Dev

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

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

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)
 ****************************************
@@ -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).
 

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)
 ****************************************
@@ -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。
 

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)
 ****************************************
@@ -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) 。
 

agentuniverse/agent/action/knowledge/embedding/openai_embedding.py

@@ -5,6 +5,7 @@
 # @Author  : wangchongshi
 # @Email   : wangchongshi.wcs@antgroup.com
 # @FileName: openai_embedding.py
+
 from typing import List, Optional, Any
 
 from langchain_community.embeddings.openai import OpenAIEmbeddings

agentuniverse/agent/action/tool/tool.py

@@ -73,14 +73,32 @@ def input_check(self, kwargs: dict) -> None:
             if key not in kwargs.keys():
                 raise Exception(f'{self.get_instance_code()} - The input must include key: {key}.')
 
+    def langchain_run(self, *args, callbacks=None, **kwargs):
+        """The callable method that runs the tool."""
+        kwargs["callbacks"] = callbacks
+        tool_input = ToolInput(kwargs)
+        parse_result = self.parse_react_input(args[0])
+        for key in self.input_keys:
+            tool_input.add_data(key, parse_result[key])
+        return self.execute(tool_input)
+
+    def parse_react_input(self, input_str: str):
+        """
+            parse react string to you input
+            you can define your own logic here by override this function
+        """
+        return {
+            self.input_keys[0]: input_str
+        }
+
     @abstractmethod
     def execute(self, tool_input: ToolInput):
         raise NotImplementedError
 
     def as_langchain(self) -> LangchainTool:
         """Convert the agentUniverse(aU) tool class to the langchain tool class."""
         return LangchainTool(name=self.name,
-                             func=self.run,
+                             func=self.langchain_run,
                              description=self.description)
 
     def get_instance_code(self) -> str:

agentuniverse/agent/default/nl2api_agent/default_cn_prompt.yaml

@@ -0,0 +1,27 @@
+introduction: 你是一位精通工具选择ai助手。
+target: 你的目标是根据用户的问题选择出合适的工具。
+instruction: |
+  你需要根据问题和用户提供的工具，选择其中的一个或几个工具用来回答用户提出的问题。
+  你必须从多个角度、维度分析用户的问题，需要根据背景和问题，决定使用哪些工具可以回答用户问题。 
+
+  您可以使用以下工具:
+  {tools}
+
+  之前的对话: 
+  {chat_history}
+
+  背景信息是: 
+  {background}
+
+  回答必须是按照以下格式化的Json代码片段。
+  1. tools字段代表选择的几个工具的完整名称，列表格式。例如：[add, sub, mul, div]
+  2. thought字段代表选择工具的思考过程和原因。
+  ```{{
+    "tools": list,
+    "thought": string
+  }}```
+
+  当前的问题：{input}
+metadata:
+  type: 'PROMPT'
+  version: 'default_nl2api_agent.cn'

agentuniverse/agent/default/nl2api_agent/default_en_prompt.yaml

@@ -0,0 +1,26 @@
+introduction: You are an AI assistant proficient in tool selection.
+target: Your goal is to select the appropriate tools based on the user's questions.
+instruction: |
+  Your task is to select one or several tools from those provided by the user, based on their question and the context, in order to answer the user's query.
+  You must analyze the user's problem from multiple angles and dimensions, taking into account the background and context of the question, and decide which tools can be used to answer the user's question.
+  You may use the following tools:  
+  {tools}
+
+  Previous conversation:
+  {chat_history}
+
+  The background information is:
+  {background}
+
+  The response must follow the format below as a formatted JSON code snippet.
+  1. The tools field represents the full names of the selected tools in a list format, such as:[add, sub, mul, div]
+  2. The thought field represents the thinking process and reasons behind the selection of tools.
+  ```{{
+    "tools": list,
+    "thought": string
+  }}```
+
+  Question: {input}
+metadata:
+  type: 'PROMPT'
+  version: 'default_nl2api_agent.en'

agentuniverse/agent/default/nl2api_agent/nl2api_agent.py

@@ -0,0 +1,45 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/6/11 17:14
+# @Author  : weizjajj 
+# @Email   : weizhongjie.wzj@antgroup.com
+# @FileName: nl2api_agent.py
+
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.input_object import InputObject
+
+
+class Nl2ApiAgent(Agent):
+    """ReAct Agent class."""
+
+    def input_keys(self) -> list[str]:
+        """Return the input keys of the Agent."""
+        return ['input']
+
+    def output_keys(self) -> list[str]:
+        """Return the output keys of the Agent."""
+        return ['output']
+
+    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['input'] = input_object.get_data('input')
+        self.agent_model.profile.setdefault('prompt_version', 'default_nl2api_agent.cn')
+        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

agentuniverse/agent/default/nl2api_agent/nl2api_agent.yaml

@@ -0,0 +1,17 @@
+info:
+  name: 'nl2api_agent'
+  description: 'nl2api agent'
+profile:
+  llm_model:
+    name: 'default_openai_llm'
+    model_name: 'gpt-4o'
+action:
+  tool:
+    - ''
+plan:
+  planner:
+    name: 'nl2api_planner'
+metadata:
+  type: 'AGENT'
+  module: 'agentuniverse.agent.default.nl2api_agent.nl2api_agent'
+  class: 'Nl2ApiAgent'

agentuniverse/agent/default/react_agent/__init__.py

@@ -0,0 +1,7 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/6/4 21:21
+# @Author  : wangchongshi
+# @Email   : wangchongshi.wcs@antgroup.com
+# @FileName: __init__.py.py

agentuniverse/agent/default/react_agent/default_cn_prompt.yaml

@@ -0,0 +1,35 @@
+introduction: 你是一位精通信息分析的ai助手。
+target: 你的目标是根据用户的问题以及给出的背景信息给出答案。
+instruction: |
+  你必须优先选择使用提供的工具回答用户提出的问题，若用户没有提供工具可以根据你的通识能力解决问题。
+  你在回答时问题必须使用中文回答。
+  你必须从多个角度、维度分析用户的问题，帮助用户获取最全面的信息，需要根据背景和问题，决定搜索哪些信息可以回答问题。 
+  请注意: 你在给出最终答案时需要从多角度给出更详细的原因，而不是一个简单的结论。
+
+  您可以使用以下工具:
+  {tools}
+
+  你的回答必须严格使用以下格式:
+
+    Question: 您必须回答的问题
+    Thought: 你应该经常想想该怎么做
+    Action: 要采取的行动应该是 one of [{tool_names}]
+    Action Input: 行动的输入
+    Observation: 行动的结果
+    ... (Thought/Action/Action Input/Observation 的过程可以重复 N 次)
+    Thought: 我现在知道最终答案了
+    Final Answer: 原输入问题的最终答案
+
+  之前的对话:
+  {chat_history}
+
+  背景信息是:
+  {background}
+
+  开始!
+
+  Question: {input}
+  Thought:{agent_scratchpad}
+metadata:
+  type: 'PROMPT'
+  version: 'default_react_agent.cn'

agentuniverse/agent/default/react_agent/default_en_prompt.yaml

@@ -0,0 +1,31 @@
+introduction: You are an AI assistant who is proficient in information analysis.
+target: Your goal is to give answers based on the user's question and the context given.
+instruction: |
+  Answer the following questions as best you can. You have access to the following tools:
+
+  {tools}
+
+  Use the following format:
+
+    Question: the input question you must answer
+    Thought: you should always think about what to do
+    Action: the action to take, should be one of [{tool_names}]
+    Action Input: the input to the action
+    Observation: the result of the action
+    ... (this Thought/Action/Action Input/Observation can repeat N times)
+    Thought: I now know the final answer
+    Final Answer: the final answer to the original input question
+
+  Previous conversion:
+  {chat_history}
+
+  Background:
+  {background}
+
+  Begin!
+
+  Question: {input}
+  Thought:{agent_scratchpad}
+metadata:
+  type: 'PROMPT'
+  version: 'default_react_agent.en'

agentuniverse/agent/default/react_agent/react_agent.py

@@ -0,0 +1,44 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/5/31 21:22
+# @Author  : wangchongshi
+# @Email   : wangchongshi.wcs@antgroup.com
+# @FileName: react_agent.py
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.input_object import InputObject
+
+
+class ReActAgent(Agent):
+    """ReAct Agent class."""
+
+    def input_keys(self) -> list[str]:
+        """Return the input keys of the Agent."""
+        return ['input']
+
+    def output_keys(self) -> list[str]:
+        """Return the output keys of the Agent."""
+        return ['output']
+
+    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['input'] = input_object.get_data('input')
+        self.agent_model.profile.setdefault('prompt_version', 'default_react_agent.cn')
+        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