件项目预计从2023年第四季度开始执行。\n2. 工业硅项目进展：云南合盛年产80万吨工业硅及配套项目一期预计于2024年完工。这一项目的完成将进一步提升合盛硅业在工业硅市场的竞争力，提高公司的整体盈利能力。\n3. 硅基新材料产业化：合盛硅业正在积极布局光伏和碳化硅领域，以实现硅基新材料的产业化。公司于2023年5月通过了2万片宽禁带半导体碳化硅衬底及外延片产业化生产线的验收，6英寸晶体良率达到了90%,外延片良率达到了95%，同时8英寸衬底也实现了量产。这显示出合盛硅业在硅基新材料领域的技术实力和市场竞争力。\n\n--------------------------\n内蒙古创源绿能科技有限公司的经营范围和业务概述\n用户的问题：请问内蒙古的新能源发展现状如何，有哪些企业在其中起主导作用？\n\n===\n1. 公司布局：内蒙古创源绿能科技有限公司由合盛硅业间接全资控股，注册资本5000万元，经营范围包括太阳能热发电装备销售、新能源原动设备制造、储能技术服务等。公司的成立标志着合盛硅业在新能源领域的布局进一步扩大。\n\n2. 项目推动：内蒙古三峡蒙能能源有限公司在库布其沙漠鄂尔多斯中北部新能源基地建设的光伏项目已并网发电，总装机规模1600万千瓦。项目建成后，每年可向京津冀地区输送清洁电能400亿千瓦时。同时，杭锦旗朔方新能源大基地规划建设面积48万余亩，总规模1300万千瓦。\n\n3. 发展状况：截至2023年12月底，内蒙古新能源装机规模突破9200万千瓦，全年新增装机超过3100万千瓦，增量创历史新高。内蒙古新能源项目建设步履不停，风光资源富集，已印发实施了新能源倍增行动实施方案、光伏治沙行动实施方案、促进新能源消纳若干举措等多项政策，为加快新能源项目建设提供了有力的政策保障。\n--------------------------\n合盛硅业的股权结构及其对内蒙古创源绿能科技有限公司的控股情况\n===\n1. 创新控股：根据企查查APP显示，合盛硅业近日间接全资控股了新成立的内蒙古创源绿能科技有限公司。该公司注册资本5000万元，经营范围包含太阳能热发电装备销售、新能源原动设备制造、储能技术服务以及合同能源管理等。\n\n2. 新材料布局：同时，合盛硅业也间接全资控股了新成立的内蒙古源芯新材料有限公司。该公司注册资本1亿元，主要经营范围为新材料技术研发、电子专用材料制造、电子专用材料研发以及电子专用设备销售等。\n\n3. 多元化发展：这两次控股行为显示出合盛硅业在业务上的多元化发展趋势，企业不仅在新能源领域进行了布局，同时也在新材料领域进行了深度探索。这对于合盛硅业的长期发展来说，无疑是一种积极的尝试和布局。\n--------------------------\n内蒙古创源绿能科技有限公司成立对合盛硅业战略发展的影响\n===\n1. 合盛硅业战略布局：合盛硅业正积极布局光伏和碳化硅领域，以实现硅基新材料的产业化。公司已通过了2万片宽禁带半导体碳化硅衬底及外延片产业化生产线的验收，并具备量产能力。同时还计划投资光伏玻璃项目和组件项目，预计的投产时间已经公布，表明公司对于硅基新材料领域的长远发展有明确的规划和目标。\n2. 创源绿能科技有限公司成立：最近，合盛硅业间接全资控股的内蒙古创源绿能科技有限公司成立，注册资本5000万元，经营范围包含太阳能热发电装备销售；新能源原动设备制造；储能技术服务；合同能源管理等。这一举措进一步强化了合盛硅业在新能源领域的布局。\n3. 内蒙古新能源发展态势：内蒙古正在全面高质量发展新能源项目，例如库布其沙漠鄂尔多斯中北部新能源基地总装机规模1600万千瓦，每年可向京津冀地区输送清洁电能400亿千瓦时。此外，全区新能源装机规模突破9200万千瓦，全年新增装机超过3100万千瓦。这些数据显示出内蒙古在新能源领域的巨大潜力和发展空间，为合盛硅业提供了广阔的市场和良好的发展环境。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 硅业巨头布局新能源：近日，合盛硅业在内蒙古设立了全资子公司——内蒙古创源绿能科技有限公司，主要经营业务为太阳能热发电装备销售等新能源相关业务。这显然是合盛硅业在新能源领域进一步深化布局的重要举措。\n\n2. 业务拓展及发展策略：合盛硅业此次的举动，不仅扩大了其在新能源领域的战略布局，同时也展现出了公司在追寻可持续发展，积极拓展绿色能源业务的决心。这种策略有利于公司在未来新能源市场的竞争中获得更大的优势。\n\n3. 对于行业影响：该公司的成立将进一步推动新能源行业的发展，尤其是在太阳能热发电装备销售等领域。同时，这也可能会对未来的新能源市场格局产生一定影响。"}], "source": "self-made"} - - +{"type": "chatml", "messages": [{"role": "user", "content": "\n你的角色是：投研分析师投研支小助,所属的国家：中国,所属的公司：蚂蚁集团,研发团队：蚂蚁集团投研团队,职责：善于从不同角度、有条理的回答用户的问题。现在，你需要仅仅使用提供的知识，专业、详细的回答用的问题。我的问题是：云计算板块个股走弱，是否建议投资云计算板块？\n-------------------------------------------------------------\n背景信息是：\n无\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n云计算板块个股走弱，是否建议投资云计算板块？\n根据搜索的信息，当前云计算板块个股表现走弱，包括金山办公、中际旭创、中科曙光、新易盛、宝信软件等均跌逾2%，同时跟踪中证云计算与大数据主题指数的云计算ETF、大数据ETF也跌近2%。然而，虽然短期内云计算板块个股表现不佳，但从长期发展趋势上来看，我国上云企业累计已超过380万家，与美国、欧洲相比，我国云计算市场仍有较大的发展空间。在新基建、东数西算等政策的推动下，中国云计算市场预期将保持快速增长。因此，对于是否投资云计算板块，需要根据投资者自身的风险承受能力和投资期限来判断，如果投资者能接受短期的市场波动并看好云计算长期的发展前景，那么云计算板块仍然是一个值得考虑的投资方向。\n--------------------------\n近期云计算板块个股的走势分析\n近期云计算板块个股走势偏弱。金山办公、中际旭创、中科曙光、新易盛、宝信软件等公司股票跌幅逾2%。同时，跟踪中证云计算与大数据主题指数的云计算ETF和大数据ETF也跌近2%。云计算板块指数报2512.868点，跌幅达2%。其中，跌幅最大的前5个股为：超讯通信跌7.70%；天威视讯跌7.46%；青云科技-U跌6.17%；卓易信息跌5.43%；神州数码跌5.43%。\n\n然而，有券商表示，目前我国上云企业累计超过380万家，与美国、欧洲相比，我国云计算市场仍有较大发展空间。在新基建、东数西算等政策推动下，预计中国云计算市场将保持快速增长。\n--------------------------\n云计算行业的未来发展趋势预测\n云计算行业的未来发展趋势可以从以下几个方面来预测：\n\n1. 效率提升：云计算本质上是提升用户对于底层IT资源的可获得性，借助更丰富的应用生态匹配自身的业务，实现效率的提升。\n\n2. 公有云与私有云的态势：阿里云公共云事业部的目标是做大规模，混合云事业部目标的是考核利润，引导客户向公有云。目前，美国市场上存量服务器规模约为2200万台，60%以上的算力是以公共云模式供给的。而中国市场的服务器存量规模大约为2000万台，仅有28%的算力以公共云模式供给的。\n\n3. AI与云计算的结合：大模型为代表的人工智能与云计算结合趋势日益明显。新的云生态也同样遵循这一逻辑，以MaaS平台和AI原生应用为切入口，降低了门槛与成本，同时由于标准化程度高，因此也更能整合碎片化与长尾需求。\n\n4. 产业数据积淀：云计算服务商由于产业数据积淀丰富，从而在产业的专业深度、服务精度、迭代速度上独树一帜，成为产业上云的领头羊。\n\n5. 行业竞争格局：强者恒强，弱者恒弱，成为行业的真实写照。中小云计算玩家由于体量与互联网巨头不在一个档次，导致基础设施、AI能力等跟不上，难寻差异化竞争力。\n\n6. 出海市场：中国云出海，依然大有可为，亚马逊与微软有了化敌为友的迹象，但不会改变整体的竞争格局，该卷的还是会继续卷。\n\n综上，云计算行业的未来发展趋势主要表现在提升效率、公有云的优先、AI与云计算的结合、产业数据积淀、行业竞争格局以及出海市场的拓展等方面。\n--------------------------\n云计算板块的投资价值评估\n云计算板块的投资价值主要体现在以下几个方面：\n\n1. 市场表现：近期云计算板块个股波动较大，但总体上呈现出强势。如新易盛近期涨幅达20%，中际旭创和金山办公等也涨幅明显。同时，跟踪中证云计算与大数据主题指数的相关ETF，如云计算50ETF、大数据ETF、云计算ETF等，近期涨幅也在3%左右。\n\n2. 行业前景：目前中国的上云企业已累计超过380万家，但相较于美国、欧洲等地，中国的云计算市场仍有较大的发展空间。受新基建、东数西算等政策影响，预计中国云计算市场将保持快速增长。\n\n3. 行业影响力：中证云计算与大数据主题指数选取了50只业务涉及提供云计算服务、大数据服务以及上述服务相关硬件设备的上市公司证券作为指数样本，反映了沪深市场云计算与大数据主题上市公司证券的整体表现，这也是评估云计算板块投资价值的一个重要指标。\n\n总结来说，云计算板块具有较高的投资价值，市场表现强势且行业前景广阔。但投资者也需要注意市场波动，合理配置资产。\n--------------------------\n近期对云计算板块的投资策略建议\n近期对云计算板块的投资策略建议如下：\n\n首先，看好具备自研能力且能够精准把握大模型新机遇的公司。例如金山云，在生成式人工智能带来的产业浪潮下，云计算作为算力底座的重要性日益凸显，而金山云致力于云计算、大数据、人工智能等核心技术研发，围绕客户在实际业务中的需求进行产品和技术迭代升级，为大模型公司、垂直行业优质客户和生态伙伴提供强大的算力底座。\n\n其次，看好能够提供行业解决方案的公司。如金山云推出的“互信VPC”对进出VPC的通信行为有着更为严格的控制，帮助客户解决合规和互信问题。围绕客户业务痛点场景，其大数据智能数据引擎KDE V3.1与云原生大数据产品KCDE V1.7，共新增50多项能力，综合能力进一步增强。\n\n再次，看好能够与前沿技术保持同频的公司，这些公司能够在互联网领域获得可持续的发展动能。如金山云与金山办公WPS AI深入合作，助力其实现训练和推理业务部署，为喜马拉雅语音模型训练场景提供GPU裸金属服务器产品，协助客户在训练场景对开源大模型进行业务摸索。\n\n最后，看好具备强大云服务能力和云安全保障能力的公司。金山云银河平台全面升级“一云多芯”方案，已兼容英特尔、鲲鹏、海光、龙芯等平台，支持全栈部署、混合部署的多形态灵活建云方案。并且，金山云银河平台通过云基准测评共计157项测试，荣获云基准测评最高等级“引领级”认证，这标志着金山云银河平台的综合产品能力处于行业卓越水平。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从短期表现来看，云计算板块个股走弱，主要个股如金山办公、中际旭创、中科曙光等都有超过2%的跌幅，同时跟踪中证云计算与大数据主题指数的相关ETF，如云计算ETF和大数据ETF也下跌近2%。这可能由于市场短期内的波动或者特定事件影响，导致投资者信心下滑，从而影响了云计算板块的短期表现。\n\n然而，从长期发展趋势来看，中国的云计算市场仍具有很大的发展空间。目前，我国上云企业累计已超过380万家，相比之下，美国和欧洲的云计算市场已经较为成熟，中国的云计算市场还有很大的增长潜力。在新基建、东数西算等政策的推动下，预计中国的云计算市场将保持快速增长。\n\n此外，云计算行业的未来发展趋势也值得我们关注。一方面，随着技术的发展，云计算正在从提升IT资源的可获得性向提供更丰富的应用生态转变，这将有助于提升企业的效率；另一方面，人工智能与云计算的结合、产业数据的积累、行业竞争格局的变化、以及出海市场的拓展等因素，也将对云计算行业的未来发展产生深远影响。\n\n因此，对于是否投资云计算板块，需要投资者根据自身的风险承受能力和投资期限来做出决策。如果投资者能够接受短期的市场波动，并且对云计算行业的长期发展前景保持乐观，那么云计算板块仍然是一个值得考虑的投资方向。"}], "source": "self-made"} From de40acdb6200a1d3bb35706f1085615462eb3208 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=89=BA=E9=A2=96=28yiying=29?= Date: Thu, 4 Jul 2024 17:49:08 +0800 Subject: [PATCH 095/118] add: methods to obtain more training dataset and fix expressing agent data --- .../v20240704/expressing_agent_data.jsonl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl b/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl index 45ad93e0..847973d4 100644 --- a/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl +++ b/dataset/peer_financial_training_dataset/v20240704/expressing_agent_data.jsonl @@ -1,10 +1,10 @@ {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-04-06，星期6\n-------------------------------------------------------------\n我的问题是：蔡崇信的访谈提到了什么？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n蔡崇信的访谈提到了什么？\n在阿里巴巴董事长蔡崇信的专访中，他表达了对公司未来发展的信心，并将电商和云计算定位为阿里巴巴的核心业务。他预测，未来五年，中国电商的市场渗透率将上升至40%或更高，推动阿里电商业务的增长。同时，他也看好中国经济的数字化进程为云计算业务提供的需求，并表示阿里正在关注AI电商和行业竞争的发展。\n\n在芯片和半导体方面，蔡崇信指出，由于美国对Nvidia等公司向中国出口高端芯片的严格限制，阿里的云业务和高端计算服务能力受到了影响。他认为这是一个大问题，但也看到解决的可能性，他预见在短期至中期，这会是一个问题，但长远来看，中国将发展自己制造这些高端GPU的能力。在限制解除前，由于已有的库存，大型语言模型的训练仍可以继续进行。\n\n此外，蔡崇信还透露，阿里正在加大对AI相关技术的投资，并致力于提升用户购买频次和订单量。\n--------------------------\n蔡崇信最近的访谈内容\n问题：蔡崇信最近的访谈有哪些重要内容？\n\n回答：\n在最近的访谈中，阿里巴巴董事会主席蔡崇信分享了对公司现状和未来发展的看法。他承认阿里巴巴在过去几年中存在一些问题，并强调了新任CEO吴泳铭对于用户体验的重视。对于中国经济，他认为中国市场仍具有巨大潜力，尽管目前存在一些挑战，例如房地产市场低迷，年轻人就业前景不明朗等。\n\n在谈到阿里巴巴的云业务时，他表示因为美国对华出口管制的影响，中国企业在高端芯片的使用上受限，这对阿里巴巴的云业务以及向客户提供高端计算服务的能力产生了影响。但他提到，中国正在加快发展自主芯片的设计和制造能力。\n\n此外，他还分享了自己的成长经历和工作体验，以及对企业文化、工作效率、领导力以及工作与生活平衡的看法。他建议年轻人要掌握一两项特长，如编程、特定领域的专业知识等，从而在人群中脱颖而出。同时，他还推荐年轻人学习数据科学、心理学等前沿学科。\n--------------------------\n蔡崇信近期对蚂蚁集团的发展规划的访谈\n问题：蔡崇信近期对蚂蚁集团的发展规划有何看法？\n\n答案：蔡崇信在最近的访谈中提到，过去几年，蚂蚁集团在一度忽视用户体验上有所欠缺，为此已经进行了重组。新的首席执行官相较于蔡崇信年轻12岁，他非常注重用户，专注于产品、界面和用户体验。蔡崇信表示，这一改变对蚂蚁集团的发展至关重要。他认为公司需要承认错误，然后改变与战略相适应的组织结构，并将公司的方向融入组织结构中。通过这种方式，他期望蚂蚁集团能够更好地服务用户，从而提升其在竞争中的地位。\n--------------------------\n蔡崇信近期对科技行业的看法和预测的访谈\n蔡崇信在近期的访谈中表示，中国在 AI 技术领域与美国存在一定差距，可能落后两年。去年10月，美国对Nvidia等公司向中国出口高端芯片的能力施加了严格的限制，这对中国的云业务及提供高端计算服务的能力产生了影响。蔡崇信认为这是一个大问题，短期和中期都会产生影响，但他也表示，中国正在努力发展自制高端GPU芯片的能力。\n\n他认为，尽管在短期内，人们已经储备了一定的库存，能够保证大型语言模型的训练在未来一年或18个月内能够继续进行，但从长远来看，中国需要自主制造这些高端芯片。\n\n蔡崇信还强调，中国不会失去世界工厂的地位，墨西哥、越南等国家相比中国仍有较大差距。他建议年轻人努力成为自己领域的前 10% 的佼佼者，以获得尊重和社会认可。\n--------------------------\n蔡崇信近期对中国经济形势的分析的访谈\n蔡崇信，阿里巴巴董事会主席、联合创始人近期在访谈中对中国经济形势进行了深入分析，并对在美运营的中国企业提出警示，重视数据隐私和网络安全等合规事项。他认为，许多西方人对中国存在绝对化的误解，事实往往介于两个极端之间。他还强调了中国市场对全球经济繁荣的重要性。\n\n关于芯片和半导体方面的问题，他指出，去年10月美国对Nvidia等公司向中国每家公司出口高端芯片的能力施加了非常严格的限制。这对阿里巴巴的云业务以及向客户提供高端计算服务的能力造成了影响。他表示，芯片短缺是一个大问题，人们正在努力解决。他认为，在限制解除之前，人们已经储备了库存，所以在未来一年或18个月内，大型语言模型的训练仍然可以继续进行。\n\n蔡崇信表示，从长远来看，中国将发展自己制造这些高端GPU的能力。他强调，不能打赌中国会输，中国市场对世界经济繁荣至关重要。\n--------------------------\n蔡崇信近期对全球金融市场的观察的访谈\n问题：请分析和总结蔡崇信对全球金融市场、阿里巴巴的发展和未来方向的观察和看法。\n\n答案：\n蔡崇信在近期的访谈中分享了他对全球金融市场、以及阿里巴巴未来发展的观察和看法。\n\n对全球金融市场，蔡崇信关注了美国对Nvidia等公司向中国出口高端芯片的限制，认为这对阿里巴巴的云业务和提供高端计算服务的能力产生了影响。他预计在短期和中期，这将是一个问题，但长期来看，中国将发展自己制造高端GPU的能力。\n\n针对阿里巴巴自身的发展，蔡崇信表示阿里巴巴过去忽视了用户体验，因此在重组过程中引入了新的CEO吴泳铭，以更加聚焦于用户为中心的产品和界面开发。他强调了人工智能在阿里的云业务中的重要性，认为拥有一个内部开发的优秀大型语言模型对于阿里的云业务至关重要。\n\n对于未来的方向，蔡崇信强调了中国消费市场的潜力。他指出，中国的制造产量约占全球的31%，但消费仅占全球的14%，存在显著的不平衡。他预期中国消费者将会变得更加重要，并且对GDP的贡献率会持续增长。\n\n此外，蔡崇信还建议年轻人学习基本的专业技能，尤其是数据科学和心理学，认为这些是在生活中取得成功所需的所有工具。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "在蔡崇信的最近一次访谈中，他分享了对阿里巴巴未来发展的信心和看法。主要内容包括：\n\n一、业务发展预测\n1. 他对电商和云计算的发展持乐观态度，认为这两项是阿里巴巴的核心业务。他预测，未来五年，中国电商市场的渗透率将上升至40%或更高，推动阿里电商业务的增长。\n2. 他看好中国经济的数字化进程，认为这将为阿里巴巴的云计算业务提供需求。\n3. 他透露阿里正在加大对AI相关技术的投资，并致力于提升用户购买频次和订单量。\n\n二、行业挑战\n1. 在芯片和半导体方面，蔡崇信指出，由于美国对Nvidia等公司向中国出口高端芯片的严格限制，阿里的云业务和高端计算服务能力受到了影响。\n2. 他认为这是一个大问题，但也看到解决方案，预测短期至中期，这会是一个问题；但长远看来，中国将发展自己制造这些高端GPU的能力。\n\n三、对蚂蚁集团的看法\n1. 他提到，蚂蚁集团在一度忽视用户体验上有所欠缺，为此已经进行了重组。新的首席执行官非常注重用户，专注于产品、界面和用户体验。\n2. 他认为这一改变对蚂蚁集团的发展至关重要，并希望通过这种方式，蚂蚁集团能够更好地服务用户，从而提升其在竞争中的地位。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：证券保险行业今年发生了啥？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n证券保险行业今年发生了啥？\n2023年，证券保险行业表现出长期向好的趋势，但也面临着转型调整的阵痛。上半年，保险公司原保险保费收入3.2万亿元，同比增长12.5%，其中人身险的原保险保费收入同比增长13.38%，显示了恢复性复苏的态势。然而，下半年可能会有调整。我国经济长期向好的基本面没有变化，因此保险行业尤其是寿险领域存在巨大增长空间。\n\n然而，人身保险发展正处于转型发展的阵痛期，面临业务增长持续承压、销售队伍转型升级、行业市场竞争加剧的多重压力。尽管伴随着社会复苏、需求回升，寿险市场慢慢回暖，但行业转型的战略窗口中存在诸多现实挑战。\n\n同时，风险也在增大。截至2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。这30家保险公司中有不少机构的资产规模在数千亿元，如果不能解决其偿付能力问题，可能会对投保人、投资者和市场产生较大的影响。\n\n政府一直在积极推动保险行业规范发展。2020年制定了《责任保险业务监管办法》，明确了责任保险的定义、保险公司的义务和保险服务的范围。2021年，国家金融监督管理总局通过了《保险集团公司监督管理办法》，旨在加强对保险集团公司的监督管理。2022年，国家金融监督管理总局在全国范围内开展了针对保险资金运用关联交易的专项检查，这是银保监会成立以来首次专门针对保险资金运用关联交易的专项检查，也是保险机构监管主体职责改革后首次大规模专项检查行动。整个行业都会持续向好发展。\n--------------------------\n2023年证券行业的重大事件\n2023年证券行业的重大事件主要包括以下几点：\n\n1. 4月，沪深交易所主板注册制首批企业上市，实现了股票发行注册制改革全面落地。同时，沪深港通交易日历优化上线实施，放开了两地市场全部共同交易日的沪深港通交易，极大地提升了交易效率。\n\n2. 5月，国家金融监督管理总局正式揭牌，标志着中国金融监管体系迈入“一行一局一会”新格局。\n\n3. 8月27日，A股政策密集落地，包括证监会阶段性收紧IPO节奏，要求上市公司募集资金应投向主营业务，严限多元化投资；财政部、税务总局公告交易印花税减半征收；证券交易所调降融资保证金比例，支持适度融资需求。\n\n4. 9月，国家金融监督管理总局发布《关于优化保险公司偿付能力监管标准的通知》，为增量险资入市进一步打开空间；同时，沪深交易所进一步规范股份减持行为。\n\n5. 10月11日，中央汇金同日增持四大行股份。\n\n6. 10月30日至31日，中央金融工作会议在北京举行，提出了推动股票发行注册制走深走实，发展多元化股权融资，大力提高上市公司质量，培育一流投资银行和投资机构等一系列资本市场改革发展的重点。\n\n此外，按照中原证券的研报，预计2023年全年肉鸡平均价格将同比上涨，这将推动养殖行业利润弹性逐步释放，相关上市公司的盈利水平将持续改善。中信证券的研报预计，2024年的公用事业行业盈利仍在扩张周期，有望在2024年跑赢市场。\n--------------------------\n2023年保险行业的重大事件\n2023年保险行业的重大事件主要体现在以下几个方面：\n\n1. 行业转型：保险机构开始进行市场细分，发掘消费者的个性化需求，向消费者提供差异化的保险产品及风险管理服务。同时，为消费者提供“好用不贵”的保险产品，也成为趋势。这可以理解为“找细分、要精准、要性价比”的转型过程。\n\n2. 行业挑战：我国人身保险发展正处于转型发展的阵痛期。面临业务增长持续承压、销售队伍转型升级、行业市场竞争加剧的多重压力。预定利率上限下调，导致阶段性承压。\n\n3. 保险公司风险：截至2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。此外，有13家保险公司的评级为C，3家评级为D，包括华夏人寿、恒大人寿、天安人寿等。若这些公司不能解决偿付能力问题，可能会对投保人、投资者和市场产生较大的影响。\n\n4. 监管加强：政府积极推动保险行业规范发展，通过了《责任保险业务监管办法》《保险集团公司监督管理办法》，并在全国范围内开展了针对保险资金运用关联交易的专项检查，这是银保监会成立以来首次专门针对保险资金运用关联交易的专项检查。行业准入门槛、偿付能力管理、业务监管，还是保险资金运用等方面，都只会越来越严格，越来越规范。\n--------------------------\n2023年证券行业的政策变动\n2023年，中国证监会发布了一系列政策，对证券行业进行了深度调整和改革。\n\n首先，证监会对全面实行注册制改革进行了深入学习和宣传，加快推进各条线制度规则的“立改废释”和体系优化，构建更加科学完备的资本市场法律体系。\n\n其次，证监会发布了券商风控指标意见稿，明确了监管方向从原来的鼓励全行业发展到扶优限劣、分类监管。这其中包括对连续3年分类评价AA级以上的券商，风险资本准备的系数可以调整为0.4，而D类则要调为2倍系数。这一政策不仅加快了行业优胜劣汰的速度，也为券商的资本使用效率提供了提升空间。\n\n再者，证监会对《证券公司风险控制指标计算标准规定》进行了修订，并向社会公开征求意见。其中包括，对券商的风险资本准备、表内外资产总额进行调整，分别给出不同的调整系数。同时，对券商在做市、资管、公募REITS等业务的计算标准进行了优化。\n\n最后，意见稿还加强了场外衍生品的监管力度，将股指期货、权益互换及卖出期权计算标准由20%调整至30%。\n\n总的来说，2023年证券行业的政策变动主要体现在全面实行注册制改革、对券商风控指标进行调整、优化风险控制指标计算标准以及加强场外衍生品的监管等方面。\n--------------------------\n2023年保险行业的政策变动\n2023年，中国保险行业的政策变动主要体现在以下几个方面：\n\n首先，财政部发布的《通知》将国有商业保险公司的经营效益类绩效评价指标“净资产收益率”的考核方式由“当年度指标”调整为“3年周期指标+当年度指标”的结合方式，以更好地引导保险公司追求长期稳健经营，防止盲目追求年度考核目标、忽视风险的短期行为。\n\n其次，《通知》将保险公司剩余期限10年期以上保单未来盈余计入核心资本的比例，从不超过35%提高至不超过40%，以鼓励保险公司发展长期保障型产品。\n\n同时，保险行业的发展也正面临一些挑战。例如，保险业投资收益及盈利水平面临增长压力，保险业投资端存量问题与新增风险并存，人身险行业转型仍处于攻坚克难阶段，财产险行业保险风险需持续关注。\n\n未来，保险行业的发展趋势主要包括金融科技进一步融入保险经营模式、行业分化效应将持续、保险行业整体业务结构持续优化。为此，专家建议保险资管公司应通过提升管理层内控和道德意识、健全完善内控系统、注重员工日常培训、建立内部监督体系等方式，加强内部控制建设，有效防范职业欺诈，提高运营效率和财务报表的可靠性，促进保险资管公司持续健康发展。\n--------------------------\n2023年证券保险行业的市场动态\n用户问题：请问2023年证券保险行业的市场动态是什么？\n\n回答：\n2023年，中国的经济形势有望由衰退转向弱复苏，这主要得益于各项政策的推出以及防疫政策的放松。然而，由于海外发达经济体衰退风险的增加以及国内地产恢复的缓慢，这种复苏态势可能会受到一些压力。\n\n在证券市场方面，随着国内经济触底反弹，A股的业绩增速有望实现同比上升。此外，预计海外发达经济体在2023年上半年停止加息，这将缓解人民币贬值压力，从而提高A股相对于海外市场的吸引力，并可能导致北向资金重新流入。因此，预计2023年国内证券市场将具备业绩和估值双重上升的基础。\n\n在保险行业方面，长期利率的复苏成为保险板块上涨的主要动力。预计负债端的拐点将在2023年第二季度出现，这表明我国经济的韧性强，潜力大，活力足，长期向好的基本面没有改变，人身险市场的长期空间仍然广阔。推荐的个股包括中国平安、中国太保和中国人寿。\n\n在投资机会方面，建议关注以下三个主线轮动：国产科技替代创新（如计算机、电子、通信、软件服务、数字经济等科创领域）；中国特色估值重塑主题（如能源、基建、房地产产业链等）；消费（如食品饮料、医药生物、酒店、旅游、交运、新能源消费等）。此外，半导体和人工智能等领域有望成为领导科技股再次走牛的主力军，具有走出史诗级行情的潜力。\n\n总的来说，2023年的证券和保险行业市场动态将主要受到经济复苏、长期利率复苏以及科技创新等因素的影响。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "2023年，中国的证券保险行业发生了许多重大事件和政策变动，行业总体呈现出长期向好的趋势，但也面临一些挑战。\n\n在保险行业方面，上半年，保险公司的原保险保费收入达到3.2万亿元，同比增长12.5%，其中人身险的原保险保费收入同比增长13.38%，显示了恢复性复苏的态势。然而，人身保险发展正处于转型发展的阵痛期，面临业务增长承压、销售队伍转型升级、行业市场竞争加剧的多重压力。\n\n风险管理方面，到2023年第一季度，有6家保险公司处于风险处置过程中，8家保险公司被认为是准问题险企，已暂停披露偿付能力报告。这些公司的资产规模在数千亿元，如果不能解决偿付能力问题，可能会对投保人、投资者和市场产生较大影响。\n\n在政策方面，政府积极推动保险行业规范发展，发布了《责任保险业务监管办法》和《保险集团公司监督管理办法》，并在全国范围内开展了针对保险资金运用关联交易的专项检查。\n\n在证券行业方面，2023年的重大事件包括沪深交易所主板注册制首批企业上市，实现了股票发行注册制改革全面落地，以及沪深港通交易日历优化上线实施，放开了两地市场全部共同交易日的沪深港通交易。\n\n政策方面，证监会发布了券商风控指标意见稿，明确了监管方向从原来的鼓励全行业发展到扶优限劣、分类监管。同时，证监会对《证券公司风险控制指标计算标准规定》进行了修订，并向社会公开征求意见。\n\n市场动态方面，随着国内经济触底反弹，A股的业绩增速有望实现同比上升。同时，保险板块由于长期利率的复苏成为上涨的主要动力，预计负债端的拐点将在2023年第二季度出现。\n\n从以上信息可以看出，2023年的证券保险行业在政策、市场、转型和风险管理等方面发生了许多重要变化，整体呈现出向好发展的趋势，但也面临着一些挑战。"}], "source": "self-made"} -{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。\n今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：在A股上市的酒企广告效果如何？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n近期在A股上市的酒企广告效果评价\n在2023年的前三季度，A股市场上的20家白酒企业总营收达到了3111.62亿元，同比增长15.91%。其中，贵州茅台和五粮液以1032.68亿元和625.36亿元的营收占据市场主导地位，两者的营收合计占总营收的54.28%。在净利润方面，这20家企业合计1189.8亿元，同比增长18.92%，其中岩石股份和伊力特的净利润增速表现最为出色，分别实现153.63%和63.88%的增长。\n\n此外，这些公司的经营性现金流净额合计达到1042.05亿元，同比大幅增加601.52亿元，其中贵州茅台和五粮液的现金流净额分别为500.02亿元和223.89亿元，显示出较强的造血能力。\n\n虽然消费需求疲软、市场竞争激烈和库存积压为白酒行业带来了挑战，但是行业整体仍保持稳健的增长态势。20家上市白酒企业的存货总额为1363.54亿元，同比增长12.63%。合同负债总额为406.67亿元，同比减少了1.91%，显示出市场对于酒类未来消费的看法偏保守与谨慎。然而，考虑到第四季度是传统的白酒消费季，市场对于名酒的业绩依然抱有乐观态度。\n\n在广告效果方面，在Q2的业绩公布中，迎驾贡酒实现了60%的高增速，成为了一大亮点。同时，皇台摘帽，剑南春等其它名酒可能借壳ST西发。这些都显示出广告对于酒企业绩的积极影响。\n\n综上所述，近期在A股上市的酒企的广告效果总体上是积极的，这些企业在市场竞争中表现出较强的实力和稳健的增长态势。\n--------------------------\n近期在A股上市的酒企广告投放情况\n近期，A股上市的酒企的广告投放情况表现积极。以贵州茅台和五粮液为例，他们联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，引发了市场的热议，并在短期内推动了白酒板块的整体涨幅达到4.44%。另一方面，根据2023年三季度的财报，20家白酒上市公司实现总营收3111.62亿元，同比增长15.91%，归母净利润合计1189.8亿元，同比增长18.92%，显示出白酒行业整体的稳步增长。其中，贵州茅台和五粮液两大龙头的营收合计占行业比达54.28%，显示出他们在行业中的领导地位。但是，面临库存积压和市场竞争激烈的挑战，白酒行业仍需面临许多挑战。\n--------------------------\n近期在A股上市的酒企广告对销售的影响\n近期在A股上市的酒企广告对销售的影响可以从以下几个方面来看：\n\n首先，广告投放有助于提升品牌知名度和影响力，从而推动销售。如迎驾贡酒，其2022年Q2业绩超预期，增速高达60%。在此期间，迎驾贡酒投入了大量的资金用于理财，累计收益达6564万元，占其最近一年净利润的6.86%。\n\n其次，现金流管理是酒企运营的重要方面，广告费用预算和现金流管理紧密相关。部分酒企在广告投放和扩产同时进行，需要有效管理现金流以保持运营正常。如贵州茅台和五粮液，虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。对于这些酒企来说，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，白酒行业竞争激烈，广告投放对于提升市场份额尤为重要。据行业分析师肖竹青预计，随着白酒行业进入深度结构性调整期，龙头企业将会拥有越来越大的市场份额，区域白酒企业会逐渐边缘化，未来白酒行业会迎来更激烈的内卷式竞争。在这种环境下，广告投放对于吸引消费者，提升市场份额显得至关重要。\n\n总的来说，广告投放是提升销售的重要工具，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。\n--------------------------\n近期在A股上市的酒企广告的创新点\n近期在A股上市的酒企广告创新主要体现在跨界联名、营销预热、吸引消费者关注等方面。如瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注，使得A股白酒板块整体收涨4.44%。此外，他们还利用“中国人的第一杯酱香拿铁”和“美酒加咖啡，就爱这一杯”等宣传语吸引消费者购买。\n\n同时，酒企也在积极探索新的销售模式和市场机会。据预测，受益于低基数、宴席需求回补、商务活动恢复、消费能力恢复，预计今年二季度营收增速或将继续回升。节假日有望接力催化需求释放，后续应密切关注端午、中秋等假期前后的销售情况。白酒销售渠道反馈行业整体回款进度正有序推进，主要酒企核心单品批发价基本保持平稳、库存相对良性。\n\n然而，值得注意的是，白酒板块的投资风险也不容忽视。白酒作为典型的可选消费品，与居民收入水平和经济活跃度紧密相关，如果经济短期遇到逆风，可能会阶段性造成行业库存提升或业绩增长受阻。因此，投资者需谨慎分析，避免盲目跟风。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从近期A股上市的酒企广告效果评价，广告投放情况，广告对销售的影响以及广告的创新点四个方面来看，广告效果总体上是积极的。\n\n首先，广告帮助提升了酒企的品牌知名度和影响力。比如迎驾贡酒在2022年Q2的业绩公布中，实现了60%的高增速，成为了一大亮点。这显示出广告对于酒企业绩的积极影响。\n\n其次，广告投放对销售有显著的推动作用。贵州茅台和五粮液联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，并在短期内推动了白酒板块的整体涨幅达到4.44%。这进一步证实了广告在提升销售方面的重要作用。\n\n然而，虽然广告能带来销售上的提升，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。例如，贵州茅台和五粮液虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。这显示出，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，酒企在广告方面的创新也取得了一定的效果。瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注。\n\n综上所述，近期在A股上市的酒企通过广告投放在提升品牌知名度、增加销售额、管理现金流以及进行创新等方面取得了一定的成效。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：在A股上市的酒企广告效果如何？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n近期在A股上市的酒企广告效果评价\n在2023年的前三季度，A股市场上的20家白酒企业总营收达到了3111.62亿元，同比增长15.91%。其中，贵州茅台和五粮液以1032.68亿元和625.36亿元的营收占据市场主导地位，两者的营收合计占总营收的54.28%。在净利润方面，这20家企业合计1189.8亿元，同比增长18.92%，其中岩石股份和伊力特的净利润增速表现最为出色，分别实现153.63%和63.88%的增长。\n\n此外，这些公司的经营性现金流净额合计达到1042.05亿元，同比大幅增加601.52亿元，其中贵州茅台和五粮液的现金流净额分别为500.02亿元和223.89亿元，显示出较强的造血能力。\n\n虽然消费需求疲软、市场竞争激烈和库存积压为白酒行业带来了挑战，但是行业整体仍保持稳健的增长态势。20家上市白酒企业的存货总额为1363.54亿元，同比增长12.63%。合同负债总额为406.67亿元，同比减少了1.91%，显示出市场对于酒类未来消费的看法偏保守与谨慎。然而，考虑到第四季度是传统的白酒消费季，市场对于名酒的业绩依然抱有乐观态度。\n\n在广告效果方面，在Q2的业绩公布中，迎驾贡酒实现了60%的高增速，成为了一大亮点。同时，皇台摘帽，剑南春等其它名酒可能借壳ST西发。这些都显示出广告对于酒企业绩的积极影响。\n\n综上所述，近期在A股上市的酒企的广告效果总体上是积极的，这些企业在市场竞争中表现出较强的实力和稳健的增长态势。\n--------------------------\n近期在A股上市的酒企广告投放情况\n近期，A股上市的酒企的广告投放情况表现积极。以贵州茅台和五粮液为例，他们联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，引发了市场的热议，并在短期内推动了白酒板块的整体涨幅达到4.44%。另一方面，根据2023年三季度的财报，20家白酒上市公司实现总营收3111.62亿元，同比增长15.91%，归母净利润合计1189.8亿元，同比增长18.92%，显示出白酒行业整体的稳步增长。其中，贵州茅台和五粮液两大龙头的营收合计占行业比达54.28%，显示出他们在行业中的领导地位。但是，面临库存积压和市场竞争激烈的挑战，白酒行业仍需面临许多挑战。\n--------------------------\n近期在A股上市的酒企广告对销售的影响\n近期在A股上市的酒企广告对销售的影响可以从以下几个方面来看：\n\n首先，广告投放有助于提升品牌知名度和影响力，从而推动销售。如迎驾贡酒，其2022年Q2业绩超预期，增速高达60%。在此期间，迎驾贡酒投入了大量的资金用于理财，累计收益达6564万元，占其最近一年净利润的6.86%。\n\n其次，现金流管理是酒企运营的重要方面，广告费用预算和现金流管理紧密相关。部分酒企在广告投放和扩产同时进行，需要有效管理现金流以保持运营正常。如贵州茅台和五粮液，虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。对于这些酒企来说，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，白酒行业竞争激烈，广告投放对于提升市场份额尤为重要。据行业分析师肖竹青预计，随着白酒行业进入深度结构性调整期，龙头企业将会拥有越来越大的市场份额，区域白酒企业会逐渐边缘化，未来白酒行业会迎来更激烈的内卷式竞争。在这种环境下，广告投放对于吸引消费者，提升市场份额显得至关重要。\n\n总的来说，广告投放是提升销售的重要工具，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。\n--------------------------\n近期在A股上市的酒企广告的创新点\n近期在A股上市的酒企广告创新主要体现在跨界联名、营销预热、吸引消费者关注等方面。如瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注，使得A股白酒板块整体收涨4.44%。此外，他们还利用“中国人的第一杯酱香拿铁”和“美酒加咖啡，就爱这一杯”等宣传语吸引消费者购买。\n\n同时，酒企也在积极探索新的销售模式和市场机会。据预测，受益于低基数、宴席需求回补、商务活动恢复、消费能力恢复，预计今年二季度营收增速或将继续回升。节假日有望接力催化需求释放，后续应密切关注端午、中秋等假期前后的销售情况。白酒销售渠道反馈行业整体回款进度正有序推进，主要酒企核心单品批发价基本保持平稳、库存相对良性。\n\n然而，值得注意的是，白酒板块的投资风险也不容忽视。白酒作为典型的可选消费品，与居民收入水平和经济活跃度紧密相关，如果经济短期遇到逆风，可能会阶段性造成行业库存提升或业绩增长受阻。因此，投资者需谨慎分析，避免盲目跟风。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从近期A股上市的酒企广告效果评价，广告投放情况，广告对销售的影响以及广告的创新点四个方面来看，广告效果总体上是积极的。\n\n首先，广告帮助提升了酒企的品牌知名度和影响力。比如迎驾贡酒在2022年Q2的业绩公布中，实现了60%的高增速，成为了一大亮点。这显示出广告对于酒企业绩的积极影响。\n\n其次，广告投放对销售有显著的推动作用。贵州茅台和五粮液联手瑞幸咖啡推出的“酱香拿铁”成功吸引了消费者的关注，并在短期内推动了白酒板块的整体涨幅达到4.44%。这进一步证实了广告在提升销售方面的重要作用。\n\n然而，虽然广告能带来销售上的提升，但酒企在进行广告投放时也需要兼顾现金流管理和行业竞争态势。例如，贵州茅台和五粮液虽有大量现金存于银行，但同时也在进行大规模的扩产，总投资金额超过370亿元。这显示出，广告投放是提升销售的重要手段，但也需要考虑其对现金流的影响。\n\n最后，酒企在广告方面的创新也取得了一定的效果。瑞幸咖啡与贵州茅台联名推出的“酱香拿铁”，从包装曝光到营销预热，成功吸引了大量消费者关注。\n\n综上所述，近期在A股上市的酒企通过广告投放在提升品牌知名度、增加销售额、管理现金流以及进行创新等方面取得了一定的成效。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-06，星期3\n-------------------------------------------------------------\n我的问题是：长期持有收益比银行还高吗？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n长期持有收益比银行还高吗？\n关于“长期持有收益比银行还高吗？”这个问题，从搜寻到的信息来看，长期持有基金的确有可能带来较高的收益。\n\n首先，统计数据显示，股票型基金的长期持有能大概率提升基金的收益，正收益比例上看，持有3-5年为最佳。虽然在3075只股票型基金中，翻倍的基金只有64只，占比2.08%，但长期持有可以提升收益的概率。\n\n其次，市场虽然会有周期性的波动，但最终会发生均值回归，即涨得过多或跌得过多的趋势最终都会回归到平均值。这就意味着，当市场处于50%分位数之下时，选择长期持有，便能大概率获得收益。\n\n此外，从2004年以来，普通股票型基金指数和偏股混合型基金指数的涨幅都超过了10倍，在近20年的时间里不断创出新高。即使在市场高位买入，只要有耐心等待一定时间，也有可能从亏损中反败为胜。\n\n最后，对于“回本就卖”的策略，虽然可以避免亏损，但可能会错失更大的收益机会。这是因为，离开市场后要重新进入可能需要面临更高的价格和风险，而长期持有则可以更好地面对市场的短期波动，增加赚钱的概率。\n\n总的来说，长期持有基金相比于银行存款，有可能带来更高的收益，但这需要投资者有足够的耐心和对市场趋势的理解。\n--------------------------\n长期持有股票的平均收益率是多少？\n根据搜索到的信息，股票的长期持有收益率是由投资者在一定的持有期内的全部股利收入和资本利得占投资本金的比重来表示的。这个比重可以通过以下公式计算：[现金股息+(股票卖出价-股票买入价)]/股票买入价×100%。这里的现金股息是指投资者持有股票期间的股息收入，股票卖出价和股票买入价的差价则代表了资本利得。因此，想要了解长期持有股票的平均收益率，你需要考虑到股息收入和股票的买卖价格的变化。\n--------------------------\n长期持有的定义和计算方法是什么？\n长期持有，是投资策略的一种，主要是指投资者选择获益率高或成长性高的股票后，进行长期持有，而不是频繁买卖。这种策略的主要思想是，不轻易抛出手中的股票，而是等待股票的长期增长带来投资收益。\n\n至于计算方法，长期持有并没有特定的计算公式。通常，投资者会根据股票的盈利能力、成长性以及市场前景等因素，决定是否将其作为长期持有的投资对象。\n--------------------------\n长期投资的风险和收益比较。\n长期投资具有一定的风险和收益。以下是根据信息整合得出的答案：\n\n风险方面：\n1. 股票投资：可能受到政治、经济、国家产业政策、技术发展等多种因素的影响，可能导致投资者利益受损。但可以通过股票投资种类的组合和投资期限的组合来降低风险。\n2. 债券投资：虽然受到的影响因素和股票投资相似，但影响程度通常较小，风险要小于股票投资。主要风险包括债券发行单位可能出现意外情况，以及银行利率变动带来的风险。\n3. 基金投资：风险和收益与基金类型有关。股票型基金的风险和收益预期通常高于债券型基金和货币市场基金。此外，基金投资还可能面临市场风险、基金运作风险、流动性风险等。\n\n收益方面：\n1. 股票投资：企业应对各种影响因素作详细的分析，选择收益较高、安全性好的股票投资。\n2. 债券投资：在进行债券投资时，应考虑购买明显高于银行储蓄存款利率的长期债券，以求收益最大化和风险最小化。\n3. 基金投资：投资人购买基金，既可能分享基金投资产生的收益，也可能承担基金投资带来的损失。\n\n综上，长期投资需充分考虑自身的风险承受能力，理性判断市场，对投资行为作出独立决策，并承担基金投资中出现的各类风险。\n--------------------------\n近期的金融市场环境对长期持有的影响。\n从搜索到的信息来看，近期的金融市场环境对长期持有的影响主要以以下几个方面进行解读：\n\n首先，如果拉长时间周期来看，长期持有的投资策略可能更有优势。过去20年间，我国累计通胀达63.01%，定期存款的累计收益率为90.82%，而偏股混合型基金指数累计收益率高达783.76%。这表明，尽管短期市场环境波动可能影响投资收益，但长期持有的投资策略，如权益投资，能够跑赢通胀并获取超额收益。\n\n其次，适当的资产配置也是提高长期投资收益的重要手段。根据招商银行的“TREE资产配置服务体系”，投资者应从自身的财富阶段和风险偏好出发，选择适合自己的投资产品，并定期进行资产配置的检视和再平衡，以保持资产组合的稳健性。\n\n此外，在市场环境变化时，长期持有的策略能够有效地减少市场波动对投资收益的影响，带来更为稳定的收益。例如，南方金添利三年持有期债基注重长期持有债券，以获取稳定的利息收入。\n\n最后，科技的应用也对长期持有的投资策略产生影响。招商银行全流程系统化、产品线上化，并积极研发市场宏观、微观层面的观测指标和定量模型，及时感知市场变化，为客户科学、高效地调整配置比例和方案，这对于长期持有的投资策略有着积极的推动作用。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "关于你的问题\"长期持有收益比银行还高吗？\"\n\n首先，我们来定义一下“长期持有”。在投资领域中，“长期持有”是指投资者选择某种具有高收益率或者高成长性的投资品种，而不是频繁地买卖，而是选择长期持有，期待通过资产的长期增长来获得投资收益。\n\n关于长期持有可能带来的收益，统计数据显示，对于股票型基金来说，长期持有能大概率提升基金的收益，正收益比例上看，持有3-5年为最佳。虽然在3075只股票型基金中，翻倍的基金只有64只，占比2.08%，但长期持有可以提升收益的概率。\n\n而对于股票的长期持有收益率，它是由投资者在一定的持有期内的全部股利收入和资本利得占投资本金的比重来表示的。这个比重可以通过以下公式计算：[现金股息+(股票卖出价-股票买入价)]/股票买入价×100%。这里的现金股息是指投资者持有股票期间的股息收入，股票卖出价和股票买入价的差价则代表了资本利得。\n\n另外，我们也需要考虑到市场环境的影响。近期的金融市场环境，尽管短期市场环境波动可能影响投资收益，但长期持有的投资策略，如权益投资，能够跑赢通胀并获取超额收益。过去20年间，我国累计通胀达63.01%，定期存款的累计收益率为90.82%，而偏股混合型基金指数累计收益率高达783.76%。\n\n当然，长期投资也具有一定的风险。例如，股票投资可能受到政治、经济、国家产业政策、技术发展等多种因素的影响；债券投资的主要风险包括债券发行单位可能出现意外情况，以及银行利率变动带来的风险；基金投资的风险和收益与基金类型有关，股票型基金的风险和收益预期通常高于债券型基金和货币市场基金。\n\n所以，从长期来看，“长期持有”的收益比银行存款有可能高，但这需要投资者有足够的耐心和对市场趋势的理解，同时也需要承担一定的风险。"}], "source": "self-made"} -{"type": "chatml", "messages": [{"role": "user", "content": "\n你的角色是：投研分析师投研支小助,所属的国家：中国,所属的公司：蚂蚁集团,研发团队：蚂蚁集团投研团队,职责：善于从不同角度、有条理的回答用户的问题。现在，你需要仅仅使用提供的知识，专业、详细的回答用的问题。我的问题是：指数基金适用于什么样的投资场景？\n-------------------------------------------------------------\n背景信息是：\n无\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n指数基金适用于什么样的投资场景？\n指数基金是一种投资工具，它的主要目标是跟踪并复制特定的股票或债券指数的表现。在投资场景中，它适用于多种情况。首先，对于那些希望投资某个具体行业，特别是高科技行业的投资者，指数基金特别是行业指数基金可以为他们提供一个优秀的投资工具。因为高科技行业的发展充满了不确定性，很难锁定单一的成功公司，但通过投资指数基金，投资者可以在整个行业范围内进行投资，享受行业的整体成长红利，而避免了单一公司决策失误带来的风险。\n\n同时，对于那些在市场底部寻求抄底机会的投资者，指数基金也是一个好的选择。当市场下跌时，很多投资者，包括机构和个人投资者，都会使用指数基金来进行投资。宽基指数基金由于覆盖面广，往往因为机构增持而规模迅速扩大，而行业或主题指数基金则更多地被个人投资者用来根据自己对行业和主题机会的判断来进行投资。\n\n但是，指数基金也存在一定的风险，因为它是满仓操作，风险相对较高。为了降低风险，投资者可以通过分期定投的方式进行投资，从而在不确定的市场环境中获得更好的投资效果。\n\n最后，对于那些对中国经济的未来预期向好的投资者，他们可以将宽基指数基金作为一种底仓的存在，而通过行业和主题指数的轮动配置来实现自己的投资判断。\n\n在总结中，指数基金适用于那些希望投资具体行业，寻求市场底部抄底机会，以及对中国经济未来预期向好的投资者。同时，通过合理的投资策略，如分期定投，投资者可以在享受投资回报的同时，有效地控制投资风险。\n--------------------------\n指数基金的投资特点和风险\n指数基金的投资特点主要体现在以下几个方面：\n\n1. 费用较低：由于指数基金采取跟踪指数的方式，基金管理人不需要花大量的时间和精力进行股票、债券等投资工具的选择以及买入与卖出的时机。这种被动的管理方式降低了基金的管理费用。另外，由于采取买入并持有的策略，只有在所跟踪指数的成份股变化时才进行调整，所以它的交易费用也相对较低。\n\n2. 风险分散：指数基金通过跟踪指数进行广泛的分散投资，这意味着任何单个股票的波动都不会对指数基金的整体表现产生太大的影响，从而降低了投资者的投资风险。\n\n3. 透明度高：由于指数基金的业绩与其所跟踪的指数收益紧密相关，投资者只需关注目标指数的涨跌就可以判断出基金净值的变动。这样的操作简单明了，适合对大势把握较好但对个股了解不多的投资者。\n\n4. 人为影响小：由于指数基金的管理过程主要是对相应的目标指数进行被动跟踪，而不是频繁地进行主动性的投资，这样可以通过较为程序化的交易来减少人为因素的影响。\n\n尽管指数基金有以上特点，但投资者也需要注意其风险，因为市场波动或经济周期等因素可能会影响到被跟踪指数的表现，从而影响到基金的收益。\n--------------------------\n指数基金适合的投资人群特性\n指数基金是一种适合多种投资者的投资工具。首先，它们适合初学者和那些没有时间进行详细研究的投资者，因为这类基金投资的是整个市场或行业的股票，不是单一的个股，这样的分散投资策略可以有效地降低风险。其次，由于指数基金的投资策略是长期持有，所以适合长期投资者。长期投资可以有效地降低交易成本，并且指数基金的长期收益表现往往比短期收益更稳定。\n\n此外，指数基金对于养老金投资者也有吸引力。由于其低成本、大容量、风险分散、风格稳定和策略透明等特性，使其成为权益资产配置的合适工具，能够帮助投资者以较低的成本获取股票市场的长期平均投资回报。\n\n最后，指数基金的运作核心是被动地跟踪指数，在充分分散个股风险的同时，获取市场的平均收益。这一特性使得投资者只需要关注目标指数的涨跌，就可以大体上判断出投资的指数基金净值的变动，业绩透明度较高。同时，由于管理过程主要是对相应的目标指数进行被动跟踪，而不是频繁地进行主动性的投资，这样在管理过程中就可以通过较为程序化的交易来减少人为因素的影响。\n--------------------------\n在不同市场环境下，指数基金的投资表现\n在不同的市场环境下，指数基金的投资表现有着其独特性。在震荡市场中，指数基金往往呈现出“越跌越买”的现象，透明度高且费率低廉的特点使其成为了投资者的重要配置工具。对于投资者来说，跟踪更精准的指数基金能避免投资方向的偏移，更好地符合他们的投资预期。因此，对于有清晰投资目标、不想承担超额收益风险的投资者，以及希望减少投资成本的投资者，指数基金是一个很好的选择。\n\n在选择指数基金时，投资者通常关注两个主要因素：跟踪误差和费用。跟踪误差是指基金实际收益与其跟踪指数之间的差距，越小越好，宽基类指数基金和行业类指数基金分别要求跟踪误差不超过1.5%和2%。费用则是考虑基金的综合费率，一般来说，费率越低，长期复利效应越显著。\n\n未来，随着全面注册制的改革推动，A股市场结构更加健康和透明，指数化投资的性价比也将更高。同时，随着市场投资标的的丰富和投资理念的成熟，被动型指数产品的规模占比有望提高，发展空间大。\n\n近年来，指数基金在零售端的规模始终保持稳步增长，2017年至2022年，指数基金的零售端整体规模5年平均增速为36.9%，其中行业指数年均指数达到53.3%。这表明，指数基金是投资者长期价值投资的重要工具，也是震荡行情下主动权益基金配置的重要补充。因此，我们可以预见，指数基金在未来还将迎来大发展。\n--------------------------\n指数基金在资产配置中的作用和地位\n指数基金在资产配置中具有重要作用和地位。自2002年我国第一只指数基金成立起，指数基金经历了四个发展高峰，分别在2009年、2012年、2018-2020年和2022年，尤其是2018年之后，债券等多种指数的全面发展推动了指数基金的持续上升。\n\n指数基金的发展并不平衡，主要体现在交易活跃的场内指数基金（ETF/LOF）占比不高。以日均交易额1000万以上且季度规模满2亿的ETF和LOF为例，其在宽指/风格板块类、行业类、指数类和商品类指数基金中的规模占比均达到60%以上，但在港股和海外指数基金中的占比较小。\n\n商品类指数基金的发行受到限制，难以满足资产配置需要。目前市场上只有13只商品基金，主要包含对标黄金现货和白银期货的贵金属基金、对标原油相关指数的能源类基金和3只商品期货ETF。\n\n指数增强公募基金的发展状况不理想，占比只有7%-8%，且超额收益率稳定性较弱。主要原因是这类基金主要集中在股票指数策略，而债券指数策略占比并不高。\n\n从机构持有国内指数基金的比例来看，2014年之后机构资金在国内基金资金来源中占比显著提高，尤其是在股票指数基金中，机构资金占比在2007年之后经历了持续提高的过程，始终稳定高于普通股票型或混合型基金的机构资金占比。\n\n指数化投资是基金组合投资专业化的必经之路，且逐渐成为国内机构投资的主流方向。目前，我国指数基金体系在不断发展，有望进一步提升顾问服务的能力，促进居民财富的优化配置，有助于证券公司财富管理向买方投顾发展和转型。\n--------------------------\n指数基金的投资策略和操作建议\n指数基金的投资策略主要是复制市场指数的投资组合，这被称为一种消极的投资策略。这种策略的优点包括简单、透明和低费率。在投资过程中，不同类型的指数基金的关注点不同。例如，策略型指数基金主要关注指数策略的长期有效性，而宽基指数基金主要关注估值等因素，行业型指数基金除了关注估值，还需要注意行业的基本面和成长性等因素。\n\n对于初次投资的投资者，建议从宽基指数基金入手，如沪深300、中证800等指数基金。定投是一个可行的参与方式，当达到止盈目标后，可以一次性赎回并开始新的定投计划。\n\n在当前市场环境下，由于A股的政策底已经被多次确认，加上国内经济的持续好转和中美关系的缓和，现在A股具有一定的配置价值。投资者可以采取分批加仓或者多次定投宽基的方式参与市场。如果看好某个细分行业，也可以配置相关的行业指数基金。\n\n在挑选指数基金产品时，投资者可以重点关注大型基金公司推出的产品。除了费率因素外，还需要关注基金的规模和跟踪误差等因素。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "指数基金是一种投资工具，主要目标是复制特定的股票或债券指数的表现。在投资场景中，它适用于多种情况。\n\n首先，对于希望投资某个具体行业，特别是高科技行业的投资者，指数基金可以为他们提供一个优秀的投资工具。因为高科技行业的发展充满了不确定性，但通过投资指数基金，投资者可以在整个行业范围内进行投资，享受行业的整体成长红利。\n\n其次，对于寻求市场底部抄底机会的投资者，指数基金也是一个好选择。当市场下跌时，许多投资者会使用指数基金来进行投资。宽基指数基金由于覆盖面广，往往因为机构增持而规模迅速扩大。\n\n此外，对于对中国经济的未来预期向好的投资者，他们可以将宽基指数基金作为一种底仓的存在，而通过行业和主题指数的轮动配置来实现自己的投资判断。\n\n然而，指数基金也存在一定的风险，因为它是满仓操作，风险相对较高。为了降低风险，投资者可以通过分期定投的方式进行投资，从而在不确定的市场环境中获得更好的投资效果。\n\n在选择指数基金时，投资者通常关注两个主要因素：跟踪误差和费用。跟踪误差是指基金实际收益与其跟踪指数之间的差距，越小越好。费用则是考虑基金的综合费率，一般来说，费率越低，长期复利效应越显著。\n\n总的来说，指数基金是适合多种投资者的投资工具，通过合理的投资策略，投资者可以在享受投资回报的同时，有效地控制投资风险。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-03，星期3\n-------------------------------------------------------------\n我的问题是：云栖大会带来什么投资机会\n-------------------------------------------------------------\n背景信息是：\n\n 根据上述收集的信息，从两部分进行资本市场大事分析\n 第一部分：基于上述几个维度，基于能够获取到的客观信息，挑选其中2-3个重点维度进行展开论述，不需要所有方面都一一提及，同时内容上要避免重复，同时更加强调时效性，表达的内容简明扼要，语义连贯。\n 第二部分：总结陈述，这里需要提炼要点，做到重点突出。然后结合市场上各类机构和专家给出的观点看法进行汇总提炼，如果市场上提到了某类资产的投资机会或者风险，则提示一下潜在的机会和主要的风险点。\n \n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n云栖大会带来什么投资机会\n事件回顾：\n云栖大会是一场关于云计算和大数据的盛会，其主要内容包括数据化转型的深入推进，以及相关的投资机会。在大会上，马云强调了贵州在未来30年的投资价值。\n\n事件解读分析：\n云栖大会的核心内容是数据化转型和云计算的发展，这将为投资者带来新的机会。随着数据化转型的深入推进，将产生更多更有价值的数据，创造更加丰富的应用场景。通过实施“数据要素x”行动，有望进一步发挥数据要素乘数效应，推进数据要素在相关行业的广泛利用。\n\n市场影响分析：\n云栖大会带来的投资机会主要集中在制造板块、品牌消费和高股息策略三个主线。制造板块中，华利集团、申洲国际、台华新材、伟星股份、新澳股份、健盛集团等优质企业有望受益。品牌消费板块中，安踏体育、李宁、比音勒芬、报喜鸟、波司登、滔搏、富安娜等高景气品牌有望迎来结构性投资机会。高股息策略中，家纺、男装、制造和港股个股在当前市盈率较低，分红能力出色，具有较好的配置价值。此外，跟踪中证云计算与大数据主题指数的云计算ETF（516510）等产品也有望受益。\n--------------------------\n2024年云栖大会的主要议题和内容\n2024年云栖大会的主要议题和内容主要围绕人工智能（AI）的发展和应用，以及其带来的挑战和影响进行讨论。\n\n1. 事件回顾：\n2023年，AI技术在多个领域取得了显著的进步，包括语言模型、医疗诊断、教育、自动驾驶和金融等。其中，以ChatGPT为代表的高级语言模型在处理复杂的数据分析、深入的语言理解与生成，以及多模态交互方面表现出色。AI在医疗领域的应用，如AI辅助的诊断工具，已经得到了广泛应用。在教育领域，AI技术被用于开发个性化学习平台，提高学习效率和质量。\n\n2. 事件解读分析：\nAI技术的快速发展也带来了一系列挑战，包括数据隐私和安全问题、就业结构的变化、以及版权问题的争议。展望未来，AI技术的发展潜力巨大，但同时也需要对技术发展进行合理的规范和引导。政府、企业和科研机构需要共同努力，制定有效的政策和标准，以确保AI技术的健康发展。\n\n3. 市场影响分析：\nAI技术的发展对资本市场产生了深远影响。在医疗领域，AI技术的应用提高了诊断效率，为医疗行业带来了巨大的市场机遇。在教育领域，AI技术的应用推动了教育行业的创新和发展。同时，AI技术的发展也对劳动市场产生了影响，一些低技能工作可能会被机器人取代，这对劳动力市场的结构和就业形势产生了影响。此外，AI技术的发展也对版权法和知识产权法产生了挑战，引发了关于创作权和版权法律适用的广泛讨论。\n--------------------------\n云栖大会对互联网行业的影响\n事件回顾：\n云栖大会是阿里巴巴集团举办的科技行业大会，被视为行业的“路标”。2023年，互联网行业发生了许多重大变化，包括ChatGPT的出现引发了大语言模型、图像视频模型的爆发，阿里巴巴和京东的组织架构调整，以及拼多多市值超过阿里巴巴等。此外，华为、小米、魅族等手机厂商也开始进军造车行业。\n\n事件解读分析：\n云栖大会的影响力在互联网行业中不可忽视，它不仅是技术盛宴，也是行业通往未来的“路标”。2023年的互联网行业变化频繁，新兴挑战者和老牌巨头都在自我变革中寻找新的定位和商机。其中，AI的普及和大模型的爆发为行业带来了新的机遇和挑战。此外，电商巨头的组织架构调整和市值变动也反映了市场的竞争态势。\n\n市场影响分析：\n这些变化对资本市场产生了深远影响。例如，阿里巴巴和京东的组织架构调整可能会影响其股价和市值。拼多多市值超过阿里巴巴可能会引发投资者对电商行业的重新评估。此外，手机厂商进军造车行业可能会对汽车和科技行业的资本市场产生影响。总的来说，这些变化可能会对相关行业的投资者产生重大影响。\n--------------------------\n云栖大会后互联网行业的投资机会\n事件回顾：\n云栖大会后，互联网行业的投资机会引起了市场的广泛关注。中国的互联网行业发展迅猛，尤其是互联网金融和互联网+等领域。随着智能手机的普及和人工智能、物联网技术的发展，行业内部正在发生深刻的技术革新。\n\n事件解读分析：\n这些技术革新为互联网行业带来了新的投资机会。在美元流动性压力缓解的背景下，互联网板块的估值正在复苏，特别是中小市值股票的估值修复速度有望领先。政策支持民营企业发展和扩大内需的推进，也将成为市场关注的重点。\n\n市场影响分析：\n互联网行业的投资机会整体看来是乐观的。建议优先关注低估值的重点公司，如腾讯、阿里巴巴、拼多多、贝壳、百度、美团、快手和京东等。然而，投资过程中也存在一些潜在风险，如政策监管超预期、宏观经济增长放缓、利率上行和流动性收紧等。因此，投资者在决策时应充分考虑这些因素，并做好风险管理和多元化投资。\n--------------------------\n云栖大会后相关公司的股票和债券市场表现\n事件回顾：\n过去一年，科技巨头们的云业务一直处于调整状态。阿里巴巴暂缓了阿里云的分拆计划，表示将采取AI驱动、公共云优先战略；腾讯创始人兼CEO马化腾重新为腾讯云战略发展方向奠定基调；京东科技集团将京东云作为独立事业部开始运作；亚马逊、微软和谷歌的云业务均受到IT支出放缓的挤压。\n\n事件解读分析：\n摩根士丹利的报告指出，大部分云业务的调整阶段仍在进行中，预计将于今年3季度、4季度结束，但最新的调查显示，调整结束的日期有所推迟。目前的调整是在为未来发展夯实基础，人工智能将在2024年下半年和2025年给云业务带来更大的再加速潜力。云业务的业绩表现正在趋于稳定，离重新加速发展又近了一步。\n\n市场影响分析：\n目前云业务的业绩增长主要表现在微软的Azure业务方面。微软最新三季度财报显示，云业务收入未如市场预期那样放缓，而保持两位数的猛增势头——Azure和其他云服务的收入三季度增长29%，高于预期3个百分点。大摩认为，微软和谷歌将成为云业务的两大受益者，云业务的受益者正在向下游延伸。此外，云业务相关的软件管理、运营维护以及通信厂商也能从中受益，如SAP、Snowflake、Datadog、MongoDB、Confluent、F5、Nutanix和Palo Alto Networks。\n--------------------------\n云栖大会对资本市场的积极、中性或消极影响分析\n事件回顾：\n云栖大会是一场关于云计算的重要会议，其中涉及到云计算可能颠覆软件产业的观点，以及百度智能云发布的一系列云计算基础设施产品，旨在降低AI原生应用开发门槛，加速企业AI原生应用落地。\n\n事件解读分析：\n云计算的发展正在改变商业模式，影响开发、部署、运行、交付应用的方式。这种转变将数据中心放在网络的核心位置，所有的应用所需要的计算能力、存储、带宽、电力都由数据中心提供。此外，大模型市场的竞争日益激烈，云计算厂商平台优势更加凸显，市场参与者的发展路线出现分化，云计算市场格局有望率先加速洗牌。\n\n市场影响分析：\n云栖大会的举办对资本市场产生了积极影响。首先，云计算的发展推动了软件产业的变革，为相关企业提供了新的发展机遇。其次，百度智能云发布的云计算基础设施产品，有助于降低AI原生应用开发门槛，加速企业AI原生应用落地，这将推动AI原生应用市场走向繁荣，对相关企业的发展产生积极影响。据统计，目前千帆平台已经累计服务超过4万家企业用户，累计帮助企业用户精调近1万个大模型，这一数据足以证明云计算市场的活跃度和发展潜力。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "基于云栖大会的相关信息，我们可以从以下两个部分进行投资机会的分析：\n\n第一部分：重点维度展开论述\n\n1. 技术创新带来的投资机会：云栖大会的核心议题包括数据化转型、云计算和人工智能（AI）的发展，这些都是当前科技领域的热点。数据化转型和云计算的深入推进将产生更多有价值的数据，创造更丰富的应用场景，为投资者带来新的机会。特别是AI技术的发展，已经在医疗、教育、自动驾驶等多个领域取得显著进步，预计将在2024年下半年和2025年给云业务带来更大的再加速潜力。\n\n2. 行业变革带来的投资机会：云栖大会后，互联网行业的投资机会引起了市场的广泛关注。中国的互联网行业发展迅猛，尤其是互联网金融和互联网+等领域。随着智能手机的普及和人工智能、物联网技术的发展，行业内部正在发生深刻的技术革新。这些技术革新为互联网行业带来了新的投资机会。\n\n第二部分：总结陈述\n\n云栖大会对资本市场产生了积极影响。首先，云计算的发展推动了软件产业的变革，为相关企业提供了新的发展机遇。其次，百度智能云发布的云计算基础设施产品，有助于降低AI原生应用开发门槛，加速企业AI原生应用落地，这将推动AI原生应用市场走向繁荣，对相关企业的发展产生积极影响。\n\n结合市场上各类机构和专家的观点，云栖大会带来的投资机会主要集中在制造板块、品牌消费和高股息策略三个主线。制造板块中，华利集团、申洲国际、台华新材、伟星股份、新澳股份、健盛集团等优质企业有望受益。品牌消费板块中，安踏体育、李宁、比音勒芬、报喜鸟、波司登、滔搏、富安娜等高景气品牌有望迎来结构性投资机会。高股息策略中，家纺、男装、制造和港股个股在当前市盈率较低，分红能力出色，具有较好的配置价值。此外，跟踪中证云计算与大数据主题指数的云计算ETF（516510）等产品也有望受益。\n\n然而，投资过程中也存在一些潜在风险，如政策监管超预期、宏观经济增长放缓、利率上行和流动性收紧等。因此，投资者在决策时应充分考虑这些因素，并做好风险管理和多元化投资。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-17，星期3\n-------------------------------------------------------------\n我的问题是：标题: 多只跨境ETF被“爆炒”!\n描述: 1月16日，多只跨境ETF放出巨大成交量，部分不足10亿跨境ETF盘中成交额已超越茅台，更有跨境基金一月成交额放大超140倍。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 多只跨境ETF被“爆炒”!\n描述: 1月16日，多只跨境ETF放出巨大成交量，部分不足10亿跨境ETF盘中成交额已超越茅台，更有跨境基金一月成交额放大超140倍。\n===\n1. 跨境ETF规模大增：根据Choice数据显示，2023年末，跨境ETF总规模达到2686.70亿元，相比2022年的1915.53亿元，增加了771.17亿元。这一显著增长反映出投资者对跨境ETF的热情提升。\n2. 高关注度领域突显：具体来看，港股创新药、港股互联网、美股科技赛道类的ETF规模增长明显。这些领域的热度反映了大众投资者的偏好和市场趋势。\n3. 市场预期潜在变化：有机构人士认为，美债收益率2023年四季度见顶回落，预计2024年全年或迎来趋势性下行，这将对利率较为敏感的资产产生影响，可能会带来新的投资机会。\n--------------------------\n2024年1月16日跨境ETF的成交额超越茅台的具体情况\n===\n1. 跨境ETF热潮：在2024年1月16日，部分市值不足10亿的跨境ETF盘中成交金额已超越贵州茅台，其中有些产品短短一个月时间成交额放大超140倍。不过，高溢价风险随之陡增，游资或开始出逃。例如，日经225ETF、东南亚科技ETF基金创出价格新高后开始走跌。\n\n2. 主力资金流出：以跨境通为例，1月16日主力资金净流出3168.88万元，占总成交额的15.17%，而游资资金净流出1377.03万元，占总成交额的6.59%。此表明，在高溢价风险的情况下，部分主力资金和游资选择了出逃。\n\n3. 散户热衷参与：与主力资金和游资不同，散户资金在此时却呈现净流入状态。以跨境通为例，其散户资金净流入4545.9万元，占总成交额的21.76%，说明在跨境ETF的炒作中，散户投资者依然保持积极态度。\n--------------------------\n2024年1月份跨境基金成交额放大超140倍的原因\n===\n1. 交易活跃度提升：2024年1月份，跨境ETF基金的成交额出现了显著的增长，甚至有些小市值的跨境ETF基金的日成交量超过了贵州茅台。例如，华夏基金旗下的日经ETF在1月16日的全天成交金额达到了47.79亿元，而其盘中市值仅有7.16亿元。同样，东南亚科技ETF基金，该基金盘中市值约6.64亿元，1月16日全天成交金额高达29.17亿元。\n\n2. 资金流向小市值：在微盘股炒作过热后，市场资金可能会寻找估值更低，或价格处在低位的小微盘品种。这在迷你基金的交易中得到了体现，投资者的资金开始追逐市值更小的跨境ETF基金产品。\n\n3. 投资风险提醒：虽然跨境ETF基金的交易活跃度提升，但是投资者应注意风险。例如，当小微盘出现明显赚钱效应后，市场资金会加速进入，进一步加强小微盘品种的表现。然而，随着价格的上涨，任何风格都不可能持续占优，微盘行情也值得警惕，尤其是微盘股的弱流动性，一旦发生风格切换可能出现“踩踏”现象。\n--------------------------\n2024年1月16日跨境ETF爆炒的市场背景\n===\n1. ETF涨幅领军：2024年1月16日，一场跨境ETF的爆炒在市场上展开，其中日经ETF(513520)、东南亚科技ETF(513730)和印度基金LOF(164824)涨幅分别超过8%、6%和5%。这表明投资者对于海外市场，尤其是亚洲市场的关注度和投资热情正在逐步提升。\n2. 板块动态：在板块方面，光伏和新能源两大板块表现抢眼，尤其是光伏30ETF(560980.SH)领涨市场。相比之下，创中盘88ETF(159804.SZ)表现不佳，成为跌幅最大的ETF。这反映了市场对绿色能源的持续看好以及对某些板块的短期调整。\n3. 市场交易情况：当日市场表现稳定，三大指数集体收涨，其中沪指上涨0.27%，深证成指上涨0.31%，创业板指上涨0.38%。总成交额达到6848亿，较上个交易日增加739亿。然而，北向资金全天净卖出41.33亿，显示外资整体上对中国市场的态度较为谨慎。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 成交量暴增的驱动因素：1月16日，多只跨境ETF的成交量大增，其中部分市值不足10亿的跨境ETF盘中成交金额已超过贵州茅台。这可能源于投资者对国际市场的投资需求增加，以及对国内市场的一些热门股票（如茅台）的投资热情有所下降。同时，跨境ETF的优势在于可以让投资者方便地参与到国际市场的投资，这也是吸引投资者的一个重要因素。\n\n2. 行业偏好的变化：在跨境ETF的投资中，投资者对某些行业的偏好可能会发生变化。比如，港股创新药、港股互联网、美股科技赛道类的跨境ETF的规模增长较为明显，这可能反映出投资者对这些行业的关注度提升。\n\n3. 市场预期的影响：市场预期的变化也会影响投资者的投资行为。比如，如果市场预期美债收益率将趋势性下行，那么对利率敏感的资产可能会受到影响，投资者可能会寻找新的投资机会，这可能会促使部分投资者转向跨境ETF。"}], "source": "self-made"} -{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2023-12-05，星期2\n-------------------------------------------------------------\n我的问题是：我是想问你，市场还会跌吗？\n-------------------------------------------------------------\n背景信息是：\n\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n我是想问你，市场还会跌吗？\n根据搜索到的信息，目前市场正在经历一个阶段性的下跌过程，与此同时，市场也在进行阶段性的筑底过程。具体表现为，市场由缓跌到急跌，这一点从指数的回撤幅度也可以看出，几大指数的回撤幅度都接近2%。尽管如此，市场的下跌并非全无好处，这也是加速赶底的表现，一旦出现反弹信号和量能走好，就可能是市场的转折点。\n\n然而，目前市场的反弹信号并未明确，且各级别的量能现状也并不理想。这预示着市场继续下行震荡的概率依然较大。因此，对于问题“市场还会跌吗？”的回答是，从当前情况来看，市场有可能会继续下行震荡。\n\n需要注意的是，尽管大盘下跌空间有限，但是局部板块需要注意风控优先的原则。例如，黄金板块在大家追高时，实际上已经出现了下跌的信号。另外，石油、新能源、白酒等板块也在提醒继续下行的风控。而对于一些关键板块，如船舶板块，可以在688-710点区间内逢低入场。\n--------------------------\n近期的股市行情走势\n根据最近的股市行情，A股三大指数集体走弱，沪指跌1.67%，收报2972.30点，创业板指跌1.98%，收报1871.10点。沪深两市下跌的同时，北证50指数逆势大涨7.28%。尽管沪指再度失守3000点大关，但资深市场人士认为二次探底才是真底，明天的走势非常关键。\n\n在此背景下，食品板块逆势崛起。西部牧业、阳光乳业、一鸣食品等多只股票录得涨停。人气股惠发食品连续录得8个涨停板，股价创历史新高。尽管该公司近年业绩乏善可陈，但其主营业务——速冻调理肉制品的市场前景被看好。\n\n此外，“平价消费”已成为近期市场的一大主线，休闲食品龙头良品铺子宣布业务端将实施17年来最大规模降价，300款产品平均降价22%，最高降幅45%。中邮证券研报表示，在“经济弱复苏+消费趋于理性”双重背景下，我国零食折扣专营店兴起，下游格局初具雏形。\n\n食品板块中，多只股票未来增长潜力获机构扎堆看好。其中，安井食品上涨空间63.48%排在首位，该公司主要从事速冻食品业务，有望在餐饮消费逐步顺周期中维持较快增长。\n\n总的来看，目前市场处于短线的寻底过程，投资者需要耐心等待，明年春节前后有望形成大级别底部。\n--------------------------\n近期的宏观经济数据\n问题：“近期的宏观经济数据体现出了中国经济的哪些特点？”\n\n答案：根据2023年10月末的数据，中国的M2余额同比增加了10.3%，达到288.23万亿元，规模以上工业增加值同比增加了4.6%，达到28.31万亿元，说明经济正在持续恢复。穆迪评级公司将中国主权信用评级展望调整为“负面”，但财政部认为，面对复杂严峻的国际形势，中国宏观经济持续恢复向好，高质量发展稳步推进，预计四季度中国经济将保持回升向好态势。此外，前三季度，全国一般公共预算收入同比增长8.9%，其中，全国税收收入增长11.9%，财政支出同比增长3.9%。这些数据表明，中国经济正在转向高质量发展，新动能正在发挥作用。至于地方政府债务，截至2022年末，全国地方政府法定债务余额35.1万亿元，全国政府债务余额61万亿元，全国政府法定负债率为50.4%，低于国际通行的60%警戒线，也低于主要市场经济国家和新兴市场国家，说明我国的债务风险总体可控。\n--------------------------\n近期的全球政治经济事件\n在您的问题中，并没有提供一个具体的问题，如果您是想了解近期的全球政治经济事件，我能从您提供的信息中提供以下概览：\n\n近期全球政治经济形势变化多端，主要表现在以下几个方面：\n\n1. 高通胀问题：海外高通胀现象短期内难以消解，这对全球经济产生了较大影响。\n\n2. 科技变革：以人工智能为代表的科技变革正在快速发展，这将对全球经济的发展模式产生深远影响。\n\n3. 企业投资变化：在这样的大环境下，全球企业在投资目的和金融需求等方面正在发生变化，例如“引进来”和“走出去”的策略。\n\n德意志银行的高级管理层也就这些变化进行了详细的讨论，关注了四大趋势以及金融服务的相应变化。\n\n以上是近期全球政治经济事件的主要信息，如果有其他具体问题，欢迎继续提问。\n--------------------------\n近期的金融政策变动\n你好，近期中国的金融政策变动主要围绕住房信贷政策的调整和优化：\n\n1. 中国人民银行和金融监管总局联合发布通知，调整优化差别化住房信贷政策。具体来说，首套住房商业性个人住房贷款最低首付比例统一为不低于20%，二套住房商业性个人住房贷款最低首付比例统一为不低于30%。此外，二套住房商业性个人住房贷款利率政策下限调整为不低于相应期限贷款市场报价利率加20个基点。\n\n2. 针对存量首套住房贷款利率，推出了降低措施。假设按揭利率下调50个基点，预计全行业每年可减少房贷利息约2000亿元，相当于增加了2022年社会消费品零售额的0.5%。对个人而言，假设存量按揭利率下调80个基点，按照100万元按揭贷款、等额本息还款计算，估算可降低借款人月供约500元，节约月供和总还款额约8%。\n\n3. 央行和银保监会印发通知，支持房地产市场平稳健康发展，包括保持房地产融资平稳有序、积极做好“保交楼”金融服务、积极配合做好受困房地产企业风险处置等措施。\n\n这些政策调整有利于降低居民购房门槛和成本，促进潜在合理购房需求的释放，有望改善银行“资产荒”和对公地产敞口资产质量，对市场预期起到积极引导作用，共同带动后市销售企稳复苏。\n--------------------------\n近期的股市技术分析\n根据搜索到的信息，近期的股市呈现出以下特点和趋势：\n\n1. A股大盘指数出现调整，上证指数跌破3000点整数关口，其中华为算力、操作系统、存储芯片、ChatGPT、鸿蒙等热门题材全线回落。\n\n2. 在大盘持续走弱的背景下，食品板块逆势崛起，西部牧业、阳光乳业、一鸣食品、安记食品、南侨食品、春雪食品等多股涨停。北交所的食品股表现更为火爆，骑士乳业、润普食品、盖世食品均收获涨停板，欧福蛋业、朱老六、康比特、田野股份、驱动力等涨超10%。\n\n3. “平价消费”已成为近期市场的一大主线，休闲食品龙头良品铺子宣布业务端将实施17年来最大规模降价，300款产品平均降价22%，最高降幅45%。\n\n4. 中国休闲食品行业市场规模持续增长，从2010年的4100亿元增长至2022年的11654亿元，预计2027年中国休闲食品行业市场规模将达12378亿元。\n\n5. 从技术分析角度看，今天的阴线出现了实体吞没上个阳线的技术信号，但目前所处走势位置来看指标并不需要过多关注，只有在重要行情末尾出现类似技术信号需要预判防止风险扩大。\n\n6. 本周的技术指标依旧收敛，最高价受到5ma压制，周线倒鸭嘴结构可能就做出来了。创业板今天跌的多跌幅0.9%，今天量能也放出近七个交易日最大量。\n\n整体来看，股市出现调整，食品板块逆势崛起，平价消费成为市场趋势，中国休闲食品行业市场规模持续增长，技术指标显示周线倒鸭嘴结构可能形成。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "首先，根据目前的股市行情，我们看到市场正在经历一个阶段性的下跌过程，尽管这也是市场筑底过程的一部分。大盘指数，如沪指已跌破3000点，而创业板指也出现了下跌。这些都表明市场在短期内可能继续处于震荡下行的状态。\n\n另一方面，我们要关注近期的宏观经济指标。目前，中国的M2余额同比增加了10.3%，规模以上工业增加值同比增加了4.6%，显示出经济正在持续恢复。尽管有的机构将中国主权信用评级展望调整为“负面”，但我们看到中国的经济基本面仍然稳健，且预计四季度中国经济将保持回升向好态势。\n\n然而，全球政治经济形势的变化以及近期的金融政策变动也会对股市产生影响。例如，海外高通胀现象短期内难以消解，这对全球经济产生了较大影响。同时，中国央行和金融监管总局调整了住房信贷政策，这也将对资金市场流动性产生影响。\n\n至于股市的技术分析，我们看到今天的阴线出现了实体吞没上个阳线的技术信号，显示出市场可能继续下行。而从市场板块来看，食品板块逆势崛起，这可能预示着市场将出现结构性机会。\n\n综合考虑以上因素，我认为市场有可能在短期内继续下行震荡。但这也是市场筑底的过程，一旦出现反弹信号和量能走好，就可能是市场的转折点。因此，投资者需要保持耐心，适当做好风险控制，并关注市场的变化，寻找结构性投资机会。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-02-20，星期2\n-------------------------------------------------------------\n我的问题是：标题: OPPO发布1+N智能体生态战略\n描述: OPPO刘作虎发布1+N智能体生态战略，这一生态由AI超级智能体和AIPro开发平台组成，基于巨型模型库和识别能力给出精准结果。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: OPPO发布1+N智能体生态战略\n描述: OPPO刘作虎发布1+N智能体生态战略，这一生态由AI超级智能体和AIPro开发平台组成，基于巨型模型库和识别能力给出精准结果。\n===\n1. AI时代的开启：OPPO首席执行官陈明永描绘了未来五年AI对手机行业的重大影响，他认为这将与当年智能手机替代功能机的影响力相媲美。他宣布2024年为AI手机元年，并宣布OPPO将成立AI中心，全力发展AI技术。\n\n2. 1+N智能生态的构建：OPPO首席产品官刘作虎发布了1+N智能体生态战略。该生态由AI超级智能体和AI Pro开发平台组成，AI超级智能体能准确理解用户意图，基于庞大的模型库和识别能力给出精准结果，AI Pro智能体开发平台将使用户可以自由创造和开发自己需要的智能体，无需编写代码。\n\n3. 共创AI手机标准：OPPO期望与行业伙伴共同定义AI手机标准，构建AI手机生态。这意味着OPPO不仅将重心放在自身产品的创新上，也致力于推动整个行业的进步，与伙伴共同开创AI手机新时代。\n--------------------------\nOPPO发布的1+N智能体生态战略具体内容是什么？\n===\n1. 战略构成：OPPO的1+N智能体生态战略由两部分组成，一是AI超级智能体，二是AI Pro开发平台。其中，AI超级智能体能够准确理解用户意图，基于庞大的模型库和识别能力给出精准结果，并形成记忆习惯。而AI Pro开发平台则为用户提供了一个自由调用组合工具和插件，创造和开发自己需要的智能体的平台，而且无需写代码。\n\n2. 用户体验提升：该战略通过AI超级智能体理解用户意图并形成记忆习惯，以满足用户需求，提高用户体验。AI超级智能体基于模型库和识别能力，可以给出精准的结果，使得用户得到更贴切的服务。\n\n3. 开启AI手机时代：OPPO CEO陈明永将2024年定义为AI手机元年，认为AI对手机行业的影响，将完全可以比肩当年智能手机替代功能机。他期待AI手机成为继功能机、智能手机之后，手机行业的第三阶段。OPPO的1+N智能体生态战略，无疑是这个阶段的一个重要开端。\n--------------------------\nOPPO的1+N智能体生态战略对其未来发展有何影响？\n===\n1. AI时代的引领者：OPPO的1+N智能体生态战略，标志着其走在了AI手机元年的前沿。这一生态由AI超级智能体和AI Pro开发平台组成，其中超级AI智能体能够准确理解用户意图并给出精准结果，让手机更懂用户。OPPO已经看到了未来五年，AI对手机行业影响的巨大潜力，完全可以比肩当年智能手机替代功能机的革新。\n\n2. 开放创新的平台：OPPO的AI Pro智能体开发平台将正式上线，用户可以在该平台上自由调用组合工具和插件，创造和开发自己需要的智能体，而且无需写代码。这一策略将无疑会吸引更多的开发者加入，共同构建AI手机生态，推动AI手机行业的发展。\n\n3. 资源重塑的决策：为了推动这一战略，OPPO专门成立了AI中心，资源将向AI集中。这表明OPPO已经把AI作为新的发展焦点，通过资源重塑，推动AI技术的研发和应用，以期在手机行业的第三阶段——AI手机阶段，占据先机。\n--------------------------\nOPPO的1+N智能体生态战略对市场和行业有何影响？\n===\n1. 战略构建：OPPO提出了1+N智能体生态战略，这一生态由AI超级智能体和AI Pro开发平台组成，旨在通过准确理解用户意图并提供精准结果，形成记忆习惯，真正理解用户需求。AI Pro智能体开发平台也即将上线，用户可以在该平台上自由调用组合工具和插件，创造和开发自己需要的智能体，而且无需编写代码。\n2. 行业影响：OPPO创始人兼首席执行官陈明永表示，2024年是AI手机元年，AI对手机行业的影响将与智能手机替代功能机时的影响相当。预示着手机行业进入了以AI为主导的第三阶段。OPPO因此专门成立了AI中心，将资源向AI领域集中，这将为其在行业内的地位和影响力带来提升。\n3. 市场响应：OPPO的1+N智能体生态战略，将使用户能够更方便地创建和开发个性化的智能体，满足用户对个性化和智能化服务的需求，进一步提升用户体验。同时，OPPO愿与行业伙伴共同定义AI手机标准，共建AI手机生态，有望推动整个行业的进步和发展。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 战略本质：OPPO发布的1+N智能体生态战略是一个双元素构成的新生态，其中AI超级智能体的主要职能是精准理解用户需求并提供个性化服务，而AIPro开发平台则提供了一个开放的平台，允许用户自由创造和开发所需的智能体，无需编写代码。\n\n2. 行业影响：OPPO首席执行官陈明永将2024年定义为AI手机元年，这一声明突显了AI在手机行业未来发展中的重要性。他预期，AI将对手机行业产生与智能手机替代功能机时期相当的影响，这意味着手机行业或将进入以AI为主导的新阶段。\n\n3. 企业发展：为了推进1+N智能体生态战略，OPPO将成立专门的AI中心，以推动AI技术的研发和应用。这一决策预示了OPPO将把AI作为新的发展重心，通过资源重塑，以期在即将到来的AI手机时代占据先机。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-24，星期3\n-------------------------------------------------------------\n我的问题是：标题: 小米汽车专利可预热电池提高充电效率\n描述: 天眼查App显示，1月23日，小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 小米汽车专利可预热电池提高充电效率\n描述: 天眼查App显示，1月23日，小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。\n===\n1. 技术创新：小米汽车科技有限公司申请的“车辆电池预研方法、装置、车辆及可读存储介质”专利获授权。这表明小米正在不断研发创新，以提高电池的充电效率，这将有助于提升小米汽车的续航能力和用户体验。\n\n2. 产品配置与市场竞争：小米汽车SU7硬件配置包括800V高压快充、骁龙8295座舱芯片、一体式激光雷达、双电机全轮驱动等，具备了一辆爆款车的基本要素。然而，随着汽车市场智能化，未来竞争将更多侧重于智能驾驶和智能座舱等软件方面。小米表示，全栈自研智能驾驶技术，并将大模型技术全面融入，可以带来更精准的判断，更强、更安全的智能驾驶体验。\n\n3. 生态优势：至2023Q3，小米已成为全球最大的消费级IoT平台，连接设备数已达6.55亿台，拥有5个及以上小米IoT设备的用户已达1300万。小米汽车的加入，将推动\"人车家全生态\"闭环，小米澎湃OS的诸多特性和完备流畅的生态体验，将全部在小米汽车上实现。小米汽车产品得到优酷、腾讯视频、哔哩哔哩等多家主流APP的生态互联支持，消费者可以体验手机与车机跨端的无感连接。\n--------------------------\n分析小米汽车科技有限公司该专利对电动汽车充电效率提升的具体作用。\n===\n1. 创新驱动：小米汽车科技有限公司近期申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权，彰显其在电动汽车技术领域的创新能力。该专利通过预热电池，提升充电性能，从而缩短充电时间，提高整体充电效率。\n2. 预热机制：该专利的核心是一个精细的预热机制。该机制首先确定目标充电桩和车辆行驶至充电桩时的预估剩余电量，然后根据充电桩的充电功率和剩余电量，确定电池的目标温度和加热速率，从而确定预估加热时长。\n3. 实施效应：最后，根据预估加热时长和车辆行驶至目标充电桩的行驶时长，对电池进行加热，以确保电池在到达充电桩时达到最佳充电状态。这种方法有望提升电动汽车的使用便捷性，可能会对电动汽车市场产生积极影响。\n--------------------------\n探寻小米汽车科技有限公司申请该专利的背景和目的。\n===\n1. 电池和车身一体化：小米汽车科技有限公司申请的“电池及车身一体化结构、车辆”专利公布。这项技术的实现将使电池包壳体的刚度和侧碰传力性能得到提高，同时，通过降低车身结构的自重，实现车辆的轻量化，提高车辆的续航里程。\n2. 电池预热技术：小米汽车申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利获授权。这个技术可以提升充电性能，减少充电时间，进而提高电池的充电效率。其方法包括预估车辆行驶至目标充电桩的剩余电量，确定电池的目标温度和第一目标加热速率，对电池进行加热。\n3. 传感器模组：北京小米移动软件有限公司申请了一项名为“传感器模组和具有其的电子设备”的专利。该传感器模组的结构设计合理，空间利用率高，内含第一传感器和第二传感器，这两个传感器与柔性电路板电连接，电路板环绕在支架上，传感器沿支架的厚度方向堆叠。\n--------------------------\n考察小米汽车科技有限公司申请该专利后，对公司自身及电动车市场可能产生的影响。\n===\n1. 技术创新驱动：小米汽车科技有限公司的新专利“车辆电池预热方法、装置、车辆及可读存储介质”已于1月23日获得授权。这一技术以提升充电性能减少充电时间，进而提高电池的充电效率为目标，体现了小米在电动车领域的技术创新力度。\n2. 技术细节揭示：该技术通过确定目标充电桩、预估剩余电量，然后根据充电功率和剩余电量确定电池的目标温度及预热速率，最后根据预估加热时长和行驶时长对电池进行加热。这一流程的设定无疑将电池预热与行驶计划紧密结合，提升了充电效率。\n3. 对电动车市场影响：这项新技术的应用不仅将提升小米汽车自身的充电效率，也将对整个电动车市场产生深远影响。它将推动电动车市场的技术创新，从而优化用户体验，提高电动车的市场接纳度。\n--------------------------\n查找其他电动车公司是否有类似的电池预热专利，进行比较。\n===\n1. 先进技术的保护：从比亚迪和小米汽车的行动中可以看出，电动车公司对电池预热的专利保护越来越重视。比如比亚迪股份有限公司获得了一项名为“车辆、能量转换装置及其控制方法”的专利，可以提升电池包的加热效率。小米汽车科技有限公司申请的“车辆电池预热方法、装置、车辆及可读存储介质”专利，旨在提升充电性能减少充电时间，进而提高电池的充电效率。\n2. 专利技术的创新性：这些公司的专利都体现出深厚的技术创新能力。比如，比亚迪的专利可以通过对车辆状态和/或车辆性能参数的检测，选择相应的加热控制模式，实现对电池包进行加热过程的精确控制。小米汽车的专利则能够预估车辆行驶至目标充电桩时的剩余电量，根据预估加热时长和车辆行驶至目标充电桩的行驶时长，对电池进行加热。\n3. 行业竞争的激烈程度：从这些公司的专利申请和授权情况来看，电动车行业的竞争已经进入到技术层面，每家公司都在努力提高自己的技术门槛，以取得市场的竞争优势。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 技术创新驱动：小米汽车科技有限公司的新专利“车辆电池预热方法、装置、车辆及可读存储介质”获得授权，表明公司在电池充电效率提升方面进行了积极的技术创新。通过预热电池，可以提高充电性能，减少充电时间，从而提升电池的充电效率。\n\n2. 产品优势凸显：该专利的授权将有助于小米汽车在电动汽车市场中获取竞争优势。电池充电效率的提升可以提升电动汽车的使用便捷性，提高续航能力，为消费者带来更优质的用户体验。\n\n3. 行业影响拓展：该专利的授权不仅对小米汽车科技有限公司自身的发展具有积极意义，同时也可能推动整个电动车行业的技术进步。预热电池的技术可以提高电动车的充电效率，对于推动电动车的市场普及具有重要作用。"}], "source": "self-made"} {"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-01-23，星期2\n-------------------------------------------------------------\n我的问题是：标题: 合盛硅业于内蒙古投资成立绿能科技公司\n描述: 近日，内蒙古创源绿能科技有限公司成立，经营范围包含太阳能热发电装备销售等。企查查股权穿透显示，该公司由合盛硅业间接全资控股。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 合盛硅业于内蒙古投资成立绿能科技公司\n描述: 近日，内蒙古创源绿能科技有限公司成立，经营范围包含太阳能热发电装备销售等。企查查股权穿透显示，该公司由合盛硅业间接全资控股。\n===\n1. 硅业巨头的新动向：合盛硅业近期在内蒙古设立全资子公司——创源绿能科技有限公司，注册资本5000万，主要经营业务为太阳能热发电装备销售等新能源相关业务。这一举措是合盛硅业在积极布局光伏和碳化硅领域，以实现硅基新材料的产业化的一部分。\n\n2. 项目投资概览：近年来，合盛硅业在多个领域出击，项目投资金额巨大。公司通过“光伏+碳化硅”的布局，致力于打造成为硅基新材料领域的领导者，开启新的增长曲线。其六大项目合计项目金额为653.38亿元，累计投入金额为229.69亿元。\n\n3. 产业链布局：合盛硅业不仅布局硅料业务，也同步向下游扩张，进入组件、光伏玻璃等领域。在中部和东部地区投资光伏玻璃项目和组件项目，预计在2023年下半年至2024年一季度陆续投产。这些举措有望进一步提升合盛硅业的产业链竞争力。\n--------------------------\n合盛硅业在内蒙古成立绿能科技公司的详细信息\n===\n1. 跨地区布局：合盛硅业近期在浙江嘉兴和内蒙古相继设立了两家全资子公司，分别是浙江合量科技有限公司和内蒙古创源绿能科技有限公司。这两家公司的经营范围主要涉及新材料技术研发、电子专用设备销售、储能技术服务等领域，展现了合盛硅业在光伏和新材料领域的战略布局。\n2. 资本运作加速：这两家新设立的公司的注册资本分别为1亿元和5000万元，显示出合盛硅业在资本运作上的决心和实力。此外，合盛硅业（万宁）有限公司也全资持有一家在内蒙古设立的新材料科技公司，进一步丰富了公司的资本结构和业务布局。\n3. 行业前景展望：随着新能源和新材料行业的快速发展，合盛硅业此次的行动无疑为公司的未来发展奠定了坚实的基础。而其在新能源和新材料领域的布局，也预示着该行业的广阔前景和巨大商业价值。\n--------------------------\n合盛硅业在新能源领域的投资布局\n===\n1. 光伏产业链布局：合盛硅业在新疆地区整合低成本资源，进一步布局多晶硅、光伏玻璃、电池片、组件等光伏产业链产品。其中，新疆中部合盛的年产20万吨多晶硅与150万吨新能源装备用超薄高透光伏玻璃项目有望于2023年第三季度投产，而东部合盛的年产20万吨高纯多晶硅项目预计将于2024年第一季度陆续投产。此外，公司的年产20GW光伏组件项目预计从2023年第四季度开始执行。\n2. 工业硅项目进展：云南合盛年产80万吨工业硅及配套项目一期预计于2024年完工。这一项目的完成将进一步提升合盛硅业在工业硅市场的竞争力，提高公司的整体盈利能力。\n3. 硅基新材料产业化：合盛硅业正在积极布局光伏和碳化硅领域，以实现硅基新材料的产业化。公司于2023年5月通过了2万片宽禁带半导体碳化硅衬底及外延片产业化生产线的验收，6英寸晶体良率达到了90%,外延片良率达到了95%，同时8英寸衬底也实现了量产。这显示出合盛硅业在硅基新材料领域的技术实力和市场竞争力。\n\n--------------------------\n内蒙古创源绿能科技有限公司的经营范围和业务概述\n用户的问题：请问内蒙古的新能源发展现状如何，有哪些企业在其中起主导作用？\n\n===\n1. 公司布局：内蒙古创源绿能科技有限公司由合盛硅业间接全资控股，注册资本5000万元，经营范围包括太阳能热发电装备销售、新能源原动设备制造、储能技术服务等。公司的成立标志着合盛硅业在新能源领域的布局进一步扩大。\n\n2. 项目推动：内蒙古三峡蒙能能源有限公司在库布其沙漠鄂尔多斯中北部新能源基地建设的光伏项目已并网发电，总装机规模1600万千瓦。项目建成后，每年可向京津冀地区输送清洁电能400亿千瓦时。同时，杭锦旗朔方新能源大基地规划建设面积48万余亩，总规模1300万千瓦。\n\n3. 发展状况：截至2023年12月底，内蒙古新能源装机规模突破9200万千瓦，全年新增装机超过3100万千瓦，增量创历史新高。内蒙古新能源项目建设步履不停，风光资源富集，已印发实施了新能源倍增行动实施方案、光伏治沙行动实施方案、促进新能源消纳若干举措等多项政策，为加快新能源项目建设提供了有力的政策保障。\n--------------------------\n合盛硅业的股权结构及其对内蒙古创源绿能科技有限公司的控股情况\n===\n1. 创新控股：根据企查查APP显示，合盛硅业近日间接全资控股了新成立的内蒙古创源绿能科技有限公司。该公司注册资本5000万元，经营范围包含太阳能热发电装备销售、新能源原动设备制造、储能技术服务以及合同能源管理等。\n\n2. 新材料布局：同时，合盛硅业也间接全资控股了新成立的内蒙古源芯新材料有限公司。该公司注册资本1亿元，主要经营范围为新材料技术研发、电子专用材料制造、电子专用材料研发以及电子专用设备销售等。\n\n3. 多元化发展：这两次控股行为显示出合盛硅业在业务上的多元化发展趋势，企业不仅在新能源领域进行了布局，同时也在新材料领域进行了深度探索。这对于合盛硅业的长期发展来说，无疑是一种积极的尝试和布局。\n--------------------------\n内蒙古创源绿能科技有限公司成立对合盛硅业战略发展的影响\n===\n1. 合盛硅业战略布局：合盛硅业正积极布局光伏和碳化硅领域，以实现硅基新材料的产业化。公司已通过了2万片宽禁带半导体碳化硅衬底及外延片产业化生产线的验收，并具备量产能力。同时还计划投资光伏玻璃项目和组件项目，预计的投产时间已经公布，表明公司对于硅基新材料领域的长远发展有明确的规划和目标。\n2. 创源绿能科技有限公司成立：最近，合盛硅业间接全资控股的内蒙古创源绿能科技有限公司成立，注册资本5000万元，经营范围包含太阳能热发电装备销售；新能源原动设备制造；储能技术服务；合同能源管理等。这一举措进一步强化了合盛硅业在新能源领域的布局。\n3. 内蒙古新能源发展态势：内蒙古正在全面高质量发展新能源项目，例如库布其沙漠鄂尔多斯中北部新能源基地总装机规模1600万千瓦，每年可向京津冀地区输送清洁电能400亿千瓦时。此外，全区新能源装机规模突破9200万千瓦，全年新增装机超过3100万千瓦。这些数据显示出内蒙古在新能源领域的巨大潜力和发展空间，为合盛硅业提供了广阔的市场和良好的发展环境。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 硅业巨头布局新能源：近日，合盛硅业在内蒙古设立了全资子公司——内蒙古创源绿能科技有限公司，主要经营业务为太阳能热发电装备销售等新能源相关业务。这显然是合盛硅业在新能源领域进一步深化布局的重要举措。\n\n2. 业务拓展及发展策略：合盛硅业此次的举动，不仅扩大了其在新能源领域的战略布局，同时也展现出了公司在追寻可持续发展，积极拓展绿色能源业务的决心。这种策略有利于公司在未来新能源市场的竞争中获得更大的优势。\n\n3. 对于行业影响：该公司的成立将进一步推动新能源行业的发展，尤其是在太阳能热发电装备销售等领域。同时，这也可能会对未来的新能源市场格局产生一定影响。"}], "source": "self-made"} -{"type": "chatml", "messages": [{"role": "user", "content": "\n你的角色是：投研分析师投研支小助,所属的国家：中国,所属的公司：蚂蚁集团,研发团队：蚂蚁集团投研团队,职责：善于从不同角度、有条理的回答用户的问题。现在，你需要仅仅使用提供的知识，专业、详细的回答用的问题。我的问题是：云计算板块个股走弱，是否建议投资云计算板块？\n-------------------------------------------------------------\n背景信息是：\n无\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n云计算板块个股走弱，是否建议投资云计算板块？\n根据搜索的信息，当前云计算板块个股表现走弱，包括金山办公、中际旭创、中科曙光、新易盛、宝信软件等均跌逾2%，同时跟踪中证云计算与大数据主题指数的云计算ETF、大数据ETF也跌近2%。然而，虽然短期内云计算板块个股表现不佳，但从长期发展趋势上来看，我国上云企业累计已超过380万家，与美国、欧洲相比，我国云计算市场仍有较大的发展空间。在新基建、东数西算等政策的推动下，中国云计算市场预期将保持快速增长。因此，对于是否投资云计算板块，需要根据投资者自身的风险承受能力和投资期限来判断，如果投资者能接受短期的市场波动并看好云计算长期的发展前景，那么云计算板块仍然是一个值得考虑的投资方向。\n--------------------------\n近期云计算板块个股的走势分析\n近期云计算板块个股走势偏弱。金山办公、中际旭创、中科曙光、新易盛、宝信软件等公司股票跌幅逾2%。同时，跟踪中证云计算与大数据主题指数的云计算ETF和大数据ETF也跌近2%。云计算板块指数报2512.868点，跌幅达2%。其中，跌幅最大的前5个股为：超讯通信跌7.70%；天威视讯跌7.46%；青云科技-U跌6.17%；卓易信息跌5.43%；神州数码跌5.43%。\n\n然而，有券商表示，目前我国上云企业累计超过380万家，与美国、欧洲相比，我国云计算市场仍有较大发展空间。在新基建、东数西算等政策推动下，预计中国云计算市场将保持快速增长。\n--------------------------\n云计算行业的未来发展趋势预测\n云计算行业的未来发展趋势可以从以下几个方面来预测：\n\n1. 效率提升：云计算本质上是提升用户对于底层IT资源的可获得性，借助更丰富的应用生态匹配自身的业务，实现效率的提升。\n\n2. 公有云与私有云的态势：阿里云公共云事业部的目标是做大规模，混合云事业部目标的是考核利润，引导客户向公有云。目前，美国市场上存量服务器规模约为2200万台，60%以上的算力是以公共云模式供给的。而中国市场的服务器存量规模大约为2000万台，仅有28%的算力以公共云模式供给的。\n\n3. AI与云计算的结合：大模型为代表的人工智能与云计算结合趋势日益明显。新的云生态也同样遵循这一逻辑，以MaaS平台和AI原生应用为切入口，降低了门槛与成本，同时由于标准化程度高，因此也更能整合碎片化与长尾需求。\n\n4. 产业数据积淀：云计算服务商由于产业数据积淀丰富，从而在产业的专业深度、服务精度、迭代速度上独树一帜，成为产业上云的领头羊。\n\n5. 行业竞争格局：强者恒强，弱者恒弱，成为行业的真实写照。中小云计算玩家由于体量与互联网巨头不在一个档次，导致基础设施、AI能力等跟不上，难寻差异化竞争力。\n\n6. 出海市场：中国云出海，依然大有可为，亚马逊与微软有了化敌为友的迹象，但不会改变整体的竞争格局，该卷的还是会继续卷。\n\n综上，云计算行业的未来发展趋势主要表现在提升效率、公有云的优先、AI与云计算的结合、产业数据积淀、行业竞争格局以及出海市场的拓展等方面。\n--------------------------\n云计算板块的投资价值评估\n云计算板块的投资价值主要体现在以下几个方面：\n\n1. 市场表现：近期云计算板块个股波动较大，但总体上呈现出强势。如新易盛近期涨幅达20%，中际旭创和金山办公等也涨幅明显。同时，跟踪中证云计算与大数据主题指数的相关ETF，如云计算50ETF、大数据ETF、云计算ETF等，近期涨幅也在3%左右。\n\n2. 行业前景：目前中国的上云企业已累计超过380万家，但相较于美国、欧洲等地，中国的云计算市场仍有较大的发展空间。受新基建、东数西算等政策影响，预计中国云计算市场将保持快速增长。\n\n3. 行业影响力：中证云计算与大数据主题指数选取了50只业务涉及提供云计算服务、大数据服务以及上述服务相关硬件设备的上市公司证券作为指数样本，反映了沪深市场云计算与大数据主题上市公司证券的整体表现，这也是评估云计算板块投资价值的一个重要指标。\n\n总结来说，云计算板块具有较高的投资价值，市场表现强势且行业前景广阔。但投资者也需要注意市场波动，合理配置资产。\n--------------------------\n近期对云计算板块的投资策略建议\n近期对云计算板块的投资策略建议如下：\n\n首先，看好具备自研能力且能够精准把握大模型新机遇的公司。例如金山云，在生成式人工智能带来的产业浪潮下，云计算作为算力底座的重要性日益凸显，而金山云致力于云计算、大数据、人工智能等核心技术研发，围绕客户在实际业务中的需求进行产品和技术迭代升级，为大模型公司、垂直行业优质客户和生态伙伴提供强大的算力底座。\n\n其次，看好能够提供行业解决方案的公司。如金山云推出的“互信VPC”对进出VPC的通信行为有着更为严格的控制，帮助客户解决合规和互信问题。围绕客户业务痛点场景，其大数据智能数据引擎KDE V3.1与云原生大数据产品KCDE V1.7，共新增50多项能力，综合能力进一步增强。\n\n再次，看好能够与前沿技术保持同频的公司，这些公司能够在互联网领域获得可持续的发展动能。如金山云与金山办公WPS AI深入合作，助力其实现训练和推理业务部署，为喜马拉雅语音模型训练场景提供GPU裸金属服务器产品，协助客户在训练场景对开源大模型进行业务摸索。\n\n最后，看好具备强大云服务能力和云安全保障能力的公司。金山云银河平台全面升级“一云多芯”方案，已兼容英特尔、鲲鹏、海光、龙芯等平台，支持全栈部署、混合部署的多形态灵活建云方案。并且，金山云银河平台通过云基准测评共计157项测试，荣获云基准测评最高等级“引领级”认证，这标志着金山云银河平台的综合产品能力处于行业卓越水平。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "从短期表现来看，云计算板块个股走弱，主要个股如金山办公、中际旭创、中科曙光等都有超过2%的跌幅，同时跟踪中证云计算与大数据主题指数的相关ETF，如云计算ETF和大数据ETF也下跌近2%。这可能由于市场短期内的波动或者特定事件影响，导致投资者信心下滑，从而影响了云计算板块的短期表现。\n\n然而，从长期发展趋势来看，中国的云计算市场仍具有很大的发展空间。目前，我国上云企业累计已超过380万家，相比之下，美国和欧洲的云计算市场已经较为成熟，中国的云计算市场还有很大的增长潜力。在新基建、东数西算等政策的推动下，预计中国的云计算市场将保持快速增长。\n\n此外，云计算行业的未来发展趋势也值得我们关注。一方面，随着技术的发展，云计算正在从提升IT资源的可获得性向提供更丰富的应用生态转变，这将有助于提升企业的效率；另一方面，人工智能与云计算的结合、产业数据的积累、行业竞争格局的变化、以及出海市场的拓展等因素，也将对云计算行业的未来发展产生深远影响。\n\n因此，对于是否投资云计算板块，需要投资者根据自身的风险承受能力和投资期限来做出决策。如果投资者能够接受短期的市场波动，并且对云计算行业的长期发展前景保持乐观，那么云计算板块仍然是一个值得考虑的投资方向。"}], "source": "self-made"} +{"type": "chatml", "messages": [{"role": "user", "content": "从现在开始，你的角色是：投研分析师\n职责：\n善于从不同角度、有条理的回答用户的问题。\n你需要仅仅使用提供的知识，专业、详细的回答用户的问题。今天的时间: 2024-02-04，星期7\n-------------------------------------------------------------\n我的问题是：标题: 特斯拉召回在美几乎所有车辆\n描述: 近日特斯拉正在召回其在美国销售的几乎所有车辆，原因是车辆仪表板上的一些警示灯的字体太小，这可能会使驾驶者增加发生碰撞的风险。\n-------------------------------------------------------------\n背景信息是：\n请自由发挥你的思路对热点事件进行解读，要求：\n- 逻辑缜密、语言高级、精炼、不啰嗦、又内容丰富、吸引眼球\n- 请用1. 2. 3. 分段，中间不要有空行，可分3段，每段至少50字，最多80字，格式是\"n. 第n段标题：第n段内容\n\"\n- 每段需要从不同方面去说明，多视角多角度\n- 能在事实性内容中出现具体标的，但不要有推荐或建议关注的倾向\n\n需要按照下面的格式输出：\n===（开始一定需要用3个=作为分隔线）\n1. （你的第一段标题）：（你的第一段解读）\n2. （你的第二段标题）：（你的第二段解读）\n...\n-------------------------------------------------------------\n仅仅可以使用下面的知识:\n标题: 特斯拉召回在美几乎所有车辆\n描述: 近日特斯拉正在召回其在美国销售的几乎所有车辆，原因是车辆仪表板上的一些警示灯的字体太小，这可能会使驾驶者增加发生碰撞的风险。\n===\n1. 召回规模广泛：特斯拉公司近期宣布，将召回在美国销售的几乎所有车辆，总计约220万辆。召回的车型包括2012-2023年款Model S、2016-2024年款Model X、2017-2023年款Model 3、2019-2024年款Model Y和2024款Cybertruck。\n\n2. 召回原因明确：这次召回的主要原因是车辆仪表板上显示的制动、驻车和防抱死制动系统（ABS）警告灯的字体大小不符合联邦安全标准，可能导致驾驶者难以读取关键的安全信息，增加发生碰撞的风险。\n\n3. 解决方案到位：特斯拉公司表示，将通过在线软件更新的方式解决此次警示灯字体问题。车主将从2024年3月30日开始收到更新通知。此次召回并非负面新闻，而是特斯拉企业负责任的表现。\n--------------------------\n特斯拉近期在美国召回车辆的详细情况\n===\n1. 召回原因及影响：特斯拉近日决定在美国召回几乎所有车型，原因是车辆仪表板上的一些警示灯的字体太小，可能导致驾驶者难以读取关键的安全信息，增加发生碰撞的风险。召回车型包括2012年至2023年款Model S、2016年至2023年款Model X、2017年至2023年款Model 3、2019年至2024年款Model Y和2024年款赛博皮卡（Cybertruck）。\n \n2. 解决措施：特斯拉已开始发布软件更新，以对警示灯字体进行更新，车主将从3月30日开始收到通知。此次召回采取的是在线软件升级方式，较传统的召回方式更为便捷，减少了客户的时间成本。\n\n3. 召回影响：特斯拉已确认有三起保修索赔可能与该问题有关，但没有撞车或人员伤亡事故的报告, 这显示出特斯拉对车辆安全的高度重视，并积极采取措施进行改正，这在一定程度上提升了特斯拉在消费者心中的品牌形象。\n--------------------------\n特斯拉召回车辆事件的原因及其对驾驶者安全的影响\n===\n1. 召回事件概述：特斯拉面临两次大规模的召回事件。第一次涉及约334569辆Model 3和Model Y，原因是有近2400起关于驾驶员方向盘失控的投诉，已经发生一起车祸。第二次涉及的是从2012年至2024年款的几乎所有车型，原因是一些警示灯的字体太小，可能会使驾驶者难以读取关键的安全信息，增加发生碰撞的风险。\n2. 召回事件的影响：召回事件不仅对特斯拉的品牌声誉构成威胁，还可能导致公司承担大量的维修成本。尤其是针对方向盘失控问题的召回，可能需要实际更换零件，而不仅仅是通过无线软件进行补救。此外，这些问题可能使得消费者对特斯拉的安全性产生怀疑，影响其销售。\n3. 召回事件的应对：特斯拉已经开始对警示灯字体问题进行在线软件更新，车主将从3月30日开始收到通知。对于方向盘失控问题，特斯拉已向调查人员提供了某些转向齿条零件编号，表明调查可能会导致实际召回。尽管这两次召回事件对特斯拉带来了挑战，但也体现了特斯拉对消费者安全的重视和应对问题的决心。\n--------------------------\n特斯拉召回车辆事件对其股价的影响\n===\n1. 召回事件的规模：特斯拉近期公布了大规模的汽车召回事件，涉及车型包括2012-2023年款Model S、2016-2024年款Model X、2017-2023年款Model 3、2019-2024年款Model Y和2024年款Cybertruck等，总计2193869辆。另外，美国国家公路交通安全管理局估计有334569辆2023年款Model 3和Model Y汽车需要接受工程分析。\n\n2. 召回原因的多元性：召回的原因多种多样，包括仪表板上显示的制动、驻车和防抱死制动系统(ABS)警告灯的字体大小不正确，以及车门解锁逻辑控制问题。在自动辅助转向功能开启情况下，驾驶员可能误用2级组合驾驶辅助功能，增加车辆发生碰撞的风险。\n\n3. 股价影响的瞬时性：这一召回事件让特斯拉的股价受到了影响，报183.16美元，跌幅超过3%。然而，由于特斯拉的市场影响力和创新能力，这种瞬时的股价影响并不代表其长期价值。\n--------------------------\n特斯拉召回车辆事件的处理和应对措施\n===\n1. 缺陷召回：特斯拉近期面临美国国家公路交通安全管理局（NHTSA）的两项召回调查。首先是该公司的Model 3和Model Y车型被发现存在驾驶员方向盘失控问题，据悉，已收到2388起相关投诉，涉及车辆总数为334569辆。此次问题的解决可能涉及实体零件的更换，这将带来更高的维修成本。\n\n2. 字体问题：其次，特斯拉几乎所有在美国销售的车辆，包括2012年至2023年款的Model S、Model X、Model 3，以及2019年至2024年款的Model Y和Cybertruck，因其警示灯字体太小，难以读取关键的安全信息，被要求进行在线软件更新。据称，特斯拉已于3月30日开始发布相关更新。\n\n3. 后续影响：在这两起事件中，特斯拉均未立即回复置评请求，但对其公众形象和财务状况都可能产生影响。尤其是方向盘失控问题，由于涉及的是实体零件更换，预计将导致特斯拉的维修成本显著提升。\n--------------------------\n\n-------------------------------------------------------------\n需要遵守的规则是：\n1. 不采用错误的信息。\n2. 不描述重复的信息。\n3. 不做对问题没有帮助的回答\n4. 简明扼要，重点突出，从不过多花哨词藻。\n5. 不在多处讲到同一个细节点，一个点只出现一次。\n6. 不说模糊的推测。\n7. 遵循背景信息中的要求。\n8. 尽量多的使用数值类信息。\n9. 不可以出现XXX、ABC等不明确的词语。\n仅仅使用提供的知识，一步一步来回答问题。"}, {"role": "assistant", "content": "===\n1. 召回行动的规模：特斯拉正在召回其在美国销售的几乎所有车辆，召回的规模之大在汽车行业中并不常见。这可能会对特斯拉的品牌形象、客户信任度、以及经济状况产生一定影响。\n\n2. 召回原因的关注：召回的原因是仪表板上的警示灯字体太小，这可能会使驾驶者在驾驶过程中忽视这些重要的警示信息，从而增加发生交通事故的风险。这个原因虽然看似细小，但却关乎到车辆的安全性，值得我们高度关注。\n\n3. 特斯拉的应对措施：尽管召回规模巨大，但特斯拉对此的应对措施值得肯定。他们选择以最快的方式解决问题，避免了更大的安全隐患。这次事件或许会暂时影响特斯拉的形象，但从长远来看，它展现了特斯拉对消费者安全的高度重视，有利于提升消费者对其产品的信任度。"}], "source": "self-made"} From b3357167850b1e12d35df229d9f62a06f54e28eb Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Thu, 4 Jul 2024 19:24:27 +0800 Subject: [PATCH 096/118] improvement: add serialize method in monitor module and tweak executing agent in peers. --- agentuniverse/base/util/monitor/monitor.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/agentuniverse/base/util/monitor/monitor.py b/agentuniverse/base/util/monitor/monitor.py index 1221496f..3980583b 100644 --- a/agentuniverse/base/util/monitor/monitor.py +++ b/agentuniverse/base/util/monitor/monitor.py @@ -99,7 +99,13 @@ def default_serializer(obj): return obj.to_dict() elif isinstance(obj, OutputObject): return obj.to_dict() - raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") + elif isinstance(obj, BaseModel): + try: + return obj.dict() + except TypeError: + raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") + else: + raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") def serialize_obj(self, obj): """Serialize an object and filter out non-serializable values.""" From f10764e9a01eef7641b912144b859343d0854ffe Mon Sep 17 00:00:00 2001 From: weizjajj Date: Fri, 5 Jul 2024 11:39:00 +0800 Subject: [PATCH 097/118] fix --- docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md | 2 +- ...\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md b/docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md index 1d2f76b9..bf49694f 100644 --- a/docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md +++ b/docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md @@ -467,7 +467,7 @@ module: 'agentuniverse.llm.default.default_openai_llm' class: 'DefaultOpenAILLM' ``` -If we need to configure and define an LLM instance based on the `gpt-3.5-turbo model`, with a maximum token limit of 1000 and a retry count of 2 for failures, the configuration would be as follows: +If we need to configure and define an LLM instance based on the `gpt-3.5-turbo model`, with a maximum token limit of 1000 and a retry count is 2, the configuration would be as follows: ```yaml name: 'demo_llm' description: 'demo openai' diff --git "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" index 77f90a8b..1d513e6e 100644 --- "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" +++ "b/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" @@ -462,7 +462,7 @@ module: 'agentuniverse.llm.default.default_openai_llm' class: 'DefaultOpenAILLM' ``` -假如我们需要配置定义一个基于`gpt-3.5-turbo`模型，最大token限制为1000，失败重试次数未2次的LLM实例，其配置如下: +假如我们需要配置定义一个基于`gpt-3.5-turbo`模型，最大token限制为1000，失败重试次数为2次的LLM实例，其配置如下: ```yaml name: 'demo_llm' description: 'demo openai' From ae62e1e9fa3d436be89a03f4bedbef969911cbc0 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Mon, 8 Jul 2024 11:44:24 +0800 Subject: [PATCH 098/118] fix: remove exception handling and retry mechanism in peer agents --- .../plan/planner/peer_planner/peer_planner.py | 41 ++++--------------- .../base/component/component_manager_base.py | 2 +- 2 files changed, 8 insertions(+), 35 deletions(-) diff --git a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py index e77a3cc5..792a0ac6 100644 --- a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py +++ b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py @@ -109,22 +109,15 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu expressingAgent = agents.get('expressing') reviewingAgent = agents.get('reviewing') - for i in range(retry_count): - LOGGER.info(f"Starting peer agents, retry_count is {i + 1}.") + for _ in range(retry_count): + LOGGER.info(f"Starting peer agents, retry_count is {_ + 1}.") if not planning_result or jump_step == "planning": if not planningAgent: LOGGER.warn("no planning agent.") planning_result = OutputObject({"framework": [agent_input.get('input')]}) else: LOGGER.info(f"Starting planning agent.") - try: - planning_result = planningAgent.run(**input_object.to_dict()) - except Exception as e: - LOGGER.error(f"Exception occurred during the planning agent's invocation, exception: {e}") - if i >= retry_count - 1: - raise e - else: - continue + planning_result = planningAgent.run(**input_object.to_dict()) input_object.add_data('planning_result', planning_result) # add planning agent log info @@ -139,14 +132,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu executing_result = OutputObject({}) else: LOGGER.info(f"Starting executing agent.") - try: - executing_result = executingAgent.run(**input_object.to_dict()) - except Exception as e: - LOGGER.error(f"Exception occurred during the executing agent's invocation, exception: {e}") - if i >= retry_count - 1: - raise e - else: - continue + executing_result = executingAgent.run(**input_object.to_dict()) input_object.add_data('executing_result', executing_result) # add executing agent log info @@ -164,14 +150,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu expressing_result = OutputObject({}) else: LOGGER.info(f"Starting expressing agent.") - try: - expressing_result = expressingAgent.run(**input_object.to_dict()) - except Exception as e: - LOGGER.error(f"Exception occurred during the expressing agent's invocation, exception: {e}") - if i >= retry_count - 1: - raise e - else: - continue + expressing_result = expressingAgent.run(**input_object.to_dict()) input_object.add_data('expressing_result', expressing_result) # add expressing agent log info @@ -192,14 +171,8 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu return result else: LOGGER.info(f"Starting reviewing agent.") - try: - reviewing_result = reviewingAgent.run(**input_object.to_dict()) - except Exception as e: - LOGGER.error(f"Exception occurred during the reviewing agent's invocation, exception: {e}") - if i >= retry_count - 1: - raise e - else: - continue + reviewing_result = reviewingAgent.run(**input_object.to_dict()) + input_object.add_data('reviewing_result', reviewing_result) # add reviewing agent log info diff --git a/agentuniverse/base/component/component_manager_base.py b/agentuniverse/base/component/component_manager_base.py index 465e492d..f3ba0efb 100644 --- a/agentuniverse/base/component/component_manager_base.py +++ b/agentuniverse/base/component/component_manager_base.py @@ -38,7 +38,7 @@ def unregister(self, component_instance_name: str): self._instance_obj_map.pop(component_instance_name) def get_instance_obj(self, component_instance_name: str, - appname: str = None, new_instance: bool = None) -> ComponentTypeVar: + appname: str = None, new_instance: bool = False) -> ComponentTypeVar: """Return the component instance object.""" appname = appname or ApplicationConfigManager().app_configer.base_info_appname instance_code = f'{appname}.{self._component_type.value.lower()}.{component_instance_name}' From 164855241751a81c6b80360a38a3853f2fa87216 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Tue, 9 Jul 2024 09:58:37 +0800 Subject: [PATCH 099/118] fix end error --- agentuniverse/agent_serve/web/request_task.py | 11 ++++++++--- agentuniverse/agent_serve/web/thread_with_result.py | 3 +++ 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/agentuniverse/agent_serve/web/request_task.py b/agentuniverse/agent_serve/web/request_task.py index 5f32ae8b..3cb13720 100644 --- a/agentuniverse/agent_serve/web/request_task.py +++ b/agentuniverse/agent_serve/web/request_task.py @@ -73,8 +73,13 @@ def receive_steps(self): ensure_ascii=False) + "\n\n" if self.canceled(): return - yield "data:" + json.dumps({"result": self.thread.result()}, - ensure_ascii=False) + "\n\n" + try: + result = self.thread.result() + yield "data:" + json.dumps({"result": result}, + ensure_ascii=False) + "\n\n " + except Exception as e: + LOGGER.error("request task execute Fail: " + str(e)) + yield "data:" + json.dumps({"error": {"error_msg": str(e)}}) + "\n\n " def append_steps(self): """Tracing async service running state and update it to database.""" @@ -225,7 +230,7 @@ def finished(self): self.__request_do__.state = TaskStateEnum.FINISHED.value @staticmethod - def query_request_state(request_id: str) -> dict|None: + def query_request_state(request_id: str) -> dict | None: """Query the request data in database by given request_id. Args: diff --git a/agentuniverse/agent_serve/web/thread_with_result.py b/agentuniverse/agent_serve/web/thread_with_result.py index 96414c9c..4d0d030e 100644 --- a/agentuniverse/agent_serve/web/thread_with_result.py +++ b/agentuniverse/agent_serve/web/thread_with_result.py @@ -30,6 +30,9 @@ def run(self): self._return = self.target(*self.args, **self.kwargs) except Exception as e: self.error = e + finally: + if 'output_stream' in self.kwargs: + self.kwargs['output_stream'].put('{"type": "EOF"}') def result(self): """Wait for target func finished, then return the result or raise an From 85652c956c801cf68953b154f4e31edf2508b6d6 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Mon, 8 Jul 2024 17:35:30 +0800 Subject: [PATCH 100/118] feat: add the data agent to build datasets and evaluate the quality of results. --- .../_picture/data_agent_eval_report.png | Bin 0 -> 109985 bytes .../_picture/data_agent_eval_report_en.png | Bin 0 -> 81302 bytes .../_picture/data_agent_eval_result.png | Bin 0 -> 140870 bytes .../_picture/data_agent_eval_result_en.png | Bin 0 -> 101822 bytes .../_picture/data_agent_flowchart.jpg | Bin 0 -> 812368 bytes .../en/8_1_1_data_governance_agent.md | 47 ++++++ ...06\346\231\272\350\203\275\344\275\223.md" | 47 ++++++ .../core/agent/data_agent_case/data_agent.py | 96 +++-------- .../agent/data_agent_case/data_agent.yaml | 8 +- .../data_agent_case/dataset_build_agent.py | 138 ++++++++++++++++ .../data_agent_case/dataset_build_agent.yaml | 12 ++ .../{eval_agent.py => dataset_eval_agent.py} | 36 ++-- .../data_agent_case/dataset_eval_agent.yaml | 14 ++ .../agent/data_agent_case/eval_agent.yaml | 14 -- .../agent/rag_agent_case/demo_rag_agent.yaml | 24 +-- .../app/core/llm/qwen_llm.yaml | 2 +- ...ent_cn.yaml => dataset_eval_agent_cn.yaml} | 78 ++++++--- .../core/prompt/dataset_eval_agent_en.yaml | 156 ++++++++++++++++++ .../app/core/prompt/demo_rag_agent_cn.yaml | 24 +++ .../app/core/prompt/demo_rag_agent_en.yaml | 25 +++ .../dataset_turn_1_2024-07-08-20:17:28.jsonl | 2 + .../dataset_turn_1_2024-07-09-11:45:42.jsonl | 2 + .../data/eval_report_2024-07-08-20:17:28.xlsx | Bin 0 -> 5263 bytes .../data/eval_report_2024-07-09-11:45:42.xlsx | Bin 0 -> 5235 bytes ...val_result_turn_1_2024-07-08-20:17:28.xlsx | Bin 0 -> 7971 bytes ...val_result_turn_1_2024-07-09-11:45:42.xlsx | Bin 0 -> 8192 bytes .../app/examples/data/query.jsonl | 3 - .../app/examples/data/queryset_cn.jsonl | 2 + .../app/examples/data/queryset_en.jsonl | 2 + .../app/examples/data_agent.py | 6 +- .../app/util/jsonl_file_utils.py | 4 +- 31 files changed, 580 insertions(+), 162 deletions(-) create mode 100644 docs/guidebook/_picture/data_agent_eval_report.png create mode 100644 docs/guidebook/_picture/data_agent_eval_report_en.png create mode 100644 docs/guidebook/_picture/data_agent_eval_result.png create mode 100644 docs/guidebook/_picture/data_agent_eval_result_en.png create mode 100644 docs/guidebook/_picture/data_agent_flowchart.jpg create mode 100644 docs/guidebook/en/8_1_1_data_governance_agent.md create mode 100644 "docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\346\262\273\347\220\206\346\231\272\350\203\275\344\275\223.md" create mode 100644 sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py create mode 100644 sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml rename sample_standard_app/app/core/agent/data_agent_case/{eval_agent.py => dataset_eval_agent.py} (94%) create mode 100644 sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml delete mode 100644 sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml rename sample_standard_app/app/core/prompt/{eval_agent_cn.yaml => dataset_eval_agent_cn.yaml} (54%) create mode 100644 sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml create mode 100644 sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml create mode 100644 sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml create mode 100644 sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl create mode 100644 sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-11:45:42.jsonl create mode 100644 sample_standard_app/app/examples/data/eval_report_2024-07-08-20:17:28.xlsx create mode 100644 sample_standard_app/app/examples/data/eval_report_2024-07-09-11:45:42.xlsx create mode 100644 sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-08-20:17:28.xlsx create mode 100644 sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-09-11:45:42.xlsx delete mode 100644 sample_standard_app/app/examples/data/query.jsonl create mode 100644 sample_standard_app/app/examples/data/queryset_cn.jsonl create mode 100644 sample_standard_app/app/examples/data/queryset_en.jsonl diff --git a/docs/guidebook/_picture/data_agent_eval_report.png b/docs/guidebook/_picture/data_agent_eval_report.png new file mode 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z{16*A6lP06w=T*RR^>i7x!7}j{6;`c?3^wOy2yt$P4{%x)|*EKNxva-*~3D2&Z(tY zD|wvp)2lx|WhpjS5zCn(tNR(L{NXp`MMJ8uKs9GJdZGbkMn2%hUd#Y-<{ z`fqxi*wXp7p)T&>N*+FFDEP-k^7%JOKrf$UPifK!iR~RdiYgCY3FFLH=es{K=z_g| zaSh>>h{92@kGMAG*PeNaOL%Pm=79r>9jukdzc@v2|m9>$s!_OpU9u list[str]: """Return the input keys of the Agent.""" - return ['dataset_path'] + return ['queryset_path'] def output_keys(self) -> list[str]: """Return the output keys of the Agent.""" - return ['prompt_answer_list', 'eval_report_list'] + return [] def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: """Agent parameter parsing. @@ -37,9 +34,7 @@ def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: Returns: dict: agent input parsed from `input_object` by the user. """ - agent_input['dataset_path'] = input_object.get_data('dataset_path') - agent_input['turn'] = input_object.get_data('turn', 1) - return agent_input + return {} def parse_result(self, planner_result: dict) -> dict: """Planner result parser. @@ -58,71 +53,34 @@ def execute(self, input_object: InputObject, agent_input: dict): input_object (InputObject): input parameters passed by the user. agent_input (dict): agent input parsed from `input_object` by the user. """ - # step1: collect q&a dataset from the candidate agent which needs to be evaluated. - prompt_answer_list = self.collect_dataset(input_object, agent_input) + date = datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + input_object.add_data('date', date) + + # step1: build q&a dataset from the candidate agent which needs to be evaluated. + prompt_answer_list = self.dataset_build_agent(input_object) input_object.add_data('prompt_answer_list', prompt_answer_list) - LOGGER.info("-------------------------------------------") - LOGGER.info("End: collect q&a dataset from the candidate agent done.") - LOGGER.info("-------------------------------------------") + # step2: evaluate q&a dataset generated by the candidate agent and generate evaluation report. + eval_report_list = self.dataset_eval_agent(input_object) + return {'prompt_answer_list': prompt_answer_list, 'eval_report_list': eval_report_list} - # step2: write the q&a dataset to json file. - for i in range(len(prompt_answer_list)): - one_turn_prompt_answer_list = prompt_answer_list[i] - json_writer = JsonFileWriter(f'data_agent_turn_{i + 1}_dataset') - json_writer.write_json_prompt_answer_list(one_turn_prompt_answer_list) - LOGGER.info(f"Progress: write the q&a dataset to local jsonl files.") + def dataset_build_agent(self, input_object: InputObject): + """Build q&a dataset from the candidate agent which needs to be evaluated.""" - # step3: evaluate q&a datasets generated by the candidate agent and generate evaluation report. - eval_report_list = self.eval_agent(input_object) - return {'prompt_answer_list': prompt_answer_list, 'eval_report_list': eval_report_list} + planner = self.agent_model.plan.get('planner', {}) + dataset_build_agent: Agent = AgentManager().get_instance_obj(planner.get('dataset_builder')) + if dataset_build_agent is None: + raise ValueError('The agent instance corresponding to `dataset_builder` parameter is empty') + output: OutputObject = dataset_build_agent.run(**input_object.to_dict()) + return output.get_data('prompt_answer_list', []) - def collect_dataset(self, input_object: InputObject, agent_input: dict) -> List[List[Tuple[str, str]]]: - """Collect q&a dataset from the candidate agent which needs to be evaluated.""" - - candidate_agent_name = self.agent_model.plan.get('planner', {}).get('candidate', '') - # get the candidate agent which needs to be evaluated - candidate_agent: Agent = AgentManager().get_instance_obj(candidate_agent_name) - if not candidate_agent: - raise ValueError('The agent instance corresponding to `candidate` parameter is empty') - - # init jsonl file reader - jsonl_file_reader = JsonFileReader(agent_input.get('dataset_path')) - # read query list - query_list = jsonl_file_reader.read_json_obj_list() - if not query_list: - raise ValueError('query list information read from dataset_path is empty') - - prompt_answer_list = [] - for i in range(agent_input.get('turn')): - LOGGER.info("-------------------------------------------") - LOGGER.info(f"Start: collect q&a dataset from the candidate agent `{candidate_agent_name}`, turn {i + 1}.") - one_turn_prompt_answer_list = [] - # single turn query and answer processing. - for j in range(len(query_list)): - query_dict: dict = query_list[j] - if query_dict: - # init the input and output key in agent - first_input_key = candidate_agent.input_keys()[0] - first_output_key = candidate_agent.output_keys()[0] - # run the target agent - output_object: OutputObject = candidate_agent.run(**query_dict) - # note: the first index of input_keys and output_keys is identified as the prompt and answer. - one_turn_prompt_answer_list.append( - (query_dict.get(first_input_key, ''), output_object.get_data(first_output_key))) - LOGGER.info(f"Progress: the turn {i + 1} query line {j + 1} has generated the answer " - f"successfully.") - LOGGER.info(f"End: the turn {i + 1} has generated the answer successfully.") - # collect q&a dataset - prompt_answer_list.append(one_turn_prompt_answer_list) - return prompt_answer_list - - def eval_agent(self, input_object: InputObject): - """Evaluate q&a datasets generated by the candidate agent and generate evaluation report.""" + def dataset_eval_agent(self, input_object: InputObject): + """Evaluate q&a dataset generated by the candidate agent and generate evaluation report.""" planner = self.agent_model.plan.get('planner', {}) - eval_agent: Agent = AgentManager().get_instance_obj(planner.get('evaluator')) - if eval_agent is None: - raise ValueError('The agent instance corresponding to `evaluator` parameter is empty') - output: OutputObject = eval_agent.run(**input_object.to_dict()) + dataset_eval_agent: Agent = AgentManager().get_instance_obj(planner.get('dataset_evaluator')) + if dataset_eval_agent is None: + LOGGER.warn("The agent instance corresponding to `dataset_evaluator` parameter is empty") + return [] + output: OutputObject = dataset_eval_agent.run(**input_object.to_dict()) return output.get_data('eval_report_json_list', []) diff --git a/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml b/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml index a67692c9..0a782015 100644 --- a/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml +++ b/sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml @@ -1,14 +1,10 @@ info: name: 'data_agent' description: 'data agent' -profile: - llm_model: - name: 'demo_llm' - model_name: 'gpt-4o' plan: planner: - candidate: 'demo_rag_agent' - evaluator: 'eval_agent' + dataset_builder: 'dataset_build_agent' + dataset_evaluator: 'dataset_eval_agent' metadata: type: 'AGENT' module: 'sample_standard_app.app.core.agent.data_agent_case.data_agent' diff --git a/sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py b/sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py new file mode 100644 index 00000000..91e84a79 --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py @@ -0,0 +1,138 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/5 15:07 +# @Author : wangchongshi +# @Email : wangchongshi.wcs@antgroup.com +# @FileName: dataset_build_agent.py +from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED +from typing import Tuple, List, Any, Optional + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.output_object import OutputObject +from agentuniverse.base.util.logging.logging_util import LOGGER +from sample_standard_app.app.util.jsonl_file_utils import JsonFileWriter, JsonFileReader + + +class DatasetBuildAgent(Agent): + """Dataset Build Agent class.""" + + executor: Optional[Any] = ThreadPoolExecutor(max_workers=1, thread_name_prefix="data_agent") + + def input_keys(self) -> list[str]: + """Return the input keys of the Agent.""" + return ['queryset_path'] + + def output_keys(self) -> list[str]: + """Return the output keys of the Agent.""" + return ['prompt_answer_list'] + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + """Agent parameter parsing. + + Args: + input_object (InputObject): input parameters passed by the user. + agent_input (dict): agent input preparsed by the agent. + Returns: + dict: agent input parsed from `input_object` by the user. + """ + agent_input['queryset_path'] = input_object.get_data('queryset_path') + agent_input['turn'] = input_object.get_data('turn', 1) + agent_input['candidate'] = input_object.get_data('candidate') + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + """Planner result parser. + + Args: + planner_result(dict): Planner result + Returns: + dict: Agent result object. + """ + return planner_result + + def execute(self, input_object: InputObject, agent_input: dict): + """Execute agent instance. + + Args: + input_object (InputObject): input parameters passed by the user. + agent_input (dict): agent input parsed from `input_object` by the user. + """ + # init thread pool + profile = self.agent_model.profile or {} + self.executor = ThreadPoolExecutor( + max_workers=profile.get('concurrency_level') if profile.get('concurrency_level') else 1, + thread_name_prefix="data_agent") + + # step1: build q&a dataset from the candidate agent which needs to be evaluated. + prompt_answer_list = self.build_dataset(agent_input) + input_object.add_data('prompt_answer_list', prompt_answer_list) + + LOGGER.info("-------------------------------------------") + LOGGER.info("End: build q&a dataset from the candidate agent done.") + LOGGER.info("-------------------------------------------") + + # step2: write the q&a dataset to json file. + date = input_object.get_data('date', '') + for i in range(len(prompt_answer_list)): + one_turn_prompt_answer_list = prompt_answer_list[i] + json_writer = JsonFileWriter(f'dataset_turn_{i + 1}_{date}') + json_writer.write_json_prompt_answer_list(one_turn_prompt_answer_list) + LOGGER.info(f"Progress: write the q&a dataset to local jsonl files.") + return {'prompt_answer_list': prompt_answer_list} + + def build_dataset(self, agent_input: dict) -> List[List[Tuple[str, str]]]: + """Build q&a dataset from the candidate agent which needs to be evaluated.""" + + candidate_agent_name = agent_input.get('candidate') \ + if agent_input.get('candidate') else self.agent_model.plan.get('planner', {}).get('candidate', '') + # get the candidate agent which needs to be evaluated + candidate_agent: Agent = AgentManager().get_instance_obj(candidate_agent_name) + if not candidate_agent: + raise ValueError('The agent instance corresponding to `candidate` parameter is empty') + + # init jsonl file reader + jsonl_file_reader = JsonFileReader(agent_input.get('queryset_path')) + # read query list + query_list = jsonl_file_reader.read_json_obj_list() + if not query_list: + raise ValueError('query list information read from queryset_path is empty') + + # init the input and output key in agent + first_input_key = candidate_agent.input_keys()[0] + first_output_key = candidate_agent.output_keys()[0] + + prompt_answer_list = [] + + for i in range(agent_input.get('turn')): + LOGGER.info("-------------------------------------------") + LOGGER.info(f"Start: build q&a dataset from the candidate agent `{candidate_agent_name}`, turn {i + 1}.") + one_turn_prompt_answer_list = [] + futures_to_query = {} + + # single turn query and answer processing. + for j in range(len(query_list)): + query_dict: dict = query_list[j] + if query_dict: + # run the target agent + future = self.executor.submit(candidate_agent.run, **query_dict) + futures_to_query[future] = query_dict + + done, not_done = wait(futures_to_query.keys(), return_when=ALL_COMPLETED) + + for future in done: + output_object: OutputObject = future.result() + # note: the first index of input_keys and output_keys is identified as the prompt and answer. + prompt = futures_to_query.get(future, {}).get(first_input_key, '') + answer = output_object.get_data(first_output_key, '') + + one_turn_prompt_answer_list.append((prompt, answer)) + LOGGER.info(f"Progress: the turn {i + 1} query: `{prompt}` has generated the answer successfully.") + + LOGGER.info(f"End: the turn {i + 1} has generated the answer successfully.") + + # build q&a dataset + prompt_answer_list.append(one_turn_prompt_answer_list) + return prompt_answer_list diff --git a/sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml b/sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml new file mode 100644 index 00000000..2d310094 --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml @@ -0,0 +1,12 @@ +info: + name: 'dataset_build_agent' + description: 'dataset build agent' +profile: + concurrency_level: 1 +plan: + planner: + candidate: 'demo_rag_agent' +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.data_agent_case.dataset_build_agent' + class: 'DatasetBuildAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/data_agent_case/eval_agent.py b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py similarity index 94% rename from sample_standard_app/app/core/agent/data_agent_case/eval_agent.py rename to sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py index ed93018d..e29156f6 100644 --- a/sample_standard_app/app/core/agent/data_agent_case/eval_agent.py +++ b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py @@ -4,13 +4,13 @@ # @Time : 2024/7/1 17:00 # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com -# @FileName: eval_agent.py +# @FileName: dataset_eval_agent.py from typing import Tuple, List -import pandas as pd from langchain_core.output_parsers import StrOutputParser from langchain_core.utils.json import parse_json_markdown from openpyxl.reader.excel import load_workbook +import pandas as pd from agentuniverse.agent.agent import Agent from agentuniverse.agent.input_object import InputObject @@ -21,11 +21,10 @@ from agentuniverse.prompt.prompt import Prompt from agentuniverse.prompt.prompt_manager import PromptManager from agentuniverse.prompt.prompt_model import AgentPromptModel -from sample_standard_app.app.util.jsonl_file_utils import JsonFileWriter -class EvalAgent(Agent): - """Evaluation Agent class.""" +class DatasetEvalAgent(Agent): + """Dataset Evaluation Agent class.""" def input_keys(self) -> list[str]: """Return the input keys of the Agent.""" @@ -78,11 +77,11 @@ def execute(self, input_object: InputObject, agent_input: dict): LOGGER.info(f"End: evaluate the dataset in multiple dimensions done.") LOGGER.info("-------------------------------------------") - # step2: write the eval results to excel file. - self.generate_eval_results_excel(prompt_answer_list, eval_dims_json_list) + # step2: write the eval results to Excel file. + self.generate_eval_results_excel(prompt_answer_list, eval_dims_json_list, input_object.get_data('date', '')) # step3: generate eval report - eval_report_json_list = self.generate_eval_report(eval_dims_json_list) + eval_report_json_list = self.generate_eval_report(eval_dims_json_list, input_object.get_data('date', '')) LOGGER.info(f"End: generate evaluation report done.") LOGGER.info("-------------------------------------------") return {'eval_report_json_list': eval_report_json_list, 'eval_dims_json_list': eval_dims_json_list} @@ -143,7 +142,7 @@ def eval(self, prompt_answer_list: List[List[Tuple[str, str]]], max_eval_lines: # calculate avg score from multiple dimensions. avg_score = sum(data['score'] for data in dimensions) except Exception as e: - LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>>{e}:{res[0]}') + LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>> res: {res}, exception: {e}') continue if len(dimensions) > 0: avg_score = avg_score / len(dimensions) @@ -192,13 +191,14 @@ def handle_llm(self) -> LLM: llm.set_by_agent_model(**self.agent_model.profile.get('llm_model')) return llm - def generate_eval_report(self, eval_dims_json_list: List[List[dict]]): + def generate_eval_report(self, eval_dims_json_list: List[List[dict]], date: str): """Integrate multidimensional evaluation scores and generate evaluation report Args: eval_dims_json_list (List[List[dict]]) : The list of evaluation results from multiple turns, the type of single turn is List[dict]. + date (str): The date of the evaluation. Returns: eval_report_json_list (List[dict]): The list of evaluation report. """ @@ -245,7 +245,7 @@ def generate_eval_report(self, eval_dims_json_list: List[List[dict]]): LOGGER.info(f"Progress: total evaluation report has generated successfully.") # generate excel report - self.generate_eval_report_excel(eval_report_json_list) + self.generate_eval_report_excel(eval_report_json_list, date) return eval_report_json_list @staticmethod @@ -279,11 +279,11 @@ def generate_total_eval_report(eval_report_json_list: List[dict]): @staticmethod def generate_eval_results_excel(prompt_answer_list: List[List[Tuple[str, str]]], - eval_dims_json_list: List[List[dict]]): + eval_dims_json_list: List[List[dict]], date: str): """Generate evaluation results in excel format.""" rows = [] - columns: List[str] = ['Line Number', 'Avg Score', 'Prompt', 'Answer'] + columns: List[str] = ['Line Number', 'Overall Score', 'Prompt', 'Answer'] if len(eval_dims_json_list) > 0 and len(eval_dims_json_list[0]) > 0: one_row_eval_result = eval_dims_json_list[0][0] dims = one_row_eval_result.get('dimensions', []) @@ -313,12 +313,12 @@ def generate_eval_results_excel(prompt_answer_list: List[List[Tuple[str, str]]], df = pd.DataFrame(rows, columns=columns) - df.to_excel(f"./data/eval_results_turn_{i + 1}.xlsx", index=False, engine='openpyxl') + df.to_excel(f"./data/eval_result_turn_{i + 1}_{date}.xlsx", index=False, engine='openpyxl') LOGGER.info(f"Progress: generate evaluation detailed results in excel format successfully.") LOGGER.info("-------------------------------------------") @staticmethod - def generate_eval_report_excel(eval_report_json_list: List[dict]): + def generate_eval_report_excel(eval_report_json_list: List[dict], date: str): """Generate excel eval report.""" rows = [] @@ -338,13 +338,13 @@ def generate_eval_report_excel(eval_report_json_list: List[dict]): df = pd.DataFrame(rows, columns=columns) - df.to_excel("./data/eval_report.xlsx", index=False, engine='openpyxl') + df.to_excel(f"./data/eval_report_{date}.xlsx", index=False, engine='openpyxl') # tweak column width - workbook = load_workbook('./data/eval_report.xlsx') + workbook = load_workbook(f"./data/eval_report_{date}.xlsx") worksheet = workbook.active column_widths = [25, 20] + [20] * (len(columns) - 2) for i, column_width in enumerate(column_widths, 1): worksheet.column_dimensions[chr(64 + i)].width = column_width - workbook.save('./data/eval_report.xlsx') + workbook.save(f"./data/eval_report_{date}.xlsx") LOGGER.info(f"Progress: generate evaluation excel file successfully.") diff --git a/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml new file mode 100644 index 00000000..23bf0a46 --- /dev/null +++ b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml @@ -0,0 +1,14 @@ +info: + name: 'dataset_eval_agent' + description: 'dataset eval agent' +profile: + prompt_version: dataset_eval_agent.cn + max_eval_lines: 10 + llm_model: + name: 'qwen_llm' + model_name: 'qwen2-72b-instruct' + temperature: 0.1 +metadata: + type: 'AGENT' + module: 'sample_standard_app.app.core.agent.data_agent_case.dataset_eval_agent' + class: 'DatasetEvalAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml b/sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml deleted file mode 100644 index 6c187edb..00000000 --- a/sample_standard_app/app/core/agent/data_agent_case/eval_agent.yaml +++ /dev/null @@ -1,14 +0,0 @@ -info: - name: 'eval_agent' - description: 'eval agent' -profile: - prompt_version: eval_agent.cn - max_eval_lines: 10 - llm_model: - name: 'demo_llm' - model_name: 'gpt-4o' - temperature: 0.1 -metadata: - type: 'AGENT' - module: 'sample_standard_app.app.core.agent.data_agent_case.eval_agent' - class: 'EvalAgent' \ No newline at end of file diff --git a/sample_standard_app/app/core/agent/rag_agent_case/demo_rag_agent.yaml b/sample_standard_app/app/core/agent/rag_agent_case/demo_rag_agent.yaml index fecdd274..d2641e06 100644 --- a/sample_standard_app/app/core/agent/rag_agent_case/demo_rag_agent.yaml +++ b/sample_standard_app/app/core/agent/rag_agent_case/demo_rag_agent.yaml @@ -2,29 +2,9 @@ info: name: 'demo_rag_agent' description: 'demo rag agent' profile: - introduction: 你是一位精通信息分析的ai助手。 - target: 你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。 - instruction: | - 你需要遵守的规则是: - 1. 必须使用中文结合查询的背景信息结合你所拥有的知识回答用户提出的问题。 - 2. 结构化答案生成，必要时通过空行提升阅读体验。 - 3. 不采用背景信息中的错误信息。 - 4. 要考虑答案和问题的相关性，不做对问题没有帮助的回答。 - 5. 详尽回答问题，重点突出，不过多花哨词藻。 - 6. 不说模糊的推测。 - 7. 尽量多的使用数值类信息。 - - 背景信息是: - {background} - - 之前的对话: - {chat_history} - - 开始! - - 需要回答的问题是: {input} + prompt_version: demo_rag_agent.cn llm_model: - name: 'default_qwen_llm' + name: 'qwen_llm' # model_name: 'gpt-4o' plan: planner: diff --git a/sample_standard_app/app/core/llm/qwen_llm.yaml b/sample_standard_app/app/core/llm/qwen_llm.yaml index 1115947c..57733656 100644 --- a/sample_standard_app/app/core/llm/qwen_llm.yaml +++ b/sample_standard_app/app/core/llm/qwen_llm.yaml @@ -1,6 +1,6 @@ name: 'qwen_llm' description: 'demo qwen llm with spi' -model_name: 'qwen-max' +model_name: 'qwen2-72b-instruct' max_tokens: 2000 metadata: type: 'LLM' diff --git a/sample_standard_app/app/core/prompt/eval_agent_cn.yaml b/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml similarity index 54% rename from sample_standard_app/app/core/prompt/eval_agent_cn.yaml rename to sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml index 869bd61b..87e56da2 100644 --- a/sample_standard_app/app/core/prompt/eval_agent_cn.yaml +++ b/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml @@ -18,7 +18,7 @@ instruction: | {answer} -------------------------------- - 质量打分共5个维度，每个维度包含了不符合准入条件的要求（0.0分）/门槛值的要求（0.0～3.0分）/加分项（0.0～2.0分） + 质量打分共6个维度，每个维度包含了不符合准入条件的要求（0.0分）/门槛值的要求（0.0～3.0分）/加分项（0.0～2.0分） 质量打分维度描述如下: @@ -36,8 +36,8 @@ instruction: | 遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 3.加分项 - 能够get到用户在问什么后： - a、对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应 + 能够理解到用户在问什么问题后： + a、对用户的所有问题（包含情绪）都给出回应 b、回应的内容整体相关性都很高 c、回复的问题全面且易懂 @@ -48,7 +48,7 @@ instruction: | 数据错误，不符合事实 2.门槛值 - 个别数据不准确，但不影响整体事实性 + 仅几个数据不准确，但不影响整体事实性 3.加分项 解读内容中所有引用数据真实可信，数据来源官方可信 @@ -58,10 +58,9 @@ instruction: | dimension requirement: 1.不符合准入条件 出现专业错误 - （如经济复苏利好股市利空债市、供不应求带来价格上涨、经济下行压力加剧通常会倒逼宏观政策加码等） 2.门槛值 - 符合业内公认的金融逻辑，前后结论不要自相矛盾，不出现明显的专业错误 + 符合业内公认的事实逻辑，前后结论不要自相矛盾，不出现明显的专业错误 3.加分项 通篇解读要一以贯之、逻辑自洽，且逻辑推演是有逐层递进的（后文分析以前文结论为依据） @@ -73,29 +72,60 @@ instruction: | 数据陈旧过时，不符合现状 2.门槛值 - 次新，符合当下现状 + 数据次新，符合当下现状 3.加分项 引用官方发布的最新数据，包含清晰的发布日期 -------------------------------- + dimension name: 结构性 + dimension requirement: + 1.不符合准入条件 + 答案如果是总分结构： + a、总论点和分论点只是抽取了原文中的部分内容，并没有真正的总结归纳 + b、生成答案结构是总分结构，但是分论点是为了回答query而硬答，与query实际并没有关系 + c、总结内容论点逻辑冲突 + + 答案如果是总结摘要结构： + a、摘要内容是抽取了部分段落，并没有真正的归纳总结 + b、摘要内容，仅仅总结成一段文本，无摘要详述 + + 2.门槛值 + 答案如果是总分结构： + a、总分结构，有总结归纳，分论点有层次 + b、答案总分结构，分论点之间有逻辑关系（互补、递进），分论点与query之间有相关性 + c、总结的内容论点间无冲突 + + 答案如果是总结摘要结构： + a、总结内容有对原文进行归纳总结，不是抽取的原文段落 + b、总结内容论点有展开叙述 + + 3.加分项 + 答案如果是总分结构： + a、总分结构，分论点有归类，有条理有逻辑 + b、总分结构，分论点为1234分层结构，有条理 + + 答案如果是总结摘要结构： + a、总结内容论点叙述详细充分，有逻辑有条理 + b、总结内容话术表达逻辑清晰，表达精炼 + -------------------------------- dimension name: 整体性 dimension requirement: 1.不符合准入条件 - 满足以下任意一点即为0分： - a、逻辑不通顺，或存在语病，或表达冗余内容超长（整体文本长度>XX字） - b、论点&总结重复/论点&论点重复：任意两条论点存在80%以上的重复度 - c、倒装、错别字、语句不通顺、逻辑问题等 - d、存在特殊符号 + 满足以下任意一点，分数为0： + a、逻辑不通顺，或存在语法问题，或表达冗余内容超长 + b、论点和论点之间存在重复：任意两条论点存在80%以上的重复度 + c、答案存在倒装、错别字、语句不通顺、逻辑问题等 + d、答案中存在特殊符号 2.门槛值 - a、语句流畅、表达可阅读性强 - b、表达直白（不反复说车轱辘话） - c、论点论据表达无重复 + a、语句流畅、表达可阅读性强 + b、答案表达直白（不反复说车轱辘话） + c、论点和论据在表达上无重复 3.加分项 - a、答案对用户query有明确回应表达 - b、语言简洁精炼，逻辑清晰 + a、答案对用户query有明确回应表达 + b、语言简洁精炼，逻辑清晰 -------------------------------- 输出格式要求： @@ -106,17 +136,17 @@ instruction: | {{ "dimensions": [ {{ - "name":质量打分维度名称, - "score":分数, - "negative point":扣分的原因和给出的优化建议 + "name":质量打分维度名称(string字符串格式), + "score":分数(float浮点数格式), + "negative point":扣分的原因和给出的优化建议(string字符串格式) }}, {{ - "name":质量打分维度, - "score":分数, - "negative point":扣分的原因和给出的优化建议 + "name":质量打分维度名称(string字符串格式), + "score":分数(float浮点数格式), + "negative point":扣分的原因和给出的优化建议(string字符串格式) }} ] }} metadata: type: 'PROMPT' - version: 'eval_agent.cn' + version: 'dataset_eval_agent.cn' diff --git a/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml b/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml new file mode 100644 index 00000000..0dcadaf3 --- /dev/null +++ b/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml @@ -0,0 +1,156 @@ +introduction: | + You are an extremely rigorous financial expert and need help me evaluate prompt (user input) and answer (model output) scores in various dimensions according to the following requirements. + + The full score for each dimension is 5 points. If you do not meet the entry conditions, you will be awarded 0 points directly, and if you meet the threshold value, you will be awarded 0-3 points. If you meet the additional requirements, you will add 0-2 points to the threshold value. + For scoring requirements, please be accurate to 1 decimal place: + 1. Failure to meet access conditions (0.0 point) + 2. Threshold value (0.0 - 3.0 points) + 3. Bonus points (0.0 - 2.0 points) + + Note: Strict scoring and high scores cannot be easily given +instruction: | + -------------------------------- + prompt: + {prompt} + + -------------------------------- + answer: + {answer} + + -------------------------------- + There are 6 dimensions in quality scoring, each dimension contains requirements for non-compliance with access conditions (0.0 points)/threshold values (0.0 - 3.0 points)/bonus points (0.0 - 2.0 points) + + The quality scoring dimensions are described as follows: + + dimension name: relevance + dimension requirement: + 1. non-compliance with access conditions + Answer is irrelevant. + + 2.threshold values + Being able to know what the user is asking, and the answer is exactly what the user is asking, including： + + 【When there is a clear intention】 + Identify user questions and respond to the questions, and the content of the reply is relevant. + When the question has the main intention, the reply content should be related to the main intention. + + 【Without clear intention】 + Conduct appropriate rhetorical confirmation of intention when encountering vague intentions and then give an accurate response. + + 3.bonus points + After being able to understand what questions users ask: + a、Respond to all user questions (including emotions) + b、The overall relevance of the response is very high + c、The answers are comprehensive and easy to understand + + -------------------------------- + dimension name: factual + dimension requirement: + 1. non-compliance with access conditions + The data is wrong and does not conform to the facts + + 2.threshold values + Only a few data are inaccurate, but they do not affect the overall factual nature + + 3.bonus points + All quoted data in the interpretation content are true and credible, and the data source is officially credible + -------------------------------- + + dimension name: rationality + dimension requirement: + 1. non-compliance with access conditions + Professional error occurred + + 2.threshold values + Adhere to the factual logic recognized by the industry, ensure that the conclusions are consistent and do not contradict each other, and avoid obvious professional errors. + + 3.bonus points + + The interpretation throughout the text must be consistent and logically coherent, with the logical reasoning advancing step by step (subsequent analysis is based on previous conclusions). + + -------------------------------- + dimension name: timeliness + dimension requirement: + 1. non-compliance with access conditions + The data is stale and no longer corresponds to the present reality. + + 2.threshold values + The data is relatively recent and aligns with the current situation. + + 3.bonus points + Cite the latest official data, including a clear publication date. + + -------------------------------- + dimension name: structural + dimension requirement: + 1. non-compliance with access conditions + If the answer follows a general-specific structure: + a、The main thesis and sub-theses only extract parts of the content from the original text and do not truly summarize or generalize. + b、The structure of the answer is general-specific, but the sub-points are forced to respond to the query without actually being related to it. + c、The summary of the content has logical conflicts in the argument points. + + If the answer is a summary abstract structure: + a、The abstract content is extracted from certain paragraphs and does not truly synthesize or summarize. + b、The abstract content is merely summarized into a single paragraph, without a detailed abstract. + + 2.threshold values + If the answer follows a general-specific structure: + a、The structure is general-specific, with summarization, and the sub-points are hierarchically organized. + b、The answer has a general-specific structure, with sub-points that have logical relationships (complementary, progressive), and the sub-points are relevant to the query. + c、Summary content without conflict between arguments + + If the answer is a summary structure: + a、The summary content summarizes the original text, not extracted original paragraphs + b、The summary content and arguments are described in an expanded manner + + 3.bonus points + If the answer follows a general-specific structure: + a、The structure is general-specific, with categorized sub-points that are orderly and logical. + b、The structure is general-specific, with the main points organized in a hierarchical structure labeled 1, 2, 3, and 4, which is orderly. + + If the answer is a summary structure: + a、The summary content and arguments are detailed and sufficient, logical and organized + b、The summary should be expressed with clear logic and concise language. + -------------------------------- + dimension name: integrity + dimension requirement: + 1. non-compliance with access conditions + If any of the following points are satisfied, the score is 0: + a、Logic is not smooth, or there are grammatical problems, or redundant content is expressed too long + b、Repetition between arguments: Any two arguments have more than 80% repetition + c、The answers include inversion, typos, inconsistent sentences, logical problems, etc. + d、There are special symbols in the answer + + 2.threshold values + a、Smooth sentences and highly readable expressions + b、Express the answer directly (do not repeat it over and over again) + c、The thesis and evidence should be expressed without repetition. + + 3.bonus points + a、The answer is clearly expressed in response to user query + b、The language is concise, and the logic is clear + + -------------------------------- + Output format requirements: + 1. According to the specific scoring dimension requirements, evaluate prompt and answer content at the same time and score. + 2. Be sure to give a negative point, combine prompt and answer, and give specific reasons for deducting points and specific optimization suggestions. + + Please score strictly in accordance with the quality scoring dimensions. High scores cannot be easily given. + Please ensure that the output only contains the following json format fields, and each field cannot be empty: + {{ + "dimensions": [ + {{ + "name": dimension name(string), + "score": score(float), + "negative point": Reasons for deductions and optimization suggestions given(string) + }}, + {{ + "name": dimension name(string), + "score": score(float), + "negative point": Reasons for deductions and optimization suggestions given(string) + }} + ] + }} +metadata: + type: 'PROMPT' + version: 'dataset_eval_agent.en' diff --git a/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml b/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml new file mode 100644 index 00000000..cda9228f --- /dev/null +++ b/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml @@ -0,0 +1,24 @@ +introduction: 你是一位精通信息分析的ai助手。 +target: 你的目标是使用中文结合查询的背景信息及你所拥有的知识回答用户提出的问题。 +instruction: | + 你需要遵守的规则是: + 1. 必须使用中文结合查询的背景信息结合你所拥有的知识回答用户提出的问题。 + 2. 结构化答案生成，必要时通过空行提升阅读体验。 + 3. 不采用背景信息中的错误信息。 + 4. 要考虑答案和问题的相关性，不做对问题没有帮助的回答。 + 5. 详尽回答问题，重点突出，不过多花哨词藻。 + 6. 不说模糊的推测。 + 7. 尽量多的使用数值类信息。 + + 背景信息是: + {background} + + 之前的对话: + {chat_history} + + 开始! + + 需要回答的问题是: {input} +metadata: + type: 'PROMPT' + version: 'demo_rag_agent.cn' diff --git a/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml b/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml new file mode 100644 index 00000000..c3b96de8 --- /dev/null +++ b/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml @@ -0,0 +1,25 @@ +introduction: You are an ai assistant who is proficient in information analysis. +target: Your goal is to use English to combine the background information of the query and your knowledge to answer questions raised by users. +instruction: | + The rules you need to follow are: + 1. You must use English combined with the background information of the query and your knowledge to answer the questions raised by the user. + 2. Structured answer generation and use blank lines to improve the reading experience if necessary. + 3. Do not use background information that is incorrect. + 4. Consider the relevance of the answer and question, do not answer questions that do not help. + 5. Answer questions in detail, with emphasis on key points. + 6. Do not make vague inferences. + 7. Try to use numerical information as much as possible. + 8. The answer should not exceed 500 words + + Background is: + {background} + + Previous conversations are: + {chat_history} + + Begin! + + The question needs to be answered is: {input} +metadata: + type: 'PROMPT' + version: 'demo_rag_agent.en' \ No newline at end of file diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl new file mode 100644 index 00000000..fd99bf03 --- /dev/null +++ b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl @@ -0,0 +1,2 @@ +{"prompt": "英伟达股票大涨的原因是什么？", "answer": "您的问题与提供的背景信息关于巴菲特减持比亚迪股票的情况不符，因此我无法直接回答“英伟达股票大涨的原因”。然而，如果要探讨股市中某个特定股票（如NVIDIA）的大涨，通常会考虑行业趋势、公司业绩、技术创新、市场需求增长、宏观经济环境等因素。对于英伟达这样的科技巨头，GPU性能提升、人工智能和数据中心需求的增长、以及新兴市场的扩张都可能是推动股价上涨的原因。但具体到英伟达，需要查看最新的行业报告和公司公告来了解详细情况。"} +{"prompt": "特斯拉股票为什么波动很大？", "answer": "特斯拉股票的波动性较大，主要有以下几个原因：\n\n1. 技术创新与行业竞争：特斯拉作为电动汽车行业的领导者，其技术创新能力和市场份额对投资者信心有直接影响。新产品的发布、技术突破或竞争对手的动作都可能导致股价大幅波动。\n\n2. 市场预期：特斯拉的股价常常受到市场对其增长前景、盈利能力和电池技术等未来预期的影响。如果市场对特斯拉的未来表现乐观，股价会上涨；反之，如果预期不达预期或出现负面新闻，股价可能会下跌。\n\n3. 宏观经济因素：全球经济状况、政策变化（如补贴政策）、能源政策调整等都会影响电动车行业的整体环境，从而影响特斯拉的股价。\n\n4. 融资活动：特斯拉的融资活动，如股权融资、债务融资，可能会导致股价短期内调整，尤其是如果融资条件优于市场预期。\n\n5. 泡沫与估值调整：特斯拉作为一家高成长性的公司，其股票估值可能在短期内被推高，一旦市场情绪变化或估值过高被认为难以维持，股价可能回调。\n\n6. 公司管理与舆论：CEO埃隆·马斯克的个人言行、公司战略调整以及媒体报道都可能成为股价波动的催化剂。\n\n总之，特斯拉股票的波动反映了市场对公司及其行业动态的快速反应，同时也体现了投资者对于特斯拉未来盈利能力、技术进步和市场地位的期待与担忧。"} \ No newline at end of file diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-11:45:42.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-11:45:42.jsonl new file mode 100644 index 00000000..92cb9bec --- /dev/null +++ b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-11:45:42.jsonl @@ -0,0 +1,2 @@ +{"prompt": "Why does Tesla stock fluctuate so much?", "answer": "The question about Tesla's stock fluctuations isn't directly addressed by the provided background information on Warren Buffett's investment strategies or his dealings with BYD. However, I can provide insights based on general knowledge and market principles.\n\nTesla's stock price is known for its significant volatility, which can be attributed to several factors:\n\n1. **Market Sentiment and Speculation**: Tesla is often seen as a bellwether for the electric vehicle (EV) industry and the broader tech sector. Positive or negative news, whether related to production numbers, new model releases, regulatory changes, or CEO Elon Musk's tweets, can significantly impact investor sentiment and lead to sharp movements in the stock price.\n\n2. **Financial Performance**: Like any publicly traded company, Tesla's quarterly earnings reports can cause substantial stock price movements. If the company beats or misses analysts' expectations regarding revenue, profitability, or guidance, it can result in immediate and sometimes dramatic reactions from the market.\n\n3. **Valuation and Growth Expectations**: Tesla has been valued at very high multiples compared to traditional automakers, reflecting investors' expectations for future growth. Any indication that this growth might slow down can lead to a reevaluation of the stock's valuation, causing price drops.\n\n4. **Competition and Market Dynamics**: The EV market is rapidly evolving, with established automakers and new entrants alike increasing their focus on electric vehicles. Changes in competitive dynamics, such as new product launches or shifts in market share, can influence Tesla's stock price.\n\n5. **Regulatory Environment**: Government policies and regulations, particularly those related to EV incentives, emissions standards, and autonomous driving technology, can have a significant impact on Tesla's business outlook and stock performance.\n\n6. **Supply Chain and Production Challenges**: Tesla has faced production bottlenecks and supply chain disruptions, especially during times of global economic uncertainty. These challenges can affect the company's ability to meet production targets and deliver vehicles, impacting stock price.\n\nIn summary, Tesla's stock fluctuations are influenced by a combination of market sentiment, financial performance, high growth expectations, competitive dynamics, regulatory changes, and operational challenges. These factors contribute to the stock's characteristic volatility."} +{"prompt": "What is the reason for the surge in Nvidia's stock?", "answer": "The question about Nvidia's stock surge isn't directly related to the provided background information on Warren Buffett's investment strategies and his transactions involving BYD shares. However, I can provide a general explanation based on common factors that might cause a surge in a company's stock price.\n\nNvidia's stock price can surge due to several reasons, including but not limited to:\n\n1. **Strong Financial Performance**: If Nvidia reports better-than-expected earnings or revenue figures, this can lead to a positive market reaction, driving up its stock price. Investors often look for signs of growth and profitability when deciding where to invest their money.\n\n2. **Innovative Product Launches**: The introduction of new, cutting-edge products or technologies can also boost investor confidence and increase demand for Nvidia's stock. For instance, advancements in graphics processing units (GPUs) for gaming, artificial intelligence, and data center applications have historically been significant drivers for Nvidia.\n\n3. **Market Expansion**: If Nvidia announces plans to enter new markets or expand into existing ones, this can signal potential for increased revenue streams, which can positively impact its stock price.\n\n4. **Strategic Acquisitions**: Acquiring other companies can strengthen Nvidia's position in the market, providing access to new technologies, customer bases, or markets. Successful acquisitions can lead to a rise in stock prices as investors anticipate future growth.\n\n5. **Industry Trends**: The technology sector, particularly segments like AI, autonomous vehicles, and cloud computing, has seen significant growth in recent years. As a leader in these areas, Nvidia benefits from positive industry trends, which can contribute to higher stock prices.\n\n6. **Analyst Recommendations**: Positive recommendations from financial analysts can influence investor sentiment and lead to increased buying activity, resulting in a stock price increase.\n\n7. **Economic Conditions**: A strong economy generally leads to more consumer spending and business investments, which can benefit companies like Nvidia that sell high-end technology products.\n\nIt's important to note that while these factors can contribute to a stock price surge, individual events or market conditions can also cause volatility. 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--git a/sample_standard_app/app/examples/data/query.jsonl b/sample_standard_app/app/examples/data/query.jsonl deleted file mode 100644 index 25db3779..00000000 --- a/sample_standard_app/app/examples/data/query.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -{"input": "我的股票为什么会跌？"} -{"input": "英伟达股票大涨的原因是什么？"} -{"input": "贵州茅台为什么不行了？"} \ No newline at end of file diff --git a/sample_standard_app/app/examples/data/queryset_cn.jsonl b/sample_standard_app/app/examples/data/queryset_cn.jsonl new file mode 100644 index 00000000..ea3ae9af --- /dev/null +++ b/sample_standard_app/app/examples/data/queryset_cn.jsonl @@ -0,0 +1,2 @@ +{"input": "英伟达股票大涨的原因是什么？"} +{"input": "特斯拉股票为什么波动很大？"} \ No newline at end of file diff --git a/sample_standard_app/app/examples/data/queryset_en.jsonl b/sample_standard_app/app/examples/data/queryset_en.jsonl new file mode 100644 index 00000000..320d7bdf --- /dev/null +++ b/sample_standard_app/app/examples/data/queryset_en.jsonl @@ -0,0 +1,2 @@ +{"input": "What is the reason for the surge in Nvidia's stock?"} +{"input": "Why does Tesla stock fluctuate so much?"} \ No newline at end of file diff --git a/sample_standard_app/app/examples/data_agent.py b/sample_standard_app/app/examples/data_agent.py index 7512a8be..851a01f1 100644 --- a/sample_standard_app/app/examples/data_agent.py +++ b/sample_standard_app/app/examples/data_agent.py @@ -12,10 +12,10 @@ AgentUniverse().start(config_path='../../config/config.toml') -def data_process(dataset_path: str, turn: int, **kwargs): +def data_process(queryset_path: str, turn: int, **kwargs): instance: Agent = AgentManager().get_instance_obj('data_agent') - instance.run(dataset_path=dataset_path, turn=turn, **kwargs) + instance.run(queryset_path=queryset_path, turn=turn, **kwargs) if __name__ == '__main__': - data_process(dataset_path="./data/query.jsonl", turn=1) + data_process(queryset_path="data/queryset_cn.jsonl", turn=1) diff --git a/sample_standard_app/app/util/jsonl_file_utils.py b/sample_standard_app/app/util/jsonl_file_utils.py index b01f78dd..2d64d739 100644 --- a/sample_standard_app/app/util/jsonl_file_utils.py +++ b/sample_standard_app/app/util/jsonl_file_utils.py @@ -60,8 +60,8 @@ def read_json_obj_list(self): class JsonFileWriter(object): - def __init__(self, output_file_name: str, extension='jsonl'): - self.outfile_path = DATA_DIR + output_file_name + '.' + extension + def __init__(self, output_file_name: str, extension='jsonl', directory=DATA_DIR): + self.outfile_path = directory + output_file_name + '.' + extension directory = os.path.dirname(self.outfile_path) if not os.path.exists(directory): os.makedirs(directory) From d3e6b96b2939695045feb56e1822ca1f307b9d58 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 9 Jul 2024 21:55:41 +0800 Subject: [PATCH 101/118] feat: add the data agent to build datasets and evaluate the quality of results. --- .../guidebook/_picture/data_agent_dataset.png | Bin 0 -> 334540 bytes .../_picture/data_agent_eval_report.png | Bin 109985 -> 96838 bytes .../data_agent_eval_report_qwen7b.png | Bin 0 -> 98726 bytes .../_picture/data_agent_eval_result.png | Bin 140870 -> 137898 bytes ...06\346\231\272\350\203\275\344\275\223.md" | 47 ------ ...73\346\231\272\350\203\275\344\275\223.md" | 157 ++++++++++++++++++ .../core/agent/data_agent_case/data_agent.py | 8 +- .../data_agent_case/dataset_build_agent.py | 28 ++-- .../data_agent_case/dataset_eval_agent.py | 90 +++++----- .../core/prompt/dataset_eval_agent_cn.yaml | 14 +- .../core/prompt/dataset_eval_agent_en.yaml | 29 ++-- .../dataset_turn_1_2024-07-08-20:17:28.jsonl | 2 - .../dataset_turn_1_2024-07-09-20:46:11.jsonl | 2 + .../data/eval_report_2024-07-08-20:17:28.xlsx | Bin 5263 -> 0 bytes .../data/eval_report_2024-07-09-20:46:11.xlsx | Bin 0 -> 8864 bytes ...val_result_turn_1_2024-07-08-20:17:28.xlsx | Bin 7971 -> 0 bytes ...val_result_turn_1_2024-07-09-20:46:11.xlsx | Bin 0 -> 8986 bytes .../app/examples/data_agent.py | 4 +- .../app/test/test_data_agent.py | 2 +- .../app/util/jsonl_file_utils.py | 10 +- 20 files changed, 253 insertions(+), 140 deletions(-) create mode 100644 docs/guidebook/_picture/data_agent_dataset.png create mode 100644 docs/guidebook/_picture/data_agent_eval_report_qwen7b.png delete mode 100644 "docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\346\262\273\347\220\206\346\231\272\350\203\275\344\275\223.md" create mode 100644 "docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" delete mode 100644 sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl create mode 100644 sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl delete mode 100644 sample_standard_app/app/examples/data/eval_report_2024-07-08-20:17:28.xlsx create mode 100644 sample_standard_app/app/examples/data/eval_report_2024-07-09-20:46:11.xlsx delete mode 100644 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z&aaNre@*UT%r!{rCD2l7)(GE780q8MN#vi{zM_1H@-y}?v40f(BI*xb(K0`6<0n>|eYMGWNewe^6c!1uE(f5FyHksGrC21;QYXzvB2* z=cVHKS~^EU>>nXQ>>p$Q82iWK**|VtVYiC3PmxpE?2MT+sHeL1>N>$C<{+&&Yr`VY ze^L?pO5^-R7saM_r3zpfEcy2?w>(OL0dyKtQeZk(Qx1FWQJMfn{2 z$Ku+)CQh6Lr>Z8i7WhBS>5>1vbmey=f6@=>(^+m%r(T_?-xg8p*2rT2zApPm;lOOB zPF*-K5ke{HCS|W5{nkr>s9)ufKhhs^B&?b4HH}W|oi182+fGzpRaeeJ49aHG~JAA~g$ z%Gh1;<^OP!h{IY@FG2XHD4n1l@%K!arbOwWPx^~O diff --git "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" index 5889bc7c..dbfc5277 100644 --- "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" +++ "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" @@ -27,10 +27,11 @@ dataAgent希望通过智能体本身的能力，对Agent数据进行**自采集 ### step1 建立智能体问题集 在dataAgent中，首先需要构建一个智能体问题集，以jsonl文件格式，其中每一行代表一次智能体调用的完整输入。 -如下所示，建立两条问题集，input对应智能体的query信息： +如下所示，建立3条问题集，input对应智能体的query信息： ```jsonl -{"input": "英伟达股票大涨的原因是什么？"} -{"input": "贵州茅台为什么不行了？"} +{"input": "帮我预测下2024欧洲杯冠军"} +{"input": "半导体还能继续持有吗"} +{"input": "北京今天天气如何"} ``` ### step2 配置dataAgent 配置dataAgent智能体，如下所示，配置文件除智能体基本配置外，主要包含两个重要项，`dataset_builder`对应生产评测数据集的智能体名称，`dataset_evaluator`对应数据集评估标注的智能体名称。 @@ -109,7 +110,7 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 ![data_agent_dataset](../_picture/data_agent_dataset.png) -[dataAgent生产的评测数据集样例地址](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl) +[dataAgent生产的评测数据集样例地址](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl) ### 完整评测结果 生产评测数据集后，dataAgent开始数据多维度评估标注，产出完整评测结果（若执行多轮dataAgent跑批任务，则产出多个完整评测结果）。 @@ -124,7 +125,11 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 - 更多维度 Score/Suggestion 以此类推 ![data_agent_eval_result](../_picture/data_agent_eval_result.png) -[dataAgent生产的完整评测结果样例地址](../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-09-20:46:11.xlsx) +评估节点发现的问题： + - 例如第1条数据在**相关性维度的suggestion**: 虽然回答了关于北京天气的问题，但提供的温度单位为华氏度，与国内用户习惯的摄氏度不符，建议转换为摄氏度并提供更全面的天气信息，如湿度、风力等。 + - 例如第3条数据在**事实性维度的suggestion**: 回答中包含事实错误，如将阿根廷球星莱昂内尔·梅西错误地归入英格兰队。优化建议是确保所有提及的数据和事实准确无误，尤其是在涉及具体人物和事件时。 + +[dataAgent生产的完整评测结果样例地址](../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10:48:30.xlsx) ### 综合评测报告 根据多轮完整的评测结果，生成一份综合评测报告。 @@ -136,12 +141,17 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 - 更多维度 Avg Score 以此类推 ![data_agent_eval_report](../_picture/data_agent_eval_report.png) -[dataAgent生产的综合评测报告样例地址](../../../sample_standard_app/app/examples/data/eval_report_2024-07-09-20:46:11.xlsx) +[dataAgent生产的综合评测报告样例地址](../../../sample_standard_app/app/examples/data/eval_report_2024-07-10-10:48:30.xlsx) ### 对比实验 -调整aU中`demo_rag_agent`中的模型从上文生产评测报告时的**qwen1.5-72b-chat**改为**qwen1.5-7b-chat**，通过dataAgent评测后，生成的综合评测报告如下: -![data_agent_eval_report](../_picture/data_agent_eval_report_qwen7b.png) -对比两次综合评测报告，可以看到各维度多轮评分均存在大幅度下降，以此方法快速分辨模型变更后，智能体效果差异。 +调整aU中`demo_rag_agent`中的模型从上文生产评测报告时的**qwen1.5-7b-chat**改为**qwen1.5-72b-chat**，通过dataAgent评测后，生成的综合评测报告如下: + +下图为模型为qwen1.5-7b-chat时，数据自治智能体产出的综合评测报告: +![data_agent_eval_report](../_picture/data_agent_eval_report.png) + +下图为模型为qwen1.5-72b-chat时，数据自治智能体产出的综合评测报告: +![data_agent_eval_report](../_picture/data_agent_eval_report_qwen72b.png) +对比两次综合评测报告，可以看到智能体模型从qwen1.5-7b-chat改为qwen1.5-72b-chat后，各维度多轮评分均存在大幅度上升，以此方法快速分辨智能体配置变更后，智能体效果差异。 ## dataAgent详细说明 @@ -153,7 +163,6 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 - [配置文件](../../../sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml) - [智能体文件](../../../sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py) - dataset_build_agent生产的评测数据以jsonl格式本地存储（jsonl文件名称为dataset_turn_{i}_{date}，`i`代表轮次，`date`代表生成时间） -- 数据治理MVP版本中，dataset_build_agent不包含智能体自适应数据采集，当前实际为"**伪智能体**"形态 ### dataset_eval_agent - [配置文件](../../../sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml) diff --git a/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py index 05cb864d..0f764e10 100644 --- a/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py +++ b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py @@ -282,7 +282,6 @@ def generate_eval_results_excel(query_answer_list: List[List[Tuple[str, str]]], eval_dims_json_list: List[List[dict]], date: str): """Generate evaluation results in excel format.""" - rows = [] columns: List[str] = ['Line Number', 'Overall Score', 'Query', 'Answer'] if len(eval_dims_json_list) > 0 and len(eval_dims_json_list[0]) > 0: one_row_eval_result = eval_dims_json_list[0][0] @@ -295,7 +294,7 @@ def generate_eval_results_excel(query_answer_list: List[List[Tuple[str, str]]], for i in range(len(eval_dims_json_list)): one_turn_eval_results = eval_dims_json_list[i] one_turn_query_answers = query_answer_list[i] - + rows = [] # write for each turn for j in range(len(one_turn_eval_results)): one_row_eval_result = one_turn_eval_results[j] diff --git a/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml b/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml index b12153b0..cda9228f 100644 --- a/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml +++ b/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml @@ -10,8 +10,6 @@ instruction: | 6. 不说模糊的推测。 7. 尽量多的使用数值类信息。 - 今天的日期是: {date} - 背景信息是: {background} diff --git a/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml b/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml index 69057613..c3b96de8 100644 --- a/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml +++ b/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml @@ -11,8 +11,6 @@ instruction: | 7. Try to use numerical information as much as possible. 8. The answer should not exceed 500 words - Today's date is: {date} - Background is: {background} diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl deleted file mode 100644 index 01f8afe8..00000000 --- a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl +++ /dev/null @@ -1,2 +0,0 @@ -{"query": "特斯拉股票为什么波动很大？", "answer": "特斯拉的股票波动大，主要受以下几个因素影响：\n\n1. **市场情绪**：特斯拉是一家备受关注的电动汽车和清洁能源公司，其创始人埃隆·马斯克的个人影响力和公司的创新形象使得市场对其期望值非常高。任何有关特斯拉的新产品发布、技术创新、产量数据或财务报告都可能引起投资者的强烈反应，导致股价大幅波动。\n\n2. **生产与交付数据**：特斯拉的季度生产与交付报告是影响其股价的重要因素。如果实际数据低于市场预期，股价可能会下跌；反之，超出预期的数据则可能导致股价上涨。\n\n3. **财务状况**：特斯拉的财务健康状况，如盈利、现金流和债务水平，也是影响股价的关键因素。例如，特斯拉在实现连续盈利之前，曾面临过多次财务压力，这直接影响了投资者的信心和股价表现。\n\n4. **竞争环境**：随着全球电动汽车市场的竞争加剧，特斯拉面临的竞争对手越来越多，包括传统汽车制造商和新兴电动车企。市场竞争的动态变化会影响特斯拉的市场份额和增长前景，从而影响股价。\n\n5. **政策与法规**：政府对新能源汽车的补贴政策、排放标准和贸易政策的变化也可能对特斯拉的业务产生重大影响，进而影响股价。\n\n6. **供应链问题**：特斯拉的供应链稳定性对生产计划至关重要。任何供应链中断，如芯片短缺或原材料价格上涨，都可能影响生产成本和交付能力，从而影响股价。\n\n7. **技术进步与风险**：特斯拉在自动驾驶、电池技术等方面的持续研发和创新是其核心竞争力之一。然而，技术进步的速度和成果的不确定性也可能带来股价波动。\n\n8. **创始人与管理层**：埃隆·马斯克作为特斯拉的灵魂人物，其言行举止、社交媒体活动甚至个人生活事件都可能成为市场关注的焦点，有时会引发股价的剧烈波动。\n\n综上所述，特斯拉的股价波动大是由于其业务特性、市场定位、财务状况、竞争环境、政策影响、供应链稳定性、技术进步和管理层等多个复杂因素共同作用的结果。"} -{"query": "英伟达股票大涨的原因是什么？", "answer": "在提供的背景信息中，并没有提及英伟达股票大涨的具体原因。但是，我可以结合当前的市场情况和英伟达的业务动态来分析可能的原因：\n\n1. **人工智能和数据中心需求增长**：近年来，随着人工智能技术的发展，对高性能计算的需求激增。英伟达作为GPU（图形处理器）领域的领导者，其产品在深度学习、机器学习等领域得到广泛应用，推动了数据中心市场的强劲需求，这可能是其股价上涨的一个重要因素。\n\n2. **游戏业务稳定**：虽然游戏市场存在周期性波动，但英伟达凭借其强大的GPU技术和市场地位，能够持续吸引游戏玩家和PC制造商，确保游戏业务的稳定收入，为股价提供支撑。\n\n3. **自动驾驶技术进展**：英伟达在自动驾驶领域的投入和进展也为其股价带来积极影响。随着自动驾驶技术的成熟和商业化应用的推进，英伟达的Drive平台有望成为该领域的重要参与者，增加公司的未来增长潜力。\n\n4. **财务表现优异**：英伟达的财务表现通常非常稳健，连续几个季度的收入和利润增长超出市场预期，增强了投资者的信心，推动股价上涨。\n\n5. **行业地位和品牌影响力**：作为全球领先的半导体公司之一，英伟达在GPU、AI芯片等多个领域的领先地位和品牌影响力，使其成为投资者眼中的优质资产，有助于维持股价的高位运行。\n\n请注意，股市波动受多种因素影响，包括宏观经济环境、行业趋势、公司业绩、市场情绪等，以上分析仅基于当前可获得的信息，实际情况可能有所不同。"} diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl new file mode 100644 index 00000000..21289684 --- /dev/null +++ b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl @@ -0,0 +1,3 @@ +{"query": "北京今天天气如何", "answer": "北京今天气温为86°F，属于高温天气，建议外出携带防晒用品，并注意补充水分。"} +{"query": "半导体还能继续持有吗", "answer": "关于这个问题，目前很难给出明确的答案。一方面，中国半导体产业的发展仍然面临诸多挑战，例如美国的制裁、需求下降、行业波动等等。另一方面，随着人工智能、新能源车等新兴领域的需求增长，中国的半导体产业也有很大的发展空间。\n因此，建议投资者在考虑是否持有半导体相关股票时，不仅要关注当前的市场情况，也要考虑到长期的产业发展趋势。同时，也需要密切关注政策动态以及国内外市场的变化，以便做出更明智的投资决策。"} +{"query": "帮我预测下2024欧洲杯冠军", "answer": "根据我的分析，以下是我对2024年欧洲杯冠军的预测：\n1. 西班牙：虽然他们的表现有所下滑，但西班牙仍然是一个强大的对手，他们有着丰富的国际比赛经验，而且他们的进攻线实力强大。\n2. 英格兰：虽然他们在最近几年的表现有些起伏不定，但他们拥有世界级的球员如哈里·凯恩和莱昂内尔·梅西，如果他们能够发挥出最佳状态，那么他们有可能赢得冠军。\n3. 法国：法国队在2018年的世界杯中赢得了冠军，这表明他们具有强大的实力。虽然姆巴佩和博格巴等球员可能会缺席比赛，但其他球员如基利安·姆巴佩和恩戈洛·坎特仍然有能力带领法国队走向胜利。\n总的来说，我认为这四支球队都有可能赢得2024年欧洲杯冠军，最终的结果取决于他们在比赛中的表现和运气。"} 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doc. --- .../_picture/data_agent_dataset_en.png | Bin 0 -> 157225 bytes .../_picture/data_agent_eval_report_en.png | Bin 81302 -> 132735 bytes .../data_agent_eval_report_en_qwen7b.png | Bin 0 -> 138859 bytes .../_picture/data_agent_eval_result_en.png | Bin 101822 -> 115659 bytes .../en/8_1_1_data_autonomous_agent.md | 169 ++++++++++++++++++ .../en/8_1_1_data_governance_agent.md | 47 ----- .../core/prompt/dataset_eval_agent_en.yaml | 9 +- .../dataset_turn_1_2024-07-09-11:45:42.jsonl | 2 - .../dataset_turn_1_2024-07-10-15:06:24.jsonl | 3 + .../data/eval_report_2024-07-09-11:45:42.xlsx | Bin 5235 -> 0 bytes .../data/eval_report_2024-07-10-15:06:24.xlsx | Bin 0 -> 8925 bytes ...val_result_turn_1_2024-07-09-11:45:42.xlsx | Bin 8192 -> 0 bytes ...val_result_turn_1_2024-07-10-15:06:24.xlsx | Bin 0 -> 13574 bytes .../app/examples/data/queryset_en.jsonl | 5 +- 14 files changed, 180 insertions(+), 55 deletions(-) create mode 100644 docs/guidebook/_picture/data_agent_dataset_en.png create mode 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b/sample_standard_app/app/examples/data/queryset_en.jsonl index 320d7bdf..dfb39bc8 100644 --- a/sample_standard_app/app/examples/data/queryset_en.jsonl +++ b/sample_standard_app/app/examples/data/queryset_en.jsonl @@ -1,2 +1,3 @@ -{"input": "What is the reason for the surge in Nvidia's stock?"} -{"input": "Why does Tesla stock fluctuate so much?"} \ No newline at end of file +{"input": "Help me predict the 2024 European Cup championship"} +{"input": "Can Tesla's stock still be bought?"} +{"input": "What's the weather in New York today?"} \ No newline at end of file From eab2feafcf22708e10b0c23e19978bf830782721 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 10 Jul 2024 16:05:43 +0800 Subject: [PATCH 108/118] feat: add the data agent English doc. --- .../_picture/data_agent_eval_result_en.png | Bin 115659 -> 127846 bytes .../en/8_1_1_data_autonomous_agent.md | 24 +++++++++--------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git 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zrir>gc@g0=!DA1&{Lk3%LzUj`^Y@z z-kuG&db^X@FUoAyy8==RPp@fCEv8R?AW%+=7YhRv z@HzC^rC9%VID9zTdPY=Ai@Vi1bPb{wrH z7`c}}yt}-Q3K`mI!o`~#qgfv^f}307t_)iL zz|K3D26+QsEc5M&e7%3O9BRuJJP`i|37eC$sY62Y`3M=ueSrTu;`5>!rdHT;t+*&l zk<@VQSBk|qLd0;X>t{8uM>CH^o91NJJQlzQx|U@GS8a9gU@y{vc#FOukFLUn1Hcruz)2h9hp)zI8tn7c|?D9b^xEp+~LUuXb ztRP@{-pD<|liB8C`&4vp`G5~5HV=dRs#x4;eDBmy{O$WU7l$1S~g52RE;Xlo)TN94mNIf`R*2*cP<{cnE z?@!PU`PT$ZYIEIbZHubB1X;{%VOtYI?YA@Gb1?6X&rfc^SL`TQK`Q@J(I2QnR&)xu z+$+lNc}eB}ww$8Af2mbpZeSnv=qn4pjQnTLMgFCKpqSfc(ohM0g;c^jL^e+_c!!U^E%S%LMq&z=m&6AZ3 z$3NM7ug6psKvcEweP;alUyp%ajN2K{q+ueGW5~XvgvQ*E|;+G?U;$ZSp#z z)P6!G6&G+JeViC#w|r)F|I=@GeOSxcGV3xE_c8&%ZGYmqo!AqR6ftEi4;@6;{QQJE z_QDX{rbaxNV`tmu=UUN8`2D{wxl)?RK{MEh0SqABJZoum Date: Thu, 11 Jul 2024 09:30:27 +0800 Subject: [PATCH 109/118] tool support agent --- agentuniverse/agent/plan/planner/planner.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/agentuniverse/agent/plan/planner/planner.py b/agentuniverse/agent/plan/planner/planner.py index 2f965da4..09b4e8eb 100644 --- a/agentuniverse/agent/plan/planner/planner.py +++ b/agentuniverse/agent/plan/planner/planner.py @@ -16,6 +16,8 @@ from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query from agentuniverse.agent.action.tool.tool_manager import ToolManager +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.memory.chat_memory import ChatMemory @@ -102,6 +104,7 @@ def run_all_actions(self, agent_model: AgentModel, planner_input: dict, input_ob action: dict = agent_model.action or dict() tools: list = action.get('tool') or list() knowledge: list = action.get('knowledge') or list() + agents: list = action.get('agent') or list() action_result: list = list() @@ -121,6 +124,14 @@ def run_all_actions(self, agent_model: AgentModel, planner_input: dict, input_ob for document in knowledge_res: action_result.append(document.text) + for agent_name in agents: + agent: Agent = AgentManager().get_instance_obj(agent_name) + if agent is None: + continue + agent_input = {key: input_object.get_data(key) for key in agent.input_keys()} + output_object = agent.run(**agent_input) + action_result.append("\n".join(output_object.get_data(key) for key in agent.output_keys())) + planner_input['background'] = planner_input['background'] or '' + "\n".join(action_result) def handle_prompt(self, agent_model: AgentModel, planner_input: dict): @@ -169,7 +180,7 @@ def stream_output(input_object: InputObject, data: dict): input_object (InputObject): Agent input object. data (dict): The data to be streamed. """ - output_stream:Queue = input_object.get_data('output_stream', None) + output_stream: Queue = input_object.get_data('output_stream', None) if output_stream is None: return output_stream.put_nowait(data) From 7861c42929b3a8e669366f5e73cd26d778f570fa Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 11 Jul 2024 09:56:03 +0800 Subject: [PATCH 110/118] tool support agent --- agentuniverse/agent/plan/planner/planner.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/agentuniverse/agent/plan/planner/planner.py b/agentuniverse/agent/plan/planner/planner.py index 09b4e8eb..062bfbcf 100644 --- a/agentuniverse/agent/plan/planner/planner.py +++ b/agentuniverse/agent/plan/planner/planner.py @@ -130,7 +130,9 @@ def run_all_actions(self, agent_model: AgentModel, planner_input: dict, input_ob continue agent_input = {key: input_object.get_data(key) for key in agent.input_keys()} output_object = agent.run(**agent_input) - action_result.append("\n".join(output_object.get_data(key) for key in agent.output_keys())) + action_result.append("\n".join([output_object.get_data(key) + for key in agent.output_keys() + if output_object.get_data(key) is not None])) planner_input['background'] = planner_input['background'] or '' + "\n".join(action_result) From 51be5eda78b73d78f7d3072147d29a2f762fa1b1 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 11 Jul 2024 10:02:09 +0800 Subject: [PATCH 111/118] tool support agent --- agentuniverse/agent/plan/planner/peer_planner/peer_planner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py index 5352bcb6..f15e6f83 100644 --- a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py +++ b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py @@ -159,7 +159,7 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu logger_info = f"\nExpressing agent execution result is :\n" logger_info += f"{expressing_result.get_data('output')}" LOGGER.info(logger_info) - self.stream_output(input_object, {"data": executing_result.to_dict(), "type": "expressing"}) + self.stream_output(input_object, {"data": expressing_result.to_dict(), "type": "expressing"}) if not reviewing_result or jump_step in ["planning", "executing", "expressing", "reviewing"]: if not reviewingAgent: From ab249f8a62cd1dd997918072269aef7f445450e1 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 11 Jul 2024 11:23:49 +0800 Subject: [PATCH 112/118] fix stream --- .../expressing_planner/expressing_planner.py | 4 +- .../plan/planner/peer_planner/peer_planner.py | 20 +++++++--- agentuniverse/agent/plan/planner/planner.py | 29 ++++++++++++--- .../plan/planner/rag_planner/rag_planner.py | 24 +----------- .../planner/react_planner/react_planner.py | 6 +-- .../planner/react_planner/stream_callback.py | 37 ++++++++++++++++--- agentuniverse/llm/openai_style_llm.py | 1 - 7 files changed, 78 insertions(+), 43 deletions(-) diff --git a/agentuniverse/agent/plan/planner/expressing_planner/expressing_planner.py b/agentuniverse/agent/plan/planner/expressing_planner/expressing_planner.py index 928b723d..6c944d43 100644 --- a/agentuniverse/agent/plan/planner/expressing_planner/expressing_planner.py +++ b/agentuniverse/agent/plan/planner/expressing_planner/expressing_planner.py @@ -51,8 +51,8 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: Inp history_messages_key="chat_history", input_messages_key=self.input_key, ) | StrOutputParser() - res = asyncio.run( - chain_with_history.ainvoke(input=planner_input, config={"configurable": {"session_id": "unused"}})) + + res = self.invoke_chain(agent_model, chain_with_history, planner_input, chat_history, input_object) return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt: diff --git a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py index f15e6f83..c8de55bb 100644 --- a/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py +++ b/agentuniverse/agent/plan/planner/peer_planner/peer_planner.py @@ -42,7 +42,7 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: Inp """ planner_config = agent_model.plan.get('planner') sub_agents = self.generate_sub_agents(planner_config) - return self.agents_run(sub_agents, planner_config, planner_input, input_object) + return self.agents_run(agent_model, sub_agents, planner_config, planner_input, input_object) @staticmethod def generate_sub_agents(planner_config: dict) -> dict: @@ -79,7 +79,8 @@ def build_expert_framework(planner_config: dict, input_object: InputObject): elif context: input_object.add_data('expert_framework', context) - def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, input_object: InputObject) -> dict: + def agents_run(self, agent_mode: AgentModel, agents: dict, planner_config: dict, agent_input: dict, + input_object: InputObject) -> dict: """Planner agents run. Args: @@ -125,7 +126,10 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu for index, one_framework in enumerate(planning_result.get_data('framework')): logger_info += f"[{index + 1}] {one_framework} \n" LOGGER.info(logger_info) - self.stream_output(input_object, {"data": planning_result.to_dict(), "type": "planning"}) + self.stream_output(input_object, {"data": { + 'output': planning_result.to_dict(), + "agent_info": agent_mode.info + }, "type": "planning"}) if not executing_result or jump_step in ["planning", "executing"]: if not executingAgent: @@ -144,7 +148,10 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu one_exec_log_info += f"[{index + 1}] output: {one_exec_res['output']}\n" logger_info += one_exec_log_info LOGGER.info(logger_info) - self.stream_output(input_object, {"data": executing_result.to_dict(), "type": "executing"}) + self.stream_output(input_object, {"data": { + 'output': executing_result.to_dict(), + "agent_info": agent_mode.info + }, "type": "executing"}) if not expressing_result or jump_step in ["planning", "executing", "expressing"]: if not expressingAgent: @@ -159,7 +166,10 @@ def agents_run(self, agents: dict, planner_config: dict, agent_input: dict, inpu logger_info = f"\nExpressing agent execution result is :\n" logger_info += f"{expressing_result.get_data('output')}" LOGGER.info(logger_info) - self.stream_output(input_object, {"data": expressing_result.to_dict(), "type": "expressing"}) + self.stream_output(input_object, {"data": { + 'output': expressing_result.get_data('output'), + "agent_info": agent_mode.info + }, "type": "expressing"}) if not reviewing_result or jump_step in ["planning", "executing", "expressing", "reviewing"]: if not reviewingAgent: diff --git a/agentuniverse/agent/plan/planner/planner.py b/agentuniverse/agent/plan/planner/planner.py index 0c4827f3..4cf36d70 100644 --- a/agentuniverse/agent/plan/planner/planner.py +++ b/agentuniverse/agent/plan/planner/planner.py @@ -6,17 +6,17 @@ # @FileName: planner.py """Base class for Planner.""" from abc import abstractmethod -import copy import logging from queue import Queue -from typing import Optional, List +from typing import Optional, List, Any + +from langchain_core.runnables import RunnableSerializable from agentuniverse.agent.action.knowledge.knowledge import Knowledge from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query from agentuniverse.agent.action.tool.tool_manager import ToolManager -from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject @@ -30,7 +30,7 @@ from agentuniverse.llm.llm import LLM from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt import Prompt -from agentuniverse.base.util.memory_util import generate_messages +from agentuniverse.base.util.memory_util import generate_messages, generate_memories logging.getLogger().setLevel(logging.ERROR) @@ -124,7 +124,7 @@ def run_all_actions(self, agent_model: AgentModel, planner_input: dict, input_ob action_result.append(document.text) for agent_name in agents: - agent: Agent = AgentManager().get_instance_obj(agent_name) + agent = AgentManager().get_instance_obj(agent_name) if agent is None: continue agent_input = {key: input_object.get_data(key) for key in agent.input_keys()} @@ -184,3 +184,22 @@ def stream_output(input_object: InputObject, data: dict): if output_stream is None: return output_stream.put_nowait(data) + + def invoke_chain(self, agent_model: AgentModel, chain: RunnableSerializable[Any, str], planner_input: dict, chat_history, + input_object: InputObject): + + if not input_object.get_data('output_stream'): + res = chain.invoke(input=planner_input, config={"configurable": {"session_id": "unused"}}) + return res + result = [] + for token in chain.stream(input=planner_input, config={"configurable": {"session_id": "unused"}}): + self.stream_output(input_object, { + 'type': 'token', + 'data': { + 'chunk': token, + 'agent_info': agent_model.info + } + }) + result.append(token) + return "".join(result) + diff --git a/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py b/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py index c731038e..984128d6 100644 --- a/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py +++ b/agentuniverse/agent/plan/planner/rag_planner/rag_planner.py @@ -5,11 +5,9 @@ # @Email : lc299034@antgroup.com # @FileName: rag_planner.py """Rag planner module.""" -from typing import Any from langchain_core.chat_history import InMemoryChatMessageHistory from langchain_core.output_parsers import StrOutputParser -from langchain_core.runnables import RunnableSerializable from langchain_core.runnables.history import RunnableWithMessageHistory from agentuniverse.agent.agent_model import AgentModel @@ -56,26 +54,8 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, history_messages_key="chat_history", input_messages_key=self.input_key, ) | StrOutputParser() - - if not input_object.get_data('output_stream'): - res = chain_with_history.invoke(input=planner_input, config={"configurable": {"session_id": "unused"}}) - return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} - else: - return self.stream(agent_model, chain_with_history, planner_input, chat_history, input_object) - - def stream(self, agent_model: AgentModel, chain: RunnableSerializable[Any, str], planner_input: dict, chat_history, - input_object: InputObject): - result = [] - for token in chain.stream(input=planner_input, config={"configurable": {"session_id": "unused"}}): - self.stream_output(input_object, { - 'type': 'token', - 'data': { - 'token': token, - 'agent_info': agent_model.info - } - }) - result.append(token) - return {**planner_input, self.output_key: ''.join(result), 'chat_history': generate_memories(chat_history)} + res = self.invoke_chain(agent_model, chain_with_history, planner_input, chat_history, input_object) + return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> ChatPrompt: """Prompt module processing. diff --git a/agentuniverse/agent/plan/planner/react_planner/react_planner.py b/agentuniverse/agent/plan/planner/react_planner/react_planner.py index b3f20211..9381b6b5 100644 --- a/agentuniverse/agent/plan/planner/react_planner/react_planner.py +++ b/agentuniverse/agent/plan/planner/react_planner/react_planner.py @@ -64,14 +64,14 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, max_iterations=agent_model.plan.get('planner').get("max_iterations", 15)) return agent_executor.invoke(input=planner_input, memory=memory.as_langchain() if memory else None, - chat_history=chat_history, config=self.get_run_config(input_object)) + chat_history=chat_history, config=self.get_run_config(agent_model, input_object)) @staticmethod - def get_run_config(input_object: InputObject) -> RunnableConfig: + def get_run_config(agent_model: AgentModel, input_object: InputObject) -> RunnableConfig: config = RunnableConfig() callbacks = [] output_stream = input_object.get_data('output_stream') - callbacks.append(StreamOutPutCallbackHandler(output_stream)) + callbacks.append(StreamOutPutCallbackHandler(output_stream, agent_info=agent_model.info)) config.setdefault("callbacks", callbacks) return config diff --git a/agentuniverse/agent/plan/planner/react_planner/stream_callback.py b/agentuniverse/agent/plan/planner/react_planner/stream_callback.py index 87ec6acd..e96f9519 100644 --- a/agentuniverse/agent/plan/planner/react_planner/stream_callback.py +++ b/agentuniverse/agent/plan/planner/react_planner/stream_callback.py @@ -16,10 +16,13 @@ class StreamOutPutCallbackHandler(BaseCallbackHandler): """Callback Handler that prints to std out.""" - def __init__(self, queue_stream: asyncio.Queue, color: Optional[str] = None) -> None: + def __init__(self, queue_stream: asyncio.Queue, color: Optional[str] = None, agent_info: dict = None) -> None: """Initialize callback handler.""" self.queueStream = queue_stream self.color = color + if agent_info is None: + agent_info = {} + self.agent_info = agent_info def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any @@ -32,7 +35,13 @@ def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any ) -> Any: - self.queueStream.put_nowait("Thought:"+action.log) + self.queueStream.put_nowait({ + "type": "ReAct", + "data": { + "output": "\nThought:" + action.log, + "agent_info": self.agent_info + } + }) def on_tool_end( self, @@ -44,9 +53,21 @@ def on_tool_end( ) -> None: """If not the final action, print out observation.""" if observation_prefix is not None: - self.queueStream.put_nowait(observation_prefix + output) + self.queueStream.put_nowait({ + " type": "ReAct", + "data": { + "output": '\n' + observation_prefix + output, + "agent_info": self.agent_info + } + }) else: - self.queueStream.put_nowait('Observation:'+output) + self.queueStream.put_nowait({ + " type": "ReAct", + "data": { + "output": '\n Observation:' + output, + "agent_info": self.agent_info + } + }) def on_text( self, @@ -61,4 +82,10 @@ def on_agent_finish( self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any ) -> None: """Run on agent end.""" - self.queueStream.put_nowait("Thought:" + finish.log + "\n") + self.queueStream.put_nowait({ + " type": "ReAct", + "data": { + "output": '\nThought:' + finish.output, + "agent_info": self.agent_info + } + }) diff --git a/agentuniverse/llm/openai_style_llm.py b/agentuniverse/llm/openai_style_llm.py index 4023802a..bc7deb2d 100644 --- a/agentuniverse/llm/openai_style_llm.py +++ b/agentuniverse/llm/openai_style_llm.py @@ -207,4 +207,3 @@ def max_context_length(self) -> int: """Return the maximum length of the context.""" if super().max_context_length(): return super().max_context_length() - return 4000 From d71c57ed905413af5daed84cf8939d4499dbe303 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 11 Jul 2024 11:31:09 +0800 Subject: [PATCH 113/118] add review,planning stream --- .../agent/plan/planner/planning_planner/planning_planner.py | 6 ++++-- .../plan/planner/reviewing_planner/reviewing_planner.py | 3 +-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py b/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py index 1ca05a0e..4cef8b56 100644 --- a/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py +++ b/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py @@ -52,9 +52,11 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, history_messages_key="chat_history", input_messages_key=self.input_key, ) | StrOutputParser() - res = asyncio.run( - chain_with_history.ainvoke(input=planner_input, config={"configurable": {"session_id": "unused"}})) + # res = asyncio.run( + # chain_with_history.ainvoke(input=planner_input, config={"configurable": {"session_id": "unused"}})) + res = self.invoke_chain(agent_model, chain_with_history, planner_input, chat_history, input_object) return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} + # return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt: """Prompt module processing. diff --git a/agentuniverse/agent/plan/planner/reviewing_planner/reviewing_planner.py b/agentuniverse/agent/plan/planner/reviewing_planner/reviewing_planner.py index 8702bee7..5f304fb2 100644 --- a/agentuniverse/agent/plan/planner/reviewing_planner/reviewing_planner.py +++ b/agentuniverse/agent/plan/planner/reviewing_planner/reviewing_planner.py @@ -51,8 +51,7 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: Inp history_messages_key="chat_history", input_messages_key=self.input_key, ) | StrOutputParser() - res = asyncio.run( - chain_with_history.ainvoke(input=planner_input, config={"configurable": {"session_id": "unused"}})) + res = self.invoke_chain(agent_model, chain_with_history, planner_input, chat_history, input_object) return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt: From 8bcf3b1dd67ad1423d0b4f4b13d1786c47db93d2 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 11 Jul 2024 11:33:45 +0800 Subject: [PATCH 114/118] remove --- .../agent/plan/planner/planning_planner/planning_planner.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py b/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py index 4cef8b56..d0af3ac4 100644 --- a/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py +++ b/agentuniverse/agent/plan/planner/planning_planner/planning_planner.py @@ -52,11 +52,8 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, history_messages_key="chat_history", input_messages_key=self.input_key, ) | StrOutputParser() - # res = asyncio.run( - # chain_with_history.ainvoke(input=planner_input, config={"configurable": {"session_id": "unused"}})) res = self.invoke_chain(agent_model, chain_with_history, planner_input, chat_history, input_object) return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} - # return {**planner_input, self.output_key: res, 'chat_history': generate_memories(chat_history)} def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt: """Prompt module processing. From 3ec6e25c16af6e55a841567f4e9c0d593a43c939 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Thu, 11 Jul 2024 11:35:09 +0800 Subject: [PATCH 115/118] fix type --- .../agent/plan/planner/react_planner/stream_callback.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/agentuniverse/agent/plan/planner/react_planner/stream_callback.py b/agentuniverse/agent/plan/planner/react_planner/stream_callback.py index e96f9519..e417bb3d 100644 --- a/agentuniverse/agent/plan/planner/react_planner/stream_callback.py +++ b/agentuniverse/agent/plan/planner/react_planner/stream_callback.py @@ -54,7 +54,7 @@ def on_tool_end( """If not the final action, print out observation.""" if observation_prefix is not None: self.queueStream.put_nowait({ - " type": "ReAct", + "type": "ReAct", "data": { "output": '\n' + observation_prefix + output, "agent_info": self.agent_info @@ -62,7 +62,7 @@ def on_tool_end( }) else: self.queueStream.put_nowait({ - " type": "ReAct", + "type": "ReAct", "data": { "output": '\n Observation:' + output, "agent_info": self.agent_info @@ -83,7 +83,7 @@ def on_agent_finish( ) -> None: """Run on agent end.""" self.queueStream.put_nowait({ - " type": "ReAct", + "type": "ReAct", "data": { "output": '\nThought:' + finish.output, "agent_info": self.agent_info From 5a05949c16f62c15f0a21958b0d7159b0b145422 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Thu, 11 Jul 2024 19:48:35 +0800 Subject: [PATCH 116/118] docs: update README & modify index of guidebook --- README.md | 31 +++++++++++++--- README_zh.md | 30 ++++++++++++++++ docs/guidebook/_picture/wechat_official.png | Bin 0 -> 33242 bytes docs/guidebook/en/0_index.md | 7 +++- docs/guidebook/en/6_1_Contact_Us.md | 21 ++++++----- docs/guidebook/en/9_1_Citation.md | 17 +++++++++ .../zh/0_\347\233\256\345\275\225.md" | 7 +++- ...24\347\263\273\346\210\221\344\273\254.md" | 33 +++++++++++++----- ...24\347\251\266\346\226\207\347\214\256.md" | 17 +++++++++ 9 files changed, 138 insertions(+), 25 deletions(-) create mode 100644 docs/guidebook/_picture/wechat_official.png create mode 100644 docs/guidebook/en/9_1_Citation.md create mode 100644 "docs/guidebook/zh/9_1_\347\240\224\347\251\266\346\226\207\347\214\256.md" diff --git a/README.md b/README.md index 23299e7b..d576ef36 100644 --- a/README.md +++ b/README.md @@ -35,6 +35,7 @@ More patterns are coming soon... * [User Guide](#User-Guide) * [API Reference](#API-Reference) * [Support](#Support) + * [Citation](#Citation) * [Acknowledgements](#Acknowledgements) **************************************** ## Quick Start @@ -64,6 +65,11 @@ For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_St [Financial Event Analysis Based on PEER Multi-Agent Mode](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) +[Andrew Ng's Reflexive Workflow Translation Agent Replication](./docs/guidebook/en/7_1_1_Translation_Case.md) + +#### 🚩 DataAgent - Data Autonomous Agent +agentUniverse has launched DataAgent (Minimum Viable Product Version). DataAgent aims to empower your agent with the capability of self-assessment and evolution through the use of intelligent agent abilities. For more details, please refer to the documentation. [DataAgent - Data Autonomous Agent](./docs/guidebook/en/8_1_1_data_autonomous_agent.md) + ### 🌟 Example Projects [agentUniverse Example Projects](sample_standard_app) @@ -98,11 +104,26 @@ For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_St #### Contact Us via Administrator Email 😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) -#### WeChat Official Account - -😊 Official Account ID: **agentUniverse智多星** - -You can get more related articles and information in our WeChat Official Account. +#### twitter +ID: [@agentuniverse_](https://x.com/agentuniverse_) + +### Citation +The agentUniverse project is supported by the following research achievements. + +BibTeX formatted +```text +@misc{wang2024peerexpertizingdomainspecifictasks, + title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, + author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu}, + year={2024}, + eprint={2407.06985}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2407.06985}, +} +``` +Overview: In the experimental section of this study, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI, and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved an 83% superior rate over BabyAGI using the GPT-3.5 turbo (16k) model, and an 81% superior rate using the GPT-4o model. For more details, please refer to the literature. +https://arxiv.org/pdf/2407.06985 ## Acknowledgements This project is partially built on excellent open-source projects such as langchain, pydantic, gunicorn, flask, SQLAlchemy, chromadb, etc. (The detailed dependency list can be found in pyproject.toml). We would like to extend special thanks to the related projects and contributors. 🙏🙏🙏 diff --git a/README_zh.md b/README_zh.md index bf096bbe..fbdddea9 100644 --- a/README_zh.md +++ b/README_zh.md @@ -34,6 +34,7 @@ * [用户指南](#用户指南) * [API参考](#API参考) * [支持](#支持) + * [文献](#文献) * [鸣谢](#鸣谢) **************************************** ## 快速开始 @@ -61,6 +62,12 @@ pip install agentUniverse [基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md) +[吴恩达反思工作流翻译智能体复刻](./docs/guidebook/zh/7_1_1_翻译案例.md) + + +#### 🚩 DataAgent - 数据自治智能体 +agentUniverse推出了DataAgent（Minimum Viable Product版本）, DataAgent旨在使用智能体能力让您的Agent拥有自我评价与演进的能力。详情见文档: [DataAgent - 数据自治智能体](./docs/guidebook/zh/8_1_1_数据自治智能体.md) + ### 🌟 示例项目 [agentUniverse 示例项目](sample_standard_app) @@ -105,7 +112,30 @@ pip install agentUniverse 😊 公众号ID：**agentUniverse智多星** +![](./docs/guidebook/_picture/wechat_official.png) + 更多相关的文章与资讯你可以在微信公众号中获取。 +#### twitter +ID: [@agentuniverse_](https://x.com/agentuniverse_) + +### 文献 +agentUniverse项目基于以下的研究成果支撑。 + +BibTeX formatted +```text +@misc{wang2024peerexpertizingdomainspecifictasks, + title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, + author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu}, + year={2024}, + eprint={2407.06985}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2407.06985}, +} +``` +文献简介：该文献在研究实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，**PEER模式在每个测评维度的平均分数均高于BabyAGI**，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。 +https://arxiv.org/pdf/2407.06985 + ## 鸣谢 本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏 diff --git a/docs/guidebook/_picture/wechat_official.png b/docs/guidebook/_picture/wechat_official.png new file mode 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z1^q^4jDd(w#CdA|3_#w6an(6G6m+5y#P<;ZAdA=4)_A_#J=O4yx#yD9{p=q6T1y5k zQQ=hMiFrdTZ3zJKl4~WCg2IWf2-Fi-#Wc;Lf6{qnG=HMNk_01~vp7OuoOQ6Xr4;FZDMV3;JiA!To* zYK+FM9_6U!xVflYDaRsgNP-grF~Kn4f?_-CypnV=@x+u$607Vo{M34HM$=#X&Xgpk z{W2Z$;w)qvkR~PpMNvIVDh_KjLz7s+yIb1x(^JsOEljF{eLrgj}rg@ literal 0 HcmV?d00001 diff --git a/docs/guidebook/en/0_index.md b/docs/guidebook/en/0_index.md index 432fd39d..b17701c7 100644 --- a/docs/guidebook/en/0_index.md +++ b/docs/guidebook/en/0_index.md @@ -43,6 +43,8 @@ * 2.4.4.1 [Logging Component](2_6_Logging_Utils.md) * 2.4.5 Data Collection * 2.4.5.1 [Monitor Module](2_5_1_Monitor_Module.md) + * 2.4.6 Data Autonomous + * 2.4.6.1 [Data Autonomous Agent](8_1_1_data_autonomous_agent.md) **3. Component Reference Manual** * 3.1 Domain Components @@ -89,9 +91,12 @@ * 6.3 [Discussion Group Based on Multi-Turn Multi-Agent Mode](6_2_1_Discussion_Group.md) * 6.4 PEER Multi-Agent Cooperation Examples * 6.4.1 [Financial Event Analysis Case](./6_4_1_Financial_Event_Analysis_Case.md) +* 6.5 [Andrew Ng's Reflexive Workflow Translation Agent Replication](./7_1_1_Translation_Case.md) **7. Series of Articles** **8. Frequently Asked Questions (FAQ)** -**[9. Contact Us](6_1_Contact_Us.md)** +**[9.Citation](9_1_Citation.md)** + +**[10. Contact Us](6_1_Contact_Us.md)** diff --git a/docs/guidebook/en/6_1_Contact_Us.md b/docs/guidebook/en/6_1_Contact_Us.md index c5a3cf33..64cbba0d 100644 --- a/docs/guidebook/en/6_1_Contact_Us.md +++ b/docs/guidebook/en/6_1_Contact_Us.md @@ -1,9 +1,12 @@ -# Contact Us -* github: https://github.com/alipay/agentUniverse -* gitee: https://gitee.com/agentUniverse/agentUniverse -* gitcode: https://gitcode.com/agentUniverse -* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ -* WeChat Official Account: agentUniverse智多星 -* DingTalk Group: -![](../_picture/dingtalk_util20250429.png) \ No newline at end of file +### Support +#### Submit Questions via GitHub Issues +😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days. + +#### Contact Us via Discord +😊 Join our [Discord Channel](https://discord.gg/DHFcdkWAhn) to interact with us. + +#### Contact Us via Administrator Email +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### twitter +ID: [@agentuniverse_](https://x.com/agentuniverse_) \ No newline at end of file diff --git a/docs/guidebook/en/9_1_Citation.md b/docs/guidebook/en/9_1_Citation.md new file mode 100644 index 00000000..3936b644 --- /dev/null +++ b/docs/guidebook/en/9_1_Citation.md @@ -0,0 +1,17 @@ +### Citation +The agentUniverse project is supported by the following research achievements. + +BibTeX formatted +```text +@misc{wang2024peerexpertizingdomainspecifictasks, + title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, + author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu}, + year={2024}, + eprint={2407.06985}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2407.06985}, +} +``` +Overview: In the experimental section of this study, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI, and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved an 83% superior rate over BabyAGI using the GPT-3.5 turbo (16k) model, and an 81% superior rate using the GPT-4o model. For more details, please refer to the literature. +https://arxiv.org/pdf/2407.06985 \ No newline at end of file diff --git "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" index 9dbf3c8b..fafa18a2 100644 --- "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" +++ "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" @@ -47,6 +47,8 @@ * 2.4.4.1 [日志组件](2_4_4_日志组件.md) * 2.4.5 数据采集 * 2.4.5.1 [monitor模块](2_5_1_监控模块.md) + * 2.4.6 数据自治 + * 2.4.6.1 [数据自治智能体](8_1_1_数据自治智能体.md) **3.组件参考手册** * 3.1 领域组件 @@ -93,9 +95,12 @@ * 6.3 [基于多轮多Agent的讨论小组](6_2_1_讨论组.md) * 6.4 PEER多Agent协作案例 * 6.4.1 [金融事件分析案例](./6_4_1_金融事件分析案例.md) +* 6.5 [吴恩达反思工作流翻译智能体复刻](./7_1_1_翻译案例.md) **7.系列文章** **8.常见问题FAQ** -**[9.联系我们](6_1_联系我们.md)** +**[9.研究文献](9_1_研究文献.md)** + +**[10.联系我们](6_1_联系我们.md)** diff --git "a/docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md" "b/docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md" index 86c11e9d..f15bbfc4 100644 --- "a/docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md" +++ "b/docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md" @@ -1,9 +1,24 @@ -# 联系我们 -* github: https://github.com/alipay/agentUniverse -* gitee: https://gitee.com/agentUniverse/agentUniverse -* gitcode: https://gitcode.com/agentUniverse -* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ -* 微信公众号: agentUniverse智多星 -* 钉钉交流群: -![](../_picture/dingtalk_util20250429.png) \ No newline at end of file +### 支持 +#### 通过github issue提交疑问 +😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 + +#### 通过Discord联系我们 +😊 加入我们的 [Discord频道](https://discord.gg/DHFcdkWAhn) 与我们进行交流。 + +#### 通过钉钉群联系我们 +😊 加入我们的钉钉答疑群与我们联系。 +![](../_picture/dingtalk_util20250429.png) + +#### 通过管理员Email联系我们 +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### 微信公众号 + +😊 公众号ID：**agentUniverse智多星** + +![](../_picture/wechat_official.png) + +更多相关的文章与资讯你可以在微信公众号中获取。 + +#### twitter +ID: [@agentuniverse_](https://x.com/agentuniverse_) \ No newline at end of file diff --git "a/docs/guidebook/zh/9_1_\347\240\224\347\251\266\346\226\207\347\214\256.md" "b/docs/guidebook/zh/9_1_\347\240\224\347\251\266\346\226\207\347\214\256.md" new file mode 100644 index 00000000..6b333775 --- /dev/null +++ "b/docs/guidebook/zh/9_1_\347\240\224\347\251\266\346\226\207\347\214\256.md" @@ -0,0 +1,17 @@ +### 文献 +agentUniverse项目基于以下的研究成果支撑。 + +BibTeX formatted +```text +@misc{wang2024peerexpertizingdomainspecifictasks, + title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, + author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu}, + year={2024}, + eprint={2407.06985}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2407.06985}, +} +``` +文献简介：该文献在研究实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，**PEER模式在每个测评维度的平均分数均高于BabyAGI**，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。 +https://arxiv.org/pdf/2407.06985 \ No newline at end of file From 0b993849e664db2c910493f9423f0467bec7c057 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Thu, 11 Jul 2024 20:17:58 +0800 Subject: [PATCH 117/118] feat: Version 0.0.11 Release --- CHANGELOG.md | 13 +++++++++++++ CHANGELOG_zh.md | 13 +++++++++++++ README.md | 4 ++-- README_zh.md | 4 ++-- pyproject.toml | 2 +- 5 files changed, 31 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6d663bf4..132b6b2f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -24,6 +24,19 @@ Note - Additional remarks regarding the version. *************************************************** # Version Update History +## [0.0.11] - 2024-07-11 +### Added +- DataAgent Autonomous Data Agent MVP Version Released + - Minimum Viable Product version, DataAgent aims to empower your agent with the capability of self-assessment and evolution through intelligent agent abilities. For detailed information, please refer to the user documentation. +- Added intermediate information streaming output capabilities in PEER and ReAct modes + +### Note +- Latest PEER research findings released + - This paper provides a detailed introduction to the mechanisms and principles of the PEER multi-agent framework. Experimental validation proves the advancement of the PEER model. For detailed information, please refer to the user documentation. +- Added use cases + - Andrew Ng's Reflexive Workflow Translation Agent Replication +- Some code optimizations and documentation updates. + ## [0.0.10] - 2024-06-28 ### Added - Added standard integration for the DeepSeek model in the LLM module. diff --git a/CHANGELOG_zh.md b/CHANGELOG_zh.md index c8fc95c9..999b56eb 100644 --- a/CHANGELOG_zh.md +++ b/CHANGELOG_zh.md @@ -24,6 +24,19 @@ Note - 对于版本的额外说明。 *************************************************** # 版本更新记录 +## [0.0.11] - 2024-07-11 +### Added +- DataAgent数据自治智能体MVP版本发布 + - Minimum Viable Product版本，DataAgent旨在使用智能体能力让您的Agent拥有自我评价与演进的能力，详细内容请查阅用户文档。 +- 增加PEER、ReAct模式中间信息流式输出能力 + +### Note +- PEER最新研究成果发布 + - 该文献详细介绍了介绍了PEER多智能体框架的机制原理，同时在实验部分通过验证证明了PEER模式的先进性，详细内容请查阅用户文档。 +- 新增使用案例 + - 吴恩达反思工作流翻译智能体复刻 +- 部分代码优化与文档更新 + ## [0.0.10] - 2024-06-28 ### Added - LLM组件新增DeepSeek模型标准接入 diff --git a/README.md b/README.md index d576ef36..563522c9 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語 ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.11-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** @@ -122,7 +122,7 @@ BibTeX formatted url={https://arxiv.org/abs/2407.06985}, } ``` -Overview: In the experimental section of this study, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI, and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved an 83% superior rate over BabyAGI using the GPT-3.5 turbo (16k) model, and an 81% superior rate using the GPT-4o model. For more details, please refer to the literature. +Overview: This document provides a detailed introduction to the mechanisms and principles of the PEER multi-agent framework. In the experimental section, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved a superior rate of 83% over BabyAGI using the GPT-3.5 Turbo (16k) model, and 81% using the GPT-4 model. For more details, please refer to the document. https://arxiv.org/pdf/2407.06985 ## Acknowledgements diff --git a/README_zh.md b/README_zh.md index fbdddea9..4cd29a73 100644 --- a/README_zh.md +++ b/README_zh.md @@ -5,7 +5,7 @@ ![](https://img.shields.io/badge/framework-agentUniverse-pink) ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python) [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE) -[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) +[![Static Badge](https://img.shields.io/badge/pypi-v0.0.11-blue?logo=pypi)](https://pypi.org/project/agentUniverse/) ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** @@ -134,7 +134,7 @@ BibTeX formatted url={https://arxiv.org/abs/2407.06985}, } ``` -文献简介：该文献在研究实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，**PEER模式在每个测评维度的平均分数均高于BabyAGI**，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。 +文献简介：该文献详细介绍了介绍了PEER多智能体框架的机制原理，同时在实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，PEER模式在每个测评维度的平均分数均高于BabyAGI，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。 https://arxiv.org/pdf/2407.06985 ## 鸣谢 diff --git a/pyproject.toml b/pyproject.toml index d3c32309..4efbd6b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "agentUniverse" -version = "0.0.10" +version = "0.0.11" description = "agentUniverse is a framework for developing applications powered by multi-agent base on large language model." authors = ["AntGroup "] From 7563f3b2608b49cc2194a43b7b93408187cd3e0a Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Fri, 12 Jul 2024 12:32:04 +0800 Subject: [PATCH 118/118] improvement: tweak jsonl and excel name format in dataAgent. --- docs/guidebook/en/8_1_1_data_autonomous_agent.md | 6 +++--- ...\262\273\346\231\272\350\203\275\344\275\223.md" | 6 +++--- .../app/core/agent/data_agent_case/data_agent.py | 2 +- ...onl => dataset_turn_1_2024-07-10-10-48-30.jsonl} | 0 ...onl => dataset_turn_1_2024-07-10-15-06-24.jsonl} | 0 ...30.xlsx => eval_report_2024-07-10-10-48-30.xlsx} | Bin ...24.xlsx => eval_report_2024-07-10-15-06-24.xlsx} | Bin ... => eval_result_turn_1_2024-07-10-10-48-30.xlsx} | Bin ... => eval_result_turn_1_2024-07-10-15-06-24.xlsx} | Bin 9 files changed, 7 insertions(+), 7 deletions(-) rename sample_standard_app/app/examples/data/{dataset_turn_1_2024-07-10-10:48:30.jsonl => dataset_turn_1_2024-07-10-10-48-30.jsonl} (100%) rename sample_standard_app/app/examples/data/{dataset_turn_1_2024-07-10-15:06:24.jsonl => dataset_turn_1_2024-07-10-15-06-24.jsonl} (100%) rename sample_standard_app/app/examples/data/{eval_report_2024-07-10-10:48:30.xlsx => eval_report_2024-07-10-10-48-30.xlsx} (100%) rename sample_standard_app/app/examples/data/{eval_report_2024-07-10-15:06:24.xlsx => eval_report_2024-07-10-15-06-24.xlsx} (100%) rename sample_standard_app/app/examples/data/{eval_result_turn_1_2024-07-10-10:48:30.xlsx => eval_result_turn_1_2024-07-10-10-48-30.xlsx} (100%) rename sample_standard_app/app/examples/data/{eval_result_turn_1_2024-07-10-15:06:24.xlsx => eval_result_turn_1_2024-07-10-15-06-24.xlsx} (100%) diff --git a/docs/guidebook/en/8_1_1_data_autonomous_agent.md b/docs/guidebook/en/8_1_1_data_autonomous_agent.md index 4b59614d..2880a1c7 100644 --- a/docs/guidebook/en/8_1_1_data_autonomous_agent.md +++ b/docs/guidebook/en/8_1_1_data_autonomous_agent.md @@ -108,7 +108,7 @@ As shown in the figure below: ![data_agent_dataset](../_picture/data_agent_dataset_en.png) -[dataAgent sample evaluation dataset](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15:06:24.jsonl) +[dataAgent sample evaluation dataset](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15-06-24.jsonl) ### Complete Evaluation Results @@ -124,7 +124,7 @@ As shown in the figure below: - More dimensions Score/Suggestion: similar to the Relevance dimension. ![data_agent_eval_result](../_picture/data_agent_eval_result_en.png) -[dataAgent sample eval result](../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15:06:24.xlsx) +[dataAgent sample eval result](../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15-06-24.xlsx) @@ -139,7 +139,7 @@ As shown in the figure below: ![data_agent_eval_report](../_picture/data_agent_eval_report_en.png) -[dataAgent sample evaluation report](../../../sample_standard_app/app/examples/data/eval_report_2024-07-10-15:06:24.xlsx) +[dataAgent sample evaluation report](../../../sample_standard_app/app/examples/data/eval_report_2024-07-10-15-06-24.xlsx) ### Comparative Experiment Adjust the llm model in demo_rag_agent within aU from the previous `qwen1.5-72b-chat` to `qwen1.5-7b-chat`, and after evaluation by dataAgent, the comprehensive evaluation reports are as follows: diff --git "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" index dbfc5277..be903610 100644 --- "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" +++ "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" @@ -110,7 +110,7 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 ![data_agent_dataset](../_picture/data_agent_dataset.png) -[dataAgent生产的评测数据集样例地址](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl) +[dataAgent生产的评测数据集样例地址](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10-48-30.jsonl) ### 完整评测结果 生产评测数据集后，dataAgent开始数据多维度评估标注，产出完整评测结果（若执行多轮dataAgent跑批任务，则产出多个完整评测结果）。 @@ -129,7 +129,7 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 - 例如第1条数据在**相关性维度的suggestion**: 虽然回答了关于北京天气的问题，但提供的温度单位为华氏度，与国内用户习惯的摄氏度不符，建议转换为摄氏度并提供更全面的天气信息，如湿度、风力等。 - 例如第3条数据在**事实性维度的suggestion**: 回答中包含事实错误，如将阿根廷球星莱昂内尔·梅西错误地归入英格兰队。优化建议是确保所有提及的数据和事实准确无误，尤其是在涉及具体人物和事件时。 -[dataAgent生产的完整评测结果样例地址](../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10:48:30.xlsx) +[dataAgent生产的完整评测结果样例地址](../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10-48-30.xlsx) ### 综合评测报告 根据多轮完整的评测结果，生成一份综合评测报告。 @@ -141,7 +141,7 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 - 更多维度 Avg Score 以此类推 ![data_agent_eval_report](../_picture/data_agent_eval_report.png) -[dataAgent生产的综合评测报告样例地址](../../../sample_standard_app/app/examples/data/eval_report_2024-07-10-10:48:30.xlsx) +[dataAgent生产的综合评测报告样例地址](../../../sample_standard_app/app/examples/data/eval_report_2024-07-10-10-48-30.xlsx) ### 对比实验 调整aU中`demo_rag_agent`中的模型从上文生产评测报告时的**qwen1.5-7b-chat**改为**qwen1.5-72b-chat**，通过dataAgent评测后，生成的综合评测报告如下: diff --git a/sample_standard_app/app/core/agent/data_agent_case/data_agent.py b/sample_standard_app/app/core/agent/data_agent_case/data_agent.py index 058ebc35..89020e67 100644 --- a/sample_standard_app/app/core/agent/data_agent_case/data_agent.py +++ b/sample_standard_app/app/core/agent/data_agent_case/data_agent.py @@ -53,7 +53,7 @@ def execute(self, input_object: InputObject, agent_input: dict): input_object (InputObject): input parameters passed by the user. agent_input (dict): agent input parsed from `input_object` by the user. """ - date = datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + date = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") input_object.add_data('date', date) # step1: build q&a dataset from the candidate agent which needs to be evaluated. diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10-48-30.jsonl similarity index 100% rename from sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10:48:30.jsonl rename to sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-10-48-30.jsonl diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15:06:24.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15-06-24.jsonl similarity index 100% rename from sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15:06:24.jsonl rename to sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15-06-24.jsonl diff --git a/sample_standard_app/app/examples/data/eval_report_2024-07-10-10:48:30.xlsx b/sample_standard_app/app/examples/data/eval_report_2024-07-10-10-48-30.xlsx similarity index 100% rename from sample_standard_app/app/examples/data/eval_report_2024-07-10-10:48:30.xlsx rename to sample_standard_app/app/examples/data/eval_report_2024-07-10-10-48-30.xlsx diff --git a/sample_standard_app/app/examples/data/eval_report_2024-07-10-15:06:24.xlsx b/sample_standard_app/app/examples/data/eval_report_2024-07-10-15-06-24.xlsx similarity index 100% rename from sample_standard_app/app/examples/data/eval_report_2024-07-10-15:06:24.xlsx rename to sample_standard_app/app/examples/data/eval_report_2024-07-10-15-06-24.xlsx diff --git a/sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10:48:30.xlsx b/sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10-48-30.xlsx similarity index 100% rename from sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10:48:30.xlsx rename to sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-10-48-30.xlsx diff --git a/sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15:06:24.xlsx b/sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15-06-24.xlsx similarity index 100% rename from sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15:06:24.xlsx rename to sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15-06-24.xlsx

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dict) -> dict: """Agent parameter parsing. @@ -67,8 +67,8 @@ def execute(self, input_object: InputObject, agent_input: dict): thread_name_prefix="data_agent") # step1: build q&a dataset from the candidate agent which needs to be evaluated. - prompt_answer_list = self.build_dataset(agent_input) - input_object.add_data('prompt_answer_list', prompt_answer_list) + query_answer_list = self.build_dataset(agent_input) + input_object.add_data('query_answer_list', query_answer_list) LOGGER.info("-------------------------------------------") LOGGER.info("End: build q&a dataset from the candidate agent done.") @@ -76,12 +76,12 @@ def execute(self, input_object: InputObject, agent_input: dict): # step2: write the q&a dataset to json file. date = input_object.get_data('date', '') - for i in range(len(prompt_answer_list)): - one_turn_prompt_answer_list = prompt_answer_list[i] + for i in range(len(query_answer_list)): + one_turn_query_answer_list = query_answer_list[i] json_writer = JsonFileWriter(f'dataset_turn_{i + 1}_{date}') - json_writer.write_json_prompt_answer_list(one_turn_prompt_answer_list) + json_writer.write_json_query_answer_list(one_turn_query_answer_list) LOGGER.info(f"Progress: write the q&a dataset to local jsonl files.") - return {'prompt_answer_list': prompt_answer_list} + return {'query_answer_list': query_answer_list} def build_dataset(self, agent_input: dict) -> List[List[Tuple[str, str]]]: """Build q&a dataset from the candidate agent which needs to be evaluated.""" @@ -104,12 +104,12 @@ def build_dataset(self, agent_input: dict) -> List[List[Tuple[str, str]]]: first_input_key = candidate_agent.input_keys()[0] first_output_key = candidate_agent.output_keys()[0] - prompt_answer_list = [] + query_answer_list = [] for i in range(agent_input.get('turn')): LOGGER.info("-------------------------------------------") LOGGER.info(f"Start: build q&a dataset from the candidate agent `{candidate_agent_name}`, turn {i + 1}.") - one_turn_prompt_answer_list = [] + one_turn_query_answer_list = [] futures_to_query = {} # single turn query and answer processing. @@ -125,14 +125,14 @@ def build_dataset(self, agent_input: dict) -> List[List[Tuple[str, str]]]: for future in done: output_object: OutputObject = future.result() # note: the first index of input_keys and output_keys is identified as the prompt and answer. - prompt = futures_to_query.get(future, {}).get(first_input_key, '') + query = futures_to_query.get(future, {}).get(first_input_key, '') answer = output_object.get_data(first_output_key, '') - one_turn_prompt_answer_list.append((prompt, answer)) - LOGGER.info(f"Progress: the turn {i + 1} query: `{prompt}` has generated the answer successfully.") + one_turn_query_answer_list.append((query, answer)) + LOGGER.info(f"Progress: the turn {i + 1} query: `{query}` has generated the answer successfully.") LOGGER.info(f"End: the turn {i + 1} has generated the answer successfully.") # build q&a dataset - prompt_answer_list.append(one_turn_prompt_answer_list) - return prompt_answer_list + query_answer_list.append(one_turn_query_answer_list) + return query_answer_list diff --git a/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py index e29156f6..05cb864d 100644 --- a/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py +++ b/sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py @@ -28,7 +28,7 @@ class DatasetEvalAgent(Agent): def input_keys(self) -> list[str]: """Return the input keys of the Agent.""" - return ['prompt_answer_list'] + return ['query_answer_list'] def output_keys(self) -> list[str]: """Return the output keys of the Agent.""" @@ -43,7 +43,7 @@ def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: Returns: dict: agent input parsed from `input_object` by the user. """ - agent_input['prompt_answer_list'] = input_object.get_data('prompt_answer_list') + agent_input['query_answer_list'] = input_object.get_data('query_answer_list') return agent_input def parse_result(self, planner_result: dict) -> dict: @@ -67,18 +67,18 @@ def execute(self, input_object: InputObject, agent_input: dict): LOGGER.info(f"Start: use the evaluator agent to evaluate q&a dataset.") LOGGER.info("-------------------------------------------") - prompt_answer_list: List[List[Tuple[str, str]]] = input_object.get_data('prompt_answer_list') + query_answer_list: List[List[Tuple[str, str]]] = input_object.get_data('query_answer_list') max_eval_lines = self.agent_model.profile.get('max_eval_lines', 100) - if len(prompt_answer_list) == 0: - raise ValueError('the `prompt_answer_list` is empty') + if len(query_answer_list) == 0: + raise ValueError('the `query_answer_list` is empty') # step1: evaluate the dataset in multiple dimensions and give specific scores. - eval_dims_json_list: List[List[dict]] = self.eval(prompt_answer_list, max_eval_lines) + eval_dims_json_list: List[List[dict]] = self.eval(query_answer_list, max_eval_lines) LOGGER.info(f"End: evaluate the dataset in multiple dimensions done.") LOGGER.info("-------------------------------------------") # step2: write the eval results to Excel file. - self.generate_eval_results_excel(prompt_answer_list, eval_dims_json_list, input_object.get_data('date', '')) + self.generate_eval_results_excel(query_answer_list, eval_dims_json_list, input_object.get_data('date', '')) # step3: generate eval report eval_report_json_list = self.generate_eval_report(eval_dims_json_list, input_object.get_data('date', '')) @@ -86,11 +86,11 @@ def execute(self, input_object: InputObject, agent_input: dict): LOGGER.info("-------------------------------------------") return {'eval_report_json_list': eval_report_json_list, 'eval_dims_json_list': eval_dims_json_list} - def eval(self, prompt_answer_list: List[List[Tuple[str, str]]], max_eval_lines: int) -> List[List[dict]]: + def eval(self, query_answer_list: List[List[Tuple[str, str]]], max_eval_lines: int) -> List[List[dict]]: """Evaluate the dataset in multiple dimensions and give specific scores. Args: - prompt_answer_list (List[List[Tuple[str, str]]]): the list of q&a pair from multiple turns, + query_answer_list (List[List[Tuple[str, str]]]): the list of q&a pair from multiple turns, the type of single turn is List[Tuple[str, str]]. max_eval_lines (int): the maximum number of lines to evaluate. @@ -102,25 +102,25 @@ def eval(self, prompt_answer_list: List[List[Tuple[str, str]]], max_eval_lines: eval_dims_json_list = [] # the q&a dataset from multiple turns. - for i in range(len(prompt_answer_list)): + for i in range(len(query_answer_list)): line_num = 0 - one_turn_prompt_answer_list = prompt_answer_list[i] + one_turn_query_answer_list = query_answer_list[i] one_turn_eval_dims_json_list = [] - # single turn prompt answer list. - for j in range(len(one_turn_prompt_answer_list)): + # single turn query answer list. + for j in range(len(one_turn_query_answer_list)): - prompt = one_turn_prompt_answer_list[j][0] - answer = one_turn_prompt_answer_list[j][1] - if prompt is None or answer is None: + query = one_turn_query_answer_list[j][0] + answer = one_turn_query_answer_list[j][1] + if query is None or answer is None: break line_num += 1 if line_num > max_eval_lines: break - if len(prompt) > 2000: - prompt = prompt[0:2000] + if len(query) > 2000: + query = query[0:2000] if len(answer) > 5000: answer = answer[0:5000] @@ -129,24 +129,24 @@ def eval(self, prompt_answer_list: List[List[Tuple[str, str]]], max_eval_lines: llm: LLM = self.handle_llm() chain = version_prompt.as_langchain() | llm.as_langchain() | StrOutputParser() - res = chain.invoke(input={'prompt': prompt, 'answer': answer}) + res = chain.invoke(input={'query': query, 'answer': answer}) dim_score_json = {'line': line_num} dimensions = [] - avg_score = 0.0 + overall_score = 0.0 try: if res is not None: data = parse_json_markdown(res) dimensions = data['dimensions'] - # calculate avg score from multiple dimensions. - avg_score = sum(data['score'] for data in dimensions) + # calculate overall score from multiple dimensions. + overall_score = sum(data['score'] for data in dimensions) except Exception as e: - LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>> res: {res}, exception: {e}') + LOGGER.warn(f'except[eval_query_answer_from_jsonl]>>> res: {res}, exception: {e}') continue if len(dimensions) > 0: - avg_score = avg_score / len(dimensions) - dim_score_json['avg_score'] = avg_score + overall_score = overall_score / len(dimensions) + dim_score_json['overall_score'] = overall_score dim_score_json['dimensions'] = dimensions LOGGER.info(f"Progress: the turn {i + 1} query line {line_num} has been evaluated successfully.") @@ -207,7 +207,7 @@ def generate_eval_report(self, eval_dims_json_list: List[List[dict]], date: str) eval_report_json_list = [] for i in range(0, len(eval_dims_json_list)): line_num = 0 - total_avg_score = 0.0 + overall_avg_score = 0.0 dim_avg_score = {} # single turn eval dim json list. one_turn_eval_dims_json_list = eval_dims_json_list[i] @@ -215,7 +215,7 @@ def generate_eval_report(self, eval_dims_json_list: List[List[dict]], date: str) one_row_eval_dims_json = one_turn_eval_dims_json_list[j] line_num += 1 - total_avg_score += one_row_eval_dims_json['avg_score'] + overall_avg_score += one_row_eval_dims_json['overall_score'] dimensions = one_row_eval_dims_json['dimensions'] for k in range(0, len(dimensions)): name = dimensions[k]['name'] @@ -225,16 +225,16 @@ def generate_eval_report(self, eval_dims_json_list: List[List[dict]], date: str) else: dim_avg_score[name] = float(score) - # calculate single turn total avg score and dim avg score. + # calculate single turn overall avg score and dim avg score. for key in dim_avg_score: dim_total_score = dim_avg_score[key] dim_avg_score[key] = dim_total_score / line_num - total_avg_score = total_avg_score / line_num + overall_avg_score = overall_avg_score / line_num # generate one turn report. one_turn_report = { - 'line_name': f"query turn {i + 1}", - 'total_avg_score': total_avg_score, + 'line_name': f"Queryset Turn {i + 1}", + 'overall_avg_score': overall_avg_score, 'dim_avg_score': dim_avg_score } LOGGER.info(f"Progress: turn {i + 1} evaluation report has generated successfully.") @@ -260,7 +260,7 @@ def generate_total_eval_report(eval_report_json_list: List[dict]): total_avg_score = 0.0 total_dim_avg_score = {} for i in range(len(eval_report_json_list)): - total_avg_score += eval_report_json_list[i]['total_avg_score'] + total_avg_score += eval_report_json_list[i]['overall_avg_score'] dim_avg_score: dict = eval_report_json_list[i]['dim_avg_score'] for key in dim_avg_score: if key in total_dim_avg_score: @@ -271,44 +271,44 @@ def generate_total_eval_report(eval_report_json_list: List[dict]): for key in total_dim_avg_score: total_dim_avg_score[key] = total_dim_avg_score[key] / len(eval_report_json_list) report_line_obj = { - 'line_name': 'total avg score', - 'total_avg_score': total_avg_score, + 'line_name': 'Turn Avg Score', + 'overall_avg_score': total_avg_score, 'dim_avg_score': total_dim_avg_score } eval_report_json_list.append(report_line_obj) @staticmethod - def generate_eval_results_excel(prompt_answer_list: List[List[Tuple[str, str]]], + def generate_eval_results_excel(query_answer_list: List[List[Tuple[str, str]]], eval_dims_json_list: List[List[dict]], date: str): """Generate evaluation results in excel format.""" rows = [] - columns: List[str] = ['Line Number', 'Overall Score', 'Prompt', 'Answer'] + columns: List[str] = ['Line Number', 'Overall Score', 'Query', 'Answer'] if len(eval_dims_json_list) > 0 and len(eval_dims_json_list[0]) > 0: one_row_eval_result = eval_dims_json_list[0][0] dims = one_row_eval_result.get('dimensions', []) # wrap excel columns for dim in dims: columns.append(dim['name'] + ' Score') - columns.append(dim['name'] + ' Negative Point') + columns.append(dim['name'] + ' Suggestion') for i in range(len(eval_dims_json_list)): one_turn_eval_results = eval_dims_json_list[i] - one_turn_prompt_answers = prompt_answer_list[i] + one_turn_query_answers = query_answer_list[i] # write for each turn for j in range(len(one_turn_eval_results)): one_row_eval_result = one_turn_eval_results[j] - one_row_prompt_answer = one_turn_prompt_answers[j] + one_row_query_answer = one_turn_query_answers[j] # wrap rows. line_number = one_row_eval_result['line'] - avg_score = one_row_eval_result['avg_score'] + overall_score = one_row_eval_result['overall_score'] dims = one_row_eval_result['dimensions'] - row = [line_number, avg_score, one_row_prompt_answer[0], one_row_prompt_answer[1]] + row = [line_number, overall_score, one_row_query_answer[0], one_row_query_answer[1]] for dim in dims: row.append(dim['score']) - row.append(dim['negative point']) + row.append(dim['suggestion']) rows.append(row) df = pd.DataFrame(rows, columns=columns) @@ -322,16 +322,16 @@ def generate_eval_report_excel(eval_report_json_list: List[dict], date: str): """Generate excel eval report.""" rows = [] - columns: List[str] = ['Line Name', 'Total Avg Score'] + columns: List[str] = ['Line Name', 'Overall Avg Score'] dim_avg_scores = eval_report_json_list[0]['dim_avg_score'] for dim, score in dim_avg_scores.items(): columns.append(dim + ' Avg Score') for item in eval_report_json_list: line_name = item['line_name'] - total_avg_score = item['total_avg_score'] + overall_avg_score = item['overall_avg_score'] dim_avg_scores = item['dim_avg_score'] - row = [line_name, total_avg_score] + row = [line_name, overall_avg_score] for dim, score in dim_avg_scores.items(): row.append(score) rows.append(row) diff --git a/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml b/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml index 87e56da2..15859e4a 100644 --- a/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml +++ b/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml @@ -1,5 +1,5 @@ introduction: ｜ - 你是一个极致严谨的金融专家，需要帮助我按照如下要求在各维度评估prompt（用户输入）和answer（模型输出）的分数。 + 你是一个极致严谨的金融专家，需要帮助我按照如下要求在各维度评估query（用户输入）和answer（模型输出）的分数。 每个维度满分为5分，不符合准入条件直接判0分，符合门槛值加0-3分，如果再符合加分项要求，在门槛值的分数基础上再加0-2分。 打分要求，请精确到小数点后1位： @@ -10,8 +10,8 @@ introduction: ｜ 注意：严格打分，不能轻松给出高分 instruction: | -------------------------------- - prompt: - {prompt} + query: + {query} -------------------------------- answer: @@ -129,8 +129,8 @@ instruction: | -------------------------------- 输出格式要求： - 1.根据具体打分维度要求，同时评价prompt和answer内容，进行打分。 - 2. 一定要给出negative point，结合prompt和answer，给出具体的扣分原因和具体的优化建议。 + 1.根据具体打分维度要求，同时评价query和answer内容，进行打分。 + 2. 一定要给出suggestion，结合query和answer，给出具体的扣分原因和具体的优化建议。 请严格按照质量打分维度要求打分，不能轻松给出高分，请确保输出只包含如下的json格式字段，且每个字段不能为空: {{ @@ -138,12 +138,12 @@ instruction: | {{ "name":质量打分维度名称(string字符串格式), "score":分数(float浮点数格式), - "negative point":扣分的原因和给出的优化建议(string字符串格式) + "suggestion":扣分的原因和给出的优化建议(string字符串格式) }}, {{ "name":质量打分维度名称(string字符串格式), "score":分数(float浮点数格式), - "negative point":扣分的原因和给出的优化建议(string字符串格式) + "suggestion":扣分的原因和给出的优化建议(string字符串格式) }} ] }} diff --git a/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml b/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml index 0dcadaf3..52c33f27 100644 --- a/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml +++ b/sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml @@ -1,5 +1,5 @@ introduction: | - You are an extremely rigorous financial expert and need help me evaluate prompt (user input) and answer (model output) scores in various dimensions according to the following requirements. + You are an extremely rigorous financial expert and need help me evaluate query (user input) and answer (model output) scores in various dimensions according to the following requirements. The full score for each dimension is 5 points. If you do not meet the entry conditions, you will be awarded 0 points directly, and if you meet the threshold value, you will be awarded 0-3 points. If you meet the additional requirements, you will add 0-2 points to the threshold value. For scoring requirements, please be accurate to 1 decimal place: @@ -10,8 +10,8 @@ introduction: | Note: Strict scoring and high scores cannot be easily given instruction: | -------------------------------- - prompt: - {prompt} + query: + {query} -------------------------------- answer: @@ -22,7 +22,7 @@ instruction: | The quality scoring dimensions are described as follows: - dimension name: relevance + dimension name: Relevance dimension requirement: 1. non-compliance with access conditions Answer is irrelevant. @@ -44,7 +44,7 @@ instruction: | c、The answers are comprehensive and easy to understand -------------------------------- - dimension name: factual + dimension name: Factual dimension requirement: 1. non-compliance with access conditions The data is wrong and does not conform to the facts @@ -54,9 +54,9 @@ instruction: | 3.bonus points All quoted data in the interpretation content are true and credible, and the data source is officially credible + -------------------------------- - - dimension name: rationality + dimension name: Rationality dimension requirement: 1. non-compliance with access conditions Professional error occurred @@ -69,7 +69,7 @@ instruction: | The interpretation throughout the text must be consistent and logically coherent, with the logical reasoning advancing step by step (subsequent analysis is based on previous conclusions). -------------------------------- - dimension name: timeliness + dimension name: Timeliness dimension requirement: 1. non-compliance with access conditions The data is stale and no longer corresponds to the present reality. @@ -81,7 +81,7 @@ instruction: | Cite the latest official data, including a clear publication date. -------------------------------- - dimension name: structural + dimension name: Structural dimension requirement: 1. non-compliance with access conditions If the answer follows a general-specific structure: @@ -111,8 +111,9 @@ instruction: | If the answer is a summary structure: a、The summary content and arguments are detailed and sufficient, logical and organized b、The summary should be expressed with clear logic and concise language. + -------------------------------- - dimension name: integrity + dimension name: Integrity dimension requirement: 1. non-compliance with access conditions If any of the following points are satisfied, the score is 0: @@ -132,8 +133,8 @@ instruction: | -------------------------------- Output format requirements: - 1. According to the specific scoring dimension requirements, evaluate prompt and answer content at the same time and score. - 2. Be sure to give a negative point, combine prompt and answer, and give specific reasons for deducting points and specific optimization suggestions. + 1. According to the specific scoring dimension requirements, evaluate query and answer content at the same time and score. + 2. Be sure to give the suggestion, combine query and answer, and give specific reasons for deducting points and specific optimization suggestions. Please score strictly in accordance with the quality scoring dimensions. High scores cannot be easily given. Please ensure that the output only contains the following json format fields, and each field cannot be empty: @@ -142,12 +143,12 @@ instruction: | {{ "name": dimension name(string), "score": score(float), - "negative point": Reasons for deductions and optimization suggestions given(string) + "suggestion": Reasons for deductions and optimization suggestions given(string) }}, {{ "name": dimension name(string), "score": score(float), - "negative point": Reasons for deductions and optimization suggestions given(string) + "suggestion": Reasons for deductions and optimization suggestions given(string) }} ] }} diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl deleted file mode 100644 index fd99bf03..00000000 --- a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-08-20:17:28.jsonl +++ /dev/null @@ -1,2 +0,0 @@ -{"prompt": "英伟达股票大涨的原因是什么？", "answer": "您的问题与提供的背景信息关于巴菲特减持比亚迪股票的情况不符，因此我无法直接回答“英伟达股票大涨的原因”。然而，如果要探讨股市中某个特定股票（如NVIDIA）的大涨，通常会考虑行业趋势、公司业绩、技术创新、市场需求增长、宏观经济环境等因素。对于英伟达这样的科技巨头，GPU性能提升、人工智能和数据中心需求的增长、以及新兴市场的扩张都可能是推动股价上涨的原因。但具体到英伟达，需要查看最新的行业报告和公司公告来了解详细情况。"} -{"prompt": "特斯拉股票为什么波动很大？", "answer": "特斯拉股票的波动性较大，主要有以下几个原因：\n\n1. 技术创新与行业竞争：特斯拉作为电动汽车行业的领导者，其技术创新能力和市场份额对投资者信心有直接影响。新产品的发布、技术突破或竞争对手的动作都可能导致股价大幅波动。\n\n2. 市场预期：特斯拉的股价常常受到市场对其增长前景、盈利能力和电池技术等未来预期的影响。如果市场对特斯拉的未来表现乐观，股价会上涨；反之，如果预期不达预期或出现负面新闻，股价可能会下跌。\n\n3. 宏观经济因素：全球经济状况、政策变化（如补贴政策）、能源政策调整等都会影响电动车行业的整体环境，从而影响特斯拉的股价。\n\n4. 融资活动：特斯拉的融资活动，如股权融资、债务融资，可能会导致股价短期内调整，尤其是如果融资条件优于市场预期。\n\n5. 泡沫与估值调整：特斯拉作为一家高成长性的公司，其股票估值可能在短期内被推高，一旦市场情绪变化或估值过高被认为难以维持，股价可能回调。\n\n6. 公司管理与舆论：CEO埃隆·马斯克的个人言行、公司战略调整以及媒体报道都可能成为股价波动的催化剂。\n\n总之，特斯拉股票的波动反映了市场对公司及其行业动态的快速反应，同时也体现了投资者对于特斯拉未来盈利能力、技术进步和市场地位的期待与担忧。"} \ No newline at end of file diff --git a/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl new file mode 100644 index 00000000..01f8afe8 --- /dev/null +++ b/sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl @@ -0,0 +1,2 @@ +{"query": "特斯拉股票为什么波动很大？", "answer": "特斯拉的股票波动大，主要受以下几个因素影响：\n\n1. **市场情绪**：特斯拉是一家备受关注的电动汽车和清洁能源公司，其创始人埃隆·马斯克的个人影响力和公司的创新形象使得市场对其期望值非常高。任何有关特斯拉的新产品发布、技术创新、产量数据或财务报告都可能引起投资者的强烈反应，导致股价大幅波动。\n\n2. **生产与交付数据**：特斯拉的季度生产与交付报告是影响其股价的重要因素。如果实际数据低于市场预期，股价可能会下跌；反之，超出预期的数据则可能导致股价上涨。\n\n3. **财务状况**：特斯拉的财务健康状况，如盈利、现金流和债务水平，也是影响股价的关键因素。例如，特斯拉在实现连续盈利之前，曾面临过多次财务压力，这直接影响了投资者的信心和股价表现。\n\n4. **竞争环境**：随着全球电动汽车市场的竞争加剧，特斯拉面临的竞争对手越来越多，包括传统汽车制造商和新兴电动车企。市场竞争的动态变化会影响特斯拉的市场份额和增长前景，从而影响股价。\n\n5. **政策与法规**：政府对新能源汽车的补贴政策、排放标准和贸易政策的变化也可能对特斯拉的业务产生重大影响，进而影响股价。\n\n6. **供应链问题**：特斯拉的供应链稳定性对生产计划至关重要。任何供应链中断，如芯片短缺或原材料价格上涨，都可能影响生产成本和交付能力，从而影响股价。\n\n7. **技术进步与风险**：特斯拉在自动驾驶、电池技术等方面的持续研发和创新是其核心竞争力之一。然而，技术进步的速度和成果的不确定性也可能带来股价波动。\n\n8. **创始人与管理层**：埃隆·马斯克作为特斯拉的灵魂人物，其言行举止、社交媒体活动甚至个人生活事件都可能成为市场关注的焦点，有时会引发股价的剧烈波动。\n\n综上所述，特斯拉的股价波动大是由于其业务特性、市场定位、财务状况、竞争环境、政策影响、供应链稳定性、技术进步和管理层等多个复杂因素共同作用的结果。"} +{"query": "英伟达股票大涨的原因是什么？", "answer": "在提供的背景信息中，并没有提及英伟达股票大涨的具体原因。但是，我可以结合当前的市场情况和英伟达的业务动态来分析可能的原因：\n\n1. **人工智能和数据中心需求增长**：近年来，随着人工智能技术的发展，对高性能计算的需求激增。英伟达作为GPU（图形处理器）领域的领导者，其产品在深度学习、机器学习等领域得到广泛应用，推动了数据中心市场的强劲需求，这可能是其股价上涨的一个重要因素。\n\n2. **游戏业务稳定**：虽然游戏市场存在周期性波动，但英伟达凭借其强大的GPU技术和市场地位，能够持续吸引游戏玩家和PC制造商，确保游戏业务的稳定收入，为股价提供支撑。\n\n3. **自动驾驶技术进展**：英伟达在自动驾驶领域的投入和进展也为其股价带来积极影响。随着自动驾驶技术的成熟和商业化应用的推进，英伟达的Drive平台有望成为该领域的重要参与者，增加公司的未来增长潜力。\n\n4. **财务表现优异**：英伟达的财务表现通常非常稳健，连续几个季度的收入和利润增长超出市场预期，增强了投资者的信心，推动股价上涨。\n\n5. **行业地位和品牌影响力**：作为全球领先的半导体公司之一，英伟达在GPU、AI芯片等多个领域的领先地位和品牌影响力，使其成为投资者眼中的优质资产，有助于维持股价的高位运行。\n\n请注意，股市波动受多种因素影响，包括宏观经济环境、行业趋势、公司业绩、市场情绪等，以上分析仅基于当前可获得的信息，实际情况可能有所不同。"} diff --git a/sample_standard_app/app/examples/data/eval_report_2024-07-08-20:17:28.xlsx b/sample_standard_app/app/examples/data/eval_report_2024-07-08-20:17:28.xlsx deleted file 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a/sample_standard_app/app/examples/data_agent.py b/sample_standard_app/app/examples/data_agent.py index 851a01f1..a3a52c7e 100644 --- a/sample_standard_app/app/examples/data_agent.py +++ b/sample_standard_app/app/examples/data_agent.py @@ -18,4 +18,6 @@ def data_process(queryset_path: str, turn: int, **kwargs): if __name__ == '__main__': - data_process(queryset_path="data/queryset_cn.jsonl", turn=1) + queryset_path_list = ["./data/queryset_cn.jsonl"] + for path in queryset_path_list: + data_process(queryset_path=path, turn=2) diff --git a/sample_standard_app/app/test/test_data_agent.py b/sample_standard_app/app/test/test_data_agent.py index f4b0d985..a19df2e8 100644 --- a/sample_standard_app/app/test/test_data_agent.py +++ b/sample_standard_app/app/test/test_data_agent.py @@ -20,7 +20,7 @@ def setUp(self) -> None: def test_data_agent(self): instance: Agent = AgentManager().get_instance_obj('data_agent') - instance.run(dataset_path='', turn=2) + instance.run(queryset_path='', turn=2) if __name__ == '__main__': diff --git a/sample_standard_app/app/util/jsonl_file_utils.py b/sample_standard_app/app/util/jsonl_file_utils.py index 2d64d739..fd089c85 100644 --- a/sample_standard_app/app/util/jsonl_file_utils.py +++ b/sample_standard_app/app/util/jsonl_file_utils.py @@ -82,10 +82,10 @@ def write_json_obj_list(self, json_obj_list: list): self.write_json_obj(json_obj_list[i]) return - def write_json_prompt_answer(self, prompt: str, answer: str): - json_obj = {"prompt": prompt, "answer": answer} + def write_json_query_answer(self, query: str, answer: str): + json_obj = {"query": query, "answer": answer} self.write_json_obj(json_obj) - def write_json_prompt_answer_list(self, prompt_answer_list: list): - for i in range(0, len(prompt_answer_list)): - self.write_json_prompt_answer(prompt_answer_list[i][0], prompt_answer_list[i][1]) + def write_json_query_answer_list(self, query_answer_list: list): + for i in range(0, len(query_answer_list)): + self.write_json_query_answer(query_answer_list[i][0], query_answer_list[i][1]) From 34581d3598f234cce69760b60a19004a5c98a267 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Tue, 9 Jul 2024 22:07:36 +0800 Subject: [PATCH 102/118] feat: add the data agent to build datasets and evaluate the quality of results. --- ...73\346\231\272\350\203\275\344\275\223.md" | 20 ++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" index 0cb9d0db..9d5542f9 100644 --- "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" +++ "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" @@ -1,7 +1,7 @@ # 数据自治智能体 ## 介绍 -**数据自治智能体全貌**：通过**自动化批量执行多轮智能体调用**，**离线自适应数据采集**，**生产评测数据集**，**生产微调数据集**，**模型推理训练**，**数据集多维度自动评估**等完整的workflow，以强化大模型和Agent的数据自治流程。 +数据自治智能体全貌：通过**自动化批量执行多轮智能体调用**，**离线自适应数据采集**，**生产评测数据集**，**生产微调数据集**，**模型推理训练**，**数据集多维度自动评估**等完整的workflow，以强化大模型和Agent的数据自治流程。 当前agentUniverse开放了**数据自治MVP（最小可行产品）版本**，根据用户指定的**问题集**，**自动化批量执行多轮智能体调用**，**生产评测数据集**，**数据集多维度自动评估**，**产出评测报告**，将数据自治思想传递到开源社区，后续将陆续开放数据自治workflow完整能力。 @@ -11,11 +11,11 @@ dataAgent希望通过智能体本身的能力，对Agent数据进行**自采集、自管理，以及智能化评判**。 -在我们开放的mvp版本中，你可以通过`dataset_build_agent`智能体对于待评测的问题集进行多轮次的批量调用，生产评测数据集；之后通过`dataset_eval_agent`智能体采用蚂蚁公布的Fin-Eva数据评测标准基于7个维度（相关性、事实性、合理性、时效性、结构性、整体性、全面性）进行智能体数据评估和标注，将多轮次评测结果进行丰富的统计学分析，以提供给用户更直观的Agent能力变化。 +在我们开放的mvp版本中，您可以通过`dataset_build_agent`智能体对于待评测的问题集进行多轮次的批量调用，生产评测数据集；之后通过`dataset_eval_agent`智能体采用蚂蚁公布的Fin-Eva数据评测标准基于7个维度（相关性、事实性、合理性、时效性、结构性、整体性、全面性）进行智能体数据评估和标注，将多轮次评测结果进行丰富的统计学分析，以提供给用户更直观的Agent能力变化。 -通过一整套mvp机制，您可以非常轻松的通过智能化能力清晰的了解您的智能体能力当前处于什么水平。 +通过一整套数据自治能力，可以非常轻松的通过智能化产出的评测报告，了解到您的智能体当前处于什么水平。 -需要特别说明的是，整个评测报告得分为参考值，在实际投产过程中，我们将多轮多次得分进行综合对比，以分辨智能体效果好坏。 +需要特别说明的是，整个评测报告分数为参考值，在实际投产过程中，我们将多轮多次得分进行综合对比，以分辨智能体效果好坏。 ## dataAgent流程图 @@ -96,11 +96,17 @@ metadata: ### step5 运行dataAgent 通过 [dataAgent代码入口](../../../sample_standard_app/app/examples/data_agent.py)，分别配置两个参数，`queryset_path`代表问题集路径，`turn`代表问题集总体执行轮次，一键启动dataAgent。 +tips: 请合理配置问题集及具体评测行数，以免造成大量算力及token损耗。 + ## dataAgent使用示例 参考上文**dataAgent执行步骤**，通过5步，启动配置好的dataAgent。 ### 评测数据集 首先生成评测数据集，以jsonl文件的格式存储智能体的q&a对。 +如下图所示： + - query: 评测数据问题 + - answer: 评测数据答案 + ![data_agent_dataset](../_picture/data_agent_dataset.png) [dataAgent生产的评测数据集样例地址](../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl) @@ -110,10 +116,10 @@ metadata: 如下图所示: - Line Number: 当前评测数据在数据集中的行数 - - Overall Score: 综合分数 = 多个维度的评估分数总和 / 维度个数 + - Overall Score: 综合分数 = 多个维度的评估分数总和 / 评估维度个数 - Query: 评测数据问题 - Answer: 评测数据答案 - - 相关性 Score: 代表评测数据答案与问题的相关相关性，分数越高越相关 + - 相关性 Score: 代表评测数据答案与问题的相关性，分数越高越相关 - 相关性 Suggestion: 代表相关性维度存在的问题和改进建议 - 更多维度 Score/Suggestion 以此类推 ![data_agent_eval_result](../_picture/data_agent_eval_result.png) @@ -135,7 +141,7 @@ metadata: ### 对比实验 调整aU中`demo_rag_agent`中的模型从上文生产评测报告时的**qwen1.5-72b-chat**改为**qwen1.5-7b-chat**，通过dataAgent评测后，生成的综合评测报告如下: ![data_agent_eval_report](../_picture/data_agent_eval_report_qwen7b.png) -对比两次综合评测报告，可以看到各维度多轮评分均存在大幅度下降，以分辨模型变更后，智能体效果差异。 +对比两次综合评测报告，可以看到各维度多轮评分均存在大幅度下降，以此方法快速分辨模型变更后，智能体效果差异。 ## dataAgent详细说明 From 122fa2758b77e7b5a86648db6880e69bdb03c352 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 10 Jul 2024 10:32:20 +0800 Subject: [PATCH 103/118] feat: add the data agent to build datasets and evaluate the quality of results. --- ...52\346\262\273\346\231\272\350\203\275\344\275\223.md" | 8 ++++---- .../app/core/prompt/dataset_eval_agent_cn.yaml | 2 ++ .../app/core/prompt/demo_rag_agent_cn.yaml | 2 ++ .../app/core/prompt/demo_rag_agent_en.yaml | 2 ++ 4 files changed, 10 insertions(+), 4 deletions(-) diff --git "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" index 9d5542f9..5889bc7c 100644 --- "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" +++ "b/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" @@ -116,10 +116,10 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 如下图所示: - Line Number: 当前评测数据在数据集中的行数 - - Overall Score: 综合分数 = 多个维度的评估分数总和 / 评估维度个数 + - Overall Score: 综合分数 = 多个维度的评估分数总和 / 评估维度个数（满分5分，分数范围0-5） - Query: 评测数据问题 - Answer: 评测数据答案 - - 相关性 Score: 代表评测数据答案与问题的相关性，分数越高越相关 + - 相关性 Score: 代表评测数据答案与问题的相关性，分数越高越相关（满分5分，分数范围0-5） - 相关性 Suggestion: 代表相关性维度存在的问题和改进建议 - 更多维度 Score/Suggestion 以此类推 ![data_agent_eval_result](../_picture/data_agent_eval_result.png) @@ -131,8 +131,8 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力 如下图所示: - Line Name: 包含两种类型，如`Queryset Turn x`代表根据问题集第x轮生成的评测数据集, `Turn Avg Score`代表多轮多维度的平均分数 - - Overall Avg Score: 单轮数据集中所有数据的综合分数总和 / 数据集中对应数据量 - - 相关性 Avg Score: 单轮数据集中所有数据的相关性分数总和 / 数据集中对应数据量 + - Overall Avg Score: 单轮数据集中所有数据的综合分数总和 / 数据集中对应数据量（满分5分，分数范围0-5） + - 相关性 Avg Score: 单轮数据集中所有数据的相关性分数总和 / 数据集中对应数据量（满分5分，分数范围0-5） - 更多维度 Avg Score 以此类推 ![data_agent_eval_report](../_picture/data_agent_eval_report.png) diff --git a/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml b/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml index 15859e4a..1588c13a 100644 --- a/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml +++ b/sample_standard_app/app/core/prompt/dataset_eval_agent_cn.yaml @@ -29,9 +29,11 @@ instruction: | 2.门槛值 能够知道到用户在问什么，并且所答即所问，包括： + 【有明确意图时】 识别出用户的问题并对问题有回复，且回复的内容有相关性 有主意图的时候，回复的内容要与主意图有相关 + 【无明确意图时】 遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 diff --git a/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml b/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml index cda9228f..b12153b0 100644 --- a/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml +++ b/sample_standard_app/app/core/prompt/demo_rag_agent_cn.yaml @@ -10,6 +10,8 @@ instruction: | 6. 不说模糊的推测。 7. 尽量多的使用数值类信息。 + 今天的日期是: {date} + 背景信息是: {background} diff --git a/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml b/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml index c3b96de8..69057613 100644 --- a/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml +++ b/sample_standard_app/app/core/prompt/demo_rag_agent_en.yaml @@ -11,6 +11,8 @@ instruction: | 7. Try to use numerical information as much as possible. 8. The answer should not exceed 500 words + Today's date is: {date} + Background is: {background} From 9587962f032d3a3dde6b50c0d15c997616a3c6bb Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 10 Jul 2024 10:36:20 +0800 Subject: [PATCH 104/118] add sql tool --- .../info_sql_database_tool.yaml | 13 +++++++ .../tool/langchain_tool/langchain_tool.py | 2 +- .../list_sql_database_tool.yaml | 13 +++++++ .../query_sql_database_tool.yaml | 13 +++++++ .../tool/langchain_tool/sql_langchain_tool.py | 39 +++++++++++++++++++ 5 files changed, 79 insertions(+), 1 deletion(-) create mode 100644 sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml create mode 100644 sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml create mode 100644 sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml create mode 100644 sample_standard_app/app/core/tool/langchain_tool/sql_langchain_tool.py diff --git a/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml b/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml new file mode 100644 index 00000000..5b1ceab1 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml @@ -0,0 +1,13 @@ +name: 'info_sql_database_tool' +description: 'sql' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: InfoSQLDatabaseTool + init_params: + db_wrapper: demo_sqldb_wrapper +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.sql_langchain_tool' + class: 'SqlLangchainTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py b/sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py index 5a8a3d7a..91ad7f41 100644 --- a/sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py +++ b/sample_standard_app/app/core/tool/langchain_tool/langchain_tool.py @@ -29,7 +29,7 @@ def execute(self, tool_input: ToolInput): def initialize_by_component_configer(self, component_configer: ToolConfiger) -> 'Tool': super().initialize_by_component_configer(component_configer) self.tool = self.init_langchain_tool(component_configer) - if not component_configer.description: + if not component_configer.description and self.tool is not None: self.description = self.tool.description return self diff --git a/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml b/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml new file mode 100644 index 00000000..5fdfe828 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml @@ -0,0 +1,13 @@ +name: 'list_sql_database_tool' +description: 'sql' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: ListSQLDatabaseTool + init_params: + db_wrapper: demo_sqldb_wrapper +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.sql_langchain_tool' + class: 'SqlLangchainTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml b/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml new file mode 100644 index 00000000..f9b013b2 --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml @@ -0,0 +1,13 @@ +name: 'query_sql_database_tool' +description: 'sql' +tool_type: 'api' +input_keys: ['input'] +langchain: + module: langchain_community.tools + class_name: QuerySQLDataBaseTool + init_params: + db_wrapper: demo_sqldb_wrapper +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.langchain_tool.sql_langchain_tool' + class: 'SqlLangchainTool' \ No newline at end of file diff --git a/sample_standard_app/app/core/tool/langchain_tool/sql_langchain_tool.py b/sample_standard_app/app/core/tool/langchain_tool/sql_langchain_tool.py new file mode 100644 index 00000000..502e44ce --- /dev/null +++ b/sample_standard_app/app/core/tool/langchain_tool/sql_langchain_tool.py @@ -0,0 +1,39 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/9 20:04 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: sql_langchain_tool.py + +from typing import Type, Optional + +from langchain_core.tools import BaseTool, Tool as LangchainTool + +from agentuniverse.agent.action.tool.tool import ToolInput +from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager +from sample_standard_app.app.core.tool.langchain_tool.langchain_tool import LangChainTool + + +class SqlLangchainTool(LangChainTool): + db_wrapper_name: Optional[str] = "" + clz: Type[BaseTool] = BaseTool + + def execute(self, tool_input: ToolInput): + if self.tool is None: + self.get_sql_database() + return super().execute(tool_input) + + def get_sql_database(self): + db_wrapper = SQLDBWrapperManager().get_instance_obj(self.db_wrapper_name) + self.tool = self.clz(db=db_wrapper.sql_database) + self.description = self.tool.description + + def as_langchain(self) -> LangchainTool: + if self.tool is None: + self.get_sql_database() + return super().as_langchain() + + def get_langchain_tool(self, init_params: dict, clz: Type[BaseTool]): + self.db_wrapper_name = init_params.get("db_wrapper") + self.clz = clz From 4b23919af292792044a5678d8456d8c364c453b5 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 10 Jul 2024 10:43:57 +0800 Subject: [PATCH 105/118] fix yaml --- .../app/core/tool/langchain_tool/info_sql_database_tool.yaml | 2 +- .../app/core/tool/langchain_tool/list_sql_database_tool.yaml | 2 +- .../app/core/tool/langchain_tool/query_sql_database_tool.yaml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml b/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml index 5b1ceab1..7ceba6e2 100644 --- a/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml +++ b/sample_standard_app/app/core/tool/langchain_tool/info_sql_database_tool.yaml @@ -1,5 +1,5 @@ name: 'info_sql_database_tool' -description: 'sql' +description: '' tool_type: 'api' input_keys: ['input'] langchain: diff --git a/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml b/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml index 5fdfe828..e2852d0b 100644 --- a/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml +++ b/sample_standard_app/app/core/tool/langchain_tool/list_sql_database_tool.yaml @@ -1,5 +1,5 @@ name: 'list_sql_database_tool' -description: 'sql' +description: '' tool_type: 'api' input_keys: ['input'] langchain: diff --git a/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml b/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml index f9b013b2..e2f739a7 100644 --- a/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml +++ b/sample_standard_app/app/core/tool/langchain_tool/query_sql_database_tool.yaml @@ -1,5 +1,5 @@ name: 'query_sql_database_tool' -description: 'sql' +description: '' tool_type: 'api' input_keys: ['input'] langchain: From 15b8351e4edb4a76b928e66bb62b06e522ec9ea1 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Wed, 10 Jul 2024 11:44:48 +0800 Subject: [PATCH 106/118] feat: add the data agent to build datasets and evaluate the quality of results. --- .../guidebook/_picture/data_agent_dataset.png | Bin 334540 -> 123163 bytes .../_picture/data_agent_eval_report.png | Bin 96838 -> 109661 bytes .../data_agent_eval_report_qwen72b.png | Bin 0 -> 112507 bytes .../data_agent_eval_report_qwen7b.png | Bin 98726 -> 0 bytes .../_picture/data_agent_eval_result.png | Bin 137898 -> 202381 bytes ...73\346\231\272\350\203\275\344\275\223.md" | 29 ++++++++++++------ .../data_agent_case/dataset_eval_agent.py | 3 +- .../app/core/prompt/demo_rag_agent_cn.yaml | 2 -- .../app/core/prompt/demo_rag_agent_en.yaml | 2 -- .../dataset_turn_1_2024-07-09-20:46:11.jsonl | 2 -- .../dataset_turn_1_2024-07-10-10:48:30.jsonl | 3 ++ ...x => eval_report_2024-07-10-10:48:30.xlsx} | Bin 8864 -> 8910 bytes ...val_result_turn_1_2024-07-09-20:46:11.xlsx | Bin 8986 -> 0 bytes ...val_result_turn_1_2024-07-10-10:48:30.xlsx | Bin 0 -> 11753 bytes .../app/examples/data/queryset_cn.jsonl | 5 +-- 15 files changed, 26 insertions(+), 20 deletions(-) create mode 100644 docs/guidebook/_picture/data_agent_eval_report_qwen72b.png delete mode 100644 docs/guidebook/_picture/data_agent_eval_report_qwen7b.png delete mode 100644 sample_standard_app/app/examples/data/dataset_turn_1_2024-07-09-20:46:11.jsonl create mode 100644 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It enhances the data processing workflow of LLM and agents through **offline adaptive LLM data collection, fine-tuning dataset generation, model inference training, and multi-dimensional automatic dataset evaluation, etc.** + +Including: +1. A foundational data framework: **DataFramework**, structured around the Dispatch-Flow-Node-Data/Event/Prompt/Answer/Eval, with capabilities for sustainable integration and expansion. +2. An agent framework: **PVRP** (Planner-Verifier-Reflector-Producer). +3. A set of capabilities: + - Adaptive data event collection: **auto_event** + - Fine-tuning dataset generation: **dataset_build** + - Uploading for training and inference: **train_dump** + - Multi-dimensional objective automatic evaluation: **eval_report** + - Model deployment and activation: **model_deploy** + - System instruction optimization: **instruct_select** + +# Quick Start +In the agentUniverse standard project template `sample_standard_app`: +## step1 +Configure the loading path of prompt in config.toml, add `agentuniverse_dataflow.prompt`, and read the built-in prompt files of agentuniverse_dataflow. +```toml +[CORE_PACKAGE] +# Perform a full component scan and registration for all the paths under this list. +default = ['sample_standard_app.app.core'] +# Scan and register agent components for all paths under this list, with priority over the default. +agent = ['sample_standard_app.app.core.agent'] +# Scan and register agent components for all paths under this list, with priority over the default. +knowledge = ['sample_standard_app.app.core.knowledge'] +# Scan and register knowledge components for all paths under this list, with priority over the default. +llm = ['sample_standard_app.app.core.llm'] +# Scan and register llm components for all paths under this list, with priority over the default. +planner = ['sample_standard_app.app.core.planner'] +# Scan and register planner components for all paths under this list, with priority over the default. +tool = ['sample_standard_app.app.core.tool'] +# Scan and register memory components for all paths under this list, with priority over the default. +memory = ['sample_standard_app.app.core.memory'] +# Scan and register service components for all paths under this list, with priority over the default. +service = ['sample_standard_app.app.core.service'] +# Scan and register prompt components for all paths under this list, with priority over the default. +prompt = ['sample_standard_app.app.core.prompt', 'agentuniverse_dataflow.prompt'] +``` +## step2 +Under the dataflow directory of sample_standard_app, dispatch is used as the entry, configure the dataflow process in `dispatch.yaml`, and then run the `dispatch.py` file (the results of the dataflow run are stored in the dispatch/data directory as jsonl). + +![picture](../_picture/dataflow_dispatch.png) + +In the `dispatch.yaml` file, users can customize and edit the dataflow task nodes that need to be run, such as the following configuration file. After dataflow runs, it performs two tasks: **adaptive data event collection and fine-tuning dataset generation**. +```yaml +name: 'main_dispatch' +description: 'dispatch with multi-dataflows which will execute one after another' +dataflows: + - ../flow/auto_event.yaml + - ../flow/dataset_build.yaml +``` + +# Dataflow Introduction +agentUniverse-dataflow currently supports six kinds of flow, namely `auto_event/dataset_build/train_dump/eval_report/model_deploy/instruct_select`, and each flow is combined through nodes to form a corresponding pipeline. +## Dataflow Flowchart +![picture](../_picture/dataflow_flowchart.jpg) + +In the yaml configuration file of each flow, each node contains llm and prompt version configuration information. **Users can customize the llm and prompt to achieve version management and quick switching.** +For example, the node in the following figure uses the QWen model of the sample project and the built-in prompt file of agentuniverse_dataflow: + +![picture](../_picture/dataflow_dataset_build.png) +## Flow Detailed Introduction +**Special instruction:** +`Auto event/dataset build/eval report/instruct select` in dataflow is a flow node that **users can directly run and experience**. The `train dump and model deploy` node mainly **provide the flow concept**. At present, they have been deployed and run through the standard model and training platform in Ant Group, and the open source community version will consider opening up later. +### Auto Event +The main function of Auto Event is to collect different data sources, perceive the log information of the corresponding agent runtime in the data source, extract valid input and output to generate specific jsonl files (currently the collection of jsonl data source is supported, and more data source types remain open. + +Auto Event contains three nodes, `Perceiver/Planner/Executor`, configured through yaml, and the example configuration file is `auto_event.yaml` in sample_standard_app. + - Perceiver Node: perceive the data source and extracting the original input and output from the data source. + - Planner Node: generate code to extract valid model input and output from the data source. + - Executor Node: execute code to extract valid model input and output from the data source. + +### Dataset Build +The main function of Dataset Build is to generate domain-specific high-quality q&a datasets. + +Dataset Build contains six nodes, `Seed/Rewrite/Extend/Dedupe/Answer/Filter`, configured through yaml, and the example configuration file is `dataset_build.yaml` in sample_standard_app. + - Seed Node: user-specified domain to produce rough query sets. + - Rewrite Node: rewrite rough query sets to standard query sets. + - Extend Node: extent query sets. + - Dedupe Node: de-duplicate query sets. + - Answer Node: LLM calls the query sets to generate the q&a datasets. + - Filter Node: professional domain evaluation criteria to filter valid datasets. + +### Train Dump +The main function of Train Dump is to upload specified data sets, sft training model, and dump q&a result sets. + +Train Dump contains four nodes, `Upload/Train/PreDeploy/Dump`, configured through yaml, and the example configuration file is `train_dump.yaml` in sample_standard_app. + - Upload Node: upload datasets to the model training platform. + - Train Node: train model according to fine-tuning data sets. + - PreDeploy Node: pre-deploy the model after training. + - Dump Node: dump q&a result sets according to query sets. + +### Eval Report +The main function of Eval Report is to evaluate the quality of q&a result sets and generate evaluation reports. + +Eval Report contains two nodes, `Eval/Report`, configured through yaml, and the example configuration file is `eval_report.yaml` in sample_standard_app. + - Eval Node: multidimensional evaluation of the quality of q&a result sets. + - Report Node: generate quality assessment reports. + +### Model Deploy +The main function of Model Deploy is to formally deploy the post-training model, dump q&a result sets. + +Model Deploy contains two nodes, `Deploy/Dump`, configured through yaml, and the example configuration file is `model_deploy.yaml` in sample_standard_app. + - Deploy Node: formal deployment of the trained model. + - Dump Node: dump q&a result sets according to query sets. + +### Instruct Select +The main function of Instruct Select is to generate instruction sets, assemble complete prompt sets, dump q&a result sets, and evaluate the quality of result sets. + +Instruct Select contains four nodes, `Seed/PromptGen/Dump/Eval`, configured through yaml, and the example configuration file is `instruct_select.yaml` in sample_standard_app. + - Seed Node: generate instruction sets based on user-specified domain. + - PromptGen Node: merge instruction sets and query sets into complete prompt sets. + - Dump Node: dump q&a result sets according to query sets. + - Eval Node: multi-dimensional evaluation of the quality of q&a result sets. \ No newline at end of file diff --git a/docs/guidebook/zh/8_1_agentUniverse-dataflow.md b/docs/guidebook/zh/8_1_agentUniverse-dataflow.md new file mode 100644 index 00000000..f542dcf4 --- /dev/null +++ b/docs/guidebook/zh/8_1_agentUniverse-dataflow.md @@ -0,0 +1,115 @@ +# 项目介绍 +agentUniverse-dataflow基于**Agentic Workflow**思想，以多智能体及flow编排的形式，通过**离线自适应数据采集、微调数据集生成、模型推理训练、数据集多维度自动评估**等工作，强化大模型和Agent的数据处理流程。 + +具体包括： +1. 一套基础数据框架：**DataFramework**, 以Dispatch-Flow-Node-Data/Event/Prompt/Answer/Eval为框架，具有可持续集成和扩展能力 +2. 一套智能体框架：**PVRP**「Planner-Verifier-Reflector-Producer」 +3. 一套能力： + - 自适应数据事件采集 **auto_event** + - 微调数据集生成 **dataset_build** + - 上传训练推理 **train_dump** + - 多维客观自动评估 **eval_report** + - 模型部署生效 **model_deploy** + - 系统指令优选 **instruct_select** + +# 快速开始 +在agentUniverse标准工程模版`sample_standard_app`中： +## step1 +配置config.toml中prompt的加载路径，增加`agentuniverse_dataflow.prompt`，读取agentuniverse_dataflow系统内置prompt文件。 +```toml +[CORE_PACKAGE] +# Perform a full component scan and registration for all the paths under this list. +default = ['sample_standard_app.app.core'] +# Scan and register agent components for all paths under this list, with priority over the default. +agent = ['sample_standard_app.app.core.agent'] +# Scan and register agent components for all paths under this list, with priority over the default. +knowledge = ['sample_standard_app.app.core.knowledge'] +# Scan and register knowledge components for all paths under this list, with priority over the default. +llm = ['sample_standard_app.app.core.llm'] +# Scan and register llm components for all paths under this list, with priority over the default. +planner = ['sample_standard_app.app.core.planner'] +# Scan and register planner components for all paths under this list, with priority over the default. +tool = ['sample_standard_app.app.core.tool'] +# Scan and register memory components for all paths under this list, with priority over the default. +memory = ['sample_standard_app.app.core.memory'] +# Scan and register service components for all paths under this list, with priority over the default. +service = ['sample_standard_app.app.core.service'] +# Scan and register prompt components for all paths under this list, with priority over the default. +prompt = ['sample_standard_app.app.core.prompt', 'agentuniverse_dataflow.prompt'] +``` +## step2 +sample_standard_app的dataflow目录下，dispatch作为入口，在`dispatch.yaml`中配置dataflow流程，之后运行`dispatch.py`文件即可（dataflow运行结果以jsonl形式存储在dispatch/data目录下）。 + +![图片](../_picture/dataflow_dispatch.png) + +在dispatch.yaml文件中，用户可以自定义编辑需要运行的dataflow任务节点，如下文配置文件dataflow运行后，执行**自适应数据事件采集和微调数据集生成**两个任务。 +```yaml +name: 'main_dispatch' +description: 'dispatch with multi-dataflows which will execute one after another' +dataflows: + - ../flow/auto_event.yaml + - ../flow/dataset_build.yaml +``` +# Dataflow介绍 +agentUniverse-dataflow目前支持6种flow，分别为`auto_event/dataset_build/train_dump/eval_report/model_deploy/instruct_select`，每个flow通过node组合形成对应的pipeline。 +## Dataflow流程图 +![图片](../_picture/dataflow_flowchart.jpg) + +在每个flow的yaml配置文件，各node节点包含llm、prompt version配置信息，用户可自定义llm基座模型和prompt，实现版本管理和快捷切换。 +例如，下图中node节点使用sample工程的qwen基座模型和agentuniverse-dataflow系统内置的prompt文件： + +![图片](../_picture/dataflow_dataset_build.png) +## 各Flow具体详情 +**特殊说明：** +dataflow中`auto event/dataset build/eval report/instruce select`为用户可以**直接运行体验的flow节点**，`train dump/model deploy`节点**理念先行**，目前在蚂蚁集团内已通过标准的模型部署和训练平台运行，开源社区版本后续考虑开放。 + +### Auto Event +Auto Event主要功能是通过采集不同数据源，感知数据源中对应agent运行时的日志信息， 提取有效输入输出生成特定jsonl文件（当前支持采集jsonl数据源，更多数据源类型持续开放）。 + +Auto Event包含3个node，`Perceiver/Planner/Executor`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的auto_event.yaml。 + - Perceiver Node: 感知数据源，提取数据源中的原始输入输出 + - Planner Node: 生成代码用来提取数据源中的有效模型输入输出 + - Executor Node: 执行代码提取数据源中的有效模型输入输出 + +### Dataset Build +Dataset Build主要功能是生成特定领域高质量模型问答数据集。 + +Dataset Build包含6个node，`Seed/Rewrite/Extend/Dedupe/Answer/Filter`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的dataset_build.yaml。 + - Seed Node: 用户指定领域，生产粗略query集 + - Rewrite Node: 粗版query集改写为标准query集 + - Extend Node: 扩充query集 + - Dedupe Node: 对query集内容去重 + - Answer Node: query集，模型调用生成问答结果集 + - Filter Node: 专业领域评价标准，过滤有效数据集 + +### Train Dump +Train Dump主要功能是上传指定数据集，sft训练模型，dump问答结果集。 + +Train Dump包含4个node，`Upload/Train/PreDeploy/Dump`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的train_dump.yaml。 + - Upload Node: 上传数据集到模型训练平台 + - Train Node: 根据微调数据集训练模型 + - PreDeploy Node: 预部署训练后模型 + - Dump Node: 指定query集，dump问答结果 + +### Eval Report +Eval Report主要功能是多维度评估问答结果集质量，生成评估报告。 + +Eval Report包含2个node，`Eval/Report`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的eval_report.yaml。 + - Eval Node: 多维度评估问答结果集质量 + - Report Node: 生产质量评估报告 + +### Model Deploy +Model Deploy主要功能是正式部署训练后模型，dump问答数据集。 + +Model Deploy包含2个node，`Deploy/Dump`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的model_deploy.yaml。 + - Deploy Node: 正式部署训练后模型 + - Dump Node: 指定query集，dump问答结果 + +### Instruct Select +Instruct Select主要功能是生成指令集，拼接完整prompt集，dump问答数据集，评估问答质量。 + +Instruct Select包含4个node，`Seed/PromptGen/Dump/Eval`，pipeline流程通过yaml配置，示例配置文件为sample_standard_app中的instruct_select.yaml。 + - Seed Node: 用户指定领域，生产指令集 + - PromptGen Node: 指令集+query集合并完整prompt集 + - Dump Node: 指定prompt集，dump问答结果 + - Eval Node: 多维度评估问答结果集质量 \ No newline at end of file diff --git a/sample_standard_app/dataflow/flow/dataset_build.yaml b/sample_standard_app/dataflow/flow/dataset_build.yaml index ae1c7f34..74204632 100644 --- a/sample_standard_app/dataflow/flow/dataset_build.yaml +++ b/sample_standard_app/dataflow/flow/dataset_build.yaml @@ -36,7 +36,7 @@ nodes: - 'dataset_build_flow.rewrite.out' dataset_out_jsonl: 'dataset_build_flow.extend.out' node_param: - extend_times: 2 + extend_times: 1 - module: 'agentuniverse_dataflow.node.data.prompt.input.dedupe' # dedupe queries which improve sft result diff --git a/sample_standard_app/dataflow/flow/eval_report.yaml b/sample_standard_app/dataflow/flow/eval_report.yaml index 8c89643c..43fd585d 100644 --- a/sample_standard_app/dataflow/flow/eval_report.yaml +++ b/sample_standard_app/dataflow/flow/eval_report.yaml @@ -10,7 +10,8 @@ nodes: param_out_jsonl: datasets_in_jsonl: # - 'train_dump_flow.aci_dump.out' - - 'dataset_build_flow.filter.out' +# - 'dataset_build_flow.filter.out' + - 'demo_jsonl' dataset_out_jsonl: 'eval_report_flow.eval.out' node_param: eval_lines: 100 From 8d2d5902b8647f76ce071b38175a2b1f4526e5bd Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Fri, 21 Jun 2024 17:19:29 +0800 Subject: [PATCH 043/118] feat: add data agent in the agentUniverse. --- agentuniverse_dataflow/node/data/event/planner.py | 4 ++-- .../dispatch/data/dataset_build_flow.dedupe.out.jsonl | 10 ++++++++++ .../dataflow/dispatch/data/demo_jsonl.jsonl | 3 +-- sample_standard_app/dataflow/dispatch/dispatch.yaml | 3 ++- 4 files changed, 15 insertions(+), 5 deletions(-) create mode 100644 sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl diff --git a/agentuniverse_dataflow/node/data/event/planner.py b/agentuniverse_dataflow/node/data/event/planner.py index a6996239..4e7c3115 100644 --- a/agentuniverse_dataflow/node/data/event/planner.py +++ b/agentuniverse_dataflow/node/data/event/planner.py @@ -39,8 +39,8 @@ def _node_preprocess(self) -> None: if self._dataset_in_handler: perceived_list = self._dataset_in_handler.read_json_obj_list() # one for plan , at least one for verification - if not perceived_list or len(perceived_list) <= 1: - raise Exception('perceived json list does not provide at least 2 samples!') + if not perceived_list or len(perceived_list) < 1: + raise Exception('perceived json list does not provide at least 1 samples!') for i in range(0, len(perceived_list)): json_obj = perceived_list[i] diff --git a/sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl b/sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl new file mode 100644 index 00000000..de1ee0ec --- /dev/null +++ b/sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl @@ -0,0 +1,10 @@ +{"prompt": "怎样判断一个企业的经济状况稳健与否？"} +{"prompt": "做出资本配置决定时，有哪些核心标志可以体现公司的盈利表现？"} +{"prompt": "金融投资领域中，风险控制措施的重要性及其实行方法是怎样的？"} +{"prompt": "加密货币对常规金融市场的冲击与后续走向如何？"} +{"prompt": "在公司收购过程中，财务审查需着重检查哪几个关键点？"} +{"prompt": "如何评估一家银行的信贷风险并制定相应的风险管理策略？"} +{"prompt": "在进行跨国投资时，应考虑哪些宏观经济指标来预测市场趋势？"} +{"prompt": "保险业中的精算模型如何影响产品定价和企业利润？"} +{"prompt": "绿色金融的发展趋势及其对传统能源行业的影响是什么？"} +{"prompt": "金融科技如何改变个人理财规划和财富管理服务？"} diff --git a/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl b/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl index 59b1da46..08b84822 100644 --- a/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl +++ b/sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl @@ -1,2 +1 @@ -{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} -{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下英伟达股票大涨的原因", "answer": "英伟达股票大涨的原因可以归纳为以下几个方面：\\n\\n1. **股票拆分**：英伟达宣布的股票拆分计划增加了股票的流动性和吸引力，使得更多投资者能够参与投资，这一举措直接导致股价在宣布后上涨了27%。\\n\\n2. **市场分析乐观**：投资机构KeyBanc Capital Markets将英伟达的目标股价上调至1200美元，显示出市场对英伟达未来业绩的强烈信心，这对股价构成显著正面影响。\\n\\n3. **季度业绩预期**：市场对英伟达即将公布的季度业绩持积极态度，分析师认为业绩表现将超出预期，这种正面预期促使投资者提前买入，推高股价。\\n\\n4. **行业领导地位**：英伟达作为人工智能和高性能计算领域的领导者，其技术优势和市场地位不断巩固，吸引了大量基于长期增长潜力的投资。\\n\\n5. **市场情绪**：英伟达股价创新高不仅提升了自身市值，还激发了市场对于人工智能板块乃至整个股市的热情，带动了全球股市上扬。\\n\\n6. **宏观经济因素**：虽然背景信息未详细提及，但通常股市整体表现、经济环境、政策支持等因素也会影响个股表现，英伟达股价上涨可能也受益于较为有利的宏观经济背景。\\n\\n综上所述，英伟达股价的大涨是多因素共同作用的结果，包括公司自身的战略决策、市场对其业绩的乐观预期、行业地位的强化以及良好的市场情绪等。"} \ No newline at end of file +{"prompt": "你是一位精通信息分析的ai助手。你的目标是判断问题对应的答案是否提供了有价值的信息，并对问题的答案做出建议和评价。需要回答的问题是: 分析下巴菲特减持比亚迪的原因", "answer": "巴菲特减持比亚迪的原因可以从以下几个方面进行分析：\\\\n\\\\n1. **估值偏高**：\\\\n - 巴菲特在公开采访中提到：“以目前的股价计算，比亚迪市值比梅赛德斯-奔驰还高，这不是一只便宜的股票。”这表明他认为比亚迪当前的估值过高，不再具备吸引力。\\\\n\\\\n2. **获利了结**：\\\\n - 巴菲特在过去7个月内多次减持比亚迪股份，可能是因为他已经从中获得了巨大的浮盈，选择在高位获利了结。这种操作符合巴菲特一贯的投资逻辑，即在高估时卖出。\\\\n\\\\n3. **规避风险**：\\\\n - 美国汇盛金融管理公司首席经济学家陈凯丰指出，巴菲特的减持行为也有规避风险的考量。随着新能源汽车市场竞争加剧，未来的不确定性增加，减持可以降低投资组合的风险。\\\\n\\\\n4. **市场竞争加剧**：\\\\n - 尽管比亚迪近年来借新能源汽车的东风高速发展，但市场竞争趋于白热化。这种竞争环境可能增加公司的经营风险，从而影响投资回报。\\\\n\\\\n5. **行业渗透率增速放缓**：\\\\n - 业内人士认为，9月电动车行业整体渗透率提升的增速已经放缓。尽管比亚迪三季报业绩优异，但未来业绩增长潜力可能受到限制，这也是巴菲特减持的一个原因。\\\\n\\\\n6. **上游原材料价格波动**：\\\\n - 比亚迪的业绩增长部分得益于上游碳酸锂价格的下滑，但这种波动性也可能带来不确定性。巴菲特可能考虑到这种不确定性，选择减持。综上所述，巴菲特减持比亚迪的原因主要包括估值偏高、获利了结、规避风险、市场竞争加剧、行业渗透率增速放缓以及上游原材料价格波动等因素。"} \ No newline at end of file diff --git a/sample_standard_app/dataflow/dispatch/dispatch.yaml b/sample_standard_app/dataflow/dispatch/dispatch.yaml index 49d65509..f73410b9 100644 --- a/sample_standard_app/dataflow/dispatch/dispatch.yaml +++ b/sample_standard_app/dataflow/dispatch/dispatch.yaml @@ -1,4 +1,5 @@ name: 'main_dispatch' description: 'dispatch with multi-dataflows which will execute one after another' dataflows: - - ../flow/auto_event.yaml \ No newline at end of file + - ../flow/eval_report.yaml + - ../flow/instruct_select.yaml \ No newline at end of file From 85d01c6f00218aa9c4a86bd4c9b6565a357b7a64 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Sat, 22 Jun 2024 12:49:42 +0800 Subject: [PATCH 044/118] Removed: move dataflow to sepc branch --- agentuniverse_dataflow/__init__.py | 7 - agentuniverse_dataflow/flow/__init__.py | 7 - agentuniverse_dataflow/flow/dataflow.py | 90 -------- agentuniverse_dataflow/node/__init__.py | 7 - agentuniverse_dataflow/node/base/__init__.py | 7 - .../node/base/data_node_base.py | 17 -- .../node/base/eval_node_base.py | 19 -- .../node/base/model_node_base.py | 35 --- agentuniverse_dataflow/node/base/node_base.py | 96 -------- agentuniverse_dataflow/node/data/__init__.py | 7 - .../node/data/answer/__init__.py | 7 - .../node/data/answer/answer.py | 39 ---- .../node/data/answer/filter.py | 122 ---------- .../node/data/base/__init__.py | 7 - .../node/data/base/prompt_answer_base.py | 32 --- .../node/data/base/prompt_base.py | 32 --- .../node/data/event/__init__.py | 7 - .../node/data/event/executor.py | 90 -------- .../node/data/event/perceiver.py | 60 ----- .../node/data/event/planner.py | 214 ------------------ .../node/data/prompt/__init__.py | 7 - .../node/data/prompt/input/__init__.py | 7 - .../node/data/prompt/input/dedupe.py | 72 ------ .../node/data/prompt/input/extend.py | 58 ----- .../node/data/prompt/input/rewrite.py | 44 ---- .../node/data/prompt/input/seed.py | 66 ------ .../node/data/prompt/instruct/__init__.py | 7 - .../node/data/prompt/instruct/prompt_gen.py | 52 ----- .../node/data/prompt/instruct/seed.py | 43 ---- agentuniverse_dataflow/node/enum/__init__.py | 7 - agentuniverse_dataflow/node/enum/enum.py | 39 ---- agentuniverse_dataflow/node/eval/__init__.py | 7 - agentuniverse_dataflow/node/eval/eval.py | 100 -------- agentuniverse_dataflow/node/eval/report.py | 67 ------ agentuniverse_dataflow/node/model/__init__.py | 7 - .../node/model/base/__init__.py | 7 - .../node/model/base/deploy_base.py | 21 -- .../node/model/base/dump_base.py | 29 --- .../node/model/base/pre_deploy_base.py | 21 -- .../node/model/base/train_base.py | 23 -- .../node/model/base/upload_base.py | 29 --- agentuniverse_dataflow/prompt/__init__.py | 7 - .../prompt/dataset_extend_node_cn.yaml | 23 -- .../prompt/dataset_filter_node_cn.yaml | 82 ------- .../prompt/dataset_input_seed_node_cn.yaml | 26 --- .../prompt/dataset_rewrite_node_cn.yaml | 27 --- .../prompt/eval_node_cn.yaml | 38 ---- .../prompt/event_planner_node_cn.yaml | 37 --- .../prompt/instruct_prompt_gen_cn.yaml | 7 - .../prompt/instruct_seed_node_cn.yaml | 32 --- agentuniverse_dataflow/util/__init__.py | 7 - .../util/constant/__init__.py | 7 - .../util/constant/eval_node_dimensions.py | 150 ------------ .../util/fileio/__init__.py | 7 - .../util/fileio/node_msg_jsonl.py | 150 ------------ agentuniverse_dataflow/util/llm/__init__.py | 7 - agentuniverse_dataflow/util/llm/llm_call.py | 58 ----- .../_picture/dataflow_dataset_build.png | Bin 99507 -> 0 bytes docs/guidebook/_picture/dataflow_dispatch.png | Bin 65843 -> 0 bytes .../guidebook/_picture/dataflow_flowchart.jpg | Bin 1402069 -> 0 bytes .../en/8_1_agentUniverse-dataflow.md | 115 ---------- .../zh/8_1_agentUniverse-dataflow.md | 115 ---------- sample_standard_app/dataflow/__init__.py | 7 - .../dataflow/dispatch/__init__.py | 7 - .../data/dataset_build_flow.dedupe.out.jsonl | 10 - .../dataflow/dispatch/data/demo_jsonl.jsonl | 1 - .../dataflow/dispatch/dispatch.py | 38 ---- .../dataflow/dispatch/dispatch.yaml | 5 - sample_standard_app/dataflow/flow/__init__.py | 7 - .../dataflow/flow/auto_event.yaml | 42 ---- .../dataflow/flow/dataset_build.yaml | 77 ------- .../dataflow/flow/eval_report.yaml | 28 --- .../dataflow/flow/instruct_select.yaml | 53 ----- .../dataflow/flow/model_deploy.yaml | 21 -- .../dataflow/flow/train_dump.yaml | 42 ---- 75 files changed, 2848 deletions(-) delete mode 100644 agentuniverse_dataflow/__init__.py delete mode 100644 agentuniverse_dataflow/flow/__init__.py delete mode 100644 agentuniverse_dataflow/flow/dataflow.py delete mode 100644 agentuniverse_dataflow/node/__init__.py delete mode 100644 agentuniverse_dataflow/node/base/__init__.py delete mode 100644 agentuniverse_dataflow/node/base/data_node_base.py delete mode 100644 agentuniverse_dataflow/node/base/eval_node_base.py delete mode 100644 agentuniverse_dataflow/node/base/model_node_base.py delete mode 100644 agentuniverse_dataflow/node/base/node_base.py delete mode 100644 agentuniverse_dataflow/node/data/__init__.py delete mode 100644 agentuniverse_dataflow/node/data/answer/__init__.py delete mode 100644 agentuniverse_dataflow/node/data/answer/answer.py delete mode 100644 agentuniverse_dataflow/node/data/answer/filter.py delete mode 100644 agentuniverse_dataflow/node/data/base/__init__.py delete mode 100644 agentuniverse_dataflow/node/data/base/prompt_answer_base.py delete mode 100644 agentuniverse_dataflow/node/data/base/prompt_base.py delete mode 100644 agentuniverse_dataflow/node/data/event/__init__.py delete mode 100644 agentuniverse_dataflow/node/data/event/executor.py delete mode 100644 agentuniverse_dataflow/node/data/event/perceiver.py delete mode 100644 agentuniverse_dataflow/node/data/event/planner.py delete mode 100644 agentuniverse_dataflow/node/data/prompt/__init__.py delete mode 100644 agentuniverse_dataflow/node/data/prompt/input/__init__.py delete mode 100644 agentuniverse_dataflow/node/data/prompt/input/dedupe.py delete mode 100644 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sample_standard_app/dataflow/dispatch/data/dataset_build_flow.dedupe.out.jsonl delete mode 100644 sample_standard_app/dataflow/dispatch/data/demo_jsonl.jsonl delete mode 100644 sample_standard_app/dataflow/dispatch/dispatch.py delete mode 100644 sample_standard_app/dataflow/dispatch/dispatch.yaml delete mode 100644 sample_standard_app/dataflow/flow/__init__.py delete mode 100644 sample_standard_app/dataflow/flow/auto_event.yaml delete mode 100644 sample_standard_app/dataflow/flow/dataset_build.yaml delete mode 100644 sample_standard_app/dataflow/flow/eval_report.yaml delete mode 100644 sample_standard_app/dataflow/flow/instruct_select.yaml delete mode 100644 sample_standard_app/dataflow/flow/model_deploy.yaml delete mode 100644 sample_standard_app/dataflow/flow/train_dump.yaml diff --git a/agentuniverse_dataflow/__init__.py b/agentuniverse_dataflow/__init__.py deleted file mode 100644 index ebd94327..00000000 --- a/agentuniverse_dataflow/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/11 20:46 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/flow/__init__.py b/agentuniverse_dataflow/flow/__init__.py deleted file mode 100644 index 582b80f7..00000000 --- a/agentuniverse_dataflow/flow/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 16:58 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/flow/dataflow.py b/agentuniverse_dataflow/flow/dataflow.py deleted file mode 100644 index 2784d82c..00000000 --- a/agentuniverse_dataflow/flow/dataflow.py +++ /dev/null @@ -1,90 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:05 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: dataflow.py -import importlib -import sys -import traceback -from typing import List, Optional - -from pydantic import BaseModel - -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.base.config.configer import Configer -from agentuniverse_dataflow.node.base.node_base import NodeBase - - -class Dataflow(BaseModel): - """The Dataflow class, which is used to define the class of data flow.""" - - _flow_name: str = None - _flow_desc: str = None - _node_sequence_list: List[NodeBase] = None - _llm_name: str = None - _configer: Optional[Configer] = None - - def __init__(self, conf_path: str = None): - super().__init__() - if conf_path: - self._configer = Configer(conf_path) - - def _flow_preprocess(self) -> None: - self._configer.load() - self._flow_name = self._configer.get('name') - self._flow_desc = self._configer.get('description') - - nodes = self._configer.get('nodes') - if not nodes: - LOGGER.error('no nodes in yaml conf') - return - - self._node_sequence_list = [] - for i in range(0, len(nodes)): - node_obj = nodes[i] - module_str = node_obj.get('module') - class_str = node_obj.get('class') - param_in_jsonl_str = node_obj.get('param_in_jsonl') - param_out_json_str = node_obj.get('param_out_jsonl') - datasets_in_jsonl_list = node_obj.get('datasets_in_jsonl') - dataset_out_jsonl_str = node_obj.get('dataset_out_jsonl') - node_param_json = node_obj.get('node_param') - - node_llm = node_obj.get('llm') - node_prompt_version = node_obj.get('prompt_version') - - module = importlib.import_module(module_str) - clz = getattr(module, class_str) - node: NodeBase = clz() - node.set_param_in_jsonl(param_in_jsonl_str) - node.set_param_out_jsonl(param_out_json_str) - node.set_datasets_in_jsonl(datasets_in_jsonl_list) - node.set_dataset_out_jsonl(dataset_out_jsonl_str) - node.set_node_param_json(node_param_json) - node.set_llm(node_llm) - node.set_prompt_version(node_prompt_version) - - self._node_sequence_list.append(node) - - return - - def _flow_process(self) -> None: - if self._node_sequence_list: - for i in range(0, len(self._node_sequence_list)): - self._node_sequence_list[i].execute() - - def _flow_postprocess(self) -> None: - pass - - def execute(self) -> None: - try: - self._flow_preprocess() - self._flow_process() - self._flow_postprocess() - except Exception as e: - # logging later - LOGGER.warn(e) - LOGGER.warn(traceback.format_exc()) - LOGGER.warn(traceback.extract_tb(sys.exc_info())) diff --git a/agentuniverse_dataflow/node/__init__.py b/agentuniverse_dataflow/node/__init__.py deleted file mode 100644 index 63761024..00000000 --- a/agentuniverse_dataflow/node/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:06 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/base/__init__.py b/agentuniverse_dataflow/node/base/__init__.py deleted file mode 100644 index 63761024..00000000 --- a/agentuniverse_dataflow/node/base/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:06 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/base/data_node_base.py b/agentuniverse_dataflow/node/base/data_node_base.py deleted file mode 100644 index 43a5cb43..00000000 --- a/agentuniverse_dataflow/node/base/data_node_base.py +++ /dev/null @@ -1,17 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:06 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: data_node.py -from agentuniverse_dataflow.node.base.node_base import NodeBase - - -class DataNodeBase(NodeBase): - """The DataBase class, which is used to define the base class of data node.""" - _batch_line_size: int = 10 - _batch_prompt_size: int = 10 - - def _node_process(self) -> None: - pass diff --git a/agentuniverse_dataflow/node/base/eval_node_base.py b/agentuniverse_dataflow/node/base/eval_node_base.py deleted file mode 100644 index 59e7223c..00000000 --- a/agentuniverse_dataflow/node/base/eval_node_base.py +++ /dev/null @@ -1,19 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:31 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: eval_node_base.py -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.node_base import NodeBase - - -class EvalNodeBase(NodeBase): - """The EvalNodeBase class, which is used to define the base class of eval node.""" - - def __init__(self): - super().__init__(node_type=NodeEnum.EVAL) - - def _node_process(self) -> None: - pass diff --git a/agentuniverse_dataflow/node/base/model_node_base.py b/agentuniverse_dataflow/node/base/model_node_base.py deleted file mode 100644 index f07e59c4..00000000 --- a/agentuniverse_dataflow/node/base/model_node_base.py +++ /dev/null @@ -1,35 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:32 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: model_node_base.py -import json - -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.node_base import NodeBase - - -class ModelNodeBase(NodeBase): - """The ModelNodeBase class, which is used to define the base class of model node.""" - - # a json line in jsonl for msg between nodes - _param_in_json_obj: json = None - _platform_mode_is_pro: bool = False - - def __init__(self): - super().__init__(node_type=NodeEnum.MODEL) - - def _node_preprocess(self) -> None: - super()._node_preprocess() - if self._param_in_handler: - self._param_in_json_obj = self._param_in_handler.read_json_obj() - - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - super()._node_postprocess() - if self._param_in_json_obj and self._param_out_handler: - self._param_out_handler.write_json_obj(self._param_in_json_obj) diff --git a/agentuniverse_dataflow/node/base/node_base.py b/agentuniverse_dataflow/node/base/node_base.py deleted file mode 100644 index 0c5da8ca..00000000 --- a/agentuniverse_dataflow/node/base/node_base.py +++ /dev/null @@ -1,96 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:06 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: node_base.py -from abc import abstractmethod -import json -from typing import Any, List, Dict, Optional - -from pydantic import BaseModel - -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileWriter, JsonFileReader - - -class NodeBase(BaseModel): - """The NodeBase class, which is used to define the base class of node.""" - - node_type: NodeEnum = None - _is_flow_start_node: bool = False - - # jsonl message between nodes - param_in_jsonl: str = None - datasets_in_jsonl: List[str] = None - param_out_jsonl: str = None - dataset_out_jsonl: str = None - - llm: str = None - prompt_version: str = None - - node_param_json: Dict[str, Any] = None - - _param_in_handler: Any = None - _param_out_handler: Any = None - _dataset_in_handler: Any = None - _dataset_out_handler: Any = None - - def set_flow_start_node(self) -> None: - self._is_flow_start_node = True - - def set_param_in_jsonl(self, param_in_jsonl: str) -> None: - self.param_in_jsonl = param_in_jsonl - - def set_param_out_jsonl(self, param_out_jsonl: str) -> None: - self.param_out_jsonl = param_out_jsonl - - def set_datasets_in_jsonl(self, datasets_in_jsonl: list[str]) -> None: - self.datasets_in_jsonl = datasets_in_jsonl - - def set_dataset_out_jsonl(self, dataset_out_jsonl: str) -> None: - self.dataset_out_jsonl = dataset_out_jsonl - - def set_node_param_json(self, node_param_json: json) -> None: - if node_param_json: - self.node_param_json = node_param_json - - def set_llm(self, llm: str) -> None: - if llm: - self.llm = llm - - def set_prompt_version(self, prompt_version: str) -> None: - if prompt_version: - self.prompt_version = prompt_version - - def _node_preprocess(self) -> None: - if self.param_in_jsonl: - self._param_in_handler = JsonFileReader(self.param_in_jsonl) - # init the first dataset as default - if self.datasets_in_jsonl and len(self.datasets_in_jsonl) > 0: - self._dataset_in_handler = JsonFileReader(self.datasets_in_jsonl[0]) - if self.param_out_jsonl: - self._param_out_handler = JsonFileWriter(self.param_out_jsonl) - if self.dataset_out_jsonl: - self._dataset_out_handler = JsonFileWriter(self.dataset_out_jsonl) - - @abstractmethod - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - pass - - def execute(self) -> None: - - self._node_preprocess() - self._node_process() - self._node_postprocess() - - def _get_node_param(self, key: str, default=None) -> Optional[any]: - if self.node_param_json: - if key in self.node_param_json: - return self.node_param_json.get(key) - - return default diff --git a/agentuniverse_dataflow/node/data/__init__.py b/agentuniverse_dataflow/node/data/__init__.py deleted file mode 100644 index fdd5faaa..00000000 --- a/agentuniverse_dataflow/node/data/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:18 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/answer/__init__.py b/agentuniverse_dataflow/node/data/answer/__init__.py deleted file mode 100644 index fdd5faaa..00000000 --- a/agentuniverse_dataflow/node/data/answer/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:18 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/answer/answer.py b/agentuniverse_dataflow/node/data/answer/answer.py deleted file mode 100644 index bda85155..00000000 --- a/agentuniverse_dataflow/node/data/answer/answer.py +++ /dev/null @@ -1,39 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- -# @Time : 2024/6/16 19:19 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: answer.py -from agentuniverse_dataflow.node.data.base.prompt_answer_base import PromptAnswerBase -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class AnswerNode(PromptAnswerBase): - """The RewriteNode class, which is used to define the class of rewrite node.""" - - def _node_process(self) -> None: - if not self._prompt_answer_list or len(self._prompt_answer_list) == 0: - return - - total_prompt_num = 0 - prompts = [] - prompt_list_num = len(self._prompt_answer_list) - prompt_answer_list = self._prompt_answer_list - self._prompt_answer_list = [] - for i in range(0, prompt_list_num): - prompt = prompt_answer_list[i][0] - prompts.append(prompt) - total_prompt_num = total_prompt_num + 1 - if total_prompt_num % self._batch_prompt_size == 0: - res = batch_call(prompts, self.llm) - for prompt, answer in zip(prompts, res): - if res is not None: - self._prompt_answer_list.append((prompt, answer)) - prompts = [] - else: - if total_prompt_num == prompt_list_num and len(prompts) > 0: - res = batch_call(prompts, self.llm) - for prompt, answer in zip(prompts, res): - if res is not None: - self._prompt_answer_list.append((prompt, answer)) - return diff --git a/agentuniverse_dataflow/node/data/answer/filter.py b/agentuniverse_dataflow/node/data/answer/filter.py deleted file mode 100644 index 41212ab4..00000000 --- a/agentuniverse_dataflow/node/data/answer/filter.py +++ /dev/null @@ -1,122 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 14:46 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: filter.py -import json - -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.data.base.prompt_answer_base import PromptAnswerBase -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader, JsonFileWriter -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class FilterNode(PromptAnswerBase): - """The FilterNode class, which is used to define the class of filter node.""" - - dimscore_threshold: int = 70 - avgscore_threshold: int = 70 - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - self.dimscore_threshold = self._get_node_param('dimscore_threshold') - self.avgscore_threshold = self._get_node_param('avgscore_threshold') - - def _node_process(self) -> None: - if not self._prompt_answer_list or len(self._prompt_answer_list) == 0: - return - - self.__quality_eval() - self.__filter_items() - - def __quality_eval(self) -> None: - prompts = [] - total_lines = 0 - jfw_quality = JsonFileWriter(self.dataset_out_jsonl + '.eval') - - list_len = len(self._prompt_answer_list) - for i in range(0, list_len): - do_req = False - prompt = self._prompt_answer_list[i][0] - answer = self._prompt_answer_list[i][1] - total_lines += 1 - - if not prompt or not answer: - continue - - prompts.append(self.generate_prompt(prompt, answer)) - - if total_lines % self._batch_prompt_size == 0: - do_req = True - elif i + 1 == list_len and len(prompts) > 0: - do_req = True - - if do_req: - res = batch_call(prompts, self.llm) - - start_line_num = total_lines - len(prompts) - for res_idx in range(0, len(res)): - try: - if res[res_idx] and res[res_idx] != '': - json_obj = parse_json_markdown(str(res[res_idx])) - json_obj['line'] = start_line_num + res_idx + 1 - jfw_quality.write_json_obj(json_obj) - except json.JSONDecodeError as e: - LOGGER.warn(f'except[__quality_eval]>>>{e}:{res[res_idx]}') - prompts = [] - - def __filter_items(self) -> None: - jfr_quality = JsonFileReader(self.dataset_out_jsonl + '.eval') - jfw_pos = JsonFileWriter(self.dataset_out_jsonl + '.pos') - jfw_neg = JsonFileWriter(self.dataset_out_jsonl + '.neg') - - prompt_answer_list = self._prompt_answer_list - self._prompt_answer_list = [] - - while True: - json_obj = jfr_quality.read_json_obj() - if json_obj: - dimensions = json_obj.get('dimensions') - - score_sum = 0.0 - is_pos = True - for i in range(0, len(dimensions)): - score = float(dimensions[i].get('score')) - score_sum = score_sum + score - if score < self.dimscore_threshold: - is_pos = False - - avg_score = score_sum / len(dimensions) - if avg_score < self.avgscore_threshold: - is_pos = False - - if is_pos: - jfw_pos.write_json_obj(json_obj) - if 'line' in json_obj: - line = json_obj['line'] - if line > 0: - self._prompt_answer_list.append(prompt_answer_list[line - 1]) - else: - jfw_neg.write_json_obj(json_obj) - - else: - break - - def generate_prompt(self, prompt_str: str, answer_str: str) -> str: - if len(prompt_str) > 2000: - prompt_str = prompt_str[0:2000] - if len(answer_str) > 5000: - answer = answer_str[0:5000] - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - - prompt = version_prompt.prompt_template.replace("", prompt_str) - prompt = prompt.replace("", answer_str) - - return prompt diff --git a/agentuniverse_dataflow/node/data/base/__init__.py b/agentuniverse_dataflow/node/data/base/__init__.py deleted file mode 100644 index 01448f22..00000000 --- a/agentuniverse_dataflow/node/data/base/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:19 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/base/prompt_answer_base.py b/agentuniverse_dataflow/node/data/base/prompt_answer_base.py deleted file mode 100644 index 7afc79a6..00000000 --- a/agentuniverse_dataflow/node/data/base/prompt_answer_base.py +++ /dev/null @@ -1,32 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:20 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: prompt_answer_base.py -from typing import List, Tuple - -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase - - -class PromptAnswerBase(DataNodeBase): - """The PromptAnswerNodeBase class, which is used to define the base class of prompt answer node.""" - - _prompt_answer_list: List[Tuple[str, str]] = None - - def __init__(self, *args, **kwargs): - super().__init__(node_type=NodeEnum.PROMPT_ANSWER) - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - if self._dataset_in_handler: - self._prompt_answer_list = self._dataset_in_handler.read_json_prompt_answer_list() - - def _node_postprocess(self) -> None: - super()._node_postprocess() - - if self._prompt_answer_list: - self._dataset_out_handler.write_json_prompt_answer_list(self._prompt_answer_list) diff --git a/agentuniverse_dataflow/node/data/base/prompt_base.py b/agentuniverse_dataflow/node/data/base/prompt_base.py deleted file mode 100644 index 1819a4b0..00000000 --- a/agentuniverse_dataflow/node/data/base/prompt_base.py +++ /dev/null @@ -1,32 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:19 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: prompt_base.py -from typing import List - -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase - - -class PromptBase(DataNodeBase): - """The PromptNodeBase class, which is used to define the base class of prompt node.""" - - _prompt_list: List[str] = None - - def __init__(self, *args, **kwargs): - super().__init__(node_type=NodeEnum.PROMPT) - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - if self._dataset_in_handler: - self._prompt_list = self._dataset_in_handler.read_json_prompt_list() - - def _node_postprocess(self) -> None: - super()._node_postprocess() - - if self._prompt_list and self._dataset_out_handler: - self._dataset_out_handler.write_json_prompt_list(self._prompt_list) diff --git a/agentuniverse_dataflow/node/data/event/__init__.py b/agentuniverse_dataflow/node/data/event/__init__.py deleted file mode 100644 index e2b69ee8..00000000 --- a/agentuniverse_dataflow/node/data/event/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:21 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/event/executor.py b/agentuniverse_dataflow/node/data/event/executor.py deleted file mode 100644 index 78d996dd..00000000 --- a/agentuniverse_dataflow/node/data/event/executor.py +++ /dev/null @@ -1,90 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:22 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: executor.py -from typing import List, Tuple, Dict - -import pandas as pd - -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader - - -class ExecutorNode(DataNodeBase): - """The ExecutorNode class, which is used to define the class of executor node.""" - - event_db: str = None - event_uri: str = None - event_sql: str = None - prompt_col: str = None - answer_col: str = None - - _data_frame: pd.DataFrame = None - _plan_dict: Dict = None - _input_data_jsonlist: List[Dict] = None - _dump_prompt_answer_list: List[Tuple[str, str]] = [] - - def __init__(self, *args, **kwargs): - super().__init__(node_type=NodeEnum.PROMPT_ANSWER) - - def _node_preprocess(self) -> None: - super()._node_preprocess() - if self._param_in_handler: - self._plan_dict = self._param_in_handler.read_json_obj() - - self.event_db = self._plan_dict.get('event_db') - self.event_uri = self._plan_dict.get('event_uri') - self.event_sql = self._plan_dict.get('event_sql') - self.prompt_col = self._plan_dict.get('prompt_col') - self.answer_col = self._plan_dict.get('answer_col') - - def _node_process(self) -> None: - if self.event_db: - if self.event_db == 'jsonl': - self._input_data_jsonlist = JsonFileReader(self.event_uri).read_json_obj_list() - self.__dump_from_plan() - - def __dump_from_plan(self): - prompt_plan_code = self._plan_dict.get('prompt_plan').get('plan_code') - answer_plan_code = self._plan_dict.get('answer_plan').get('plan_code') - prompt_input_var = self._plan_dict.get('prompt_plan').get('input_var') - answer_input_var = self._plan_dict.get('answer_plan').get('input_var') - prompt_output_var = self._plan_dict.get('prompt_plan').get('output_var') - answer_output_var = self._plan_dict.get('answer_plan').get('output_var') - - for i in range(0, len(self._input_data_jsonlist)): - prompt_related_str = self._input_data_jsonlist[i].get(self.prompt_col) - answer_related_str = self._input_data_jsonlist[i].get(self.answer_col) - - prompt_namespace = { - prompt_input_var: prompt_related_str, - prompt_output_var: '' - } - answer_namespace = { - answer_input_var: answer_related_str, - answer_output_var: '' - } - - try: - exec(prompt_plan_code, globals(), prompt_namespace) - exec(answer_plan_code, globals(), answer_namespace) - except Exception as e: - LOGGER.warn( - f'exception from exec>>>{e}:\nprompt_plan_code:{prompt_plan_code}\nanswer_plan_code:{answer_plan_code}') - break - - prompt = prompt_namespace.get(prompt_output_var) - answer = answer_namespace.get(answer_output_var) - self._dump_prompt_answer_list.append((prompt, answer)) - - if self._dataset_out_handler: - self._dataset_out_handler.write_json_prompt_answer_list(self._dump_prompt_answer_list) - self._dump_prompt_answer_list.clear() - - def _node_postprocess(self) -> None: - super()._node_postprocess() diff --git a/agentuniverse_dataflow/node/data/event/perceiver.py b/agentuniverse_dataflow/node/data/event/perceiver.py deleted file mode 100644 index c9384c7b..00000000 --- a/agentuniverse_dataflow/node/data/event/perceiver.py +++ /dev/null @@ -1,60 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:25 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: perceiver.py -from typing import List, Dict - -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader - - -class PerceiverNode(DataNodeBase): - """The PerceiverNode class, which is used to define the class of perceiver node.""" - - event_db: str = None - event_uri: str = None - event_sql: str = None - prompt_col: str = None - answer_col: str = None - - _input_data_jsonlist: List[Dict] = None - - def __init__(self, *args, **kwargs): - super().__init__(node_type=NodeEnum.PROMPT_ANSWER) - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - self.event_db = self._get_node_param('event_db') - self.event_uri = self._get_node_param('event_uri') - self.event_sql = self._get_node_param('event_sql') - self.prompt_col = self._get_node_param('prompt_col') - self.answer_col = self._get_node_param('answer_col') - - def _node_process(self) -> None: - if self.event_db: - if self.event_db == 'jsonl': - self._input_data_jsonlist = JsonFileReader(self.event_uri).read_json_obj_list() - - def _node_postprocess(self) -> None: - super()._node_postprocess() - if self._input_data_jsonlist is not None and self._dataset_out_handler: - for i in range(0, len(self._input_data_jsonlist)): - prompt_related_str = self._input_data_jsonlist[i].get(self.prompt_col) - answer_related_str = self._input_data_jsonlist[i].get(self.answer_col) - - self._dataset_out_handler.write_json_prompt_answer(prompt_related_str, answer_related_str) - - if self._param_out_handler: - param_obj = { - 'event_db': self.event_db, - 'event_uri': self.event_uri, - 'event_sql': self.event_sql, - 'prompt_col': self.prompt_col, - 'answer_col': self.answer_col - } - self._param_out_handler.write_json_obj(param_obj) diff --git a/agentuniverse_dataflow/node/data/event/planner.py b/agentuniverse_dataflow/node/data/event/planner.py deleted file mode 100644 index 4e7c3115..00000000 --- a/agentuniverse_dataflow/node/data/event/planner.py +++ /dev/null @@ -1,214 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/16 19:26 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: planner.py -import json -from typing import List, Dict - -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader -from agentuniverse_dataflow.node.enum.enum import NodeEnum -from agentuniverse_dataflow.node.base.data_node_base import DataNodeBase -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class PlannerNode(DataNodeBase): - """The PlannerNode class, which is used to define the class of planner node.""" - - _perceived_prompt_list: List[str] = [] - _perceived_answer_list: List[str] = [] - _perceived_list_size: int = 0 - - _new_plan_dict: Dict = {} - _last_plan_dict: Dict = None - _verify_lines: int = None - - def __init__(self, *args, **kwargs): - super().__init__(node_type=NodeEnum.PROMPT_ANSWER) - - def _node_preprocess(self) -> None: - super()._node_preprocess() - self._verify_lines = int(self._get_node_param('verify_lines')) - if self._dataset_in_handler: - perceived_list = self._dataset_in_handler.read_json_obj_list() - # one for plan , at least one for verification - if not perceived_list or len(perceived_list) < 1: - raise Exception('perceived json list does not provide at least 1 samples!') - - for i in range(0, len(perceived_list)): - json_obj = perceived_list[i] - self._perceived_prompt_list.append(json_obj['prompt']) - self._perceived_answer_list.append(json_obj['answer']) - self._perceived_list_size += 1 - - if self.param_out_jsonl: - jr = JsonFileReader(self.param_out_jsonl) - self._last_plan_dict = jr.read_json_obj() - if self._param_in_handler: - self._new_plan_dict.update(self._param_in_handler.read_json_obj()) - - self._new_plan_dict['prompt_plan'] = { - 'input_var': 'input_str', - 'output_var': 'prompt', - 'plan_code': None - } - self._new_plan_dict['answer_plan'] = { - 'input_var': 'input_str', - 'output_var': 'answer', - 'plan_code': None - } - - def _node_process(self) -> None: - # check whether last plan works or not - if self.__check_last_plan(): - self._new_plan_dict = self._last_plan_dict - return - - prompt_plan_code = self.__generate_plan_and_verify(self._new_plan_dict.get('prompt_plan'), - self._perceived_prompt_list) - answer_plan_code = self.__generate_plan_and_verify(self._new_plan_dict.get('answer_plan'), - self._perceived_answer_list) - if prompt_plan_code and answer_plan_code: - self._new_plan_dict['prompt_plan']['plan_code'] = prompt_plan_code - self._new_plan_dict['answer_plan']['plan_code'] = answer_plan_code - - def __check_last_plan(self) -> bool: - if not self._last_plan_dict: - return False - - last_plan = self._last_plan_dict.get('prompt_plan') - last_plan_code = last_plan.get('plan_code') - last_output_var = last_plan.get('output_var') - dict_obj = self.execute_from_plan(self._perceived_prompt_list, 0, 1, last_plan_code, last_output_var) - if dict_obj.get('reflection'): - return False - - last_plan = self._last_plan_dict.get('answer_plan') - last_plan_code = last_plan.get('plan_code') - last_output_var = last_plan.get('output_var') - dict_obj = self.execute_from_plan(self._perceived_answer_list, 0, 1, last_plan_code, last_output_var) - if dict_obj.get('reflection'): - return False - - return True - - def __generate_plan_and_verify(self, plan_list: str, data_list: list) -> str: - - output_var = plan_list.get('output_var') - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - - prompt_template = getattr(version_prompt, 'data_event_plan', '').replace('', output_var) - prompt_reflection_template = prompt_template.replace('', data_list[0]) - prompt = prompt_reflection_template.replace('', 'None') - - llm_retry = 10 - reflection_retry = 10 - last_reflection = None - while True: - if llm_retry <= 0 or reflection_retry <= 0: - return None - - LOGGER.debug(prompt) - res = batch_call([prompt], self.llm) - LOGGER.debug(res) - - try: - res_obj = parse_json_markdown(res[0].strip()) - LOGGER.debug(res_obj) - except json.decoder.JSONDecodeError as e: - reflection = 'exception: ' + str(e) + '\ncode: json.loads' - if reflection == last_reflection: - reflection = 'None' - prompt = prompt_reflection_template.replace('', reflection) - last_reflection = reflection - LOGGER.warn(e) - continue - - plan_code = res_obj["plan_code"].strip() - LOGGER.debug(f'plan_code>>>{plan_code}') - - reflection_retry -= 1 - start_idx = 1 - verify_lines = 10 if not self._verify_lines else self._verify_lines - if verify_lines > self._perceived_list_size - start_idx: - verify_lines = self._perceived_list_size - start_idx - - dict_obj = self.execute_from_plan(data_list, start_idx, verify_lines, plan_code, output_var) - reflection = dict_obj.get('reflection') - LOGGER.debug(f'reflection:{reflection}') - - if reflection: - if reflection == last_reflection: - reflection = 'None' - prompt = prompt_reflection_template.replace('', reflection) - last_reflection = reflection - else: - return plan_code - - return None - - def _node_postprocess(self) -> None: - super()._node_postprocess() - - if self._param_out_handler: - self._param_out_handler.write_json_obj(self._new_plan_dict) - - def check_data_reflection(self, data_str: str) -> str: - if not data_str or data_str == '': - return 'Output cannot be empty' - - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - - prompt = getattr(version_prompt, 'exec_result_verification', '').replace('', data_str) - LOGGER.debug(prompt) - res = batch_call([prompt], self.llm) - LOGGER.debug(res[0]) - json_obj = parse_json_markdown(res[0]) - if json_obj.get('success'): - return None - else: - return json_obj.get('reflection') - - def execute_from_plan(self, data_list: list, start_idx: int, execute_lines: int, plan_code: str, - output_var: str) -> Dict: - reflection = None - output_list = [] - for i in range(start_idx, start_idx + execute_lines): - namespace = { - 'input_str': data_list[i], - output_var: '', - } - - try: - exec(plan_code, globals(), namespace) - except Exception as e: - LOGGER.warn(f'except from exec>>>{e}') - reflection = 'exec Exception:' + str(e) + '\ncode:' + plan_code - break - - output = namespace.get(output_var) - LOGGER.debug(f'after exec >>>{output}') - - if output == '': - reflection = "The output is empty" - break - if not isinstance(output, str): - reflection = 'The type of output must be a string，the current output type is: ' + str( - type(output)) + ', the original code is as follows: ' + plan_code - break - - result_str = output_var + ':' + output - reflection = self.check_data_reflection(result_str) - if reflection: - break - else: - output_list.append(output) - - return {'reflection': reflection, 'output_list': output_list} diff --git a/agentuniverse_dataflow/node/data/prompt/__init__.py b/agentuniverse_dataflow/node/data/prompt/__init__.py deleted file mode 100644 index 3cddf9e6..00000000 --- a/agentuniverse_dataflow/node/data/prompt/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 14:54 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/prompt/input/__init__.py b/agentuniverse_dataflow/node/data/prompt/input/__init__.py deleted file mode 100644 index 3cddf9e6..00000000 --- a/agentuniverse_dataflow/node/data/prompt/input/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 14:54 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/prompt/input/dedupe.py b/agentuniverse_dataflow/node/data/prompt/input/dedupe.py deleted file mode 100644 index bd6f4a8d..00000000 --- a/agentuniverse_dataflow/node/data/prompt/input/dedupe.py +++ /dev/null @@ -1,72 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 17:17 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: dedupe.py -from collections import Counter - -from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase - - -class DedupeNode(PromptBase): - """The DedupeNode class, which is used to define the class of dedupe node.""" - - diversify_hamming_threshold: int = 18 - freq_top_percent: float = 1.0 - freq_least_count: int = 100 - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - self.diversify_hamming_threshold = self._get_node_param('diversify_hamming_threshold') - self.freq_top_percent = self._get_node_param('freq_top_percent') - self.freq_least_count = self._get_node_param('freq_least_count') - - def _node_process(self) -> None: - if not self._prompt_list or len(self._prompt_list) == 0: - return - - try: - from simhash import Simhash - except ImportError: - raise ImportError( - "simhash is required at the DedupeNode. Please: `pip install simhash`" - ) - - # calculate the simhash value for each document. - simhashes = [(doc, Simhash(doc)) for doc in self._prompt_list] - - unique_documents = [] - replaced_documents = [] - for doc1, sh1 in simhashes: - if all(sh1.distance(sh2) > self.diversify_hamming_threshold for _, sh2 in unique_documents): - unique_documents.append((doc1, sh1)) - dis = sh1.distance(unique_documents[0][1]) - least_hamming = self.diversify_hamming_threshold - least_hamming_doc = None - for i in range(0, len(unique_documents)): - sh2 = unique_documents[i][1] - hamming_dist = sh1.distance(sh2) - if hamming_dist < least_hamming: - least_hamming = hamming_dist - least_hamming_doc = unique_documents[i][0] - replaced_documents.append(least_hamming_doc) - - freq_counter = Counter(replaced_documents) - sorted_docs = sorted(freq_counter.items(), key=lambda x: x[1], reverse=True) - - orig_len = len(sorted_docs) - freq_top_num = int(orig_len * self.freq_top_percent) - if freq_top_num < self.freq_least_count: - freq_top_num = self.freq_least_count - if self.freq_least_count > orig_len: - freq_top_num = orig_len - - freq_top_docs = sorted_docs[:freq_top_num] - - self._prompt_list = [] - for doc, freq in freq_top_docs: - if doc is not None: - self._prompt_list.append(doc) diff --git a/agentuniverse_dataflow/node/data/prompt/input/extend.py b/agentuniverse_dataflow/node/data/prompt/input/extend.py deleted file mode 100644 index 4dd49a6f..00000000 --- a/agentuniverse_dataflow/node/data/prompt/input/extend.py +++ /dev/null @@ -1,58 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 17:30 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: extend.py -import json -import math - -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class ExtendNode(PromptBase): - """The ExtendNode class, which is used to define the class of extend node.""" - - extend_times: int = 1 - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - self.extend_times = self._get_node_param('extend_times') - - def _node_process(self) -> None: - if not self._prompt_list or len(self._prompt_list) == 0: - return - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - prompt_with_extend_times = version_prompt.prompt_template.replace('', str(self.extend_times)) - - input_list = '' - input_len = len(self._prompt_list) - inputs = self._prompt_list - prompts = [] - batch_size = math.ceil(20 / self.extend_times) - for i in range(0, input_len): - input_list = input_list + inputs[i] + '\n' - if (i + 1) % batch_size == 0 or i == input_len - 1: - prompts.append(prompt_with_extend_times.replace('', input_list)) - input_list = '' - - responses = batch_call(prompts, self.llm) - - for i in range(0, len(responses)): - try: - if responses[i] != '' and responses[i] is not None: - data = parse_json_markdown(responses[i]) - if 'extend_inputs' in data: - extend_inputs = data['extend_inputs'] - self._prompt_list.extend(extend_inputs) - except Exception as e: - LOGGER.warn(f'except[]>>>{e}:{responses[i]}') - continue diff --git a/agentuniverse_dataflow/node/data/prompt/input/rewrite.py b/agentuniverse_dataflow/node/data/prompt/input/rewrite.py deleted file mode 100644 index c087f942..00000000 --- a/agentuniverse_dataflow/node/data/prompt/input/rewrite.py +++ /dev/null @@ -1,44 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 17:34 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: rewrite.py -import json - -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class RewriteNode(PromptBase): - """The RewriteNode class, which is used to define the class of rewrite node.""" - - def _node_process(self) -> None: - if not self._prompt_list or len(self._prompt_list) == 0: - return - - prompts = [] - inputs = self._prompt_list - inputs_all = '' - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - for i in range(0, len(inputs)): - inputs_all = inputs_all + inputs[i] + '\n' - if (i + 1) % self._batch_prompt_size == 0 or i == len(inputs) - 1: - prompts.append(version_prompt.prompt_template.replace('', inputs_all)) - inputs_all = '' - - responses = batch_call(prompts, self.llm) - - self._prompt_list = [] - for i in range(0, len(responses)): - try: - rewrite_input = parse_json_markdown(responses[i])['rewrite_inputs'] - self._prompt_list.extend(rewrite_input) - except json.JSONDecodeError as e: - # rewrite_inputs_all.extend(inputs) - continue diff --git a/agentuniverse_dataflow/node/data/prompt/input/seed.py b/agentuniverse_dataflow/node/data/prompt/input/seed.py deleted file mode 100644 index 7f02dbb6..00000000 --- a/agentuniverse_dataflow/node/data/prompt/input/seed.py +++ /dev/null @@ -1,66 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 17:45 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: seed.py -import json - -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class SeedNode(PromptBase): - """The SeedNode class, which is used to define the class of seed node.""" - - seeds_num: int = 100 - seed_gen_requirement: str = "金融领域" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - if 'seeds_num' in kwargs: - self.seeds_num = kwargs['seeds_num'] - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - seeds_num = self._get_node_param('seeds_num') - if seeds_num: - self.seeds_num = seeds_num - seed_gen_requirement = self._get_node_param('seed_gen_requirement') - if seed_gen_requirement: - self.seed_gen_requirement = seed_gen_requirement - - def _node_process(self) -> None: - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - prompt_with_seed_requirement = version_prompt.prompt_template.replace('', - self.seed_gen_requirement) - prompts = [] - - for i in range(0, self.seeds_num): - if (i + 1) % self._batch_line_size == 0: - prompt = prompt_with_seed_requirement.replace('', str(self._batch_line_size)) - prompts.append(prompt) - elif i == self.seeds_num - 1: - left_num = (i + 1) % self._batch_line_size - prompt = prompt_with_seed_requirement.replace('', str(left_num)) - prompts.append(prompt) - - responses = batch_call(prompts, self.llm) - - self._prompt_list = [] - for i in range(0, len(responses)): - try: - seeds = parse_json_markdown(responses[i])['seeds'] - self._prompt_list.extend(seeds) - except TypeError as e: - continue - except json.decoder.JSONDecodeError as e: - LOGGER.warn(f'except>>>{e}:{responses}') - continue diff --git a/agentuniverse_dataflow/node/data/prompt/instruct/__init__.py b/agentuniverse_dataflow/node/data/prompt/instruct/__init__.py deleted file mode 100644 index 3cddf9e6..00000000 --- a/agentuniverse_dataflow/node/data/prompt/instruct/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 14:54 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/data/prompt/instruct/prompt_gen.py b/agentuniverse_dataflow/node/data/prompt/instruct/prompt_gen.py deleted file mode 100644 index ef503f7c..00000000 --- a/agentuniverse_dataflow/node/data/prompt/instruct/prompt_gen.py +++ /dev/null @@ -1,52 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 14:57 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: prompt_gen.py -from typing import List - -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader - - -class PromptGenNode(PromptBase): - """The PromptGenNode class, which is used to define the class of prompt generate node.""" - - _instruct_list: List[str] = None - _input_sample: str = None - - _instruct_num: int = None - - def _node_preprocess(self) -> None: - - super()._node_preprocess() - - if not self.datasets_in_jsonl or len(self.datasets_in_jsonl) != 2: - raise Exception(f"Node param {self.datasets_in_jsonl} should contain 2 elements:1.instruct 2.input") - - instruct_in_handler = JsonFileReader(self.datasets_in_jsonl[0]) - input_in_handler = JsonFileReader(self.datasets_in_jsonl[1]) - - if instruct_in_handler: - self._instruct_list = instruct_in_handler.read_json_prompt_list() - if input_in_handler: - self._input_sample = input_in_handler.read_json_prompt() - - self._instruct_num = self._get_node_param('instruct_num') - - def _node_process(self) -> None: - - self._prompt_list = [] - for instruct_idx in range(0, min(self._instruct_num, len(self._instruct_list))): - prompt = self.generate_prompt_from_instruct_and_input(self._instruct_list[instruct_idx], self._input_sample) - self._prompt_list.append(prompt) - - def generate_prompt_from_instruct_and_input(self, instruct='', input=''): - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - prompt = version_prompt.prompt_template.replace("", instruct) - prompt = prompt.replace("", input) - return prompt.strip() diff --git a/agentuniverse_dataflow/node/data/prompt/instruct/seed.py b/agentuniverse_dataflow/node/data/prompt/instruct/seed.py deleted file mode 100644 index bb48270d..00000000 --- a/agentuniverse_dataflow/node/data/prompt/instruct/seed.py +++ /dev/null @@ -1,43 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 15:02 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: seed.py -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.data.base.prompt_base import PromptBase -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class SeedNode(PromptBase): - """The SeedNode class, which is used to define the class of extend node.""" - - extend_times: int = 20 - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - self.extend_times = self._get_node_param('extend_times') - - def _node_process(self) -> None: - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - prompt_with_extend_times = version_prompt.prompt_template.replace('', - str(self.extend_times)) - - prompts = [prompt_with_extend_times] - responses = batch_call(prompts, self.llm) - self._prompt_list = [] - if len(responses) == 1: - try: - if responses[0] != '' and responses[0] is not None: - data = parse_json_markdown(responses[0]) - if 'instructs' in data: - extend_instructs = data['instructs'] - self._prompt_list.extend(extend_instructs) - except Exception as e: - LOGGER.warn(f'except[]>>>{e}:{responses[0]}') diff --git a/agentuniverse_dataflow/node/enum/__init__.py b/agentuniverse_dataflow/node/enum/__init__.py deleted file mode 100644 index 8138e72d..00000000 --- a/agentuniverse_dataflow/node/enum/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:07 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/enum/enum.py b/agentuniverse_dataflow/node/enum/enum.py deleted file mode 100644 index 556a1e28..00000000 --- a/agentuniverse_dataflow/node/enum/enum.py +++ /dev/null @@ -1,39 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:07 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: enum.py -from enum import Enum - - -class NodeEnum(Enum): - """The enumeration of the supported nodes.""" - - # DATA - PROMPT = "PROMPT" - PROMPT_ANSWER = "PROMPT_ANSWER" - - # MODEL - MODEL = "MODEL" - - # CRITIC - EVAL = "EVAL" - QUALITY = "QUALITY" - - @staticmethod - def to_value_list(): - """Return the value list of the enumeration.""" - return [item.value for item in NodeEnum] - - -class InOutEnum(Enum): - """The enumeration of the supported inputs and outputs.""" - - JSONL = "JSONL" - - @staticmethod - def to_value_list(): - """Return the value list of the enumeration.""" - return [item.value for item in InOutEnum] diff --git a/agentuniverse_dataflow/node/eval/__init__.py b/agentuniverse_dataflow/node/eval/__init__.py deleted file mode 100644 index f52c0757..00000000 --- a/agentuniverse_dataflow/node/eval/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 18:00 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/eval/eval.py b/agentuniverse_dataflow/node/eval/eval.py deleted file mode 100644 index b87cae3d..00000000 --- a/agentuniverse_dataflow/node/eval/eval.py +++ /dev/null @@ -1,100 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 18:07 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: eval.py -from typing import List, Tuple - -from langchain.output_parsers.json import parse_json_markdown - -from agentuniverse.prompt.prompt import Prompt -from agentuniverse.prompt.prompt_manager import PromptManager -from agentuniverse_dataflow.node.base.eval_node_base import EvalNodeBase -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse_dataflow.util.constant.eval_node_dimensions import get_eval_dims -from agentuniverse_dataflow.util.llm.llm_call import batch_call - - -class EvalNode(EvalNodeBase): - """The EvalNode class, which is used to define the class of eval node.""" - - _prompt_answer_list: List[Tuple[str, str]] = None - _eval_dims_json_list: List[str] = None - _eval_lines: int = 100 - - def set_eval_lines(self, eval_lines: int) -> None: - if eval_lines > 0: - self._eval_lines = eval_lines - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - self._eval_lines = self._get_node_param('eval_lines') - if self._dataset_in_handler: - self._prompt_answer_list = self._dataset_in_handler.read_json_prompt_answer_list() - - def _node_postprocess(self) -> None: - super()._node_postprocess() - - if self._dataset_out_handler and self._eval_dims_json_list: - self._dataset_out_handler.write_json_obj_list(self._eval_dims_json_list) - - def _node_process(self) -> None: - if not self._prompt_answer_list or len(self._prompt_answer_list) == 0: - return - - eval_dims = get_eval_dims() - - line_num = 0 - self._eval_dims_json_list = [] - for i in range(0, len(self._prompt_answer_list)): - prompt = self._prompt_answer_list[i][0] - answer = self._prompt_answer_list[i][1] - - if prompt is None: - break - - line_num += 1 - if line_num > self._eval_lines: - break - - if len(prompt) > 2000: - prompt = prompt[0:2000] - if len(answer) > 5000: - answer = answer[0:5000] - - version_prompt: Prompt = PromptManager().get_instance_obj(self.prompt_version) - - eval_prompt_temp = version_prompt.prompt_template.replace('', prompt) - eval_prompt_temp = eval_prompt_temp.replace('', answer) - dim_prompts = [] - - for i in range(0, len(eval_dims)): - eval_dim_name = eval_dims[i][0] - eval_dim_requirement = eval_dims[i][1] - dim_prompt = f'dimension name: {eval_dim_name} \n dimension requirement: {eval_dim_requirement}' - dim_prompts.append(dim_prompt) - - eval_prompt_temp = eval_prompt_temp.replace('', '\n'.join(dim_prompts)) - res = batch_call([eval_prompt_temp], self.llm) - - dim_score_json = {'line': line_num} - dimensions = [] - avg_score = 0.0 - - try: - if res[0] != '' and res[0] is not None: - data = parse_json_markdown(res[0]) - dimensions = data['dimensions'] - avg_score = sum(data['score'] for data in dimensions) - except Exception as e: - LOGGER.warn(f'except[eval_prompt_answer_from_jsonl]>>>{e}:{res[0]}') - continue - if len(dimensions) > 0: - avg_score = avg_score / len(dimensions) - dim_score_json['avg_score'] = avg_score - dim_score_json['dimensions'] = dimensions - - self._eval_dims_json_list.append(dim_score_json) diff --git a/agentuniverse_dataflow/node/eval/report.py b/agentuniverse_dataflow/node/eval/report.py deleted file mode 100644 index e616c8de..00000000 --- a/agentuniverse_dataflow/node/eval/report.py +++ /dev/null @@ -1,67 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 18:02 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: report.py -from typing import List - -from agentuniverse_dataflow.node.base.eval_node_base import EvalNodeBase -from agentuniverse_dataflow.util.fileio.node_msg_jsonl import JsonFileReader - - -class ReportNode(EvalNodeBase): - """The ReportNode class, which is used to define the class of report node.""" - - _eval_report_json_list: List[str] = None - - def _node_preprocess(self) -> None: - super()._node_preprocess() - if not self.datasets_in_jsonl or len(self.datasets_in_jsonl) == 0: - raise Exception(f"No input datasets: {self.datasets_in_jsonl}") - - def _node_postprocess(self) -> None: - super()._node_postprocess() - - if self._dataset_out_handler and self._eval_report_json_list: - self._dataset_out_handler.write_json_obj_list(self._eval_report_json_list) - - def _node_process(self) -> None: - self._eval_report_json_list = [] - for i in range(0, len(self.datasets_in_jsonl)): - jfr = JsonFileReader(self.datasets_in_jsonl[i]) - line_objs = jfr.read_json_obj_list() - if line_objs is None: - break - - line_num = 0 - total_avg_score = 0.0 - dim_avg_score = {} - for j in range(0, len(line_objs)): - json_obj = line_objs[j] - line_num += 1 - total_avg_score += json_obj['avg_score'] - dimensions = json_obj['dimensions'] - - dim_num = len(dimensions) - for k in range(0, dim_num): - name = dimensions[k]['name'] - score = dimensions[k]['score'] - if name in dim_avg_score: - dim_avg_score[name] += float(score) - else: - dim_avg_score[name] = float(score) - - for key in dim_avg_score: - dim_total_score = dim_avg_score[key] - dim_avg_score[key] = dim_total_score / line_num - - total_avg_score = total_avg_score / line_num - - report_line_obj = { - 'input_file': self.datasets_in_jsonl[i], - 'total_avg_score': total_avg_score, - 'dim_avg_score': dim_avg_score - } - self._eval_report_json_list.append(report_line_obj) diff --git a/agentuniverse_dataflow/node/model/__init__.py b/agentuniverse_dataflow/node/model/__init__.py deleted file mode 100644 index cdbd34bd..00000000 --- a/agentuniverse_dataflow/node/model/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:53 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/model/base/__init__.py b/agentuniverse_dataflow/node/model/base/__init__.py deleted file mode 100644 index cdbd34bd..00000000 --- a/agentuniverse_dataflow/node/model/base/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:53 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/node/model/base/deploy_base.py b/agentuniverse_dataflow/node/model/base/deploy_base.py deleted file mode 100644 index bc1afdfc..00000000 --- a/agentuniverse_dataflow/node/model/base/deploy_base.py +++ /dev/null @@ -1,21 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:53 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: deploy_base.py -from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase - - -class DeployBase(ModelNodeBase): - """The DeployBase class, which is used to define the base class of deploy node.""" - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - super()._node_postprocess() diff --git a/agentuniverse_dataflow/node/model/base/dump_base.py b/agentuniverse_dataflow/node/model/base/dump_base.py deleted file mode 100644 index 34d1f00c..00000000 --- a/agentuniverse_dataflow/node/model/base/dump_base.py +++ /dev/null @@ -1,29 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:54 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: dump_base.py -from typing import List, Tuple - -from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase - - -class DumpBase(ModelNodeBase): - """The DumpBase class, which is used to define the base class of dump node.""" - - _prompt_answer_out_list: List[Tuple[str, str]] = None - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - self._param_in_json_obj = None - super()._node_postprocess() - - if self._prompt_answer_out_list and self._dataset_out_handler: - self._dataset_out_handler.write_json_prompt_answer_list(self._prompt_answer_out_list) diff --git a/agentuniverse_dataflow/node/model/base/pre_deploy_base.py b/agentuniverse_dataflow/node/model/base/pre_deploy_base.py deleted file mode 100644 index b46bcaf5..00000000 --- a/agentuniverse_dataflow/node/model/base/pre_deploy_base.py +++ /dev/null @@ -1,21 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:54 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: pre_deploy_base.py -from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase - - -class PreDeployBase(ModelNodeBase): - """The PreDeployBase class, which is used to define the base class of pre-deploy node.""" - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - super()._node_postprocess() diff --git a/agentuniverse_dataflow/node/model/base/train_base.py b/agentuniverse_dataflow/node/model/base/train_base.py deleted file mode 100644 index 5d4f09c0..00000000 --- a/agentuniverse_dataflow/node/model/base/train_base.py +++ /dev/null @@ -1,23 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:54 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: train_base.py -from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase - - -class TrainBase(ModelNodeBase): - """The TrainBase class, which is used to define the base class of train node.""" - - train_out_artifact: str = None - - def _node_preprocess(self) -> None: - super()._node_preprocess() - - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - super()._node_postprocess() diff --git a/agentuniverse_dataflow/node/model/base/upload_base.py b/agentuniverse_dataflow/node/model/base/upload_base.py deleted file mode 100644 index 2b00d9d1..00000000 --- a/agentuniverse_dataflow/node/model/base/upload_base.py +++ /dev/null @@ -1,29 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:59 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: upload_base.py -from agentuniverse_dataflow.node.base.model_node_base import ModelNodeBase - - -class UploadBase(ModelNodeBase): - """The UploadBase class, which is used to define the base class of upload node.""" - - dataset_jsonl_filename: str = None - - def __init__(self, **kwargs): - super().__init__() - self.set_flow_start_node() - - def _node_preprocess(self) -> None: - super()._node_preprocess() - if self.datasets_in_jsonl and len(self.datasets_in_jsonl) > 0: - self.dataset_jsonl_filename = self.datasets_in_jsonl[0] + '.jsonl' - - def _node_process(self) -> None: - pass - - def _node_postprocess(self) -> None: - super()._node_postprocess() diff --git a/agentuniverse_dataflow/prompt/__init__.py b/agentuniverse_dataflow/prompt/__init__.py deleted file mode 100644 index 68b2996a..00000000 --- a/agentuniverse_dataflow/prompt/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 10:45 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/prompt/dataset_extend_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_extend_node_cn.yaml deleted file mode 100644 index 845ffd6f..00000000 --- a/agentuniverse_dataflow/prompt/dataset_extend_node_cn.yaml +++ /dev/null @@ -1,23 +0,0 @@ -introduction: 你是一个极致严谨的金融专家，对于输入的字符串列表，请基于以下要求扩展，用来提交给大模型从而能获取高质量结果，请确保输出字符串只包含扩展后的内容且每一行是一条，如不能有“扩展后的问题:”之类的表述。 -instruction: | - 扩展要求： - 1.请确保扩展出来的输出字符串和输入字符串语义上完全不一样 - 2.请确保扩展出来的输出字符串和输入字符串大部分字符不相同 - 3.请确保扩展出来的输出字符串是对原输入有多样性大幅提升 - 4.请确保扩展出来的输出字符串和原输入对比是完全不同的金融话题 - 5.扩展倍数：以输入作为参考，输出尽量扩展条数为输入的倍 - - - 输入列表： - - - 请确保输出包含且只包含如下的json格式: - { - "extend_inputs": - [ - "xxx" - ] - } -metadata: - type: 'PROMPT' - version: 'dataset_extend_node.cn' diff --git a/agentuniverse_dataflow/prompt/dataset_filter_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_filter_node_cn.yaml deleted file mode 100644 index bd63c65a..00000000 --- a/agentuniverse_dataflow/prompt/dataset_filter_node_cn.yaml +++ /dev/null @@ -1,82 +0,0 @@ -introduction: 你是一个极致严谨的金融专家，需要帮助按照如下要求评估answer在各维度的质量打分。 -instruction: | - 各维度质量打分请分别以0~100分来评估： - 1.不符合要求的打0～20分 - 2.基本符合要求的打20～40分 - 3.比较优秀的打40～60分 - 4.特别突出的打60～80分 - 5.极致卓越的打80～100分 - - prompt: - - - answer: - - - 质量打分维度描述: - - 1.合规性: - 文本内容应遵守所有相关的法律法规、行业准则和公司政策。 - 必须遵循金融监管机构的要求，包括但不限于披露、报告和交易规则。 - 应保证文本中的任何建议或信息不违反利益冲突和道德行为准则。 - - 2.安全性: - 保护文本中包含的敏感信息，包括个人信息和财务数据，确保其不会被非授权人员访问。 - 应实施数据加密、访问控制和其他安全措施以防止数据泄露或未授权使用。 - - 3.事实性: - 文本中如果有声明、数据和引用都应是可查证的、基于可靠数据源和事实的。 - 避免这些信息是推测性的语言，确保所有信息都有确凿的证据支持。 - - 4.结构性: - 文本应具有清晰、逻辑性强的结构，包括合适的标题、子标题和段落。 - 信息的呈现应有助于理解，遵循从总体到细节的组织方式。 - - 5.合理性: - 呈现的分析或论点应基于合理的假设和逻辑推理。 - 结论应当合理，不偏离数据和证据所能支持的范围。 - - 6.严谨性: - 文本应遵循高标准的专业准则，精确使用术语，严谨处理数据。 - 所提供的分析和结论应深入、准确，无逻辑漏洞。 - - 7.重复性: - 文本内容在反复使用或引用时应保持一致，能够被同样的方式理解和解释。 - 术语定义和使用标准应一致，确保在不同场合和时间的解读不发生变化。 - - 8.时效性: - 文本内容应反映最新的信息和数据，及时更新以匹配当前的金融市场和环境。 - 如有数据和信息必须标明对应的时点，确保读者明白其相关性和有效性。 - - 9.全面性: - 文本应全面覆盖主题，考虑到所有相关方面和潜在影响。 - 应包含足够的背景信息、上下文和可能的影响分析。 - - 10.相关性: - 评估answer相对于prompt的相关性 - a.不符合准入条件：答非所问，不相关 - b.门槛值：能够知道到用户在问什么，并且所答即所问，包括： - 【有明确意图时】识别出用户的问题并对问题有回复，且回复的内容有相关性；有主意图的时候，回复的内容要与主意图有相关 - 【无明确意图时】遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 - c.加分项 - 能够get到用户在问什么后：对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应;回应的内容整体相关性都很高;回复的问题全面且易懂 - - - 请确保严格要求，不轻易给出高分，请确保输出包含且只包含如下字段且每个字段不能为空长度也不超过200的json格式: - { - "dimensions": [ - { - "name":质量打分维度, - "score":分数, - "negative substr":扣分最多的子串 - }, - { - "name":质量打分维度, - "score":分数, - "negative substr":扣分最多的子串 - } - ] - } -metadata: - type: 'PROMPT' - version: 'dataset_filter_node.cn' diff --git a/agentuniverse_dataflow/prompt/dataset_input_seed_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_input_seed_node_cn.yaml deleted file mode 100644 index 5ba9d53d..00000000 --- a/agentuniverse_dataflow/prompt/dataset_input_seed_node_cn.yaml +++ /dev/null @@ -1,26 +0,0 @@ -introduction: 你是一个极致严谨的金融专家，请尽量确保生成条金融问题用来提交给大模型从而能获取更多高质量的问题，请确保输出字符串是中文的且只包含生成后的问题，如不能有“生成的问题：”之类的表述。 -instruction: | - 输出表达要求： - 1.同义词替换：用同义词替换原始查询中的某些词汇，以扩大搜索结果的覆盖范围。 - 2.标准化处理：将查询中的词汇标准化，比如将复数形式转换为单数形式，或者使用基本形式的词替换其它形式的词。 - 3.去噪声词：从查询中去除不重要的词汇，如介词和连接词，以突出关键信息。 - 4.查询扩展：在原始查询基础上添加额外的词汇或短语，以提高查询的详细度或覆盖面。 - 5.重排和重构：根据特定的上下文或目标，对查询中的词语进行重新排序或重构，以改善其表达的清晰度或逻辑性。 - 6.意图识别：识别用户查询的潜在意图，然后根据这一意图进行相应的改写。 - 7.表达形式：尽可能的丰富指令的表达方式和长度，既有非常简洁的表达也有略长的表达 - 8.格式处理：规范化格式，提出不必要的标点、空格、回车、乱码等 - 9.结构形式：请保持相似的角色和指令结构或者框架，使的大模型能遵从相关回答结构 - - 一句话描述生成的种子： - - - 请确保输出包含且只包含如下的json格式: - { - "seeds": - [ - "xxx" - ] - } -metadata: - type: 'PROMPT' - version: 'dataset_input_seed_node.cn' diff --git a/agentuniverse_dataflow/prompt/dataset_rewrite_node_cn.yaml b/agentuniverse_dataflow/prompt/dataset_rewrite_node_cn.yaml deleted file mode 100644 index 713ee074..00000000 --- a/agentuniverse_dataflow/prompt/dataset_rewrite_node_cn.yaml +++ /dev/null @@ -1,27 +0,0 @@ -introduction: 你是一个极致严谨的金融专家，对于输入的一列字符串，请基于以下要求改写，用来提交给大模型从而能获取高质量结果，请确保输出每一行字符串与输入每一行字符串一一对应且只包含改写后的内容，如不能有“改写后的问题：”之类的表述。 -instruction: | - - 改写要求： - 1.同义词替换：用同义词替换原始查询中的某些词汇，以扩大搜索结果的覆盖范围。 - 2.标准化处理：将查询中的词汇标准化，比如将复数形式转换为单数形式，或者使用基本形式的词替换其它形式的词。 - 3.去噪声词：从查询中去除不重要的词汇，如介词和连接词，以突出关键信息。 - 4.查询扩展：在原始查询基础上添加额外的词汇或短语，以提高查询的详细度或覆盖面。 - 5.重排和重构：根据特定的上下文或目标，对查询中的词语进行重新排序或重构，以改善其表达的清晰度或逻辑性。 - 6.意图识别：识别用户查询的潜在意图，然后根据这一意图进行相应的改写。 - 7.表达形式：尽可能的丰富指令的表达方式和长度，既有非常简洁的表达也有略长的表达 - 8.格式处理：规范化格式，提出不必要的标点、空格、回车、乱码等 - 9.结构形式：请保持相似的角色和指令结构或者框架，使的大模型能遵从相关回答结构 - - 输入字符串包括： - - - 请确保输出包含且只包含如下的json格式: - { - "rewrite_inputs": - [ - "xxx" - ] - } -metadata: - type: 'PROMPT' - version: 'dataset_rewrite_node.cn' diff --git a/agentuniverse_dataflow/prompt/eval_node_cn.yaml b/agentuniverse_dataflow/prompt/eval_node_cn.yaml deleted file mode 100644 index 7db7aebf..00000000 --- a/agentuniverse_dataflow/prompt/eval_node_cn.yaml +++ /dev/null @@ -1,38 +0,0 @@ -introduction: 你是一个极致严谨的金融专家，需要帮助按照如下要求评估answer在各维度的质量打分。 -instruction: | - 打分要求，请精确到小数点后1位： - 1.不符合准入条件（0.0分） - 2.门槛值（0.0～3.0分） - 3.加分项（0.0～2.0分） - - prompt: - - - answer: - - - 质量打分维度描述: - - - - - 输出格式要求： - - 请确保严格要求，不轻易给出高分，请确保输出只包含如下的json格式字段，且每个字段不能为空长度也不超过200，不包含其他内容: - { - "dimensions": [ - { - "name":质量打分维度名称, - "score":分数, - "negative substr":扣分最多的子串 - }, - { - "name":质量打分维度, - "score":分数, - "negative substr":扣分最多的子串 - } - ] - } -metadata: - type: 'PROMPT' - version: 'eval_node.cn' diff --git a/agentuniverse_dataflow/prompt/event_planner_node_cn.yaml b/agentuniverse_dataflow/prompt/event_planner_node_cn.yaml deleted file mode 100644 index 0e30f999..00000000 --- a/agentuniverse_dataflow/prompt/event_planner_node_cn.yaml +++ /dev/null @@ -1,37 +0,0 @@ -data_event_plan: | - 你是一个极致严谨的金融指令工程专家，我们认为大模型输入input为prompt输出output为answer, prompt包括指令、上下文和用户输入， answer是针对prompt输入后LLM的回答 - 你需要针对输入str，规划对应的plan从而基于plan的代码输出满足要求的str - - 如下是输入： - input_str: - - - ### - 请严格遵循如下输出要求： - 1.请特别注意输入字符串是否是json编码的字符串并做相应的处理, 如果不是请不要解码避免引起不必要的麻烦 - 2.请确保代码输出的所有变量都是人可阅读的str类型, 如果不是请解析并提取到输出，同时尽量不要遗漏输入中的可阅读内容 - 3.请确保提供最简洁安全的代码片段, 请不要打印到屏幕 - 4.请参考之前的反馈: - - ### - 请确保你的回答包含且仅包含如下json格式的内容并严格遵循json规范: - { - "plan_code": 输入变量为input_str,输出变量为的python exec可执行的代码片段 - } -exec_result_verification: | - 你是一个极致严谨的数据质量专家,如下输入字符串每一行都是一条数据,你的工作是一行一行来确认是不是符合如下要求: - 1.人可以读取理解的文本 - 2.输入字段是完全解码的 - - 输入: - - - ### - 请确保你的回答包含且仅包含如下json格式的内容: - { - "success": 如果输入每行符合上面要求则为true否则为false, - "reflection": 不符合上面要求的原因和改进建议 - } -metadata: - type: 'PROMPT' - version: 'event_planner_node.cn' diff --git a/agentuniverse_dataflow/prompt/instruct_prompt_gen_cn.yaml b/agentuniverse_dataflow/prompt/instruct_prompt_gen_cn.yaml deleted file mode 100644 index c2f72358..00000000 --- a/agentuniverse_dataflow/prompt/instruct_prompt_gen_cn.yaml +++ /dev/null @@ -1,7 +0,0 @@ -instruction: | - - - -metadata: - type: 'PROMPT' - version: 'instruct_prompt_gen_node.cn' diff --git a/agentuniverse_dataflow/prompt/instruct_seed_node_cn.yaml b/agentuniverse_dataflow/prompt/instruct_seed_node_cn.yaml deleted file mode 100644 index b75b6040..00000000 --- a/agentuniverse_dataflow/prompt/instruct_seed_node_cn.yaml +++ /dev/null @@ -1,32 +0,0 @@ -introduction: 你是一个极致严谨的金融专家。 -instruction: | - 请参考输入尽量按照扩展要求确保生成金融指令用来提交给大模型从而能获取更多高质量的问题，请确保输出字符串是中文的且只包含生成后的指令，如不能有“生成的指令：”“输出：”之类的表述。 - 同时请确保指令中没有具体的金融领域对象，只包含对输出的要求不包含输入的金融问题本身，角色的描述也可以扩展同类表述。 - - 指令输出要求： - 1.清晰表述：指令中使用清晰、明确的描述，避免模糊的词语，例如可以加上数字，具体的风格 - 2.指令位置：把指令放在prompt开头，并用三个引号或者三个井号等特殊的内容将待处理的内容和指令分开 - 3.输出格式：为了得到稳定的输出，指令中请尽量指定好输出格式 - 4.角色扮演：指令中请包含角色扮演技巧，以及针对角色扮演的专业要求 - 5.用户角色：指令中告诉模型用户是什么角色，从而得到的答案会令你感到惊艳 - 6.语义差异：请确保扩展出来的输出字符串和输入字符串语义上完全不一样 - 7.多样性：请确保扩展出来的输出字符串是对原输入有多样性大幅提升 - 8.字符串：请确保扩展出来的输出字符串和原输入对比是完全不同的金融话题 - 9.扩展倍数：以输入作为参考，输出尽量扩展条数为输入的倍 - - - 指令输入参考： - 你是一个极致严谨的金融专家，请尽量找出问题对应的金融解读框架，并基于解读框架一步一步结构化详细回答如下问题: - - 指令输出格式要求： - - 请确保输出只包含如下的json格式，不包含其他内容: - { - "instructs": - [ - "xxx" - ] - } -metadata: - type: 'PROMPT' - version: 'instruct_seed_node.cn' diff --git a/agentuniverse_dataflow/util/__init__.py b/agentuniverse_dataflow/util/__init__.py deleted file mode 100644 index cc148e69..00000000 --- a/agentuniverse_dataflow/util/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:12 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/util/constant/__init__.py b/agentuniverse_dataflow/util/constant/__init__.py deleted file mode 100644 index 34f082dc..00000000 --- a/agentuniverse_dataflow/util/constant/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/18 14:41 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/util/constant/eval_node_dimensions.py b/agentuniverse_dataflow/util/constant/eval_node_dimensions.py deleted file mode 100644 index a3b2312e..00000000 --- a/agentuniverse_dataflow/util/constant/eval_node_dimensions.py +++ /dev/null @@ -1,150 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/18 14:41 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: eval_node_dimensions.py - -_EVAL_DIM_RELATIVE_NAME = "相关性" -_EVAL_DIM_RELATIVE_REQUIREMENT = """ -1.不符合准入条件 -答非所问，不相关 - -2.门槛值 -能够知道到用户在问什么，并且所答即所问，包括： -【有明确意图时】 -识别出用户的问题并对问题有回复，且回复的内容有相关性 -有主意图的时候，回复的内容要与主意图有相关 -【无明确意图时】 -遇到模糊意图时进行恰当的意图反问确认，并给予准确回应 - -3.加分项 -能够get到用户在问什么后： -a、对用户的所有问题（包含情绪，只有一个问题，那就是回复了就算）都给出回应 -b、回应的内容整体相关性都很高 -c、回复的问题全面且易懂 -""" - -_EVAL_DIM_FACTUALITY_NAME = "事实性" -_EVAL_DIM_FACTUALITY_REQUIREMENT = """ -1.不符合准入条件 -数据错误，不符合事实 - -2.门槛值 -个别数据不准确，但不影响整体事实性 - -3.加分项 -解读内容中所有引用数据真实可信，数据来源官方可信 -""" - -_EVAL_DIM_CONSTRUCTIVE_NAME = "结构性" -_EVAL_DIM_CONSTITUTIVE_REQUIREMENT = """ -1.不符合准入条件 -总分结构： -a、总分论点只是抽取了原文中的部分内容，并没有真正的总结归纳 -b、生成模型是总分结构，但是总论点对分论点的总结性小于50% -c、生成模型是总分结构，但是分论点是为了回答query而硬答，与query实际并没有关系。 -d、总结内容论点逻辑冲突； -总结摘要结构： -a、摘要内容是抽取了部分段落，并没有真正的归纳总结 -b、摘要内容，仅仅总结成一段文本，无摘要详述 - -2.门槛值 -总分结构： -a、总分结构，有总结和归纳，分论点有层次或者结构 -b、总分结构，总论点对分论点的总结行大于50% -c、总分结构，分论点之间有逻辑关系（互补、递进），分论点与query之间有相关性 -d、总结内容论点间无冲突； -总结摘要结构： -a、摘要内容有对原文进行归纳总结，不是抽取的原文段落 -b、摘要内容论点有展开叙述 - -3.加分项 -总分结构： -a、总分结构，分论点有归类抽象有条理有逻辑 -b、总分结构，分论点为1234分层结构，有条理 - -摘要结构： -a、摘要内容论点叙述详细充分，有逻辑有条理 -b、总结表达：总结内容话术表达逻辑清晰，表达精炼，字数<= XX字 -""" - -_EVAL_DIM_RATIONALITY_NAME = "合理性" -_EVAL_DIM_RATIONALITY_REQUIREMENT = """ -1.不符合准入条件 -出现专业错误 -（如经济复苏利好股市利空债市、供不应求带来价格上涨、经济下行压力加剧通常会倒逼宏观政策加码等） - -2.门槛值 -符合业内公认的金融逻辑，前后结论不要自相矛盾，不出现明显的专业错误 - -3.加分项 -通篇解读要一以贯之、逻辑自洽，且逻辑推演是有逐层递进的（后文分析以前文结论为依据） -""" - -_EVAL_DIM_TIMELINESS_NAME = "时效性" -_EVAL_DIM_TIMELINESS_REQUIREMENT = """ -1.不符合准入条件 -数据陈旧过时，不符合现状 - -2.门槛值 -次新，符合当下现状 - -3.加分项 -引用官方发布的最新数据，包含清晰的发布日期 -""" - -_EVAL_DIM_COMPREHENSIVE_NAME = "全面性" -_EVAL_DIM_COMPREHENSIVE_REQUIREMENT = """ -3分门槛：从2～3个维度分析，分析类问题的论据较为饱满 -3分～5分看主观感受 -分子维度占分母维度的百分比进行打分： -●信息查询类：暂不涉及 -●标的分析类：暂不涉及 -●宏观分析类： -包含指标与市场预期相比（必填项） 、指标整体对经济\政策的指示意义（必填项）、经济指标同比\环比的趋势分析、指标子项细拆分析（如PMI各个分项）表征含义、对股债资产的影响。尽可能包含更多维度的分析。必填项缺失得零分 -●市场分析类： -尽可能包含以下2-3个维度以上的分析：尽可能从以下多个驱动力维度展开分析，基本面、政策面、情绪面（突发事件）、估值面、资金面，同时驱动力维度的判别要准确 -●政策解读类： -尽可能包含以下2-3个维度以上的分析：宏观政策类：包含政策出台背景（原因）、政策对基本面\流动性\情绪面（风险偏好）的影响、政策对股债资产、相关行业的影响。宏观会议类：包含与历届同类会议的措辞对比、对经济的定调、对货币、财政、房地产、金融、资本市场、防风险等重要政策的定调、整体对股债市场、相关行业的影响 -●大事解读类： -尽可能包含以下2-3个维度以上的分析：行业政策类：包含政策出台背景（原因）、政策对行业基本面\流动性\情绪面（风险偏好）的影响、政策对行业的短期\中长期多空影响。其它非经济\政策类大事对股市的影响：事件背景\原因、事件对A股基本面、政策面、情绪面、估值面、资金面的影响（不需要面面俱到，只需要对驱动力维度的归属准确） -●报告解读类： -尽可能包含以下2-3个维度以上的分析： -财报解读类：分析角度主要包括企业盈利能力、企业成长能力、企业营运水平和企业财务报表质量。企业盈利能力指标包括毛利率、净利率和ROE等。企业成长能力指标包括营业收入增速和净利润增速。企业营运能力分析包括存货周转天数和应收账款周转天数。企业财务报表质量指标包括资产负债率和利息覆盖率。以上指标为常用分析指标，主要供参考，分析解读围绕财务报表即可； -研报解读类：分析师分析一般会从业绩分析、行业分析、市场前景、技术发展、政策、行业事件、估值分析、策略建议等视角，这些视角都可以拆细，通常选取其中主要矛盾和市场核心关注点展开分析； -政府报告解读类：一般包括央行货币执行报告、财政预算报告等，这块解读没有固定框架，主流分析方法主要对政府报告内容引用、概括和提炼精神，能提到报告主要内容多个点即可。 -●策略建议类：暂不涉及 -●泛金融类：暂不涉及 -""" - -_EVAL_DIM_OVERALL_NAME = "整体性" -_EVAL_DIM_OVERALL_REQUIREMENT = """ -1.不符合准入条件 -满足以下任意一点即为0分： -a、逻辑不通顺，或存在语病，或表达冗余内容超长（整体文本长度>XX字） -b、论点&总结重复/论点&论点重复：任意两条论点存在80%以上的重复度 -c、倒装、错别字、语句不通顺、逻辑问题等 -d、存在特殊符号 - -2.门槛值 -a、语句流畅、表达可阅读性强 -b、表达直白（不反复说车轱辘话） -c、论点论据表达无重复 - -3.加分项 -a、答案对用户query有明确回应表达 -b、语言简洁精炼，逻辑清晰 -""" - - -def get_eval_dims(): - eval_dims = [(_EVAL_DIM_RELATIVE_NAME, _EVAL_DIM_RELATIVE_REQUIREMENT), - (_EVAL_DIM_FACTUALITY_NAME, _EVAL_DIM_FACTUALITY_REQUIREMENT), - (_EVAL_DIM_CONSTRUCTIVE_NAME, _EVAL_DIM_CONSTITUTIVE_REQUIREMENT), - (_EVAL_DIM_RATIONALITY_NAME, _EVAL_DIM_RATIONALITY_REQUIREMENT), - (_EVAL_DIM_TIMELINESS_NAME, _EVAL_DIM_TIMELINESS_REQUIREMENT), - (_EVAL_DIM_COMPREHENSIVE_NAME, _EVAL_DIM_COMPREHENSIVE_REQUIREMENT), - (_EVAL_DIM_OVERALL_NAME, _EVAL_DIM_OVERALL_REQUIREMENT)] - return eval_dims diff --git a/agentuniverse_dataflow/util/fileio/__init__.py b/agentuniverse_dataflow/util/fileio/__init__.py deleted file mode 100644 index cc148e69..00000000 --- a/agentuniverse_dataflow/util/fileio/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:12 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/util/fileio/node_msg_jsonl.py b/agentuniverse_dataflow/util/fileio/node_msg_jsonl.py deleted file mode 100644 index 94cbb43b..00000000 --- a/agentuniverse_dataflow/util/fileio/node_msg_jsonl.py +++ /dev/null @@ -1,150 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/14 17:14 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: node_msg_jsonl.py -import json -import os -import sys - -from agentuniverse.base.util.logging.logging_util import LOGGER - -sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) - -DATA_DIR = 'data/' -os.makedirs(DATA_DIR, exist_ok=True) - - -class JsonFileOps(object): - def __init__(self): - return - - @classmethod - def is_file_exist(cls, input_file_name, extension='jsonl'): - infile = DATA_DIR + input_file_name + '.' + extension - return os.path.exists(infile) - - @classmethod - def rm_file_if_exist(cls, input_file_name, extension='jsonl'): - infile = DATA_DIR + input_file_name + '.' + extension - if os.path.exists(infile): - os.remove(infile) - return - - -class JsonFileWriter(object): - def __init__(self, output_file_name: str, extension='jsonl'): - self.outfile_path = DATA_DIR + output_file_name + '.' + extension - # create directory if not exist - directory = os.path.dirname(self.outfile_path) - if not os.path.exists(directory): - os.makedirs(directory) - - self.outfile_handler = open(self.outfile_path, 'w', encoding='utf-8') - - def write_json_obj(self, json_obj: dict): - try: - # Confirm that it's a json string and then write - # json.loads(json_obj) - json_line = json.dumps(json_obj, ensure_ascii=False) - self.outfile_handler.write(json_line.strip() + '\n') - self.outfile_handler.flush() - except Exception as e: - LOGGER.warn(f"except[write_json_obj]>>>{e}:{json_obj}") - - return - - def write_json_obj_list(self, json_obj_list: list): - for i in range(0, len(json_obj_list)): - self.write_json_obj(json_obj_list[i]) - return - - def write_json_prompt(self, prompt: str): - json_obj = {"prompt": prompt} - self.write_json_obj(json_obj) - - def write_json_prompt_list(self, prompt_list: list): - for i in range(0, len(prompt_list)): - self.write_json_prompt(prompt_list[i]) - - def write_json_prompt_answer(self, prompt: str, answer: str): - json_obj = {"prompt": prompt, "answer": answer} - self.write_json_obj(json_obj) - - def write_json_prompt_answer_list(self, prompt_answer_list: list): - for i in range(0, len(prompt_answer_list)): - self.write_json_prompt_answer(prompt_answer_list[i][0], prompt_answer_list[i][1]) - - -class JsonFileReader(object): - def __init__(self, input_file_name: str, extension='jsonl'): - self.infile_handler = None - self.filename = None - if JsonFileOps.is_file_exist(input_file_name, extension): - self.filename = input_file_name + '.' + extension - self.infile_handler = open(DATA_DIR + self.filename, 'r', encoding='utf-8') - - def read_json_obj(self): - if not self.infile_handler: - raise Exception(f"None json file to read: {self.filename}") - return None - - json_line = self.infile_handler.readline() - if json_line: - try: - json_obj = json.loads(json_line.strip()) - return json_obj - except Exception as e: - LOGGER.warn(f"except[read_json_line]>>>{e}:{json_line}") - return json.loads('{}') - else: - return None - - def read_json_obj_list(self): - obj_list = [] - - while True: - obj = self.read_json_obj() - if obj is None: - break - obj_list.append(obj) - - return obj_list - - def read_json_prompt(self): - json_obj = self.read_json_obj() - if json_obj is not None: - return json_obj.get('prompt') - else: - return None - - def read_json_prompt_list(self): - prompts = [] - while True: - prompt = self.read_json_prompt() - if prompt is None: - break - prompts.append(prompt) - - return prompts - - def read_json_prompt_answer(self): - json_obj = self.read_json_obj() - if json_obj is not None: - prompt = json_obj.get('prompt') if 'prompt' in json_obj else None - answer = json_obj.get('answer') if 'answer' in json_obj else None - return prompt, answer - else: - return None, None - - def read_json_prompt_answer_list(self): - prompt_answer_list = [] - while True: - prompt, answer = self.read_json_prompt_answer() - if prompt is None: - break - prompt_answer_list.append((prompt, answer)) - - return prompt_answer_list diff --git a/agentuniverse_dataflow/util/llm/__init__.py b/agentuniverse_dataflow/util/llm/__init__.py deleted file mode 100644 index ae852a5c..00000000 --- a/agentuniverse_dataflow/util/llm/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 20:47 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: __init__.py.py diff --git a/agentuniverse_dataflow/util/llm/llm_call.py b/agentuniverse_dataflow/util/llm/llm_call.py deleted file mode 100644 index ac1cc24b..00000000 --- a/agentuniverse_dataflow/util/llm/llm_call.py +++ /dev/null @@ -1,58 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/6/17 20:47 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: llm_call.py -import asyncio - -from agentuniverse.base.util.logging.logging_util import LOGGER -from agentuniverse.llm.llm import LLM -from agentuniverse.llm.llm_manager import LLMManager - - -def batch_call(prompts: list[str], llm_name: str): - return asyncio.run(async_batch_call(prompts, llm_name)) - - -async def async_batch_call(prompts: list[str], llm_name: str): - tasks = [] - - prompt_len = len(prompts) - for i in range(0, prompt_len): - llm: LLM = LLMManager().get_instance_obj(component_instance_name=llm_name, new_instance=True) - if llm is None: - raise Exception('LLM not found for agentuniverse data.') - messages = [{"role": "user", "content": prompts[i]}] - tasks.append(llm.acall(messages=messages, timeout=700)) - - task = asyncio.create_task(show_progress(len(prompts), asyncio.get_running_loop())) - outputs = await asyncio.gather(*tasks, return_exceptions=True) - task.cancel() - - results = [] - for i, output in enumerate(outputs): - if isinstance(output, Exception): - LOGGER.warn(f'>>>except[async_llm_call]:{output}') - results.append(None) - else: - results.append(output.text) - - print_progress(len(results), prompt_len) - - return results - - -def print_progress(completed: int, task_count: int): - progress = (completed / task_count) * 100 - LOGGER.info(f"\r>>>llm progress: {completed}/{task_count} = {progress:.2f}%") - 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zCNZ8CwUt>+{`M~tiLt5Zuw9wa`vRqSF}aA&8hMQ2it6JBj)~LOjjG;wd>8uI^(qoP z+Lj>U)j;Al$-1WqOyOg-8BG}c+QlS5+`Ra$f1Is~gBn#Kxe(41b*8`LHA3=9ILcv%YP5*EUAV`e&65+`}UNB?(2-FzLp;Qs*5k6&2; literal 0 HcmV?d00001 diff --git a/docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md b/docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md new file mode 100644 index 00000000..a3dc6d1d --- /dev/null +++ b/docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md @@ -0,0 +1,75 @@ +# Financial Event Analysis Case +## Case Description +This case is based on PeerPlanner and showcases a multi-agent collaborative example for analyzing financial events. Regarding the topic of "Buffett's 2023 Reduction in BYD Shares"， it demonstrates how to use the PEER multi-agent collaboration model in agentUniverse and details the configuration and output examples for each agent in PEER. + +This case study utilizes the GPT-4o model by OPENAI. Before using it, you need to configure the `OPENAI_API_KEY` in your environment variables. + +## Agents +### Planning Agent +Reference the original code files: +- [Configuration file](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_planning_agent.yaml) +- [Prompt file](../../../sample_standard_app/app/core/prompt/planning_agent_cn.yaml) + +The Planning Agent is responsible for breaking down the original financial problem into multiple sub-problems that can be individually solved and provided to the subsequent Executing Agent. In this case, the original question "Analyze the reasons for Buffett's reduction in BYD shares" can be decomposed into several sub-questions as shown in the diagram below: +![planning_result](../_picture/6_4_1_planning_result.png) +You can debug the Planning Agent individually in the [test file](../../../sample_standard_app/app/test/test_planning_agent.py). + +### Executing Agent +Reference the original code files: +- [Configuration file](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_executing_agent.yaml) +- [Prompt file](../../../sample_standard_app/app/core/prompt/executing_agent_cn.yaml) + +In this Agent, we provide a tool [google_search_tool](../../../sample_standard_app/app/core/tool/google_search_tool.py) for searching information on Google. To use this tool, you should configure `SERPER_API_KEY` in your environment. For convenience, if `SERPER_API_KEY ` is not configured, this tool will return a pre-set query result related to this case, which you can find in the [mock_search_tool](../../../sample_standard_app/app/core/tool/mock_search_tool.py). + + +The Executing Agent is responsible for solving the sub-problems broken down by the Planning Agent. In this case, the execution results of the Executing Agent are as follows: +![executing_result](../_picture/6_4_1_executing_result.png) +The result is lengthy, so only the execution results of the first two questions are shown here. You can debug the Executing Agent individually in the [test file](../../../sample_standard_app/app/test/test_executing_agent.py) to obtain the complete results. + +### Expressing Agent +Reference the original code files: +- [Configuration file](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_expressing_agent.yaml) +- [Prompt file](../../../sample_standard_app/app/core/prompt/expressing_agent_cn.yaml) + +The Expressing Agent is responsible for summarizing all the results output by the Executing Agent and formulating them into an answer to the original question according to the requirements in the prompt file. In this case, the output result of the Expressing Agent is as follows: +![expressing_result](../_picture/6_4_1_expressing_result.png) +You can debug the Expressing Agent individually in the [test file](../../../sample_standard_app/app/test/test_expressing_agent.py). + +### Reviewing Agent +Reference the original code files: +- [Configuration file](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_reviewing_agent.yaml) + +The Reviewing Agent is responsible for evaluating whether the result produced by the Expressing Agent is an effective answer to the original question. In this case, the Reviewing Agent accepted the answer from the Expressing Agent: +![reviewing_result](../_picture/6_4_1_reviewing_result.png) +You can debug the Reviewing Agent individually in the [test file](../../../sample_standard_app/app/test/test_reviewing_agent.py). + +### PEER Agent +```yaml +info: + name: 'demo_peer_agent' + description: 'demo peer agent' +plan: + planner: + name: 'peer_planner' + eval_threshold: 60 + retry_count: 2 + planning: 'demo_planning_agent' + executing: 'demo_executing_agent' + expressing: 'demo_expressing_agent' + reviewing: 'demo_reviewing_agent' +metadata: + type: 'AGENT' + module: 'agentuniverse.agent.default.peer_agent.peer_agent' + class: 'PeerAgent' +``` +Users can configure the four Agents mentioned above into a complete PEER Agent through the `peer_planner` collaboration model. The configurations include: +- name: Fixed as `peer_planner`, indicating the use of the PEER multi-agent collaboration model. +- eval_threshold: The minimum score for the Reviewing Agent to accept the answer. +- retry_count: The number of retries for the PEER Agent if the Reviewing Agent does not accept the answer. +- planning：The Agent responsible for the Plan part. +- executing：The Agent responsible for the Execute part. +- expressing：The Agent responsible for the Express part. +- reviewing：The Agent responsible for the Review part. + +You can run the complete case in the [example file](../../../sample_standard_app/app/examples/peer_chat_bot.py). + diff --git "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" index 43a9b654..3e4f1ca4 100644 --- "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" +++ "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" @@ -86,6 +86,8 @@ * 6.2 ReAct类Agent案例 * 6.2.1 [Python代码生成与执行Agent](7_1_1_Python自动执行案例.md) * 6.3 [基于多轮多Agent的讨论小组](6_2_1_讨论组.md) +* 6.4 PEER多Agent协作案例 + * 6.4.1 [] **7.系列文章** diff --git "a/docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md" "b/docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md" new file mode 100644 index 00000000..6f7da1ce --- /dev/null +++ "b/docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md" @@ -0,0 +1,75 @@ +# 金融事件分析案例 +## 案例说明 +本案例基于PeerPlanner，搭建了一个用于分析金融事件的多智能体协作案例，并以“巴菲特2023年减持比亚迪”事件为例，展示了如何在agentUniverse中使用PEER多智能体协作模式，并详细展示了PEER中每种智能体的配置方式及输出样例。 + +该案例基于OPENAI的gpt-4o模型，使用前需要您在环境变量中配置`OPENAI_API_KEY`。 + +## Agents +### Planning Agent +原始代码文件可参考 +- [配置文件](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_planning_agent.yaml) +- [提示词](../../../sample_standard_app/app/core/prompt/planning_agent_cn.yaml) + +Planning Agent负责将原始的金融问题拆分为多个可被单独解决的子问题，提供给后续的Executing Agent执行。在这个案例中，原始问题“分析下巴菲特减持比亚迪的原因”可以被拆解为下图中的数个子问题： +![planning_result](../_picture/6_4_1_planning_result.png) +您可以在[测试文件](../../../sample_standard_app/app/test/test_planning_agent.py)中单独调试Planning Agent。 + +### Executing Agent +原始代码文件可参考 +- [配置文件](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_executing_agent.yaml) +- [提示词](../../../sample_standard_app/app/core/prompt/executing_agent_cn.yaml) + +在这个Agent中，我们提供了一个用于在google上搜索信息的工具[google_search_tool](../../../sample_standard_app/app/core/tool/google_search_tool.py)，该工具的使用需要在环境信息中配置`SERPER_API_KEY`。为了方便您进行简单的尝试，当环境配置中没有`SERPER_API_KEY`时，该工具会返回一段预设好的关于本案例问题的查询结果，具体内容您可以在[mock_search_tool](../../../sample_standard_app/app/core/tool/mock_search_tool.py)中查看。 + + +Executing Agent负责解决Planning Agent拆分出的子问题。在本案例中，Executing Agent对拆解问题的执行结果如下： +![executing_result](../_picture/6_4_1_executing_result.png) +结果较长，这里只展示了前两问的执行结果。您可以在[测试文件](../../../sample_standard_app/app/test/test_executing_agent.py)中单独调试Executing Agent获得完整的结果。 + +### Expressing Agent +原始代码文件可参考 +- [配置文件](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_expressing_agent.yaml) +- [提示词](../../../sample_standard_app/app/core/prompt/expressing_agent_cn.yaml) + +Expressing Agent负责将Executing Agent输出的所有结果进行汇总，并根据提示词中的要求总结表达为对原始问题的回答，在该案例中，Expressing Agent的输出结果如下： +![expressing_result](../_picture/6_4_1_expressing_result.png) +您可以在[测试文件](../../../sample_standard_app/app/test/test_expressing_agent.py)中单独调试Expressing Agent。 + +### Reviewing Agent +原始代码文件可参考 +- [配置文件](../../../sample_standard_app/app/core/agent/peer_agent_case/demo_reviewing_agent.yaml) + +Reviewing Agent负责对Expressing Agent产出的结果进行评价，看是否对于原问题是有效的回答，在本案例中，Reviewing Agent接受了Expressing Agent的答案： +![reviewing_result](../_picture/6_4_1_reviewing_result.png) +您可以在[测试文件](../../../sample_standard_app/app/test/test_reviewing_agent.py)中单独调试Reviewing Agent。 + +### PEER Agent +```yaml +info: + name: 'demo_peer_agent' + description: 'demo peer agent' +plan: + planner: + name: 'peer_planner' + eval_threshold: 60 + retry_count: 2 + planning: 'demo_planning_agent' + executing: 'demo_executing_agent' + expressing: 'demo_expressing_agent' + reviewing: 'demo_reviewing_agent' +metadata: + type: 'AGENT' + module: 'agentuniverse.agent.default.peer_agent.peer_agent' + class: 'PeerAgent' +``` +用户可以通过配置文件的形式将上文中的四个Agent经由`peer_planner`的协作模式，组装为完整的PEER Agent。其中： +- name: 固定为`peer_planner`，表示该Agent使用了PEER多智能体协作模式。 +- eval_threshold: 表示Reviewing Agent采纳答案时的最低分数。 +- retry_count: 表示Reviewing Agent未采纳答案后，PEER Agent的重试次数 +- planning：负责Plan部分的Agent名称 +- executing：负责Execute部分的Agent名称 +- expressing：负责Express部分的Agent名称 +- reviewing：负责Review部分的Agent名称 + +您可以在[示例文件](../../../sample_standard_app/app/examples/peer_chat_bot.py)中完整运行本案例。 + From bab76de8b6737ef1693ea7176894bffb43cde998 Mon Sep 17 00:00:00 2001 From: "fanen.lhy" Date: Tue, 18 Jun 2024 12:26:08 +0800 Subject: [PATCH 028/118] Add: peer case docs --- docs/guidebook/en/0_index.md | 2 ++ "docs/guidebook/zh/0_\347\233\256\345\275\225.md" | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/guidebook/en/0_index.md b/docs/guidebook/en/0_index.md index 6fa4e741..ff85fc87 100644 --- a/docs/guidebook/en/0_index.md +++ b/docs/guidebook/en/0_index.md @@ -82,6 +82,8 @@ * 6.2 ReAct-Type Agent Examples * 6.2.1 [Python Code Generation and Execution Agent](7_1_1_Python_Auto_Runner.md) * 6.3 [Discussion Group Based on Multi-Turn Multi-Agent Mode](6_2_1_Discussion_Group.md) +* 6.4 PEER Multi-Agent Cooperation Examples + * 6.4.1 [Financial Event Analysis Case](./6_4_1_Financial_Event_Analysis_Case.md) **7. Series of Articles** diff --git "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" index 3e4f1ca4..27c590f0 100644 --- "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" +++ "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md" @@ -87,7 +87,7 @@ * 6.2.1 [Python代码生成与执行Agent](7_1_1_Python自动执行案例.md) * 6.3 [基于多轮多Agent的讨论小组](6_2_1_讨论组.md) * 6.4 PEER多Agent协作案例 - * 6.4.1 [] + * 6.4.1 [金融事件分析案例](./6_4_1_金融事件分析案例.md) **7.系列文章** From b60477757caf27846882d7e6e473fb5ed7dd0910 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Tue, 18 Jun 2024 14:02:24 +0800 Subject: [PATCH 029/118] docs: update README --- README.md | 3 ++- README_jp.md | 1 + README_zh.md | 1 + 3 files changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ee6656b2..92d45c9e 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,8 @@ For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md). ## Use Cases [Legal Consultation Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) [Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) -[Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) +[Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) +[Financial Event Analysis Based on PEER Multi-Agent Mode](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) ## Guidebook For more detailed information, please refer to the [Guidebook](docs/guidebook/en/0_index.md). diff --git a/README_jp.md b/README_jp.md index ec284e38..957e2f4e 100644 --- a/README_jp.md +++ b/README_jp.md @@ -47,6 +47,7 @@ pip install agentUniverse [法律相談Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) [Pythonコード生成と実行Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) [多回多Agentによるディスカッショングループ](./docs/guidebook/en/6_2_1_Discussion_Group.md) +[PEERマルチAgentモードに基づいた金融イベント分析](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) ## ガイドブック 詳細情報については、[ガイドブック](docs/guidebook/en/0_index.md)を参照してください。 diff --git a/README_zh.md b/README_zh.md index d35db732..490395a2 100644 --- a/README_zh.md +++ b/README_zh.md @@ -51,6 +51,7 @@ pip install agentUniverse [法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) +[基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md)] ## 用户指南 更多详细信息，请参阅[指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 From 751c85e927ee7f4f1395b901c8789f4f30de3d36 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Fri, 14 Jun 2024 18:33:14 +0800 Subject: [PATCH 030/118] docs: update readme --- README_jp.md | 2 ++ README_zh.md | 2 ++ 2 files changed, 4 insertions(+) diff --git a/README_jp.md b/README_jp.md index 957e2f4e..0162d123 100644 --- a/README_jp.md +++ b/README_jp.md @@ -45,7 +45,9 @@ pip install agentUniverse ## 使用ケース [法律相談Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) + [Pythonコード生成と実行Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) + [多回多Agentによるディスカッショングループ](./docs/guidebook/en/6_2_1_Discussion_Group.md) [PEERマルチAgentモードに基づいた金融イベント分析](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) diff --git a/README_zh.md b/README_zh.md index 490395a2..7c02ffcd 100644 --- a/README_zh.md +++ b/README_zh.md @@ -49,7 +49,9 @@ pip install agentUniverse ## 使用案例 [法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) + [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) + [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) [基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md)] From c1e0ff7985b298b357cca90a4dcdd03e22a015b3 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Mon, 17 Jun 2024 21:14:32 +0800 Subject: [PATCH 031/118] docs: update README for new version --- README.md | 106 ++++++++++++------ README_zh.md | 98 +++++++++++----- docs/guidebook/en/1_Core_Features.md | 12 ++ docs/guidebook/en/1_Why_Use_agentUniverse.md | 10 ++ .../zh/1_1_\347\256\200\344\273\213.md" | 99 +++++++++++++--- ...0\351\200\211\346\213\251agentUniverse.md" | 10 ++ ...70\345\277\203\347\211\271\346\200\247.md" | 12 ++ 7 files changed, 270 insertions(+), 77 deletions(-) create mode 100644 docs/guidebook/en/1_Core_Features.md create mode 100644 docs/guidebook/en/1_Why_Use_agentUniverse.md create mode 100644 "docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" create mode 100644 "docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" diff --git a/README.md b/README.md index 92d45c9e..e1e70f75 100644 --- a/README.md +++ b/README.md @@ -10,58 +10,100 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語 ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** -## Overview -agentUniverse is a framework for developing applications powered by multi-agent base on large language model. It provides all the essential components for building a single agent, and a multi-agent collaboration mechanism which serves as a pattern factory that allowing developers to buid and customize multi-agent collaboration patterns. With this framework, developers can easily construct multi-agent applications, and share the pattern practices from different technical and business fields. +## What is agentUniverse? -The framework will come with serveral pre-install multi-agent collaboration patterns which have been proven effective in real business scenarios, and will continue to be enriched in the future. Patterns that are currently about to be released include: +**agentUniverse is a multi-agent framework based on large language models.** agentUniverse provides you with the flexible and easily extensible capability to build single agents. At its core, agentUniverse features a rich set of multi-agent collaboration mode components (which can be viewed as a Collaboration Mode Factory, or Pattern Factory). These components allow agents to maximize their effectiveness by specializing in different domains to solve problems. agentUniverse also focuses on the integration of domain expertise, helping you seamlessly incorporate domain knowledge into the work of your agents.🎉🎉🎉 -- PEER pattern： -This pattern utilizes four distinct agent roles: Plan, Execute, Express, and Review, to achieve a multi-step breakdown and sequential execution of a complex task. It also performs autonomous iteration based on evaluative feedback which enhancing performance in reasoning and analytical tasks. +**🌈🌈🌈agentUniverse helps developers and enterprises to easily build powerful collaborative agents that perform at an expert level in their respective domains.** +![](docs/guidebook/_picture/agent_universe_framework_resize.jpg) -- DOE pattern： -This pattern consists of three agents: Data-fining agent, which is designed to solve data-intensive and high-computational-precision task; Opinion-inject agent, which combines the data results from first agent and the expert opinions which are pre-collected and structured; the third agent, Express agent generates the final result base on given document type and language style. - -More patterns are coming soon... +We encourage you to practice and share different domain Patterns within the community. The framework comes pre-loaded with several multi-agent collaboration mode components that have been validated in real-world industries and will continue to expand in the future. The components that will be available soon include: -![](docs/guidebook/_picture/agent_universe_framework_resize.jpg) +* PEER Mode Component: This pattern uses agents with different responsibilities—Plan, Execute, Express, and Review—to break down complex problems into manageable steps, execute the steps in sequence, and iteratively improve based on feedback, enhancing the performance of reasoning and analysis tasks. Typical use cases: Event interpretation, industry analysis. +* DOE Mode Component: This pattern employs three agents—Data-fining, Opinion-inject, and Express—to improve the effectiveness of tasks that are data-intensive, require high computational precision, and incorporate expert opinions. Typical use cases: Financial report generation. -## agentUniverseSample Project -[agentUniverse Sample Project](sample_standard_app/README.md) +More patterns are coming soon... -## Quick Installation +**************************************** +## Table of Contents +* [Quick Start](#Quick Start) +* [Cases and Example Projects](#Cases and Example Projects) +* [More](#More) + * [Why Use agentUniverse](#Why Use agentUniverse) + * [Core Features](#Core Features) + * [User Guide](#User Guide) + * [API Reference](#API Reference) + * [Support](#Support) + * [Acknowledgements](#Acknowledgements) +**************************************** +## Quick Start Using pip: ```shell pip install agentUniverse ``` -## Quick Start We will show you how to: -* Prepare the environment and application project + +* Prepare the environment and application projects * Build a simple agent -* Use pattern components to complete multi-agent collaboration -* Test and optimize the performance of the agent -* Quickly serve the agent -For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md). +* Use mode components for multi-agent collaboration +* Test and tune the execution effectiveness of an agent +* Quickly deploy an agent as a service -## Use Cases +For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_Start.md). + +**************************************** +## Cases and Example Projects +### 🌟 Use Cases [Legal Consultation Agent](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md) [Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md) [Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md) [Financial Event Analysis Based on PEER Multi-Agent Mode](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md) -## Guidebook -For more detailed information, please refer to the [Guidebook](docs/guidebook/en/0_index.md). +### 🌟 Example Projects +[agentUniverse Example Projects](sample_standard_app) -## API Reference -[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) +### 🌟 Product Cases Built with agentUniverse +['Zhi Xiao Zhu' AI Assistant for Financial Professionals](https://zhu.alipay.com/) +**************************************** +## More +### Why Use agentUniverse +💡 [Why Use agentUniverse?](./docs/guidebook/en/1_Why_Use_agentUniverse.md) -## More Ways to Contact Us -* github: https://github.com/alipay/agentUniverse -* gitee: https://gitee.com/agentUniverse/agentUniverse -* gitcode: https://gitcode.com/agentUniverse -* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ -* WeChat Official Account: agentUniverse智多星 -* DingTalk Group: +### Core Features + +* **Rich Multi-Agent Collaboration Modes:** Provides industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express). It also supports user-defined patterns for new modes, enabling organic collaboration among multiple agents. +* **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents. +* **Seamless Integration of Domain Expertise:** Offers capabilities for domain-specific prompts, knowledge construction, and management, and supports domain-level SOP orchestration and embedding, aligning agents to the expert level in their fields. + +💡 For more specific details, see the [Core Features of agentUniverse](./docs/guidebook/en/1_Core_Features.md). + +### User Guide +💡 For more detailed information, please read the [User Guide](./docs/guidebook/en/0_index.md). + +### API Reference +💡 Please read the [API Reference](https://agentuniverse.readthedocs.io/en/latest/). + +### Support +#### Submit Questions via GitHub Issues +😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days. + +#### Contact Us via Discord +😊 Join our [Discord Channel](https://discord.gg/VfhEvJzQ) to interact with us. + +#### Contact Us via DingTalk +😊 Join our DingTalk support group to get in touch with us. ![](./docs/guidebook/_picture/dingtalk_util20250429.png) + +#### Contact Us via Administrator Email +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### WeChat Official Account + +😊 Official Account ID: **agentUniverse智多星** + +You can get more related articles and information in our WeChat Official Account. + +## Acknowledgements +This project is partially built on excellent open-source projects such as langchain, pydantic, gunicorn, flask, SQLAlchemy, chromadb, etc. (The detailed dependency list can be found in pyproject.toml). We would like to extend special thanks to the related projects and contributors. 🙏🙏🙏 diff --git a/README_zh.md b/README_zh.md index 7c02ffcd..53bac792 100644 --- a/README_zh.md +++ b/README_zh.md @@ -10,34 +10,38 @@ ![](docs/guidebook/_picture/logo_bar.jpg) **************************************** -## 项目介绍 +## agentUniverse是什么？ -本框架是一个大模型多智能体框架。核心提供了多智能体协作编排组件，其相当于一个模式工厂（pattern factory），允许开发者对多智能体协作模式进行开发定制，同时附带了搭建单一智能体的全部关键组件。开发者可以基于本框架轻松构建多智能体应用，并通过社区对不同领域的pattern实践进行交流共享。 +**agentUniverse 是一个基于大型语言模型的多智能体框架。** agentUniverse为您提供灵活易拓展的单智能体构建能力；agentUniverse核心拥有丰富的多智能体协同模式组件（可视为一个协同模式工厂Pattern Factory），它能让智能体们各司其职在解决不同领域问题时发挥最大的能力；同时agentUniverse专注于领域经验的融合，帮助您轻松将领域经验融入到智能体的工作中。🎉🎉🎉 -框架将预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： - -- PEER 模式组件： -该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 - - -- DOE 模式组件： -该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 - -更多模式组件持续推出中... +**🌈🌈🌈agentUniverse帮助开发者、企业轻松构建出领域专家级别的强大智能体协同为您工作。** ![](docs/guidebook/_picture/agent_universe_framework_resize.jpg) +我们期待您通过社区对不同领域的Pattern进行实践与交流共享，框架也预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： +* PEER 模式组件： 该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 +* DOE 模式组件： 该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 -## agentUniverse 示例项目 -[agentUniverse 示例项目](sample_standard_app/README_zh.md) +更多模式组件持续推出中... -## 快速安装 +**************************************** +## 目录 +* [快速开始](#快速开始) +* [案例与样例工程](#案例与样例工程) +* [更多](#更多) + * [为什么使用agentUniverse](#为什么使用agentUniverse) + * [核心特性](#核心特性) + * [用户指南](#用户指南) + * [API参考](#API参考) + * [支持](#支持) + * [鸣谢](#鸣谢) +**************************************** +## 快速开始 使用pip： ```shell pip install agentUniverse ``` -## 快速开始 我们将向您展示如何： * 进行环境与应用工程准备 * 构建一个简单的agent @@ -46,8 +50,9 @@ pip install agentUniverse * 对agent进行快速服务化 详情请阅读[快速开始](docs/guidebook/zh/1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。 - -## 使用案例 +**************************************** +## 案例与样例工程 +### 🌟 使用案例 [法律咨询Agent](./docs/guidebook/zh/7_1_1_法律咨询案例.md) [Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md) @@ -55,18 +60,51 @@ pip install agentUniverse [基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md) [基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md)] -## 用户指南 -更多详细信息，请参阅[指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 +### 🌟 示例项目 +[agentUniverse 示例项目](sample_standard_app) + +### 🌟 使用aU构建的产品案例 +[支小助 金融从业专家AI助手](https://zhu.alipay.com/) +**************************************** +## 更多 +### 为什么使用agentUniverse +💡 [为什么使用agentUniverse?](./docs/guidebook/zh/1_为什么选择agentUniverse.md) -## API 参考 -[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) +### 核心特性 -## 更多方式联系我们 -* github: https://github.com/alipay/agentUniverse -* gitee: https://gitee.com/agentUniverse/agentUniverse -* gitcode: https://gitcode.com/agentUniverse -* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ -* 微信公众号: agentUniverse智多星 -* 钉钉答疑群: +* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作； + +* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体； + +* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； + +💡 更多特定见[agentUniverse核心特性](docs/guidebook/zh/1_核心特性.md)部分。 + +### 用户指南 +💡 更多详细信息，请阅读[用户指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。 + +### API参考 +💡 请阅读[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) 。 + +### 支持 +#### 通过github issue提交疑问 +😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 + +#### 通过Discord联系我们 +😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。 + +#### 通过钉钉群联系我们 +😊 加入我们的钉钉答疑群与我们联系。 ![](./docs/guidebook/_picture/dingtalk_util20250429.png) + +#### 通过管理员Email联系我们 +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### 微信公众号 + +😊 公众号ID：**agentUniverse智多星** + +更多相关的文章与资讯你可以在微信公众号中获取。 + +## 鸣谢 +本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏 diff --git a/docs/guidebook/en/1_Core_Features.md b/docs/guidebook/en/1_Core_Features.md new file mode 100644 index 00000000..92d7f23e --- /dev/null +++ b/docs/guidebook/en/1_Core_Features.md @@ -0,0 +1,12 @@ +# Core Features +* **Agent-Oriented Design:** The framework is designed with agents in mind, customizing agents through the perception, planning, action, and memory modules. +* **Rich Multi-Agent Collaboration Modes:** Offers industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express), and supports user-defined orchestration of new modes, enabling organic collaboration among multiple agents. +* **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents. +* **Seamless Integration of Domain Expertise:** Provides capabilities for domain-specific prompts, knowledge construction and management, and supports domain-level SOP orchestration and embedding to align agents to expert levels in their fields. +* **Multi-LLM and Extensible:** Built-in support for multiple models such as the OpenAI series, Qwen series, Baichuan series, Kimi series, etc., while offering standard extensions to integrate any third-party model services and privately deployed models. +* **Multi-Data Source and Extensible:** Supports various data sources, whether traditional ones like SQLite, MySQL, Oracle, or vector databases like ChromaDB, Milvu. It also provides standard extension methods to integrate any third-party data services and other databases. +* **Convenient Ecosystem Integration:** The framework integrates mature and practically tested large model application ecosystem capabilities, shielding most of the underlying configurations and implementations. This includes LLM ecosystem components like LangChain, llama_index, as well as technical components like Grpc and SLS. +* **Observable and Feedback Capable:** With the monitor component, all service interactions and model interactions within agentUniverse can be recorded and observed. Combined with the framework's automatic agent evaluation capabilities, you can easily grasp the performance gains from agent or model iterations and feed them back into service iterations and model training. +* **Enterprise Private Component Extensions:** The framework offers a private technical component extension and loading mechanism, allowing internal components like self-developed RPC protocol components, messaging components, and logging components to be integrated via configuration. +* **One-Click Service Deployment:** Supports one-click startup of a web server through the capabilities of Flask and Gunicorn, making it easy to integrate agent capabilities into any existing system. +* **Standard Container Delivery:** Provides a standard Docker image and offers recommended integration solutions based on Docker and Kubernetes (K8S) capabilities. \ No newline at end of file diff --git a/docs/guidebook/en/1_Why_Use_agentUniverse.md b/docs/guidebook/en/1_Why_Use_agentUniverse.md new file mode 100644 index 00000000..3c91c45f --- /dev/null +++ b/docs/guidebook/en/1_Why_Use_agentUniverse.md @@ -0,0 +1,10 @@ +# Why Use agentUniverse +With agentUniverse, you can easily build powerful collaborative agents that perform at an expert level in their respective domains. + +agentUniverse provides all the essential components for building single agents. All visible components are extensible and customizable, allowing them to seamlessly connect with large model ecosystem components and middleware, helping you build powerful single agents. + +agentUniverse features a rich set of multi-agent collaboration capabilities. Different collaboration modes are specialized in solving domain-specific problems, with multi-agents playing different roles and working together towards a common goal. This greatly enhances the agents' performance in completing tasks. + +agentUniverse focuses on the construction and management of domain prompts, domain knowledge, and domain SOPs. Through these capabilities, domain expertise is infused into the collaborative work of all agents, ensuring that agents have expert-level proficiency in specific domains. + +In addition, agentUniverse already has numerous industry-grade products and practices in the financial sector. We will share and open up collaboration modes, domain expertise, and solutions that have been validated in the financial industry as case studies. \ No newline at end of file diff --git "a/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" "b/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" index d0d25ee7..9047edce 100644 --- "a/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" +++ "b/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" @@ -1,25 +1,94 @@ -# agentUniverse项目简介 -## 项目介绍 -本框架是一个大模型多智能体框架。核心提供了多智能体协作编排组件，其相当于一个模式工厂（pattern factory），允许开发者对多智能体协作模式进行开发定制，同时附带了搭建单一智能体的全部关键组件。开发者可以基于本框架轻松构建多智能体应用，并通过社区对不同领域的pattern实践进行交流共享。 +## agentUniverse是什么？ -框架将预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： +**agentUniverse 是一个基于大型语言模型的多智能体框架。** agentUniverse为您提供灵活易拓展的单智能体构建能力；agentUniverse核心拥有丰富的多智能体协同模式组件（可视为一个协同模式工厂Pattern Factory），它能让智能体们各司其职在解决不同领域问题时发挥最大的能力；同时agentUniverse专注于领域经验的融合，帮助您轻松将领域经验融入到智能体的工作中。🎉🎉🎉 -- PEER 模式组件： -该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 +**🌈🌈🌈agentUniverse帮助开发者、企业轻松构建出领域专家级别的强大智能体协同为您工作。** +![](../_picture/agent_universe_framework_resize.jpg) -- DOE 模式组件： -该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 +我们期待您通过社区对不同领域的Pattern进行实践与交流共享，框架也预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括： +* PEER 模式组件： 该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析 +* DOE 模式组件： 该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成 +**************************************** +## 目录 +* [快速开始](#快速开始) +* [案例与样例工程](#案例与样例工程) +* [更多](#更多) + * [为什么使用agentUniverse](#为什么使用agentUniverse) + * [核心特性](#核心特性) + * [用户指南](#用户指南) + * [API参考](#API参考) + * [支持](#支持) + * [鸣谢](#鸣谢) +**************************************** +## 快速开始 +使用pip： +```shell +pip install agentUniverse +``` -更多模式组件持续推出中... +我们将向您展示如何： +* 进行环境与应用工程准备 +* 构建一个简单的agent +* 使用模式组件完成多agent协同 +* 对agent执行效果进行测试调优 +* 对agent进行快速服务化 -![](../_picture/agent_universe_framework_resize.jpg) +详情请阅读[快速开始](1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。 +**************************************** +## 案例与样例工程 +### 🌟 使用案例 +[法律咨询Agent](7_1_1_法律咨询案例.md) + +[Python代码生成与执行Agent](7_1_1_Python自动执行案例.md) + +[基于多轮多Agent的讨论小组](6_2_1_讨论组.md) + +### 🌟 示例项目 +[agentUniverse 示例项目](../../../sample_standard_app) + +### 🌟 使用aU构建的产品案例 +[支小助 金融从业专家AI助手](https://zhu.alipay.com/) +**************************************** +## 更多 +### 为什么使用agentUniverse +💡[为什么使用agentUniverse?](1_为什么选择agentUniverse.md) + +### 核心特性 + +* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作； + +* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体； + +* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； + +💡更多特定见[agentUniverse核心特性](1_核心特性.md)部分。 + +### 用户指南 +💡更多详细信息，请阅读[用户指南](0_%E7%9B%AE%E5%BD%95.md) 。 + +### API参考 +💡请阅读[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) 。 + +### 支持 +#### 通过github issue提交疑问 +😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 + +#### 通过Discord联系我们 +😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。 + +#### 通过钉钉群联系我们 +😊 加入我们的钉钉答疑群与我们联系。 +![](../_picture/dingtalk_util20250429.png) + +#### 通过管理员Email联系我们 +😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) + +#### 微信公众号 -除了丰富的协作模式外agentUniverse还包含如下主要特性： +😊 公众号ID：**agentUniverse智多星** -* **快速简单的研发体验**： 通过配置化与简单的接口实现，您可以基于本框架快速完成单智能体搭建、多智能体协同过程定义与服务化； -* **丰富的组件与扩展定制**： 框架提供了大量常见的领域组件（LLM、知识、工具、记忆、协同模式等组件）与技术组件（DB、RPC、Message等组件）的默认实现；提供了基于所有组件类型的扩展标准，您可以对任何部分进行定制并用来强化您的agent能力； -* **prompt友好与管理**：框架内置了一整套prompt管理机制，您可以对于prompt进行多版本管理、切换与专家经验领域prompt化。 +更多相关的文章与资讯你可以在微信公众号中获取。 ## 鸣谢 -本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等开源项目（详细依赖列表可见requirements.txt）实现，在此特别感谢相关项目与关联方。 \ No newline at end of file +本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏 \ No newline at end of file diff --git "a/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" "b/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" new file mode 100644 index 00000000..e5f0e4d7 --- /dev/null +++ "b/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" @@ -0,0 +1,10 @@ +# 为什么选择agentUniverse +通过agentUniverse您可以轻松构建出领域专家级别的强大智能体协同为您工作。 + +agentUniverse提供了搭建单智能体的全部关键组件，所有可见的组件都是可扩展与定制的，它们可以非常轻松的连接大模型生态组件与中间件，帮助您构建能强大的单智能体。 + +agentUniverse拥有丰富的多智能体协同能力，不同的协同模式专精于解决专属场景下的领域问题，多智能体将扮演不同的角色将各司其职围绕着任务目标协同工作，这将大大提升智能体们完成任务的表现； + +agentUniverse专注于领域prompt、领域知识、领域SOP构建管理的能力，通过这些能力将领域经验注入到所有智能体的协同工作中去，保证智能体在特定领域拥有对齐人工专家的能力； + +此外agentUniverse在金融领域已经有非常多的产业级产品与实践，我们会将金融领域经过产业验证的协同模式、领域经验、解决方案作为案例分享开放。 \ No newline at end of file diff --git "a/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" "b/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" new file mode 100644 index 00000000..3899fd53 --- /dev/null +++ "b/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" @@ -0,0 +1,12 @@ +# 核心特性 +* **面向智能体设计：** 框架所有设计均从智能体领域出发，通过感知、规划、行动、记忆模块定制智能体； +* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作； +* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体； +* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别； +* **面向多LLM且可扩展：** 内置OpenAi系列、qwen系列、Baichuan系列、Kimi系列等数种模型LLM接入方式，同时提供模型扩展标准，支持任意第三方模型服务与私有部署模型接入； +* **面向多数据源且可扩展：** 框架支持多种数据源，无论是SQLite, MySQL, Oracle等这一类传统数据源、还是ChromaDB、Milvu这一类向量数据库，同时提供数据源扩展标准，支持任意第三方数据服务与其他数据库自定义接入； +* **便捷的生态融合能力：** 框架将当前比较成熟先进、经得起实际考验的大模型应用生态能力融合，屏蔽了大部分底层配置与实现，无论是LangChain、llama_index这类LLM生态组件，还是如Grpc、SLS这一类技术组件。 +* **效果可观测与可反馈：** 通过monitor组件所有agentUniverse的服务交互、模型交互均可被记录观测，结合框架提供的智能体自动评测能力，您可以轻松的掌握智能体或模型迭代带来的效果增益，并反馈至服务迭代与模型训练中。 +* **面向企业私有组件扩展：** 框架提供私有技术组件扩展加载机制，通过配置可以将企业内部如自研RPC协议组件、消息组件、日志组件接入； +* **支持一键服务化：** 框架通过Flask+Gunicore的能力支持一键启动WebServer，通过服务化能力您可以将agent的能力融合至任何现有的系统中去； +* **面向标准容器交付：** 框架提供了标准的Docker镜像，同时基于Docker与K8S能力给出推荐集成方案。 \ No newline at end of file From 57dd5f0fd637f42c03f849d2053640ec03609a71 Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Mon, 17 Jun 2024 21:21:12 +0800 Subject: [PATCH 032/118] docs: update readme for new version. --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index e1e70f75..f2b410d0 100644 --- a/README.md +++ b/README.md @@ -27,13 +27,13 @@ More patterns are coming soon... **************************************** ## Table of Contents -* [Quick Start](#Quick Start) -* [Cases and Example Projects](#Cases and Example Projects) +* [Quick Start](#Quick-Start) +* [Cases and Example Projects](#Cases-and-Example-Projects) * [More](#More) - * [Why Use agentUniverse](#Why Use agentUniverse) - * [Core Features](#Core Features) - * [User Guide](#User Guide) - * [API Reference](#API Reference) + * [Why Use agentUniverse](#Why-Use-agentUniverse) + * [Core Features](#Core-Features) + * [User Guide](#User-Guide) + * [API Reference](#API-Reference) * [Support](#Support) * [Acknowledgements](#Acknowledgements) **************************************** From ede51e27bb33b1d8fee952139d700b211a0a5f9b Mon Sep 17 00:00:00 2001 From: Jerry Z H Date: Mon, 17 Jun 2024 22:19:07 +0800 Subject: [PATCH 033/118] docs: update Discord code --- README.md | 6 +----- README_jp.md | 4 +--- README_zh.md | 2 +- 3 files changed, 3 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index f2b410d0..3059c234 100644 --- a/README.md +++ b/README.md @@ -90,11 +90,7 @@ For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_St 😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days. #### Contact Us via Discord -😊 Join our [Discord Channel](https://discord.gg/VfhEvJzQ) to interact with us. - -#### Contact Us via DingTalk -😊 Join our DingTalk support group to get in touch with us. -![](./docs/guidebook/_picture/dingtalk_util20250429.png) +😊 Join our [Discord Channel](https://discord.gg/DHFcdkWAhn) to interact with us. #### Contact Us via Administrator Email 😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com) diff --git a/README_jp.md b/README_jp.md index 0162d123..833c9750 100644 --- a/README_jp.md +++ b/README_jp.md @@ -62,7 +62,5 @@ pip install agentUniverse * gitee: https://gitee.com/agentUniverse/agentUniverse * gitcode: https://gitcode.com/agentUniverse * Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions -* Discord: https://discord.gg/VfhEvJzQ +* Discord: https://discord.gg/DHFcdkWAhn * WeChat公式アカウント: agentUniverse智多星 -* DingTalkグループ: -![](./docs/guidebook/_picture/dingtalk_util20250429.png) diff --git a/README_zh.md b/README_zh.md index 53bac792..eecb53d9 100644 --- a/README_zh.md +++ b/README_zh.md @@ -91,7 +91,7 @@ pip install agentUniverse 😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。 #### 通过Discord联系我们 -😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。 +😊 加入我们的 [Discord频道](https://discord.gg/DHFcdkWAhn) 与我们进行交流。 #### 通过钉钉群联系我们 😊 加入我们的钉钉答疑群与我们联系。 From 8c4fd613de39c5e9a2496bdc42d1d47e78362bb5 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 12:58:56 +0800 Subject: [PATCH 034/118] add tool doc --- ...20\347\232\204\345\267\245\345\205\267.md" | 140 ++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 "docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" new file mode 100644 index 00000000..4fb390b2 --- /dev/null +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -0,0 +1,140 @@ +# 支持的工具 + +当前au的 sample工程当中，集成了以下工具： + +## 1. 搜索工具 + +### 1.1 谷歌搜索 +[工具地址](../../../sample_standard_app/app/core/tool/google_search_tool.yaml) +详细配置信息： + +```yaml +name: 'google_search_tool' +description: | + 该工具可以用来进行谷歌搜索，工具的输入是你想搜索的内容。 + 工具输入示例： + 示例1: 你想要搜索上海的天气时，工具的输入应该是：上海今天的天气 + 示例2: 你想要搜索日本的天气时，工具的输入应该是：日本的天气 +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.google_search_tool' + class: 'GoogleSearchTool' +``` +为了使用该api,你必须申请(https://serper.dev)BING_SUBSCRIPTION_KEY,并配置在环境变量当中。 +配置方法： +1. 通过python代码配置 +必须配置：SERPER_API_KEY +```python +import os +os.environ['SERPER_API_KEY'] = 'xxxx' +``` +2. 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +SERPER_API_KEY="xxxx" +``` + + +### 1.2 Bing(必应)搜索 +当前集成了Bing官方的搜索 +[工具地址](../../../sample_standard_app/app/core/tool/bing_search_tool.yaml) +工具配置： +```yaml +name: 'bing_search_tool' +description: 'demo bing search tool' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.bing_search_tool' + class: 'BingSearchTool' +``` +为了使用该api,你必须申请BING_SUBSCRIPTION_KEY,并配置在环境变量当中。 +配置方法： +1. 通过python代码配置 +必须配置：BING_SUBSCRIPTION_KEY +```python +import os +os.environ['BING_SUBSCRIPTION_KEY'] = 'xxxx' +``` +2. 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +BING_SUBSCRIPTION_KEY="xxxx" +``` + + + +### 1.3 Search API +支持多种搜索工具，例如： +- [百度搜索](../../../sample_standard_app/app/core/tool/search_api_baidu_tool.yaml) +- [Bing搜索](../../../sample_standard_app/app/core/tool/search_api_bing_tool.yaml) +其他搜索引擎还包括: 谷歌搜索、亚马逊搜索、YouTube搜索等,详细信息可以参考:https://www.searchapi.io/ +工具配置： +```yaml +name: 'search_api_baidu_tool' +description: '百度(必应)搜索工具，输入为一个要搜索内容的字符串，例如：input=黄金价格是多少' +tool_type: 'api' +input_keys: ['input'] +engine: 'baidu' +search_type: 'json' +search_params: + num: 10 +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.search_api_tool' + class: 'SearchAPITool' +``` +参数说明: +search_type: 代表你期望返回的搜索结果的格式，json代表你期望为json格式，common代表你期望为string字符串格式。 +search_params: 代表你需要额外的传递给搜索引擎的参数，比如在百度搜索中，num代表了返回的搜索结果条数，详细参数需要参考https://www.searchapi.io/ 。 +engine: 你期望使用的搜索引擎，baidu、google、bing、amazon、youtube... +为了使用该api,你必须从[官网](https://www.searchapi.io/)申请SEARCH_API_KEY,并配置在环境变量当中。 +配置方法： +1. 通过python代码配置 +必须配置：SEARCH_API_KEY +```python +import os +os.environ['SEARCHAPI_API_KEY'] = 'xxxxxx' +``` +2. 通过配置文件配置 +在项目的config目录下的custom_key.toml当中，添加配置： +```toml +SEARCHAPI_API_KEY="xxxxxx" +``` + + +## 2. 代码工具 + +### 2.1 PythonRepl +[工具地址](../../../sample_standard_app/app/core/tool/python_repl_tool.yaml) +该工具可以执行一段Python代码,工具的配置信息: +```yaml +name: 'python_runner' +description: '使用该工具可以执行python代码，可以在pycharm中直接运行的代码.工具的输入必须时一段有效的python代码. 如何你想要查看工具的执行结果, 必须在python代码中使用print(...)打印你想查看的内容。 + 工具输入示例： + 你想要计算1+3等于多少时，工具的输入应该是： + ```py + print(1+3) + ``` + 你想要获取百度页面的信息时，工具的输入应该是: + ```py + import requests + resp=requests.get("https://www.baidu.com") + print(resp.content) + ```' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.python_repl' + class: 'PythonREPLTool' +``` + +该工具可以直接使用，无需任何key，但是为了系统安全，请不要在生产环境使用该工具 + + + + From cebfbf016eff10d86707412ab99668c834c77df0 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 13:16:46 +0800 Subject: [PATCH 035/118] add en doc --- docs/guidebook/en/2_2_3_Integrated_Tools.md | 138 ++++++++++++++++++ ...20\347\232\204\345\267\245\345\205\267.md" | 2 +- 2 files changed, 139 insertions(+), 1 deletion(-) create mode 100644 docs/guidebook/en/2_2_3_Integrated_Tools.md diff --git a/docs/guidebook/en/2_2_3_Integrated_Tools.md b/docs/guidebook/en/2_2_3_Integrated_Tools.md new file mode 100644 index 00000000..6f7e977e --- /dev/null +++ b/docs/guidebook/en/2_2_3_Integrated_Tools.md @@ -0,0 +1,138 @@ +# Integrated Tools + +In the current au's sample project, the following tools are integrated. + +## 1. Search Tools + +### 1.1 Google Search +[工具地址](../../../sample_standard_app/app/core/tool/google_search_tool.yaml) +Detailed Configuration Information: + +```yaml +name: 'google_search_tool' +description: | + This tool can be used to perform Google searches. The tool's input is the content you want to search for. + Example inputs for the tool: + Example 1: If you want to search for the weather in Shanghai, the tool's input should be: "Shanghai weather today" + Example 2: If you want to search for the weather in Japan, the tool's input should be: "Japan weather" +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.google_search_tool' + class: 'GoogleSearchTool' +``` +To use this API, you must apply for a BING_SUBSCRIPTION_KEY at https://serper.dev and configure it in your environment variables. +Configuration method: +1. Configure via Python code You must configure: SERPER_API_KEY +```python +import os +os.environ['SERPER_API_KEY'] = 'xxxx' +``` +2. Configure via configuration file In the custom_key.toml file located in the config directory of the project, add the configuration: +```toml +SERPER_API_KEY="xxxx" +``` + + +### 1.2 Bing Search +Currently, it integrates with the official Bing search. +[工具地址](../../../sample_standard_app/app/core/tool/bing_search_tool.yaml) +Tool configuration: +```yaml +name: 'bing_search_tool' +description: 'demo bing search tool' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.bing_search_tool' + class: 'BingSearchTool' +``` +To use this API, you must apply for BING_SUBSCRIPTION_KEY and configure it in environment variables. +Configuration method: +1. Configure through Python code +Mandatory configuration: BING_SUBSCRIPTION_KEY +```python +import os +os.environ['BING_SUBSCRIPTION_KEY'] = 'xxxx' +``` +2. Configure through configuration file +In custom_key.toml under config directory of the project, add configuration: +```toml +BING_SUBSCRIPTION_KEY="xxxx" +``` + + + +### 1.3 Search API +Supports multiple search tools, such as: +- [Baidu search](../../../sample_standard_app/app/core/tool/search_api_baidu_tool.yaml) +- [Bing search](../../../sample_standard_app/app/core/tool/search_api_bing_tool.yaml) +Other search engines also include: Google search, Amazon search, YouTube search, etc. For more information, please refer to: https://www.searchapi.io/ +Tool configuration: +```yaml +name: 'search_api_baidu_tool' +description: 'Baidu (Bing) search tool, input is a string of content to be searched, e.g.: input="What is the price of gold?"' +tool_type: 'api' +input_keys: ['input'] +engine: 'baidu' +search_type: 'json' +search_params: + num: 10 +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.search_api_tool' + class: 'SearchAPITool' +``` +Parameter description: + +search_type: Represents the format of the expected search results, where json represents the expectation for JSON format and common represents the expectation for string string format. +search_params: Represents additional parameters that need to be passed to the search engine, such as in Baidu search, num represents the number of returned search results, detailed parameters need to be referenced at [https://www.searchapi.io/]. +engine: The search engine you expect to use, including baidu, google, bing, amazon, youtube, ... To use this API, you must apply for SEARCH_API_KEY from the official website ([https://www.searchapi.io/]) and configure it in environment variables. +Configuration method: +You must configure：SEARCHAPI_API_KEY +1. Configure via Python code : +```python +import os +os.environ['SEARCHAPI_API_KEY'] = 'xxxxxx' +``` +2. Configure through configuration file +Add configuration in custom_key.toml under the config directory of the project: +```toml +SEARCHAPI_API_KEY="xxxxxx" +``` + + +## 2. 代码工具 + +### 2.1 PythonRepl +[Tool address](../../../sample_standard_app/app/core/tool/python_repl_tool.yaml) +This tool can execute a piece of Python code, the configuration information of the tool: +```yaml +name: 'python_runner' +description: 'The tool can execute Python code, which can be directly run in PyCharm. The input to the tool must be valid Python code. If you want to view the execution result of the tool, you must use print(...) to print the content you want to view in the Python code. + Example of tool input: + When you want to calculate what 1 + 3 equals, the input to the tool should be: + ```py + print(1+3) + ``` + When you want to get information about the Baidu page, the input to the tool should be: + ```py + import requests + resp=requests.get("https://www.baidu.com") + print(resp.content) + ```' +tool_type: 'api' +input_keys: ['input'] +metadata: + type: 'TOOL' + module: 'sample_standard_app.app.core.tool.python_repl' + class: 'PythonREPLTool' +``` + +This tool can be used directly without any key, but for system security, please do not use this tool in production environments. + + + + diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index 4fb390b2..12057d5d 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -1,4 +1,4 @@ -# 支持的工具 +# 集成的工具 当前au的 sample工程当中，集成了以下工具： From ba905acfc3f6adb188c39100affcba596f0b1063 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 14:47:52 +0800 Subject: [PATCH 036/118] =?UTF-8?q?=E6=9A=82=E5=AD=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../agent/action/knowledge/knowledge.py | 37 +++++++++++++++++++ agentuniverse/agent/agent.py | 36 ++++++++++++++++++ .../app/test/test_rag_agent.py | 10 +++-- 3 files changed, 79 insertions(+), 4 deletions(-) diff --git a/agentuniverse/agent/action/knowledge/knowledge.py b/agentuniverse/agent/action/knowledge/knowledge.py index f8e631a7..f8bc3bb7 100644 --- a/agentuniverse/agent/action/knowledge/knowledge.py +++ b/agentuniverse/agent/action/knowledge/knowledge.py @@ -7,6 +7,9 @@ # @FileName: knowledge.py from typing import Optional, Dict, List, Any +from langchain_core.utils.json import parse_json_markdown +from langchain.tools import Tool as LangchainTool + from agentuniverse.agent.action.knowledge.reader.reader import Reader from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query @@ -77,3 +80,37 @@ def initialize_by_component_configer(self, component_configer: KnowledgeConfiger if component_configer.ext_info: self.ext_info = component_configer.ext_info return self + + def langchain_query(self, query: str) -> str: + """Query the knowledge using LangChain. + + Query documents from the store and return the results. + """ + parse_query = parse_json_markdown(query) + query = Query(**parse_query) + knowledge = self.store.query(query) + res = ['This is Query Result'] + for doc in knowledge: + res.append(doc.text) + return "\n=========================================\n".join(res) + + def as_langchain_tool(self) -> Any: + """Convert the Knowledge object to a LangChain tool. + + Returns: + Any: the LangChain tool object + """ + args_description = """ + This is a knowledge base tool, which stores the content you may need. To use this tool, you need to give a json string with the following format: + ```json + { + "query_str": "", + "top_k": , + } + ``` + """ + return LangchainTool( + name=self.name, + description=self.description + args_description, + func=self.langchain_query, + ) diff --git a/agentuniverse/agent/agent.py b/agentuniverse/agent/agent.py index 2f4099fc..40f34354 100644 --- a/agentuniverse/agent/agent.py +++ b/agentuniverse/agent/agent.py @@ -6,10 +6,13 @@ # @Email : lc299034@antgroup.com # @FileName: agent.py """The definition of agent paradigm.""" +import json from abc import abstractmethod from datetime import datetime from typing import Optional +from langchain_core.utils.json import parse_json_markdown + from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.output_object import OutputObject @@ -21,6 +24,7 @@ import ApplicationConfigManager from agentuniverse.base.config.component_configer.configers.agent_configer \ import AgentConfiger +from agentuniverse.base.util.logging.logging_util import LOGGER from agentuniverse.llm.llm import LLM @@ -156,3 +160,35 @@ def initialize_by_component_configer(self, component_configer: AgentConfiger) -> plan=plan, memory=memory, action=action) self.agent_model = agent_model return self + + def langchain_run(self, input: str, callbacks=None, **kwargs): + """Run the agent model using LangChain.""" + try: + parse_result = parse_json_markdown(input) + except Exception as e: + LOGGER.error(f"langchain run parse_json_markdown error,input(parse_result) error({str(e)})") + return "Error , Your Action Input is not a valid JSON string" + output_object = self.run(**parse_result, callbacks=callbacks, **kwargs) + result_dict = {} + for key in self.output_keys(): + result_dict[key] = output_object.get_data(key) + return result_dict + + def as_langchain_tool(self): + """Convert to LangChain tool.""" + from langchain.agents.tools import Tool + format_dict = {} + for key in self.input_keys(): + format_dict.setdefault(key, "input val") + format_str = json.dumps(format_dict) + + args_description = f""" + to use this tool,your input must be a json string,must contain all keys of {self.input_keys()}, + and the value of the key must be a json string,the format of the json string is as follows: + ```{format_str}``` + """ + return Tool( + name=self.agent_model.info.get("name"), + func=self.langchain_run, + description=self.agent_model.info.get("description") + args_description + ) diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 06c3d9b8..2b7178d5 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -24,10 +24,12 @@ def setUp(self) -> None: def test_rag_agent(self): """Test demo rag agent.""" instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') - output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') - res_info = f"\nRag agent execution result is :\n" - res_info += output_object.get_data('output') - print(res_info) + # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') + # res_info = f"\nRag agent execution result is :\n" + # res_info += output_object.get_data('output') + # print(res_info) + tool = instance.as_langchain_tool() + print(tool.description) if __name__ == '__main__': From 0fecf96d852e40ba899174e8d26d69a34be82a85 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 16:06:47 +0800 Subject: [PATCH 037/118] add knowlege --- .../agent/action/knowledge/knowledge.py | 4 ++-- .../plan/planner/react_planner/react_planner.py | 12 ++++++++++++ sample_standard_app/app/test/test_rag_agent.py | 17 ++++++++++------- 3 files changed, 24 insertions(+), 9 deletions(-) diff --git a/agentuniverse/agent/action/knowledge/knowledge.py b/agentuniverse/agent/action/knowledge/knowledge.py index f8bc3bb7..d6fc4920 100644 --- a/agentuniverse/agent/action/knowledge/knowledge.py +++ b/agentuniverse/agent/action/knowledge/knowledge.py @@ -5,7 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: knowledge.py -from typing import Optional, Dict, List, Any +from typing import Optional, Dict, List from langchain_core.utils.json import parse_json_markdown from langchain.tools import Tool as LangchainTool @@ -94,7 +94,7 @@ def langchain_query(self, query: str) -> str: res.append(doc.text) return "\n=========================================\n".join(res) - def as_langchain_tool(self) -> Any: + def as_langchain_tool(self) -> LangchainTool: """Convert the Knowledge object to a LangChain tool. Returns: diff --git a/agentuniverse/agent/plan/planner/react_planner/react_planner.py b/agentuniverse/agent/plan/planner/react_planner/react_planner.py index ef1f76f9..77e2b0f2 100644 --- a/agentuniverse/agent/plan/planner/react_planner/react_planner.py +++ b/agentuniverse/agent/plan/planner/react_planner/react_planner.py @@ -11,8 +11,11 @@ from langchain.tools import Tool as LangchainTool from langchain_core.runnables import RunnableConfig +from agentuniverse.agent.action.knowledge.knowledge import Knowledge +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.action.tool.tool import Tool from agentuniverse.agent.action.tool.tool_manager import ToolManager +from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.memory.chat_memory import ChatMemory @@ -78,6 +81,15 @@ def acquire_tools(action) -> list[LangchainTool]: for tool_name in tool_names: tool: Tool = ToolManager().get_instance_obj(tool_name) lc_tools.append(tool.as_langchain()) + knowledge: list = action.get('knowledge') or list() + for knowledge_name in knowledge: + knowledge_tool: Knowledge = KnowledgeManager().get_instance_obj(knowledge_name) + lc_tools.append(knowledge_tool.as_langchain_tool()) + + agents: list = action.get('agent') or list() + for agent_name in agents: + agent_tool: Agent = AgentModel.load_by_name(agent_name) + lc_tools.append(agent_tool.as_langchain_tool()) return lc_tools def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt: diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 2b7178d5..1d97de49 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -7,6 +7,8 @@ # @FileName: test_rag_agent.py import unittest +from agentuniverse.agent.action.knowledge.knowledge import Knowledge +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.output_object import OutputObject @@ -23,13 +25,14 @@ def setUp(self) -> None: def test_rag_agent(self): """Test demo rag agent.""" - instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') - # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') - # res_info = f"\nRag agent execution result is :\n" - # res_info += output_object.get_data('output') - # print(res_info) - tool = instance.as_langchain_tool() - print(tool.description) + # instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') + # # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') + # # res_info = f"\nRag agent execution result is :\n" + # # res_info += output_object.get_data('output') + # # print(res_info) + # tool = instance.as_langchain_tool() + knowledge: Knowledge = KnowledgeManager().get_instance_obj('demo_knowledge') + print(knowledge.as_langchain_tool().description) if __name__ == '__main__': From 6707da4e1b9099ea2dd70475f7b7ed1b56aa3262 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 16:07:50 +0800 Subject: [PATCH 038/118] fix output --- sample_standard_app/app/test/test_rag_agent.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 1d97de49..3c678aa1 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -23,16 +23,13 @@ class RagAgentTest(unittest.TestCase): def setUp(self) -> None: AgentUniverse().start(config_path='../../config/config.toml') - def test_rag_agent(self): + def test_rag_fgent(self): """Test demo rag agent.""" - # instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') - # # output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') - # # res_info = f"\nRag agent execution result is :\n" - # # res_info += output_object.get_data('output') - # # print(res_info) - # tool = instance.as_langchain_tool() - knowledge: Knowledge = KnowledgeManager().get_instance_obj('demo_knowledge') - print(knowledge.as_langchain_tool().description) + instance: Agent = AgentManager().get_instance_obj('demo_rag_agent') + output_object: OutputObject = instance.run(input='分析下巴菲特减持比亚迪的原因') + res_info = f"\nRag agent execution result is :\n" + res_info += output_object.get_data('output') + print(res_info) if __name__ == '__main__': From 310587015dc088bb06fe8d6b3b248eadd336fd1d Mon Sep 17 00:00:00 2001 From: weizjajj Date: Mon, 17 Jun 2024 16:08:42 +0800 Subject: [PATCH 039/118] remove unused import --- sample_standard_app/app/test/test_rag_agent.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/sample_standard_app/app/test/test_rag_agent.py b/sample_standard_app/app/test/test_rag_agent.py index 3c678aa1..14ba678f 100644 --- a/sample_standard_app/app/test/test_rag_agent.py +++ b/sample_standard_app/app/test/test_rag_agent.py @@ -7,8 +7,6 @@ # @FileName: test_rag_agent.py import unittest -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager from agentuniverse.agent.agent import Agent from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.output_object import OutputObject From 83ba37d556b2a0ae9526de514b85c9f349e717b8 Mon Sep 17 00:00:00 2001 From: weizjajj Date: Wed, 19 Jun 2024 17:53:42 +0800 Subject: [PATCH 040/118] add deepseek support --- .../planner/react_planner/react_planner.py | 3 +- .../llm/default/deep_seek_openai_style_llm.py | 70 +++++++++++++++++++ .../default/deep_seek_openai_style_llm.yaml | 8 +++ agentuniverse/llm/llm.py | 2 + agentuniverse/llm/openai_style_llm.py | 19 +++++ ...20\347\232\204\345\267\245\345\205\267.md" | 2 +- .../rag_agent_case/demo_multillm_agent.yaml | 6 +- .../agent/rag_agent_case/law_rag_agent.yaml | 2 +- .../react_agent_case/demo_react_agent.yaml | 6 ++ .../core/knowledge/civil_law_knowledge.yaml | 2 +- .../knowledge/criminal_law_knowledge.yaml | 2 +- .../app/core/llm/deep_seek_opneai_llm.yaml | 11 +++ .../app/test/test_deep_seek_llm.py | 56 +++++++++++++++ 13 files changed, 182 insertions(+), 7 deletions(-) create mode 100644 agentuniverse/llm/default/deep_seek_openai_style_llm.py create mode 100644 agentuniverse/llm/default/deep_seek_openai_style_llm.yaml create mode 100644 sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml create mode 100644 sample_standard_app/app/test/test_deep_seek_llm.py diff --git a/agentuniverse/agent/plan/planner/react_planner/react_planner.py b/agentuniverse/agent/plan/planner/react_planner/react_planner.py index 77e2b0f2..b3f20211 100644 --- a/agentuniverse/agent/plan/planner/react_planner/react_planner.py +++ b/agentuniverse/agent/plan/planner/react_planner/react_planner.py @@ -16,6 +16,7 @@ from agentuniverse.agent.action.tool.tool import Tool from agentuniverse.agent.action.tool.tool_manager import ToolManager from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager from agentuniverse.agent.agent_model import AgentModel from agentuniverse.agent.input_object import InputObject from agentuniverse.agent.memory.chat_memory import ChatMemory @@ -88,7 +89,7 @@ def acquire_tools(action) -> list[LangchainTool]: agents: list = action.get('agent') or list() for agent_name in agents: - agent_tool: Agent = AgentModel.load_by_name(agent_name) + agent_tool: Agent = AgentManager().get_instance_obj(agent_name) lc_tools.append(agent_tool.as_langchain_tool()) return lc_tools diff --git a/agentuniverse/llm/default/deep_seek_openai_style_llm.py b/agentuniverse/llm/default/deep_seek_openai_style_llm.py new file mode 100644 index 00000000..485ec685 --- /dev/null +++ b/agentuniverse/llm/default/deep_seek_openai_style_llm.py @@ -0,0 +1,70 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/5/21 17:49 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: claude_llm.py + +from typing import Optional, Any, Union, Iterator, AsyncIterator + +from pydantic import Field + +from agentuniverse.base.annotation.trace import trace_llm +from agentuniverse.base.util.env_util import get_from_env +from agentuniverse.llm.llm_output import LLMOutput +from agentuniverse.llm.openai_style_llm import OpenAIStyleLLM + +DEEpSEEkMAXCONTETNLENGTH = { + "deepseek-chat": 32000, + "deepseek-coder": 32000, + "claude-3-haiku-20240307": 200000, + "claude-2.1": 200000, + "claude-2.0": 100000, + "claude-instant-1.2": 100000 +} + + +class DefaultDeepSeekLLM(OpenAIStyleLLM): + """The agentUniverse default openai llm module. + + LLM parameters, such as name/description/model_name/max_tokens, + are injected into this class by the default_openai_llm.yaml configuration. + """ + + api_key: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_API_KEY")) + organization: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_ORGANIZATION")) + api_base: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_API_BASE")) + proxy: Optional[str] = Field(default_factory=lambda: get_from_env("DEEPSEEK_PROXY")) + + @trace_llm + def call(self, messages: list, **kwargs: Any) -> Union[LLMOutput, Iterator[LLMOutput]]: + """ The call method of the LLM. + + Users can customize how the model interacts by overriding call method of the LLM class. + + Args: + messages (list): The messages to send to the LLM. + **kwargs: Arbitrary keyword arguments. + """ + return super().call(messages, **kwargs) + + @trace_llm + async def acall(self, messages: list, **kwargs: Any) -> Union[LLMOutput, AsyncIterator[LLMOutput]]: + """ The async call method of the LLM. + + Users can customize how the model interacts by overriding acall method of the LLM class. + + Args: + messages (list): The messages to send to the LLM. + **kwargs: Arbitrary keyword arguments. + """ + return await super().acall(messages, **kwargs) + + def max_context_length(self) -> int: + """Max context length. + + The total length of input tokens and generated tokens is limited by the openai model's context length. + """ + return DEEpSEEkMAXCONTETNLENGTH.get(self.model_name, 4096) + diff --git a/agentuniverse/llm/default/deep_seek_openai_style_llm.yaml b/agentuniverse/llm/default/deep_seek_openai_style_llm.yaml new file mode 100644 index 00000000..3921577a --- /dev/null +++ b/agentuniverse/llm/default/deep_seek_openai_style_llm.yaml @@ -0,0 +1,8 @@ +name: 'default_deepseek_llm' +description: 'default default_deepseek_llm llm with spi' +model_name: 'deepseek-chat' +max_tokens: 1000 +metadata: + type: 'LLM' + module: 'agentuniverse.llm.default.deep_seek_openai_style_llm' + class: 'DefaultDeepSeekLLM' \ No newline at end of file diff --git a/agentuniverse/llm/llm.py b/agentuniverse/llm/llm.py index 53c4fd67..4d33c629 100644 --- a/agentuniverse/llm/llm.py +++ b/agentuniverse/llm/llm.py @@ -103,6 +103,8 @@ def initialize_by_component_configer(self, component_configer: LLMConfiger) -> ' if component_configer.ext_info: self.ext_info = component_configer.ext_info self.tracing = component_configer.tracing + if 'max_context_length' in component_configer.configer.value: + self._max_context_length = component_configer.configer.value['max_context_length'] return self def set_by_agent_model(self, **kwargs) -> None: diff --git a/agentuniverse/llm/openai_style_llm.py b/agentuniverse/llm/openai_style_llm.py index 2d5a3ea0..0981e47a 100644 --- a/agentuniverse/llm/openai_style_llm.py +++ b/agentuniverse/llm/openai_style_llm.py @@ -14,6 +14,8 @@ from langchain_core.language_models.base import BaseLanguageModel from openai import OpenAI, AsyncOpenAI +from agentuniverse.base.config.component_configer.configers.llm_configer import LLMConfiger +from agentuniverse.base.util.env_util import get_from_env from agentuniverse.llm.llm import LLM, LLMOutput from agentuniverse.llm.openai_style_langchain_instance import LangchainOpenAIStyleInstance @@ -161,6 +163,17 @@ async def agenerate_stream_result(self, stream: AsyncIterator) -> AsyncIterator[ await self.aclose() + def initialize_by_component_configer(self, component_configer: LLMConfiger) -> 'LLM': + if 'api_base' in component_configer.configer.value: + self.api_base = component_configer.configer.value.get('api_base') + elif 'api_base_env' in component_configer.configer.value: + self.api_base = get_from_env(component_configer.configer.value.get('api_base_env')) + if 'api_key' in component_configer .configer.value: + self.api_key = component_configer.configer.value.get('api_key') + elif 'api_key_env' in component_configer.configer.value: + self.api_key = get_from_env(component_configer.configer.value.get('api_key_env')) + return super().initialize_by_component_configer(component_configer) + def get_num_tokens(self, text: str) -> int: """Get the number of tokens present in the text. @@ -187,3 +200,9 @@ async def aclose(self): """Async close the client.""" if hasattr(self, 'async_client') and self.async_client: await self.async_client.close() + + def max_context_length(self) -> int: + """Return the maximum length of the context.""" + if super().max_context_length(): + return super().max_context_length() + return 4000 diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index 12057d5d..ceba884c 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -22,7 +22,7 @@ metadata: module: 'sample_standard_app.app.core.tool.google_search_tool' class: 'GoogleSearchTool' ``` -为了使用该api,你必须申请(https://serper.dev)BING_SUBSCRIPTION_KEY,并配置在环境变量当中。 +为了使用该api,你必须申请(https://serper.dev)SERPER_API_KEY并配置在环境变量当中。 配置方法： 1. 通过python代码配置 必须配置：SERPER_API_KEY diff --git a/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml b/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml index 2fd13785..8732f8a1 100644 --- a/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml +++ b/sample_standard_app/app/core/agent/rag_agent_case/demo_multillm_agent.yaml @@ -15,8 +15,10 @@ profile: # model_name: 'Baichuan2-Turbo' # name: 'qwen_llm' # model_name: 'qwen-turbo' - name: 'wenxin_llm' - model_name: 'ERNIE-3.5-8K' +# name: 'wenxin_llm' +# model_name: 'ERNIE-3.5-8K' +# name: 'deep_seek_llm' + name: 'default_deepseek_llm' plan: planner: name: 'rag_planner' diff --git a/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml b/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml index bf917fcd..03ced866 100644 --- a/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml +++ b/sample_standard_app/app/core/agent/rag_agent_case/law_rag_agent.yaml @@ -1,6 +1,6 @@ info: name: 'law_rag_agent' - description: 'law rag agent' + description: '一个法律顾问，可以根据给出的事件，以及提供的背景知识做出客观的司法判断。' profile: introduction: 你是一位精通信息分析的ai法律顾问。 target: 你的目标是根据给出的事件，以及提供的背景知识做出客观的司法判断。 diff --git a/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml b/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml index 8a38838d..b9a4e02a 100644 --- a/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml +++ b/sample_standard_app/app/core/agent/react_agent_case/demo_react_agent.yaml @@ -5,11 +5,17 @@ profile: prompt_version: qwen_react_agent.cn llm_model: name: 'default_qwen_llm' + model_name: 'qwen-max' temperature: 0 action: tool: - 'google_search_tool' - 'python_runner' + knowledge: + - 'civil_law_knowledge' + - 'criminal_law_knowledge' + agent: + - 'law_rag_agent' plan: planner: name: 'react_planner' diff --git a/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml b/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml index 86ddd6b0..3cf789e9 100644 --- a/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml +++ b/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml @@ -1,5 +1,5 @@ name: 'civil_law_knowledge' -description: 'civil law knowledge' +description: 民法知识库，可以查询民法典法律相关内容' metadata: type: 'KNOWLEDGE' module: 'sample_standard_app.app.core.knowledge.civil_law_knowledge' diff --git a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml b/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml index 5a1b2a8c..184b16f9 100644 --- a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml +++ b/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml @@ -1,5 +1,5 @@ name: 'criminal_law_knowledge' -description: 'criminal law knowledge' +description: '刑法知识库，可以查询与刑法相关的内容' metadata: type: 'KNOWLEDGE' module: 'sample_standard_app.app.core.knowledge.criminal_law_knowledge' diff --git a/sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml b/sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml new file mode 100644 index 00000000..a4aebb15 --- /dev/null +++ b/sample_standard_app/app/core/llm/deep_seek_opneai_llm.yaml @@ -0,0 +1,11 @@ +name: 'deep_seek_llm' +description: 'demo deep_seek llm with spi' +model_name: 'deepseek-chat' +max_tokens: 4000 +max_context_length: 32000 +api_key_env: 'DEEPSEEK_API_KEY' +api_base_env: 'DEEPSEEK_API_BASE' +metadata: + type: 'LLM' + module: 'agentuniverse.llm.openai_style_llm' + class: 'OpenAIStyleLLM' \ No newline at end of file diff --git a/sample_standard_app/app/test/test_deep_seek_llm.py b/sample_standard_app/app/test/test_deep_seek_llm.py new file mode 100644 index 00000000..8afb55c8 --- /dev/null +++ b/sample_standard_app/app/test/test_deep_seek_llm.py @@ -0,0 +1,56 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/5/22 16:25 +# @Author : weizjajj +# @Email : weizhongjie.wzj@antgroup.com +# @FileName: test_wenxin_llm.py + + +import asyncio +import time +import unittest + +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.llm.default.wenxin_llm import WenXinLLM +from agentuniverse.llm.llm_manager import LLMManager + + +class DeepSeekLLMTest(unittest.TestCase): + """Test cases for the reviewing agent""" + + def setUp(self) -> None: + AgentUniverse().start(config_path='../../config/config.toml') + + def test_reviewing_agent(self): + llm: WenXinLLM = LLMManager().get_instance_obj("deep_seek_llm") + res = llm.call( + messages=[{"role": "user", "content": "你好"}], + streaming=True + ) + print(res) + for item in res: + print(item) + + langchain_llm = llm.as_langchain() + print(langchain_llm.invoke(input="你好")) + time.sleep(1) + asyncio.run(self.call_stream()) + + async def call_stream(self): + llm: WenXinLLM = LLMManager().get_instance_obj("deep_seek_llm") + res = await llm.acall( + messages=[{"role": "user", "content": "你好"}], + streaming=True + ) + print(res) + async for item in res: + print(item) + + langchain_llm = llm.as_langchain() + res = await langchain_llm.ainvoke(input="你好") + print(res) + + +if __name__ == '__main__': + unittest.main() From 377f19cca54913a9037c80ee131a92a243074922 Mon Sep 17 00:00:00 2001 From: "fanen.lhy" Date: Thu, 20 Jun 2024 22:38:35 +0800 Subject: [PATCH 041/118] Fix: Pydantic warning in chroma store Fix: Lock the numpy version to Major 1 Change: Default gunicorn config bind address from 127.0.0.1 to 0.0.0.0 --- agentuniverse/agent/action/knowledge/store/chroma_store.py | 5 +++-- pyproject.toml | 1 + sample_standard_app/config/gunicorn_config.toml | 2 +- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/agentuniverse/agent/action/knowledge/store/chroma_store.py b/agentuniverse/agent/action/knowledge/store/chroma_store.py index dcb67a6a..d389150a 100644 --- a/agentuniverse/agent/action/knowledge/store/chroma_store.py +++ b/agentuniverse/agent/action/knowledge/store/chroma_store.py @@ -6,10 +6,11 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: chroma_store.py from typing import List, Any, Optional +from pydantic import SkipValidation import chromadb from chromadb import QueryResult -from chromadb.api.models import Collection +from chromadb.api.models.Collection import Collection from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query @@ -28,7 +29,7 @@ class ChromaStore(Store): """ collection_name: Optional[str] = 'chroma_db' - collection: Collection = None + collection: SkipValidation[Collection] = None persist_path: Optional[str] = None def __init__(self, **kwargs): diff --git a/pyproject.toml b/pyproject.toml index 30e82d41..302c6b67 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,7 @@ dashscope = "^1.19.1" anthropic = "^0.26.0" ollama = '^0.2.1' langchain-anthropic = '^0.1.13' +numpy = '^1.26.0' pandas = "^2.2.2" pyarrow = "^16.1.0" diff --git a/sample_standard_app/config/gunicorn_config.toml b/sample_standard_app/config/gunicorn_config.toml index 2dd3b915..3b8836de 100644 --- a/sample_standard_app/config/gunicorn_config.toml +++ b/sample_standard_app/config/gunicorn_config.toml @@ -1,5 +1,5 @@ [GUNICORN_CONFIG] -bind = '127.0.0.1:8888' +bind = '0.0.0.0:8888' backlog = 2048 worker_class = 'gthread' threads = 4 From 42725008d20137f788720fb8ba828f753a541b12 Mon Sep 17 00:00:00 2001 From: wangchongshi Date: Fri, 21 Jun 2024 16:14:34 +0800 Subject: [PATCH 042/118] feat: add data agent in the agentUniverse. --- .../node/data/prompt/input/extend.py | 2 +- agentuniverse_dataflow/util/llm/llm_call.py | 2 +- .../_picture/dataflow_dataset_build.png | Bin 0 -> 99507 bytes docs/guidebook/_picture/dataflow_dispatch.png | Bin 0 -> 65843 bytes .../guidebook/_picture/dataflow_flowchart.jpg | Bin 0 -> 1402069 bytes .../en/8_1_agentUniverse-dataflow.md | 115 ++++++++++++++++++ .../zh/8_1_agentUniverse-dataflow.md | 115 ++++++++++++++++++ .../dataflow/flow/dataset_build.yaml | 2 +- .../dataflow/flow/eval_report.yaml | 3 +- 9 files changed, 235 insertions(+), 4 deletions(-) create mode 100644 docs/guidebook/_picture/dataflow_dataset_build.png create mode 100644 docs/guidebook/_picture/dataflow_dispatch.png create mode 100644 docs/guidebook/_picture/dataflow_flowchart.jpg create mode 100644 docs/guidebook/en/8_1_agentUniverse-dataflow.md create mode 100644 docs/guidebook/zh/8_1_agentUniverse-dataflow.md diff --git a/agentuniverse_dataflow/node/data/prompt/input/extend.py b/agentuniverse_dataflow/node/data/prompt/input/extend.py index 2af1732e..4dd49a6f 100644 --- a/agentuniverse_dataflow/node/data/prompt/input/extend.py +++ b/agentuniverse_dataflow/node/data/prompt/input/extend.py @@ -20,7 +20,7 @@ class ExtendNode(PromptBase): """The ExtendNode class, which is used to define the class of extend node.""" - extend_times: int = 4 + extend_times: int = 1 def _node_preprocess(self) -> None: super()._node_preprocess() diff --git a/agentuniverse_dataflow/util/llm/llm_call.py b/agentuniverse_dataflow/util/llm/llm_call.py index 7cea21f8..ac1cc24b 100644 --- a/agentuniverse_dataflow/util/llm/llm_call.py +++ b/agentuniverse_dataflow/util/llm/llm_call.py @@ -25,7 +25,7 @@ async def async_batch_call(prompts: list[str], llm_name: str): if llm is None: raise Exception('LLM not found for agentuniverse data.') messages = [{"role": "user", "content": prompts[i]}] - tasks.append(llm.acall(messages=messages, timeout=200)) + tasks.append(llm.acall(messages=messages, 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