diff --git a/agentuniverse/agent/action/knowledge/doc_processor/__init__.py b/agentuniverse/agent/action/knowledge/doc_processor/__init__.py new file mode 100644 index 00000000..4eced121 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/23 13:59 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.py b/agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.py new file mode 100644 index 00000000..f3e30c8a --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.py @@ -0,0 +1,50 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/5 14:37 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: text_splitter.py +from typing import List, Optional +from langchain.text_splitter import CharacterTextSplitter as Splitter + +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import \ + DocProcessor +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class CharacterTextSplitter(DocProcessor): + chunk_size: int = 200 + chunk_overlap: int = 20 + separator: str = "/n/n" + splitter: Optional[Splitter] = None + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.splitter = Splitter(separator=self.separator, + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap) + + def _process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + lc_doc_list = self.splitter.split_documents(Document.as_langchain_list( + origin_docs + )) + return Document.from_langchain_list(lc_doc_list) + + def _initialize_by_component_configer(self, + doc_processor_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(doc_processor_configer) + if hasattr(doc_processor_configer, "chunk_size"): + self.chunk_size = doc_processor_configer.chunk_size + if hasattr(doc_processor_configer, "chunk_overlap"): + self.chunk_overlap = doc_processor_configer.chunk_overlap + if hasattr(doc_processor_configer, "separator"): + self.separator = doc_processor_configer.separator + self.splitter = Splitter(separator=self.separator, + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap) + return self diff --git a/agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml b/agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml new file mode 100644 index 00000000..de2f679f --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml @@ -0,0 +1,9 @@ +name: 'character_text_splitter' +description: 'langchain character text splitter' +chunk_size: 200 +chunk_overlap: 20 +separators: "/n/n" +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.character_text_splitter' + class: 'CharacterTextSplitter' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.py b/agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.py new file mode 100644 index 00000000..d5e7934e --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.py @@ -0,0 +1,67 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/5 15:48 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: dashscope_reranker.py + +from typing import List, Optional +import dashscope +from http import HTTPStatus + +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import \ + DocProcessor +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + +MODEL_NAME_MAP = { + "gte_rerank": dashscope.TextReRank.Models.gte_rerank +} + + +class DashscopeReranker(DocProcessor): + model_name: str = "gte_rerank" + top_n: int = 10 + + def _process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + if not query or not query.query_str: + raise Exception("Dashscope reranker need an origin string query.") + if len(origin_docs)<1: + return origin_docs + documents_texts = [] + for _doc in origin_docs: + documents_texts.append(_doc.text) + resp = dashscope.TextReRank.call( + model=MODEL_NAME_MAP.get(self.model_name), + query=query.query_str, + documents=documents_texts, + top_n=self.top_n, + return_documents=False + ) + if resp.status_code == HTTPStatus.OK: + results = resp.output.results + else: + raise Exception(f"Dashscope rerank api call error: {resp}") + rerank_docs = [] + for _result in results: + index = _result.index + if origin_docs[index].metadata: + origin_docs[index].metadata["relevance_score"] = _result.relevance_score + else: + origin_docs[index].metadata = {"relevance_score": _result.relevance_score} + rerank_docs.append(origin_docs[index]) + + return rerank_docs + + def _initialize_by_component_configer(self, + doc_processor_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(doc_processor_configer) + if hasattr(doc_processor_configer, "model_name"): + self.model_name = doc_processor_configer.model_name + if hasattr(doc_processor_configer, "top_n"): + self.top_n = doc_processor_configer.top_n + return self diff --git a/agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml b/agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml new file mode 100644 index 00000000..bd58b979 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml @@ -0,0 +1,6 @@ +name: 'dashscope_reranker' +description: 'reranker use dashscope api' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.dashscope_reranker' + class: 'DashscopeReranker' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/doc_processor/doc_processor.py b/agentuniverse/agent/action/knowledge/doc_processor/doc_processor.py new file mode 100644 index 00000000..d9e0c02c --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/doc_processor.py @@ -0,0 +1,56 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/23 14:00 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: doc_processor.py + +from abc import abstractmethod +from typing import List, Optional + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class DocProcessor(ComponentBase): + """The basic class for doc processor. + """ + + component_type: ComponentEnum = ComponentEnum.DOC_PROCESSOR + name: Optional[str] = None + description: Optional[str] = None + + class Config: + arbitrary_types_allowed = True + + def process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + """Process input documents，return should also be a document list.""" + return self._process_docs(origin_docs, query) + + @abstractmethod + def _process_docs(self, origin_docs: List[Document], + query: Query = None) -> \ + List[Document]: + """Process input documents，return should also be a document list.""" + pass + + def _initialize_by_component_configer(self, + doc_processor_configer: ComponentConfiger) \ + -> 'DocProcessor': + """Initialize the DocProcessor by the ComponentConfiger object. + + Args: + doc_processor_configer(ComponentConfiger): A configer contains DocProcessor + basic info. + Returns: + DocProcessor: A DocProcessor instance. + """ + self.name = doc_processor_configer.name + self.description = doc_processor_configer.description + return self diff --git a/agentuniverse/agent/action/knowledge/doc_processor/doc_processor_manager.py b/agentuniverse/agent/action/knowledge/doc_processor/doc_processor_manager.py new file mode 100644 index 00000000..aa88b6d7 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/doc_processor_manager.py @@ -0,0 +1,20 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/23 14:12 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: doc_processor_manager.py + +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.base.component.component_enum import ComponentEnum +from agentuniverse.base.component.component_manager_base import ComponentManagerBase +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import DocProcessor + + +@singleton +class DocProcessorManager(ComponentManagerBase[DocProcessor]): + """A singleton manager class of the DocProcessor.""" + + def __init__(self): + super().__init__(ComponentEnum.DOC_PROCESSOR) \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.py b/agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.py new file mode 100644 index 00000000..d7f55af1 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.py @@ -0,0 +1,74 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/6 10:44 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: jieba_keyword_extractor.py +from typing import List + +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import \ + DocProcessor +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + +import jieba +import jieba.analyse + +# nltk english stopwords +stop_words = {'or', "mustn't", 'how', 'their', 'again', 'few', 'other', 'who', + 'being', 'theirs', 'during', 'if', 'on', 'she', 'wouldn', 'why', + 'above', 'll', "weren't", 'your', 'are', 'an', 'over', 'his', + 'hasn', 'off', 'you', 'he', 'was', "you'd", 'me', 'ain', 'any', + 'what', 'most', 're', 'haven', "isn't", 'there', "it's", 'same', + 'm', 'only', 'my', 'needn', 'too', 'into', 'in', 'by', 'between', + "that'll", "mightn't", "aren't", 'am', 'up', 'having', "you'll", + "you're", 'these', 'mustn', 'himself', 'down', 'such', 'wasn', + 'ourselves', 'did', 'because', 'should', 'won', 'about', 'aren', + 'don', 'while', 't', 'isn', 'have', 'whom', 'myself', 'itself', + 'this', 'will', 'and', 'further', 'no', 'where', 'ma', 'yours', + 'been', "didn't", 'that', 'had', 'when', 'we', 'herself', 'some', + 'has', "she's", "needn't", "should've", 'of', "won't", 'both', + 'which', "haven't", 'yourself', 'through', 'the', 'from', + "you've", 'for', 'then', 'hadn', 'a', 'them', 'as', 'after', + 'themselves', "shouldn't", 'they', 'y', 'doesn', 'didn', 'here', + 'ours', 'own', 'it', "hadn't", 'each', 'our', 'shouldn', 'all', + 'out', 'before', 'couldn', 'd', "doesn't", 'hers', "hasn't", + 'than', 'at', "don't", 'not', 'to', 'is', 'with', 'until', + 'does', 'yourselves', 'under', 'below', 'i', 'those', "wouldn't", + 'once', "couldn't", 'just', 's', 'shan', "wasn't", 'him', 'so', + 'can', 'doing', 'o', 'her', 'were', 'now', 'very', 'weren', + 'its', 'against', 'do', 've', 'be', 'mightn', 'but', "shan't", + 'nor', 'more'} + +chinese_stopwords = {'的', '了', '在', '是', '我', '有', '和', '就', '不', + '人', '都', '一', '一个', '上', '也', '很', '到', '说', + '要', '去', '你', '会', '着', '没有', '看', '好', '自己', + '请问', '您', '他', '于', '及', '即', '为', '最', '从', '以', + '了', '将', '与', '吗', '吧', '中', '#', '什么', '怎么', '哪个', + '哪些', '啥', '相关'} + + +class JiebaKeywordExtractor(DocProcessor): + top_k: int = 3 + + def _process_docs(self, origin_docs: List[Document], query: Query = None) \ + -> List[Document]: + for _doc in origin_docs: + words = jieba.lcut(_doc.text) + filtered_words = [word for word in words if word not in + chinese_stopwords and word.lower() not in stop_words] + keywords = jieba.analyse.extract_tags(" ".join(filtered_words), + topK=self.top_k) + _doc.keywords.update(keywords) + + return origin_docs + + def _initialize_by_component_configer(self, + doc_processor_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(doc_processor_configer) + if hasattr(doc_processor_configer, "top_k"): + self.top_k = doc_processor_configer.top_k + return self diff --git a/agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml b/agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml new file mode 100644 index 00000000..3a8bc8a4 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml @@ -0,0 +1,7 @@ +name: 'jieba_keyword_extractor' +description: 'extract keywords from text' +top_k: 3 +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.jieba_keyword_extractor' + class: 'JiebaKeywordExtractor' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.py b/agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.py new file mode 100644 index 00000000..167c3c47 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.py @@ -0,0 +1,58 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- +from typing import List + +# @Time : 2024/7/31 16:19 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: recursive_character_text_splitter.py +from typing import List, Optional +from langchain.text_splitter import RecursiveCharacterTextSplitter as Splitter + +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import \ + DocProcessor +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class RecursiveCharacterTextSplitter(DocProcessor): + chunk_size: int = 200 + chunk_overlap: int = 20 + separators: List[str] = ["\n\n", "\n", " ", ""] + splitter: Optional[Splitter] = None + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.splitter = Splitter(separators=self.separators, + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap) + + def _process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + lc_doc_list = self.splitter.split_documents(Document.as_langchain_list( + origin_docs + )) + return Document.from_langchain_list(lc_doc_list) + + def _initialize_by_component_configer(self, + doc_processor_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(doc_processor_configer) + if hasattr(doc_processor_configer, "chunk_size"): + self.chunk_size = doc_processor_configer.chunk_size + if hasattr(doc_processor_configer, "chunk_overlap"): + self.chunk_overlap = doc_processor_configer.chunk_overlap + if hasattr(doc_processor_configer, "separators"): + self.separators = doc_processor_configer.separators + self.splitter = Splitter(separators=self.separators, + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap) + return self + + + + + + + diff --git a/agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml b/agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml new file mode 100644 index 00000000..b51f45da --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml @@ -0,0 +1,11 @@ +name: 'recursive_character_text_splitter' +description: 'langchain recursive character text splitter' +chunk_size: 200 +chunk_overlap: 20 +separators: + - "\n\n" + - "\n" +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.recursive_character_text_splitter' + class: 'RecursiveCharacterTextSplitter' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/doc_processor/token_text_splitter.py b/agentuniverse/agent/action/knowledge/doc_processor/token_text_splitter.py new file mode 100644 index 00000000..7609702d --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/token_text_splitter.py @@ -0,0 +1,59 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/5 15:37 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: token_text_splitter.py +from typing import List, Optional +from langchain.text_splitter import TokenTextSplitter as Splitter + +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import \ + DocProcessor +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class TokenTextSplitter(DocProcessor): + chunk_size: int = 200 + chunk_overlap: int = 20 + encoding_name: str = 'gpt2' + model_name: Optional[str] = None + splitter: Optional[Splitter] = None + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.splitter = Splitter( + encoding_name=self.encoding_name, + model_name=self.model_name, + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap + ) + + def _process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + lc_doc_list = self.splitter.split_documents(Document.as_langchain_list( + origin_docs + )) + return Document.from_langchain_list(lc_doc_list) + + def _initialize_by_component_configer(self, + doc_processor_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(doc_processor_configer) + if hasattr(doc_processor_configer, "chunk_size"): + self.chunk_size = doc_processor_configer.chunk_size + if hasattr(doc_processor_configer, "chunk_overlap"): + self.chunk_overlap = doc_processor_configer.chunk_overlap + if hasattr(doc_processor_configer, "encoding_name"): + self.encoding_name = doc_processor_configer.encoding_name + if hasattr(doc_processor_configer, "model_name"): + self.model_name = doc_processor_configer.model_name + self.splitter = Splitter( + encoding_name=self.encoding_name, + model_name=self.model_name, + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap + ) + return self diff --git a/agentuniverse/agent/action/knowledge/doc_processor/token_text_splitter.yaml b/agentuniverse/agent/action/knowledge/doc_processor/token_text_splitter.yaml new file mode 100644 index 00000000..160955df --- /dev/null +++ b/agentuniverse/agent/action/knowledge/doc_processor/token_text_splitter.yaml @@ -0,0 +1,8 @@ +name: 'token_text_splitter' +description: 'langchain token text splitter' +chunk_size: 200 +chunk_overlap: 20 +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.token_text_splitter' + class: 'TokenTextSplitter' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py b/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py index e46ee14b..faa1d1d7 100644 --- a/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py +++ b/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.py @@ -32,8 +32,7 @@ class DashscopeEmbedding(Embedding): default_factory=lambda: get_from_env("DASHSCOPE_API_KEY") ) - - def get_embeddings(self, texts: List[str]) -> List[List[float]]: + def get_embeddings(self, texts: List[str], **kwargs) -> List[List[float]]: """ Retrieve text embeddings for a list of input texts. @@ -61,7 +60,7 @@ def post(post_params): }, data=json.dumps(post_params, ensure_ascii=False).encode( "utf-8"), - timeout=120 + timeout=300 ) resp_json = response.json() return resp_json @@ -72,9 +71,14 @@ def post(post_params): "model": self.embedding_model_name, "input": {}, "parameters": { - "text_type": "query" } } + if self.embedding_model_name == "text-embedding-v3": + post_params["parameters"][ + "dimension"] = self.embedding_dims if self.embedding_dims else 1024 + post_params["parameters"][ + "output_type"] = kwargs["output_type"] if "output_type" in kwargs else "dense" + post_params["parameters"]["text_type"] = kwargs["text_type"] if "text_type" in kwargs else "document" for batch in batched(texts): post_params["input"]["texts"] = batch @@ -92,7 +96,7 @@ def post(post_params): f"error message:{error_message}") return result - async def async_get_embeddings(self, texts: List[str]) -> List[List[float]]: + async def async_get_embeddings(self, texts: List[str], **kwargs) -> List[List[float]]: """ Async version of get_embeddings. @@ -121,7 +125,7 @@ async def async_post(post_params): }, data=json.dumps(post_params, ensure_ascii=False).encode( "utf-8"), - timeout=120, + timeout=300, ) as resp: resp_json = await resp.json() return resp_json @@ -135,6 +139,14 @@ async def async_post(post_params): "text_type": "query" } } + if self.embedding_model_name == "text-embedding-v3": + post_params["parameters"][ + "dimension"] = self.embedding_dims if self.embedding_dims else 1024 + post_params["parameters"][ + "output_type"] = kwargs[ + "output_type"] if "output_type" in kwargs else "dense" + post_params["parameters"]["text_type"] = kwargs[ + "text_type"] if "text_type" in kwargs else "document" for batch in batched(texts): post_params["input"]["texts"] = batch diff --git a/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.yaml b/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.yaml new file mode 100644 index 00000000..76618ae5 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/embedding/dashscope_embedding.yaml @@ -0,0 +1,7 @@ +name: 'dashscope_embedding' +description: 'embedding use dashscope api' +embedding_model_name: 'text-embedding-v2' +metadata: + type: 'EMBEDDING' + module: 'agentuniverse.agent.action.knowledge.embedding.dashscope_embedding' + class: 'DashscopeEmbedding' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/embedding/embedding.py b/agentuniverse/agent/action/knowledge/embedding/embedding.py index 6c6980d5..15429341 100644 --- a/agentuniverse/agent/action/knowledge/embedding/embedding.py +++ b/agentuniverse/agent/action/knowledge/embedding/embedding.py @@ -8,27 +8,57 @@ from abc import abstractmethod from typing import List, Optional -from pydantic import BaseModel from langchain_core.embeddings import Embeddings as LCEmbeddings +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger -class Embedding(BaseModel): + +class Embedding(ComponentBase): """The basic class for embedding. Attributes: embedding_model_name (Optional[str]): The name of the embedding model. """ + component_type: ComponentEnum = ComponentEnum.EMBEDDING + name: Optional[str] = None + description: Optional[str] = None embedding_model_name: Optional[str] = None + embedding_dims: Optional[int] = None @abstractmethod - def get_embeddings(self, text: List[str]) -> List[List[float]]: + def get_embeddings(self, text: List[str], **kwargs) -> List[List[float]]: """Get embeddings.""" @abstractmethod - async def async_get_embeddings(self, texts: List[str]) -> List[List[float]]: + async def async_get_embeddings(self, texts: List[str], **kwargs) -> List[ + List[float]]: """Asynchronously get embeddings.""" def as_langchain(self) -> LCEmbeddings: """Convert the agentUniverse(aU) embedding class to the langchain embedding class.""" pass + + def _initialize_by_component_configer(self, + embedding_configer: ComponentConfiger) \ + -> 'Embedding': + """Initialize the embedding by the ComponentConfiger object. + + Args: + embedding_configer(ComponentConfiger): A configer contains embedding + basic info. + Returns: + Embedding: A embedding instance. + """ + if embedding_configer.name: + self.name = embedding_configer.name + if embedding_configer.description: + self.description = embedding_configer.description + if hasattr(embedding_configer, "embedding_dims"): + self.embedding_dims = embedding_configer.embedding_dims + if hasattr(embedding_configer, "embedding_model_name"): + self.embedding_model_name = embedding_configer.embedding_model_name + return self diff --git a/agentuniverse/agent/action/knowledge/embedding/embedding_manager.py b/agentuniverse/agent/action/knowledge/embedding/embedding_manager.py new file mode 100644 index 00000000..8279e3fa --- /dev/null +++ b/agentuniverse/agent/action/knowledge/embedding/embedding_manager.py @@ -0,0 +1,21 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/23 11:28 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: embedding_manager.py + +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.base.component.component_enum import ComponentEnum +from agentuniverse.base.component.component_manager_base import ComponentManagerBase +from agentuniverse.agent.action.knowledge.embedding.embedding import Embedding + + +@singleton +class EmbeddingManager(ComponentManagerBase[Embedding]): + """A singleton manager class of the embedding.""" + + def __init__(self): + super().__init__(ComponentEnum.EMBEDDING) + diff --git a/agentuniverse/agent/action/knowledge/embedding/openai_embedding.py b/agentuniverse/agent/action/knowledge/embedding/openai_embedding.py index f54bbd0d..8575e6fc 100644 --- a/agentuniverse/agent/action/knowledge/embedding/openai_embedding.py +++ b/agentuniverse/agent/action/knowledge/embedding/openai_embedding.py @@ -14,6 +14,8 @@ from agentuniverse.agent.action.knowledge.embedding.embedding import Embedding from agentuniverse.base.util.env_util import get_from_env +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger class OpenAIEmbedding(Embedding): @@ -95,3 +97,21 @@ def as_langchain(self) -> OpenAIEmbeddings: """Convert the agentUniverse(aU) openai embedding class to the langchain openai embedding class.""" return OpenAIEmbeddings(openai_api_key=self.openai_api_key, client=self.client.embeddings, async_client=self.async_client.embeddings) + + def _initialize_by_component_configer(self, + embedding_configer: ComponentConfiger) \ + -> 'Embedding': + """Initialize the embedding by the ComponentConfiger object. + + Args: + embedding_configer(ComponentConfiger): A configer contains embedding + basic info. + Returns: + Embedding: A embedding instance. + """ + super()._initialize_by_component_configer(embedding_configer) + if hasattr(embedding_configer, "embedding_dims"): + self.dimensions = embedding_configer.embedding_dims + if hasattr(embedding_configer, "openai_client_args"): + self.openai_client_args = embedding_configer.openai_client_args + return self diff --git a/agentuniverse/agent/action/knowledge/embedding/openai_embedding.yaml b/agentuniverse/agent/action/knowledge/embedding/openai_embedding.yaml new file mode 100644 index 00000000..a7cf7af0 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/embedding/openai_embedding.yaml @@ -0,0 +1,7 @@ +name: 'openai_embedding' +description: 'embedding use openai api' +embedding_model_name: 'text-embedding-v2' +metadata: + type: 'EMBEDDING' + module: 'agentuniverse.agent.action.knowledge.embedding.openai_embedding' + class: 'OpenAIEmbedding' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/knowledge.py b/agentuniverse/agent/action/knowledge/knowledge.py index 633b24cb..e9a60b41 100644 --- a/agentuniverse/agent/action/knowledge/knowledge.py +++ b/agentuniverse/agent/action/knowledge/knowledge.py @@ -5,54 +5,162 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: knowledge.py -from typing import Optional, Dict, List +import os +import re +from typing import Optional, Dict, List, Any +from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED 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.base.config.component_configer.component_configer import ComponentConfiger from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query -from agentuniverse.agent.action.knowledge.store.store import Store +from agentuniverse.agent.action.knowledge.store.store_manager import StoreManager +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor import DocProcessor +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor_manager import DocProcessorManager +from agentuniverse.agent.action.knowledge.query_paraphraser.query_paraphraser import QueryParaphraser +from agentuniverse.agent.action.knowledge.query_paraphraser.query_paraphraser_manager import QueryParaphraserManager +from agentuniverse.agent.action.knowledge.rag_router.rag_router_manager import RagRouterManager +from agentuniverse.agent.action.knowledge.reader.reader import Reader +from agentuniverse.agent.action.knowledge.reader.reader_manager import ReaderManager from agentuniverse.base.annotation.trace import trace_knowledge 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 import ApplicationConfigManager -from agentuniverse.base.config.component_configer.configers.knowledge_configer import KnowledgeConfiger +from agentuniverse.base.util.logging.logging_util import LOGGER class Knowledge(ComponentBase): - """The basic class for knowledge model. + """ + The basic class for the knowledge model. Attributes: name (str): The name of the knowledge. description (str): The description of the knowledge. - store (Store): The store of the knowledge, which is used to store knowledge + stores (List[str]): The stores for the knowledge, which are used to store knowledge and provide retrieval capabilities, such as ChromaDB store or Redis Store. - reader (Reader): The reader of the knowledge, which is used to load data and generate knowledge. + query_paraphrasers (List[str]): Query paraphrasers used to paraphrase the original query string, + such as extracting keywords and splitting into sub-queries. + + insert_processors (List[str]): DocProcessors used in the knowledge insertion step, + such as text splitter and text cleaner. + + rag_router (str): RAG router used to decide which stores to use in + the RAG step. + + post_processors (List[str]): DocProcessors used in the RAG step to process retrieved + documents, such as reranking and filtering. + + readers (Dict[str, str]): The readers of the knowledge, which are used to load data and generate knowledge. + Each reader refers to a specific file type. + + insert_executor (ThreadPoolExecutor): Used for performing insert and search + operations concurrently in multiple stores. ext_info (Optional[Dict]): The extended information of the knowledge. """ + class Config: + arbitrary_types_allowed = True name: str = "" description: Optional[str] = None - store: Store = None - reader: Reader = None + stores: List[str] = [] + query_paraphrasers: List[str] = [] + insert_processors: List[str] = [] + rag_router: str = "base_router" + post_processors: List[str] = [] + readers: Dict[str, str] = dict() + insert_executor: Optional[ThreadPoolExecutor] = None + query_executor: Optional[ThreadPoolExecutor] = None ext_info: Optional[Dict] = None def __init__(self, **kwargs): super().__init__(component_type=ComponentEnum.KNOWLEDGE, **kwargs) + self.insert_executor = ThreadPoolExecutor( + max_workers=5, + thread_name_prefix="Knowledge store" + ) + self.query_executor = ThreadPoolExecutor( + max_workers=10, + thread_name_prefix="Knowledge query" + ) + + def _load_data(self, *args: Any, **kwargs: Any) -> List[Document]: + # check if source is a local file or remote url + if kwargs.get("source_path"): + source_path = kwargs.get("source_path") + else: + raise Exception("No file to load.") + url_pattern = re.compile( + r'^(https?:\/\/)?' + r'((([a-zA-Z0-9]{1,256}\.[a-zA-Z0-9]{1,6})|' + r'(\d{1,3}\.){3}\d{1,3})' + r'(:\d{1,5})?)' + r'(\/[a-zA-Z0-9@:%._\+~#=]*)*\/?' + r'(\?[a-zA-Z0-9@:%._\+~#&//=]*)?$' + ) + + if url_pattern.match(source_path): + source_type = "url" + elif os.path.isfile(source_path): + source_type = os.path.splitext(source_path)[1][1:] + else: + raise Exception(f"Knowledge load data error: Unknown source type:{source_path}") + if source_type in self.readers: + reader = ReaderManager().get_instance_obj(self.readers[source_type]) + else: + reader = ReaderManager().get_file_default_reader(source_type) + return reader.load_data(source_path) + + def _insert_process(self, origin_docs: List[Document]) -> List[Document]: + for _processor_code in self.insert_processors: + doc_processor: DocProcessor = DocProcessorManager().get_instance_obj(_processor_code) + origin_docs = doc_processor.process_docs(origin_docs) + return origin_docs + + def _rag_post_process(self, origin_docs: List[Document], query: Query): + for _processor_code in self.post_processors: + doc_processor: DocProcessor = DocProcessorManager().get_instance_obj(_processor_code) + origin_docs = doc_processor.process_docs(origin_docs, query=query) + return origin_docs + + def _paraphrase_query(self, origin_query: Query) -> Query: + for _paraphraser_code in self.query_paraphrasers: + query_paraphraser: QueryParaphraser = QueryParaphraserManager().get_instance_obj( + _paraphraser_code) + origin_query = query_paraphraser.query_paraphrase(origin_query) + return origin_query def insert_knowledge(self, **kwargs) -> None: """Insert the knowledge. Load data by the reader and insert the documents into the store. """ - document_list: List[Document] = self.reader.load_data() - self.store.insert_documents(document_list, **kwargs) + document_list: List[Document] = self._load_data(**kwargs) + document_list = self._insert_process(document_list) + futures = [] + if "stores" in kwargs: + stores = kwargs["stores"] + else: + stores = self.stores + for _store_code in stores: + futures.append( + self.insert_executor.submit( + StoreManager().get_instance_obj(_store_code).insert_document, + document_list)) + wait(futures, return_when=ALL_COMPLETED) + for future in futures: + try: + future.result() + except Exception as e: + LOGGER.error(f"Exception occurred in knowledge insert: {e}") + LOGGER.info("Knowledge insert complete.") + + def _route_rag(self, query: Query): + return RagRouterManager().get_instance_obj(self.rag_router).rag_route(query, self.stores) @trace_knowledge def query_knowledge(self, **kwargs) -> List[Document]: @@ -61,26 +169,61 @@ def query_knowledge(self, **kwargs) -> List[Document]: Query documents from the store and return the results. """ query = Query(**kwargs) - return self.store.query(query) + query = self._paraphrase_query(query) + query_tasks = self._route_rag(query) + + futures = [] + for query_task in query_tasks: + futures.append( + self.query_executor.submit( + StoreManager().get_instance_obj(query_task[1]).query, + query_task[0])) + wait(futures, return_when=ALL_COMPLETED) + retrieved_docs = {} + for future in futures: + try: + task_result = future.result() + for _doc in task_result: + if _doc.id not in retrieved_docs: + retrieved_docs[_doc.id] = _doc + except Exception as e: + LOGGER.error(f"Exception occurred in knowledge query: {e}") + retrieved_docs = list(retrieved_docs.values()) + retrieved_docs = self._rag_post_process(retrieved_docs, query) + return retrieved_docs + + def to_llm(self, retrieved_docs: List[Document]) -> Any: + """Transfer list docs to llm input""" + retrieved_texts = [doc.text for doc in retrieved_docs] + return "\n=========================================\n".join(retrieved_texts) - def get_instance_code(self) -> str: - """Return the full name of the knowledge.""" - appname = ApplicationConfigManager().app_configer.base_info_appname - return f'{appname}.{self.component_type.value.lower()}.{self.name}' + def _initialize_by_component_configer(self, + knowledge_configer: ComponentConfiger) \ + -> 'Knowledge': + """Initialize the reader by the ComponentConfiger object. - def initialize_by_component_configer(self, component_configer: KnowledgeConfiger) -> 'Knowledge': - """Initialize the Knowledge by the ComponentConfiger object. Args: - component_configer(KnowledgeConfiger): the ComponentConfiger object + reader_configer(ComponentConfiger): A configer contains reader + basic info. Returns: - Knowledge: the Knowledge object + Reader: A reader instance. """ - if component_configer.name: - self.name = component_configer.name - if component_configer.description: - self.description = component_configer.description - if component_configer.ext_info: - self.ext_info = component_configer.ext_info + if knowledge_configer.name: + self.name = knowledge_configer.name + if knowledge_configer.description: + self.description = knowledge_configer.description + if hasattr(knowledge_configer, "stores"): + self.stores = knowledge_configer.stores + if hasattr(knowledge_configer, "query_paraphrasers"): + self.query_paraphrasers = knowledge_configer.query_paraphrasers + if hasattr(knowledge_configer, "insert_processors"): + self.insert_processors = knowledge_configer.insert_processors + if hasattr(knowledge_configer, "rag_router"): + self.rag_router = knowledge_configer.rag_router + if hasattr(knowledge_configer, "post_processors"): + self.post_processors = knowledge_configer.post_processors + if hasattr(knowledge_configer, "readers"): + self.readers = knowledge_configer.readers return self def langchain_query(self, query: str) -> str: @@ -89,12 +232,8 @@ def langchain_query(self, query: str) -> str: 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) + knowledge = self.query_knowledge(**parse_query) + return "This is Query Result:\n"+self.to_llm(knowledge) def as_langchain_tool(self) -> LangchainTool: """Convert the Knowledge object to a LangChain tool. @@ -107,7 +246,7 @@ def as_langchain_tool(self) -> LangchainTool: ```json { "query_str": "", - "top_k": , + "similarity_top_k": , } ``` """ diff --git a/agentuniverse/agent/action/knowledge/knowledge_manager.py b/agentuniverse/agent/action/knowledge/knowledge_manager.py index 21b345a3..3e4a7b1b 100644 --- a/agentuniverse/agent/action/knowledge/knowledge_manager.py +++ b/agentuniverse/agent/action/knowledge/knowledge_manager.py @@ -5,13 +5,14 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: knowledge_manager.py +from agentuniverse.agent.action.knowledge.knowledge import Knowledge from agentuniverse.base.annotation.singleton import singleton from agentuniverse.base.component.component_enum import ComponentEnum from agentuniverse.base.component.component_manager_base import ComponentManagerBase @singleton -class KnowledgeManager(ComponentManagerBase): +class KnowledgeManager(ComponentManagerBase[Knowledge]): """The KnowledgeManager class, which is used to manage the knowledge.""" def __init__(self): diff --git a/agentuniverse/agent/action/knowledge/query_paraphraser/__init__.py b/agentuniverse/agent/action/knowledge/query_paraphraser/__init__.py new file mode 100644 index 00000000..27d651c5 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/query_paraphraser/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 10:31 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.py b/agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.py new file mode 100644 index 00000000..610c4409 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.py @@ -0,0 +1,39 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/12 15:44 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: query_keyword_extractor.py + +from typing import Optional + +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor_manager import \ + DocProcessorManager +from agentuniverse.agent.action.knowledge.query_paraphraser.query_paraphraser import QueryParaphraser +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class QueryKeywordExtractor(QueryParaphraser): + keyword_extractor: Optional[str] = "jieba_keyword_extractor" + + def query_paraphrase(self, origin_query: Query) -> Query: + + keyword_extractor_instance = DocProcessorManager().get_instance_obj(self.keyword_extractor) + + keywords = keyword_extractor_instance.process_docs( + [Document(text=origin_query.query_str)] + )[0].keywords + + origin_query.keywords.update(keywords) + return origin_query + + def _initialize_by_component_configer(self, + query_paraphraser_configer: ComponentConfiger) -> 'QueryParaphraser': + super()._initialize_by_component_configer(query_paraphraser_configer) + if hasattr(query_paraphraser_configer, "keyword_extractor"): + self.keyword_extractor = query_paraphraser_configer.keyword_extractor + return self diff --git a/agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml b/agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml new file mode 100644 index 00000000..ca84963d --- /dev/null +++ b/agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml @@ -0,0 +1,6 @@ +name: 'query_keyword_extractor' +description: 'extract keywords from query origin str' +metadata: + type: 'QUERY_PARAPHRASER' + module: 'agentuniverse.agent.action.knowledge.query_paraphraser.query_keyword_extractor' + class: 'QueryKeywordExtractor' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/query_paraphraser/query_paraphraser.py b/agentuniverse/agent/action/knowledge/query_paraphraser/query_paraphraser.py new file mode 100644 index 00000000..27add137 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/query_paraphraser/query_paraphraser.py @@ -0,0 +1,46 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 10:59 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: query_paraphraser.py + +from abc import abstractmethod +from typing import Optional + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger + + +class QueryParaphraser(ComponentBase): + """The basic class for query paraphraser.""" + + component_type: ComponentEnum = ComponentEnum.QUERY_PARAPHRASER + name: Optional[str] = None + description: Optional[str] = None + + @abstractmethod + def query_paraphrase(self, origin_query: Query) -> Query: + """Paraphrase the origin query string to different style.""" + + + def _initialize_by_component_configer(self, + query_paraphraser_config: ComponentConfiger) \ + -> 'QueryParaphraser': + """Initialize the QueryParaphraser by the ComponentConfiger object. + + Args: + query_paraphraser_config(ComponentConfiger): A configer contains query_paraphraser + basic info. + Returns: + QueryParaphraser: A query_paraphraser instance. + """ + if query_paraphraser_config.name: + self.name = query_paraphraser_config.name + if query_paraphraser_config.description: + self.description = query_paraphraser_config.description + + return self diff --git a/agentuniverse/agent/action/knowledge/query_paraphraser/query_paraphraser_manager.py b/agentuniverse/agent/action/knowledge/query_paraphraser/query_paraphraser_manager.py new file mode 100644 index 00000000..6fc555fe --- /dev/null +++ b/agentuniverse/agent/action/knowledge/query_paraphraser/query_paraphraser_manager.py @@ -0,0 +1,20 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 11:37 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: query_paraphraser_manager.py + +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.base.component.component_enum import ComponentEnum +from agentuniverse.base.component.component_manager_base import ComponentManagerBase +from agentuniverse.agent.action.knowledge.query_paraphraser.query_paraphraser import QueryParaphraser + + +@singleton +class QueryParaphraserManager(ComponentManagerBase[QueryParaphraser]): + """A singleton manager class of the QueryParaphraser.""" + + def __init__(self): + super().__init__(ComponentEnum.QUERY_PARAPHRASER) \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/rag_router/__init__.py b/agentuniverse/agent/action/knowledge/rag_router/__init__.py new file mode 100644 index 00000000..46b387e9 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/rag_router/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 11:59 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse/agent/action/knowledge/rag_router/base_router.py b/agentuniverse/agent/action/knowledge/rag_router/base_router.py new file mode 100644 index 00000000..8ee9a72d --- /dev/null +++ b/agentuniverse/agent/action/knowledge/rag_router/base_router.py @@ -0,0 +1,19 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/13 11:31 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: base_router.py + +from typing import List, Tuple + +from agentuniverse.agent.action.knowledge.rag_router.rag_router import \ + RagRouter +from agentuniverse.agent.action.knowledge.store.query import Query + + +class BaseRouter(RagRouter): + def _rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + return [(query, store) for store in store_list] diff --git a/agentuniverse/agent/action/knowledge/rag_router/base_router.yaml b/agentuniverse/agent/action/knowledge/rag_router/base_router.yaml new file mode 100644 index 00000000..25d0cd2a --- /dev/null +++ b/agentuniverse/agent/action/knowledge/rag_router/base_router.yaml @@ -0,0 +1,6 @@ +name: 'base_router' +description: 'base rag router map query to all store' +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.base_router' + class: 'BaseRouter' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py b/agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py new file mode 100644 index 00000000..23c27a08 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py @@ -0,0 +1,60 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/13 16:25 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: nlu_rag_router.py + +import json +from typing import List, Tuple, Optional + +from agentuniverse.agent.agent_manager import AgentManager +from agentuniverse.agent.action.knowledge.store.store_manager import StoreManager +from agentuniverse.agent.action.knowledge.rag_router.rag_router import \ + RagRouter +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class NluRagRouter(RagRouter): + llm: Optional[dict] = None + agent_name: str = 'nlu_rag_route_agent' + store_amount: int = 1 + + def _rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + + agent = AgentManager().get_instance_obj(self.agent_name) + if self.llm: + agent.agent_model.profile['llm_model'] = self.llm + else: + agent.agent_model.profile['llm_model'] = {"name": "__default_instance__"} + store_info = {} + for _store in store_list: + store_info[_store] = StoreManager().get_instance_obj(_store).description + agent_result = agent.run( + query=query.query_str, + store_info=json.dumps(store_info), + store_amount=self.store_amount + ) + target_store_list = agent_result.output.split(",") + if len(target_store_list) > self.store_amount: + target_store_list = target_store_list[:self.store_amount] + filtered_store_list = [_store for _store in target_store_list if _store in store_list] + + return [(query, store) for store in filtered_store_list] + + def _initialize_by_component_configer(self, + rag_router_config: ComponentConfiger) -> 'RagRouter': + super()._initialize_by_component_configer(rag_router_config) + if hasattr(rag_router_config, "llm"): + self.llm = rag_router_config.llm + if hasattr(rag_router_config, "agent_name"): + self.agent_name = rag_router_config.agent_name + if hasattr(rag_router_config, "store_amount"): + self.store_amount = rag_router_config.store_amount + + + diff --git a/agentuniverse/agent/action/knowledge/rag_router/rag_router.py b/agentuniverse/agent/action/knowledge/rag_router/rag_router.py new file mode 100644 index 00000000..e6456113 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/rag_router/rag_router.py @@ -0,0 +1,52 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 12:00 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: rag_router.py + +from abc import abstractmethod +from typing import List, Optional, Tuple + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger + + +class RagRouter(ComponentBase): + """The basic class for rag router.""" + + component_type: ComponentEnum = ComponentEnum.RAG_ROUTER + name: Optional[str] = None + description: Optional[str] = None + + def rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + """Accept query a list of store instance name, and return a list of + query-store pair.""" + return self._rag_route(query, store_list) + + def _rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + """Accept query a list of store instance name, and return a list of + query-store pair.""" + pass + + def _initialize_by_component_configer(self, + rag_router_config: ComponentConfiger) \ + -> 'RagRouter': + """Initialize the rag router by the ComponentConfiger object. + + Args: + rag_router_config(ComponentConfiger): A configer contains rag router + basic info. + Returns: + RagRouter: A rag router instance. + """ + if rag_router_config.name: + self.name = rag_router_config.name + if rag_router_config.description: + self.description = rag_router_config.description + return self diff --git a/agentuniverse/agent/action/knowledge/rag_router/rag_router_manager.py b/agentuniverse/agent/action/knowledge/rag_router/rag_router_manager.py new file mode 100644 index 00000000..179e8d4b --- /dev/null +++ b/agentuniverse/agent/action/knowledge/rag_router/rag_router_manager.py @@ -0,0 +1,20 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/25 14:16 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: rag_router_manager.py + +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.base.component.component_enum import ComponentEnum +from agentuniverse.base.component.component_manager_base import ComponentManagerBase +from agentuniverse.agent.action.knowledge.rag_router.rag_router import RagRouter + + +@singleton +class RagRouterManager(ComponentManagerBase[RagRouter]): + """A singleton manager class of the RagRouter.""" + + def __init__(self): + super().__init__(ComponentEnum.RAG_ROUTER) \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/reader/file/docx_reader.py b/agentuniverse/agent/action/knowledge/reader/file/docx_reader.py index 07ba7e1a..ffa69212 100644 --- a/agentuniverse/agent/action/knowledge/reader/file/docx_reader.py +++ b/agentuniverse/agent/action/knowledge/reader/file/docx_reader.py @@ -5,6 +5,7 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: doc_parser.py +from typing import Union from pathlib import Path from typing import List, Optional, Dict @@ -15,7 +16,7 @@ class DocxReader(Reader): """Docx reader.""" - def load_data(self, file: Path, ext_info: Optional[Dict] = None) -> List[Document]: + def _load_data(self, file: Union[str, Path], ext_info: Optional[Dict] = None) -> List[Document]: """Parse the docx file. Note: @@ -30,6 +31,8 @@ def load_data(self, file: Path, ext_info: Optional[Dict] = None) -> List[Documen "`pip install docx2txt`" ) + if isinstance(file, str): + file = Path(file) text = docx2txt.process(file) metadata = {"file_name": file.name} if ext_info is not None: diff --git a/agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml b/agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml new file mode 100644 index 00000000..a0e66a4e --- /dev/null +++ b/agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml @@ -0,0 +1,6 @@ +name: 'default_docx_reader' +description: 'default docx reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.docx_reader' + class: 'DocxReader' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/reader/file/file_reader.py b/agentuniverse/agent/action/knowledge/reader/file/file_reader.py index 7c156fb6..f706f04f 100644 --- a/agentuniverse/agent/action/knowledge/reader/file/file_reader.py +++ b/agentuniverse/agent/action/knowledge/reader/file/file_reader.py @@ -33,7 +33,7 @@ class FileReader(Reader): file_readers: Dict[str, Type[Reader]] = DEFAULT_FILE_READERS - def load_data(self, file_paths: List[Path], ext_info: Optional[Dict] = None) -> List[Document]: + def _load_data(self, file_paths: List[Path], ext_info: Optional[Dict] = None) -> List[Document]: document_list = [] for file_path in file_paths: file_suffix = file_path.suffix.lower() diff --git a/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.py b/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.py index e9215f41..c72f08dc 100644 --- a/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.py +++ b/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.py @@ -6,7 +6,7 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: pdf_parser.py from pathlib import Path -from typing import List, Optional, Dict +from typing import List, Optional, Dict, Union from agentuniverse.agent.action.knowledge.reader.reader import Reader from agentuniverse.agent.action.knowledge.store.document import Document @@ -15,7 +15,7 @@ class PdfReader(Reader): """PDF reader.""" - def load_data(self, file: Path, ext_info: Optional[Dict] = None) -> List[Document]: + def _load_data(self, file: Union[str, Path], ext_info: Optional[Dict] = None) -> List[Document]: """Parse the pdf file. Note: @@ -27,6 +27,8 @@ def load_data(self, file: Path, ext_info: Optional[Dict] = None) -> List[Documen raise ImportError( "pypdf is required to read PDF files: `pip install pypdf`" ) + if isinstance(file, str): + file = Path(file) with open(file, "rb") as fp: # Create a PDF object pdf = pypdf.PdfReader(fp) diff --git a/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml b/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml new file mode 100644 index 00000000..70bad75e --- /dev/null +++ b/agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml @@ -0,0 +1,6 @@ +name: 'default_pdf_reader' +description: 'default pdf reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.pdf_reader' + class: 'PdfReader' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.py b/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.py index a1e609cc..d339f47f 100644 --- a/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.py +++ b/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.py @@ -6,7 +6,7 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: pptx_parser.py from pathlib import Path -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Union from agentuniverse.agent.action.knowledge.reader.reader import Reader from agentuniverse.agent.action.knowledge.store.document import Document @@ -15,7 +15,7 @@ class PptxReader(Reader): """Pptx reader.""" - def load_data(self, file: Path, ext_info: Optional[Dict] = None) -> List[Document]: + def _load_data(self, file: Union[str, Path], ext_info: Optional[Dict] = None) -> List[Document]: """Parse the pptx file. Note: @@ -27,6 +27,8 @@ def load_data(self, file: Path, ext_info: Optional[Dict] = None) -> List[Documen raise ImportError( "python-pptx is required to read pptx files: `pip install python-pptx`" ) + if isinstance(file, str): + file = Path(file) presentation = Presentation(file) document_list = [] for slide_number, slide in enumerate(presentation.slides, start=1): diff --git a/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml b/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml new file mode 100644 index 00000000..ec0987ca --- /dev/null +++ b/agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml @@ -0,0 +1,6 @@ +name: 'default_pptx_reader' +description: 'default pptx reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.pptx_reader' + class: 'PptxReader' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml b/agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml new file mode 100644 index 00000000..f957d514 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml @@ -0,0 +1,6 @@ +name: 'default_txt_reader' +description: 'default txt reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.txt_reader' + class: 'TxtReader' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.py b/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.py index 48219203..99c9431a 100644 --- a/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.py +++ b/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.py @@ -19,7 +19,7 @@ class WebPdfReader(Reader): The pdf file will be downloaded and then parsed by `pdfminer.six`. """ - def load_data(self, web_pdf_url: str) -> List[Document]: + def _load_data(self, web_pdf_url: str) -> List[Document]: if web_pdf_url is None: return [] response = requests.get(web_pdf_url) diff --git a/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml b/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml new file mode 100644 index 00000000..0ee9083f --- /dev/null +++ b/agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml @@ -0,0 +1,6 @@ +name: 'default_web_pdf_reader' +description: 'default web pdf reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.web_pdf_reader' + class: 'WebPdfReader' \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/reader/reader.py b/agentuniverse/agent/action/knowledge/reader/reader.py index edd1ba75..23961d95 100644 --- a/agentuniverse/agent/action/knowledge/reader/reader.py +++ b/agentuniverse/agent/action/knowledge/reader/reader.py @@ -6,16 +6,39 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: reader.py from abc import abstractmethod -from typing import List, Any - -from pydantic import BaseModel +from typing import List, Any, Optional from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger -class Reader(BaseModel): +class Reader(ComponentBase): """The basic class for the knowledge reader.""" + component_type: ComponentEnum = ComponentEnum.READER + name: Optional[str] = None + description: Optional[str] = None - @abstractmethod def load_data(self, *args: Any, **kwargs: Any) -> List[Document]: """Load data from the input params.""" + return self._load_data(*args, **kwargs) + + @abstractmethod + def _load_data(self, *args: Any, **kwargs: Any) -> List[Document]: + """Load data from the input params.""" + + def _initialize_by_component_configer(self, + reader_configer: ComponentConfiger) \ + -> 'Reader': + """Initialize the reader by the ComponentConfiger object. + + Args: + reader_configer(ComponentConfiger): A configer contains reader + basic info. + Returns: + Reader: A reader instance. + """ + self.name = reader_configer.name + self.description = reader_configer.description + return self diff --git a/agentuniverse/agent/action/knowledge/reader/reader_manager.py b/agentuniverse/agent/action/knowledge/reader/reader_manager.py new file mode 100644 index 00000000..a17ec2fa --- /dev/null +++ b/agentuniverse/agent/action/knowledge/reader/reader_manager.py @@ -0,0 +1,35 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 11:41 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: reader_manager.py + +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.base.component.component_enum import ComponentEnum +from agentuniverse.base.component.component_manager_base import \ + ComponentManagerBase, ComponentTypeVar +from agentuniverse.agent.action.knowledge.reader.reader import Reader + + +@singleton +class ReaderManager(ComponentManagerBase[Reader]): + """A singleton manager class of the reader.""" + DEFAULT_READER = { + "pdf": "default_pdf_reader", + "pptx": "default_pptx_reader", + "docx": "default_docx_reader", + "txt": "default_txt_reader" + } + + def __init__(self): + super().__init__(ComponentEnum.READER) + + def get_file_default_reader(self, + file_type: str, + new_instance: bool = False) -> Reader | None: + if file_type in self.DEFAULT_READER: + return self.get_instance_obj(self.DEFAULT_READER[file_type]) + else: + return None diff --git a/agentuniverse/agent/action/knowledge/store/chroma_store.py b/agentuniverse/agent/action/knowledge/store/chroma_store.py index df81e146..084ee759 100644 --- a/agentuniverse/agent/action/knowledge/store/chroma_store.py +++ b/agentuniverse/agent/action/knowledge/store/chroma_store.py @@ -5,16 +5,21 @@ # @Author : wangchongshi # @Email : wangchongshi.wcs@antgroup.com # @FileName: chroma_store.py +from urllib.parse import urlparse from typing import List, Any, Optional from pydantic import SkipValidation import chromadb from chromadb import QueryResult +from chromadb.config import Settings from chromadb.api.models.Collection import Collection +from agentuniverse.agent.action.knowledge.embedding.embedding_manager import EmbeddingManager from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query from agentuniverse.agent.action.knowledge.store.store import Store +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger class ChromaStore(Store): @@ -31,20 +36,33 @@ class ChromaStore(Store): collection_name: Optional[str] = 'chroma_db' collection: SkipValidation[Collection] = None persist_path: Optional[str] = None - - def __init__(self, **kwargs): - """Initialize the chroma store class.""" - super().__init__(**kwargs) - if self.collection is None: - # default to create a new collection or get an existed collection. - self.collection = self.client.get_or_create_collection(name=self.collection_name) + embedding_model: Optional[str] = None + similarity_top_k: Optional[int] = 10 def _new_client(self) -> Any: """Initialize the chroma client.""" - if self.persist_path is None: - return chromadb.PersistentClient() + if self.persist_path.startswith('http') or \ + self.persist_path.startswith('https'): + # Remote database URL + parsed_url = urlparse(self.persist_path) + settings = Settings( + chroma_api_impl="chromadb.api.fastapi.FastAPI", + chroma_server_host=parsed_url.hostname, + chroma_server_http_port=str(parsed_url.port) + ) else: - return chromadb.PersistentClient(path=self.persist_path) + settings = Settings( + is_persistent=True, + persist_directory=self.persist_path + ) + + client = chromadb.Client(settings) + if self.collection is None: + # default to create a new collection or get an existed collection. + self.collection = client.get_or_create_collection( + name=self.collection_name + ) + return client def query(self, query: Query, **kwargs) -> List[Document]: """Query the chroma collection with the given query and return the top k results. @@ -61,23 +79,25 @@ def query(self, query: Query, **kwargs) -> List[Document]: List[Document]: List of documents retrieved by the query. """ - embedding = query.embedding + embedding = query.embeddings if self.embedding_model is not None and len(embedding) == 0: - embedding = self.embedding_model.get_embeddings([query.query_str])[0] + embedding = EmbeddingManager().get_instance_obj( + self.embedding_model + ).get_embeddings([query.query_str], text_type="query")[0] if len(embedding) > 0: query_result = self.collection.query( - n_results=query.similarity_top_k, - query_embeddings=[embedding] + n_results=query.similarity_top_k if query.similarity_top_k else self.similarity_top_k, + query_embeddings=embedding ) else: query_result = self.collection.query( - n_results=query.similarity_top_k, + n_results=query.similarity_top_k if query.similarity_top_k else self.similarity_top_k, query_texts=[query.query_str] ) # convert to the agentUniverse(aU) document format return self.to_documents(query_result) - def insert_documents(self, documents: List[Document], **kwargs: Any): + def insert_document(self, documents: List[Document], **kwargs: Any): """Insert documents to the chroma collection. Args: @@ -92,7 +112,9 @@ def insert_documents(self, documents: List[Document], **kwargs: Any): for document in documents: embedding = document.embedding if self.embedding_model is not None and len(embedding) == 0: - embedding = self.embedding_model.get_embeddings([document.text])[0] + embedding = EmbeddingManager().get_instance_obj( + self.embedding_model + ).get_embeddings([document.text])[0] self.collection.add( documents=[document.text], metadatas=[document.metadata], @@ -105,7 +127,9 @@ def upsert_document(self, documents: List[Document], **kwargs): for document in documents: embedding = document.embedding if self.embedding_model is not None and len(embedding) == 0: - embedding = self.embedding_model.get_embeddings([document.text])[0] + embedding = EmbeddingManager().get_instance_obj( + self.embedding_model + ).get_embeddings([document.text])[0] self.collection.upsert( documents=[document.text], metadatas=[document.metadata], @@ -118,7 +142,9 @@ def update_document(self, documents: List[Document], **kwargs): for document in documents: embedding = document.embedding if self.embedding_model is not None and len(embedding) == 0: - embedding = self.embedding_model.get_embeddings([document.text])[0] + embedding = EmbeddingManager().get_instance_obj( + self.embedding_model + ).get_embeddings([document.text])[0] self.collection.update( documents=[document.text], metadatas=[document.metadata], @@ -136,8 +162,24 @@ def to_documents(query_result: QueryResult) -> List[Document]: for i in range(len(query_result['ids'][0])): documents.append(Document(id=query_result['ids'][0][i], text=query_result['documents'][0][i], - embedding=query_result['embeddings'][0][i] - if query_result['embeddings'] is not None else [], + embedding=query_result['embeddings'][0][ + i] + if query_result[ + 'embeddings'] is not None else [], metadata=query_result['metadatas'][0][i] - if query_result['metadatas'] is not None else None)) + if query_result[ + 'metadatas'] is not None else None)) return documents + + def _initialize_by_component_configer(self, + chroma_store_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(chroma_store_configer) + if hasattr(chroma_store_configer, "collection_name"): + self.collection_name = chroma_store_configer.collection_name + if hasattr(chroma_store_configer, "persist_path"): + self.persist_path = chroma_store_configer.persist_path + if hasattr(chroma_store_configer, "embedding_model"): + self.embedding_model = chroma_store_configer.embedding_model + if hasattr(chroma_store_configer, "similarity_top_k"): + self.similarity_top_k = chroma_store_configer.similarity_top_k + return self diff --git a/agentuniverse/agent/action/knowledge/store/document.py b/agentuniverse/agent/action/knowledge/store/document.py index 5b7d6fd9..743eeb35 100644 --- a/agentuniverse/agent/action/knowledge/store/document.py +++ b/agentuniverse/agent/action/knowledge/store/document.py @@ -5,7 +5,7 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: document.py import uuid -from typing import Dict, Any, Optional, List +from typing import Dict, Any, Optional, List, Set from langchain_core.documents.base import Document as LCDocument from pydantic import BaseModel, Field, model_validator @@ -20,11 +20,14 @@ class Document(BaseModel): metadata (Dict[str, Any]): Metadata associated with the document. embedding (List[float]): Embedding data associated with the document """ + class Config: + arbitrary_types_allowed = True id: str = None text: Optional[str] = "" metadata: Optional[Dict[str, Any]] = None embedding: List[float] = Field(default_factory=list) + keywords: Set[str] = Field(default_factory=set) @model_validator(mode='before') def create_id(cls, values): diff --git a/agentuniverse/agent/action/knowledge/store/image_document.py b/agentuniverse/agent/action/knowledge/store/image_document.py new file mode 100644 index 00000000..fe013d9b --- /dev/null +++ b/agentuniverse/agent/action/knowledge/store/image_document.py @@ -0,0 +1,27 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/22 18:16 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: image_document.py +from typing import Optional, List, Union +from pydantic import Field +from PIL.Image import Image + +from .document import Document + +class ImageDocument(Document): + """The basic class for an ImageDocument. + + Attributes: + origin_image (Optional[Union[Image, str]]): The original image or base64. + image_embedding (List[float]): Embedding data associated with the image. + ocr_text (Optional[str]): Text extracted from the image using OCR. + ocr_text_embedding (Optional[List[float]]): Embedding data associated with the OCR text. + """ + + origin_image: Optional[Union[Image, str]] + image_embedding: List[float] = Field(default_factory=list) + ocr_text: Optional[str] + ocr_text_embedding: Optional[List[float]] \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/store/milvus_store.py b/agentuniverse/agent/action/knowledge/store/milvus_store.py index ced1ac14..5bd592ec 100644 --- a/agentuniverse/agent/action/knowledge/store/milvus_store.py +++ b/agentuniverse/agent/action/knowledge/store/milvus_store.py @@ -5,7 +5,7 @@ # @Author : fanen.lhy # @Email : fanen.lhy@antgroup.com # @FileName: milvus_store.py -from typing import List, Optional +from typing import List, Optional, Any try: from pymilvus import connections, Collection, CollectionSchema, \ @@ -17,6 +17,9 @@ from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query from agentuniverse.agent.action.knowledge.store.store import Store +from agentuniverse.agent.action.knowledge.embedding.embedding_manager import EmbeddingManager +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger DEFAULT_CONNECTION_ARGS = { "host": "localhost", @@ -36,24 +39,29 @@ class MilvusStore(Store): + connection_args: Optional[dict] = None + search_args: Optional[dict] = None + index_params: Optional[dict] = None collection_name: Optional[str] = 'milvus_db' collection: Collection = None connection_name: str = 'default_connection' + embedding_model: Optional[str] = None + similarity_top_k: Optional[int] = 10 - def __init__( - self, - connection_args: dict = None, - **kwargs - ): - """Initialize the Milvus store class.""" - super().__init__(**kwargs) - if not connection_args: - connection_args = DEFAULT_CONNECTION_ARGS - host = connection_args["host"] - port = connection_args["port"] - db_name = connection_args.get("db_name", "default") + + def _connect_to_milvus(self, connection_args: dict): + """Connect to Milvus server.""" + if not connections.has_connection(self.connection_name): + connections.connect( + alias=self.connection_name, **connection_args + ) + + def _new_client(self) -> Any: + host = self.connection_args["host"] + port = self.connection_args["port"] + db_name = self.connection_args.get("db_name", "default") self.connection_name = f"{host}_{port}_{db_name}" - self._connect_to_milvus(connection_args) + self._connect_to_milvus(self.connection_args) if utility.has_collection(self.collection_name, using=self.connection_name): self.collection = Collection( @@ -61,13 +69,28 @@ def __init__( ) self.collection.load() - - def _connect_to_milvus(self, connection_args: dict): - """Connect to Milvus server.""" - if not connections.has_connection(self.connection_name): - connections.connect( - alias=self.connection_name, **connection_args - ) + def _initialize_by_component_configer(self, + milvus_store_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(milvus_store_configer) + if hasattr(milvus_store_configer, "connection_args"): + self.connection_args = milvus_store_configer.connection_args + else: + self.connection_args = DEFAULT_CONNECTION_ARGS + if hasattr(milvus_store_configer, "search_args"): + self.search_args = milvus_store_configer.search_args + else: + self.search_args = DEFAULT_SEARCH_ARGS + if hasattr(milvus_store_configer, "index_params"): + self.index_params = milvus_store_configer.index_params + else: + self.index_params = DEFAULT_INDEX_PARAMS + if hasattr(milvus_store_configer, "collection_name"): + self.collection_name = milvus_store_configer.collection_name + if hasattr(milvus_store_configer, "embedding_model"): + self.embedding_model = milvus_store_configer.embedding_model + if hasattr(milvus_store_configer, "similarity_top_k"): + self.similarity_top_k = milvus_store_configer.similarity_top_k + return self def _create_or_load_collection(self, dim: int = 128, @@ -96,7 +119,7 @@ def _create_or_load_collection(self, ) else: if not index_params: - index_params = DEFAULT_INDEX_PARAMS + index_params = self.index_params fields = [ FieldSchema(name="id", dtype=DataType.VARCHAR, max_length=100, is_primary=True), @@ -133,18 +156,23 @@ def query(self, """ if not self.collection: return self.to_documents([]) - embedding = query.embedding + embedding = query.embeddings if self.embedding_model is not None and len(embedding) == 0: - embedding = self.embedding_model.get_embeddings([query.query_str])[ - 0] + if not self.embedding_model: + raise Exception("Milvus store can only save vector, " + "you should provide embedding in your document or specify an embedding model.") + embedding = EmbeddingManager().get_instance_obj( + self.embedding_model + ).get_embeddings([query.query_str], text_type="query")[0] + if not search_args: - search_args = DEFAULT_SEARCH_ARGS + search_args = self.search_args if len(embedding) > 0: query_result = self.collection.search( - data=[embedding], + data=embedding, anns_field="embedding", param=search_args, - limit=query.similarity_top_k, + limit=query.similarity_top_k if query.similarity_top_k else self.similarity_top_k, output_fields=["id", "text", "embedding", "metadata"] ) else: @@ -173,8 +201,15 @@ def upsert_document(self, """ for document in documents: embedding = document.embedding - if self.embedding_model is not None and len(embedding) == 0: - embedding = self.embedding_model.get_embeddings([document.text])[0] + if len(embedding) == 0: + if not self.embedding_model: + raise Exception("Milvus store can only save vector, " + "you should provide embedding in your document or specify an embedding model.") + else: + embedding = EmbeddingManager().get_instance_obj( + self.embedding_model + ).get_embeddings([document.text])[0] + if not self.collection: self._create_or_load_collection( dim=len(embedding), @@ -196,7 +231,7 @@ def upsert_document(self, self.collection.insert(entities) self.collection.load() - def insert_documents(self, + def insert_document(self, documents: List[Document], max_length: int = 65535, index_params: dict = None, diff --git a/agentuniverse/agent/action/knowledge/store/query.py b/agentuniverse/agent/action/knowledge/store/query.py index aa2d2ce5..1e59fc26 100644 --- a/agentuniverse/agent/action/knowledge/store/query.py +++ b/agentuniverse/agent/action/knowledge/store/query.py @@ -6,7 +6,8 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: query.py -from typing import Optional, List +from PIL.Image import Image +from typing import Optional, List, Set from pydantic import BaseModel, Field @@ -15,11 +16,21 @@ class Query(BaseModel): """The basic class for the knowledge query. Attributes: - query_str (Optional[str]): The query string. - similarity_top_k (int): The number of top results to return. - embedding (List[float]): The specific embedding data of the query. + query_str (Optional[str]): Origin query str. + query_text_bundles (Optional[List[str]]): The query string list. + query_image_bundles (Optional[List[Image]]): The query image list. + keywords: (Optional[Set[str]]): Keywords used to search with inverted index. + embeddings (List[List[float]]): A list of embedded queries. + ext_info (dict): extra information used in query. """ + class Config: + arbitrary_types_allowed = True query_str: Optional[str] = None - similarity_top_k: int = 2 - embedding: List[float] = Field(default_factory=list) + query_text_bundles: Optional[List[str]] = Field(default_factory=list) + query_image_bundles: Optional[List[Image]] = Field(default_factory=list) + keywords: Optional[Set[str]] = Field(default_factory=set) + embeddings: List[List[float]] = Field(default_factory=list) + ext_info: dict = {} + + similarity_top_k: Optional[int] = None diff --git a/agentuniverse/agent/action/knowledge/store/sqlite_store.py b/agentuniverse/agent/action/knowledge/store/sqlite_store.py new file mode 100644 index 00000000..a30a6d97 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/store/sqlite_store.py @@ -0,0 +1,237 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/8 14:01 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: sqlite_store.py +import sqlite3 +import json +import math +from typing import List, Optional, Set +from collections import Counter + +import jieba + +from agentuniverse.agent.action.knowledge.store.store import Store +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor_manager import DocProcessorManager +from agentuniverse.base.config.component_configer.component_configer import \ + ComponentConfiger + + +class SQLiteStore(Store): + db_path: str = 'sqlite_store.db' + conn: Optional[sqlite3.Connection] = None + k1: float = 1.5 + b: float = 0.75 + keyword_extractor: Optional[str] = None + similarity_top_k: int = 10 + + def _new_client(self): + self.conn = sqlite3.connect(self.db_path, check_same_thread=False) + self._create_tables() + + def _create_tables(self): + with self.conn: + self.conn.execute(''' + CREATE TABLE IF NOT EXISTS documents ( + id TEXT PRIMARY KEY, + text TEXT, + word_count INT, + metadata TEXT + ) + ''') + self.conn.execute(''' + CREATE TABLE IF NOT EXISTS inverted_index ( + term TEXT, + doc_id TEXT, + FOREIGN KEY (doc_id) REFERENCES documents (id) + ) + ''') + + def _initialize_by_component_configer(self, + sqlite_store_configer: ComponentConfiger) -> 'DocProcessor': + super()._initialize_by_component_configer(sqlite_store_configer) + if hasattr(sqlite_store_configer, "db_path"): + self.db_path = sqlite_store_configer.db_path + if hasattr(sqlite_store_configer, "k1"): + self.k1 = sqlite_store_configer.k1 + if hasattr(sqlite_store_configer, "b"): + self.b = sqlite_store_configer.b + if hasattr(sqlite_store_configer, "keyword_extractor"): + self.keyword_extractor = sqlite_store_configer.keyword_extractor + if hasattr(sqlite_store_configer, "similarity_top_k"): + self.similarity_top_k = sqlite_store_configer.similarity_top_k + return self + + + def _get_all_docs_count(self) -> int: + with self.conn: + cursor = self.conn.cursor() + cursor.execute('SELECT COUNT(*) FROM documents') + count = cursor.fetchone()[0] + cursor.close() + return count + + def _get_all_docs_words_count(self) -> int: + with self.conn: + cursor = self.conn.cursor() + cursor.execute('SELECT SUM(word_count) FROM documents') + total_word_count = cursor.fetchone()[0] + cursor.close() + return total_word_count if total_word_count is not None else 0 + + def compute_bm25(self, query_text, doc_text, inverted_index, + total_doc_count, total_word_count): + k1 = self.k1 + b = self.b + query_words = jieba.lcut(query_text) + doc_words = jieba.lcut(doc_text) + doc_length = len(doc_words) + avg_doc_length = total_word_count / total_doc_count + bm25_score = 0 + + doc_counter = Counter(doc_words) + + for term in query_words: + if term in inverted_index: + term_freq = doc_counter[term] + num_docs_with_term = len(inverted_index[term]) + idf = math.log((total_doc_count - num_docs_with_term + 0.5) / ( + num_docs_with_term + 0.5) + 1) + bm25_term_score = idf * (term_freq * (k1 + 1)) / ( + term_freq + k1 * ( + 1 - b + b * (doc_length / avg_doc_length))) + bm25_score += bm25_term_score + + return bm25_score + + + def _get_document_keyword(self, document: Document) -> Set[str]: + if not self.keyword_extractor: + raise Exception( + "You must specify a keyword extractor in sqlite store query") + else: + _doc = DocProcessorManager().get_instance_obj( + self.keyword_extractor) \ + .process_docs([document]) + return _doc[0].keywords + + def insert_document(self, documents: List[Document], **kwargs): + with self.conn: + for document in documents: + metadata = json.dumps( + document.metadata) if document.metadata else None + self.conn.execute( + 'INSERT OR REPLACE INTO documents (id, text, word_count, metadata) VALUES (?, ?, ?, ?)', + (document.id, document.text, len(jieba.lcut(document.text)), metadata) + ) + self._get_document_keyword(document) + for term in set(document.keywords): + self.conn.execute( + 'INSERT INTO inverted_index (term, doc_id) VALUES (?, ?)', + (term, document.id) + ) + + def delete_document(self, document_id: int): + with self.conn: + self.conn.execute( + 'DELETE FROM documents WHERE id = ?', + (document_id,) + ) + self.conn.execute( + 'DELETE FROM inverted_index WHERE doc_id = ?', + (document_id,) + ) + + def upsert_document(self, documents: List[Document], **kwargs): + with self.conn: + for document in documents: + metadata = json.dumps( + document.metadata) if document.metadata else None + self.conn.execute( + 'INSERT OR REPLACE INTO documents (id, text, word_count, metadata) VALUES (?, ?, ?, ?)', + ( + document.id, document.text, len(jieba.lcut(document.text)), + metadata) + ) + self.conn.execute( + 'DELETE FROM inverted_index WHERE doc_id = ?', + (document.id,) + ) + self._get_document_keyword(document) + for term in set(document.keywords): + self.conn.execute( + 'INSERT INTO inverted_index (term, doc_id) VALUES (?, ?)', + (term, document.id) + ) + + def query(self, query: Query, **kwargs) -> List[Document]: + if len(query.keywords) > 0: + query_terms = query.keywords + else: + query_terms = self._get_document_keyword(Document(text=query.query_str)) + query.keywords = query_terms + + # Get doc_id from inverted index. + relevant_docs = set() + inverted_index = {} + with self.conn: + for keyword in query_terms: + cursor = self.conn.cursor() + cursor.execute( + 'SELECT doc_id FROM inverted_index WHERE term = ?', + (keyword,)) + docs_with_keyword = [row[0] for row in cursor.fetchall()] + inverted_index[keyword] = docs_with_keyword + relevant_docs.update(docs_with_keyword) + cursor.close() + + # Count every document's bm25. + doc_scores = [] + total_doc_count = self._get_all_docs_count() + total_word_count = self._get_all_docs_words_count() + for doc_id in relevant_docs: + cursor = self.conn.cursor() + cursor.execute('SELECT text FROM documents WHERE id = ?', + (doc_id,)) + doc_text = cursor.fetchone()[0] + cursor.close() + + bm25_score = self.compute_bm25(query.query_str, doc_text, + inverted_index, total_doc_count, total_word_count) + doc_scores.append((doc_id, bm25_score)) + + # Order the docs with bm25, and return top k. + doc_scores.sort(key=lambda x: x[1], reverse=True) + top_docs = doc_scores[:self.similarity_top_k] + results = [] + for doc_id, score in top_docs: + cursor = self.conn.cursor() + cursor.execute('SELECT * FROM documents WHERE id = ?', + (doc_id,)) + doc_row = cursor.fetchone() + cursor.close() + + document = Document(id=doc_row[0], text=doc_row[1], + word_count=doc_row[2], + metadata=json.loads(doc_row[3])) + results.append(document) + + return results + + @staticmethod + def to_documents(query_result) -> List[Document]: + """Convert the query results of sqlite to the agentUniverse(aU) document format.""" + + if query_result is None: + return [] + documents = [] + for i in range(len(query_result['ids'][0])): + documents.append(Document(id=query_result['ids'][0][i], + text=query_result['documents'][0][i], + embedding=[], + metadata=json.loads(query_result[2]) if query_result[2] else None)) + return documents diff --git a/agentuniverse/agent/action/knowledge/store/store.py b/agentuniverse/agent/action/knowledge/store/store.py index c37ec73b..af28676a 100644 --- a/agentuniverse/agent/action/knowledge/store/store.py +++ b/agentuniverse/agent/action/knowledge/store/store.py @@ -6,56 +6,60 @@ # @Email : wangchongshi.wcs@antgroup.com # @FileName: store.py from typing import Any, List, Optional -from pydantic import BaseModel -from agentuniverse.agent.action.knowledge.embedding.embedding import Embedding +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger from agentuniverse.agent.action.knowledge.store.document import Document from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent_serve.web.post_fork_queue import add_post_fork -class Store(BaseModel): +class Store(ComponentBase): """The basic class for the knowledge store. Store of the knowledge, store class is used to store knowledge and provide retrieval capabilities, vector storage, such as ChromaDB store, or non-vector storage, such as Redis Store. - - Attributes: - client (Any): The client of the store, - client is usually used to connect to the storage and provides knowledge insertion, - update, deletion, and query operations - - async_client (Any): The async client of the store, - the function is the same as that of the `client` parameter in asynchronous mode. - - embedding_model (Embedding): The embedding model of the store, - used to provided embedding operations on texts to generate a list of floats. """ - + component_type: ComponentEnum = ComponentEnum.STORE + name: Optional[str] = None + description: Optional[str] = None client: Any = None async_client: Any = None - embedding_model: Optional[Embedding] = None class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True - def __init__(self, **kwargs): - """Initialize the store class.""" - super().__init__(**kwargs) - if self.client is None: - self.client = self._new_client() - if self.async_client is None: - self.async_client = self._new_async_client() - def _new_client(self) -> Any: - """Initialize the client.""" + """Initialize the client，like database connection or local file create.""" pass def _new_async_client(self) -> Any: """Initialize the async client.""" pass + def _initialize_by_component_configer(self, + store_configer: ComponentConfiger) \ + -> 'Store': + """Initialize the store by the ComponentConfiger object. + + Args: + store_configer(ComponentConfiger): A configer contains store + basic info. The client will be created in post fork due to + conflicts that may occur in a multithreaded scenario. + Returns: + Store: A store instance. + """ + if store_configer.name: + self.name = store_configer.name + if store_configer.description: + self.description = store_configer.description + add_post_fork(self._new_client) + add_post_fork(self._new_async_client) + return self + def query(self, query: Query, **kwargs) -> List[Document]: """Query documents.""" raise NotImplementedError @@ -64,22 +68,14 @@ async def async_query(self, query: Query, **kwargs) -> List[Document]: """Asynchronously query documents.""" raise NotImplementedError - def insert_documents(self, documents: List[Document], **kwargs): + def insert_document(self, documents: List[Document], **kwargs): """Insert documents into the store.""" raise NotImplementedError - async def async_insert_documents(self, documents: List[Document], **kwargs): + async def async_insert_document(self, documents: List[Document], **kwargs): """Asynchronously insert documents into the store.""" raise NotImplementedError - def insert_document(self, document: Document, **kwargs): - """Insert document into the store.""" - raise NotImplementedError - - async def async_insert_document(self, document: Document, **kwargs): - """Asynchronously insert document into the store.""" - raise NotImplementedError - def delete_document(self, document_id: str, **kwargs): """Delete the specific document by the document id.""" raise NotImplementedError diff --git a/agentuniverse/agent/action/knowledge/store/store_manager.py b/agentuniverse/agent/action/knowledge/store/store_manager.py new file mode 100644 index 00000000..9eebfae5 --- /dev/null +++ b/agentuniverse/agent/action/knowledge/store/store_manager.py @@ -0,0 +1,20 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/7/24 11:45 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: store_manager.py + +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.base.component.component_enum import ComponentEnum +from agentuniverse.base.component.component_manager_base import ComponentManagerBase +from agentuniverse.agent.action.knowledge.store.store import Store + + +@singleton +class StoreManager(ComponentManagerBase[Store]): + """A singleton manager class of the reader.""" + + def __init__(self): + super().__init__(ComponentEnum.STORE) \ No newline at end of file diff --git a/agentuniverse/agent/action/knowledge/store/temp.py b/agentuniverse/agent/action/knowledge/store/temp.py new file mode 100644 index 00000000..12da6fbb --- /dev/null +++ b/agentuniverse/agent/action/knowledge/store/temp.py @@ -0,0 +1,68 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/8 15:23 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: temp.py +import jieba +from collections import defaultdict, Counter +import math + + +# 构建倒排索引 +def build_inverted_index(corpus): + inverted_index = defaultdict(list) + for idx, document in enumerate(corpus): + words = jieba.lcut(document) + for word in set(words): + inverted_index[word].append(idx) + return inverted_index + + +# 计算 BM25 +def compute_bm25(query, doc_id, corpus, inverted_index, k1=1.5, b=0.75): + query_words = jieba.lcut(query) + doc_words = jieba.lcut(corpus[doc_id]) + doc_length = len(doc_words) + avg_doc_length = sum(len(jieba.lcut(doc)) for doc in corpus) / len(corpus) + bm25_score = 0 + + doc_counter = Counter(doc_words) + + for term in query_words: + if term in inverted_index: + term_freq = doc_counter[term] + num_docs_with_term = len(inverted_index[term]) + idf = math.log((len(corpus) - num_docs_with_term + 0.5) / ( + num_docs_with_term + 0.5) + 1) + bm25_term_score = idf * (term_freq * (k1 + 1)) / ( + term_freq + k1 * ( + 1 - b + b * (doc_length / avg_doc_length))) + bm25_score += bm25_term_score + + return bm25_score + + +# 示例数据 +corpus = [ + "自然语言处理是人工智能的一个重要方向", + "我喜欢学习新的技术", + "自然语言处理包括很多技术，比如分词、词性标注、命名实体识别等" +] + +# 构建倒排索引 +inverted_index = build_inverted_index(corpus) + +# 查询 +query = "自然语言处理" +scores = {idx: compute_bm25(query, idx, corpus, inverted_index) for idx in + range(len(corpus))} + +# 排序并输出最相关的文档 +sorted_docs = sorted(scores.items(), key=lambda item: item[1], reverse=True) +for doc_id, score in sorted_docs: + print(f"文档ID: {doc_id}, BM25分数: {score}, 内容: {corpus[doc_id]}") + +if __name__ == "__main__": + print("test") \ No newline at end of file diff --git a/agentuniverse/agent/default/rag_route_agent/__init__.py b/agentuniverse/agent/default/rag_route_agent/__init__.py new file mode 100644 index 00000000..80aebaa4 --- /dev/null +++ b/agentuniverse/agent/default/rag_route_agent/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/13 11:13 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_agent.py b/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_agent.py new file mode 100644 index 00000000..e57bddd5 --- /dev/null +++ b/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_agent.py @@ -0,0 +1,48 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/13 11:20 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: nlu_rag_route_agent.py + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject + + +class NluRagRouteAgent(Agent): + """Rag Agent class.""" + + def input_keys(self) -> list[str]: + """Return the input keys of the Agent.""" + return ['query', 'store_info', 'store_amount'] + + 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['query'] = input_object.get_data('query') + agent_input['store_info'] = input_object.get_data('store_info') + agent_input['store_amount'] = input_object.get_data('store_amount') + self.agent_model.profile.setdefault('prompt_version', + 'nlu_rag_route_prompt.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 diff --git a/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_agent.yaml b/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_agent.yaml new file mode 100644 index 00000000..98b645c1 --- /dev/null +++ b/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_agent.yaml @@ -0,0 +1,14 @@ +info: + name: 'nlu_rag_route_agent' + description: 'agent used to route query to different store' +profile: + llm_model: + name: 'default_openai_llm' + model_name: 'gpt-4o' +plan: + planner: + name: 'rag_planner' +metadata: + type: 'AGENT' + module: 'agentuniverse.agent.default.rag_route_agent.nlu_rag_route_agent' + class: 'NluRagRouteAgent' \ No newline at end of file diff --git a/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_prompt.yaml b/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_prompt.yaml new file mode 100644 index 00000000..0cd80d78 --- /dev/null +++ b/agentuniverse/agent/default/rag_route_agent/nlu_rag_route_prompt.yaml @@ -0,0 +1,20 @@ +introduction: 你是一位精通信息分析的ai助手。 +target: 你的目标是根据检索内容和数据库内容的描述选择和检索内容相关的数据库。 +instruction: | + 你需要根据给定的检索内容和数据库相关信息，选取存储信息和检索内容相关且总数不超过指定数量的数据库。 + 查询内容为字符串。 + 数据库相关信息为json string的格式，其中key为数据库名称，value为数据库存储内容的描述。 + 你需要遵守的规则是: + 1. 仅回答最终选取的数据库名称，多个名称间用`,`进行拼接，不要回答选取过程和无关内容。 + 2. 选取的数据库一定不能超过指定的数量。 + 3. 要深层次考虑检索问题和数据库存储内容的关联性，可以用你的通识知识进行判断。 + + -------------------------------------- + 开始! + 查询内容是: {query} + 数据库相关信息为: {store_info} + 最终选取数据库数量不超过{store_amount}个。 + +metadata: + type: 'PROMPT' + version: 'nlu_rag_route_prompt.cn' \ No newline at end of file diff --git a/agentuniverse/agent/plan/planner/planner.py b/agentuniverse/agent/plan/planner/planner.py index 6a14b901..43a8669e 100644 --- a/agentuniverse/agent/plan/planner/planner.py +++ b/agentuniverse/agent/plan/planner/planner.py @@ -118,10 +118,11 @@ def run_all_actions(self, agent_model: AgentModel, planner_input: dict, input_ob knowledge: Knowledge = KnowledgeManager().get_instance_obj(knowledge_name) if knowledge is None: continue - knowledge_res: List[Document] = knowledge.query_knowledge(query_str=input_object.get_data(self.input_key), - **input_object.to_dict()) - for document in knowledge_res: - action_result.append(document.text) + knowledge_res: List[Document] = knowledge.query_knowledge( + query_str=input_object.get_data(self.input_key), + **input_object.to_dict() + ) + action_result.append(knowledge.to_llm(knowledge_res)) for agent_name in agents: agent = AgentManager().get_instance_obj(agent_name) diff --git a/agentuniverse/base/agentuniverse.py b/agentuniverse/base/agentuniverse.py index eef8aa44..33551f06 100644 --- a/agentuniverse/base/agentuniverse.py +++ b/agentuniverse/base/agentuniverse.py @@ -27,6 +27,7 @@ from agentuniverse.agent_serve.web.request_task import RequestLibrary from agentuniverse.agent_serve.web.rpc.grpc.grpc_server_booster import set_grpc_config from agentuniverse.agent_serve.web.web_booster import ACTIVATE_OPTIONS +from agentuniverse.agent_serve.web.post_fork_queue import POST_FORK_QUEUE @singleton @@ -42,9 +43,16 @@ def __init__(self): self.__system_default_planner_package = ['agentuniverse.agent.plan.planner'] self.__system_default_memory_package = ['agentuniverse.agent.memory.default'] self.__system_default_prompt_package = ['agentuniverse.agent', 'agentuniverse.base.util'] + self.__system_default_embedding_package = ['agentuniverse.agent.action.knowledge.embedding'] + self.__system_default_doc_processor_package = ['agentuniverse.agent.action.knowledge.doc_processor'] + self.__system_default_reader_package = ['agentuniverse.agent.action.knowledge.reader.file'] + self.__system_default_rag_router_package = ['agentuniverse.agent.action.knowledge.rag_router'] + self.__system_default_query_paraphraser_package = ['agentuniverse.agent.action.knowledge.query_paraphraser'] - def start(self, config_path: str = None): - """Start the agentUniverse framework.""" + def start(self, config_path: str = None, core_mode: bool = False): + """Start the agentUniverse framework. + + """ # get default config path project_root_path = get_project_root_path() sys.path.append(str(project_root_path.parent)) @@ -104,6 +112,10 @@ def start(self, config_path: str = None): # scan and register the components self.__scan_and_register(self.__config_container.app_configer) + if core_mode: + + for _func, args, kwargs in POST_FORK_QUEUE: + _func(*args, **kwargs) def __scan_and_register(self, app_configer: AppConfiger): """Scan the component directory and register the components. @@ -125,6 +137,17 @@ def __scan_and_register(self, app_configer: AppConfiger): + self.__system_default_memory_package) core_prompt_package_list = ((app_configer.core_prompt_package_list or app_configer.core_default_package_list) + self.__system_default_prompt_package) + core_embedding_package_list = ((app_configer.core_embedding_package_list or app_configer.core_default_package_list) + + self.__system_default_embedding_package) + core_doc_processor_package_list = ((app_configer.core_doc_processor_package_list or app_configer.core_default_package_list) + + self.__system_default_doc_processor_package) + core_reader_package_list = ((app_configer.core_reader_package_list or app_configer.core_default_package_list) + + self.__system_default_reader_package) + core_store_package_list = app_configer.core_store_package_list or app_configer.core_default_package_list + core_rag_router_package_list = ((app_configer.core_rag_router_package_list or app_configer.core_default_package_list) + + self.__system_default_rag_router_package) + core_query_paraphraser_package_list = ((app_configer.core_query_paraphraser_package_list or app_configer.core_default_package_list) + + self.__system_default_query_paraphraser_package) component_package_map = { ComponentEnum.AGENT: core_agent_package_list, @@ -135,7 +158,13 @@ def __scan_and_register(self, app_configer: AppConfiger): ComponentEnum.SERVICE: core_service_package_list, ComponentEnum.SQLDB_WRAPPER: core_sqldb_wrapper_package_list, ComponentEnum.MEMORY: core_memory_package_list, - ComponentEnum.PROMPT: core_prompt_package_list + ComponentEnum.PROMPT: core_prompt_package_list, + ComponentEnum.EMBEDDING: core_embedding_package_list, + ComponentEnum.DOC_PROCESSOR: core_doc_processor_package_list, + ComponentEnum.READER: core_reader_package_list, + ComponentEnum.STORE: core_store_package_list, + ComponentEnum.RAG_ROUTER: core_rag_router_package_list, + ComponentEnum.QUERY_PARAPHRASER: core_query_paraphraser_package_list } component_configer_list_map = {} diff --git a/agentuniverse/base/component/component_base.py b/agentuniverse/base/component/component_base.py index 381a2379..1c26a635 100644 --- a/agentuniverse/base/component/component_base.py +++ b/agentuniverse/base/component/component_base.py @@ -20,9 +20,11 @@ class ComponentBase(BaseModel): component_type: ComponentEnum # component yaml path component_config_path: Optional[str] = None + + # Used to indicate whether this object is the default object for the corresponding component. + default_symbol: bool = False # pydantic protected_namespaces config model_config = ConfigDict(protected_namespaces=()) - def get_instance_code(self) -> str: """Return the full name of the component.""" appname = ApplicationConfigManager().app_configer.base_info_appname @@ -36,4 +38,14 @@ def initialize_by_component_configer(self, component_configer: ComponentConfiger Returns: ComponentBase: the component object """ + if hasattr(component_configer, "default_symbol"): + self.default_symbol = component_configer.default_symbol + self._initialize_by_component_configer(component_configer) + + return self + + def _initialize_by_component_configer(self, component_configer: ComponentConfiger) -> 'ComponentBase': pass + + def is_default_object(self): + return self.default_symbol diff --git a/agentuniverse/base/component/component_configer_util.py b/agentuniverse/base/component/component_configer_util.py index 7809275f..8ce32896 100644 --- a/agentuniverse/base/component/component_configer_util.py +++ b/agentuniverse/base/component/component_configer_util.py @@ -29,6 +29,13 @@ from agentuniverse.base.component.component_enum import ComponentEnum from agentuniverse.llm.llm_manager import LLMManager from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse.agent.action.knowledge.embedding.embedding_manager import EmbeddingManager +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor_manager import DocProcessorManager +from agentuniverse.agent.action.knowledge.reader.reader_manager import ReaderManager +from agentuniverse.agent.action.knowledge.query_paraphraser.query_paraphraser_manager import QueryParaphraserManager +from agentuniverse.agent.action.knowledge.store.store_manager import StoreManager +from agentuniverse.agent.action.knowledge.rag_router.rag_router_manager import RagRouterManager + class ComponentConfigerUtil(object): @@ -44,6 +51,12 @@ class ComponentConfigerUtil(object): ComponentEnum.SERVICE: ServiceConfiger, ComponentEnum.PROMPT: PromptConfiger, ComponentEnum.SQLDB_WRAPPER: SQLDBWrapperConfiger, + ComponentEnum.EMBEDDING: ComponentConfiger, + ComponentEnum.DOC_PROCESSOR: ComponentConfiger, + ComponentEnum.READER: ComponentConfiger, + ComponentEnum.STORE: ComponentConfiger, + ComponentEnum.RAG_ROUTER: ComponentConfiger, + ComponentEnum.QUERY_PARAPHRASER: ComponentConfiger, ComponentEnum.DEFAULT: ComponentConfiger } @@ -56,7 +69,13 @@ class ComponentConfigerUtil(object): ComponentEnum.MEMORY: MemoryManager, ComponentEnum.SERVICE: ServiceManager, ComponentEnum.SQLDB_WRAPPER: SQLDBWrapperManager, - ComponentEnum.PROMPT: PromptManager + ComponentEnum.PROMPT: PromptManager, + ComponentEnum.EMBEDDING: EmbeddingManager, + ComponentEnum.DOC_PROCESSOR: DocProcessorManager, + ComponentEnum.READER: ReaderManager, + ComponentEnum.STORE: StoreManager, + ComponentEnum.RAG_ROUTER: RagRouterManager, + ComponentEnum.QUERY_PARAPHRASER: QueryParaphraserManager, } @classmethod diff --git a/agentuniverse/base/component/component_enum.py b/agentuniverse/base/component/component_enum.py index 9371214c..b0cc512d 100644 --- a/agentuniverse/base/component/component_enum.py +++ b/agentuniverse/base/component/component_enum.py @@ -21,6 +21,12 @@ class ComponentEnum(Enum): PROMPT = "PROMPT" SQLDB_WRAPPER = "SQLDB_WRAPPER" PRODUCT = "PRODUCT" + EMBEDDING = "EMBEDDING" + DOC_PROCESSOR = "DOC_PROCESSOR" + READER = "READER" + STORE = "STORE" + RAG_ROUTER = "RAG_ROUTER" + QUERY_PARAPHRASER = "QUERY_PARAPHRASER" @staticmethod def to_value_list(): diff --git a/agentuniverse/base/component/component_manager_base.py b/agentuniverse/base/component/component_manager_base.py index f3ba0efb..b28a3444 100644 --- a/agentuniverse/base/component/component_manager_base.py +++ b/agentuniverse/base/component/component_manager_base.py @@ -32,6 +32,8 @@ def register(self, component_instance_name: str, component_instance_obj: Compone raise ValueError(f"{self._component_type.value} component object instance with name " f"'{component_instance_name}' already exists.") self._instance_obj_map[component_instance_name] = component_instance_obj + if component_instance_obj.default_symbol: + self._instance_obj_map["__default_instance__"] = component_instance_obj def unregister(self, component_instance_name: str): """Unregister the component instance abstractmethod.""" @@ -40,12 +42,20 @@ def unregister(self, component_instance_name: str): def get_instance_obj(self, component_instance_name: str, appname: str = None, new_instance: bool = False) -> ComponentTypeVar: """Return the component instance object.""" + if component_instance_name == "__default_instance__": + return self.get_default_instance(new_instance) appname = appname or ApplicationConfigManager().app_configer.base_info_appname instance_code = f'{appname}.{self._component_type.value.lower()}.{component_instance_name}' if new_instance: return copy.deepcopy(self._instance_obj_map.get(instance_code)) return self._instance_obj_map.get(instance_code) + def get_default_instance(self, new_instance: bool = False) -> ComponentTypeVar: + """Return the default instance of component.""" + if new_instance: + return copy.deepcopy(self._instance_obj_map.get("__default_instance__")) + return self._instance_obj_map.get("__default_instance__") + def get_instance_name_list(self) -> list[str]: """Return the component instance list.""" return list(self._instance_obj_map.keys()) diff --git a/agentuniverse/base/config/application_configer/app_configer.py b/agentuniverse/base/config/application_configer/app_configer.py index 2a77a4c1..d9bd946c 100644 --- a/agentuniverse/base/config/application_configer/app_configer.py +++ b/agentuniverse/base/config/application_configer/app_configer.py @@ -27,6 +27,12 @@ def __init__(self): self.__core_sqldb_wrapper_package_list: Optional[list[str]] = None self.__core_prompt_package_list: Optional[list[str]] = None self.__core_product_package_list: Optional[list[str]] = None + self.__core_embedding_package_list: Optional[list[str]] = None + self.__core_doc_processor_package_list: Optional[list[str]] = None + self.__core_reader_package_list: Optional[list[str]] = None + self.__core_store_package_list: Optional[list[str]] = None + self.__core_rag_router_package_list: Optional[list[str]] = None + self.__core_query_paraphraser_package_list: Optional[list[str]] = None @property def base_info_appname(self) -> Optional[str]: @@ -86,6 +92,36 @@ def core_prompt_package_list(self) -> Optional[list[str]]: def core_product_package_list(self) -> Optional[list[str]]: return self.__core_product_package_list + @property + def core_embedding_package_list(self) -> Optional[list[str]]: + """Return the embedding package list of the core.""" + return self.__core_embedding_package_list + + @property + def core_doc_processor_package_list(self) -> Optional[list[str]]: + """Return the document processor package list of the core.""" + return self.__core_doc_processor_package_list + + @property + def core_reader_package_list(self) -> Optional[list[str]]: + """Return the reader package list of the core.""" + return self.__core_reader_package_list + + @property + def core_store_package_list(self) -> Optional[list[str]]: + """Return the store package list of the core.""" + return self.__core_store_package_list + + @property + def core_rag_router_package_list(self) -> Optional[list[str]]: + """Return the RAG router package list of the core.""" + return self.__core_rag_router_package_list + + @property + def core_query_paraphraser_package_list(self) -> Optional[list[str]]: + """Return the query paraphraser package list of the core.""" + return self.__core_query_paraphraser_package_list + def load_by_configer(self, configer: Configer) -> 'AppConfiger': """Load the AppConfiger by the given Configer. @@ -107,4 +143,10 @@ def load_by_configer(self, configer: Configer) -> 'AppConfiger': self.__core_sqldb_wrapper_package_list = configer.value.get('CORE_PACKAGE', {}).get('sqldb_wrapper') self.__core_prompt_package_list = configer.value.get('CORE_PACKAGE', {}).get('prompt') self.__core_product_package_list = configer.value.get('CORE_PACKAGE', {}).get('product') + self.__core_embedding_package_list = configer.value.get('CORE_PACKAGE', {}).get( 'embedding') + self.__core_doc_processor_package_list = configer.value.get('CORE_PACKAGE', {}).get('doc_processor') + self.__core_reader_package_list = configer.value.get('CORE_PACKAGE', {}).get('reader') + self.__core_store_package_list = configer.value.get('CORE_PACKAGE', {}).get('store') + self.__core_rag_router_package_list = configer.value.get('CORE_PACKAGE', {}).get('rag_router') + self.__core_query_paraphraser_package_list = configer.value.get('CORE_PACKAGE', {}).get('query_paraphraser') return self diff --git a/agentuniverse/base/config/component_configer/configers/knowledge_configer.py b/agentuniverse/base/config/component_configer/configers/knowledge_configer.py index 3e1eca72..1b99c80b 100644 --- a/agentuniverse/base/config/component_configer/configers/knowledge_configer.py +++ b/agentuniverse/base/config/component_configer/configers/knowledge_configer.py @@ -5,7 +5,7 @@ # @Author : jerry.zzw # @Email : jerry.zzw@antgroup.com # @FileName: knowledge_configer.py -from typing import Optional, Dict +from typing import Optional, Dict, List from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger from agentuniverse.base.config.configer import Configer @@ -19,6 +19,12 @@ def __init__(self, configer: Optional[Configer] = None): self.__name: Optional[str] = None self.__description: Optional[str] = None self.__ext_info: Optional[Dict] = None + self.stores: List[str] = [] + self.query_paraphrasers: Optional[List[str]] = [] + self.insert_processors: Optional[List[str]] = [] + self.rag_router: str = "base_router" + self.post_processors: List[str] = [] + self.readers: Dict[str, str] = dict() @property def name(self) -> Optional[str]: diff --git a/agentuniverse/base/config/custom_configer/custom_key_configer.py b/agentuniverse/base/config/custom_configer/custom_key_configer.py index d1de4836..a1f796cf 100644 --- a/agentuniverse/base/config/custom_configer/custom_key_configer.py +++ b/agentuniverse/base/config/custom_configer/custom_key_configer.py @@ -19,7 +19,11 @@ def __init__(self, config_path: str = None): self._Configer__value = {} super().__init__(config_path) if config_path: - self.load() + try: + self.load() + except FileNotFoundError as e: + print(f"Custom key file {config_path} read error, " + f"skip load custom key.") if self._Configer__value.get("KEY_LIST"): for key, value in self._Configer__value.get("KEY_LIST").items(): os.environ[key] = str(value) \ No newline at end of file diff --git a/agentuniverse/llm/llm.py b/agentuniverse/llm/llm.py index 680832bf..9aedda8d 100644 --- a/agentuniverse/llm/llm.py +++ b/agentuniverse/llm/llm.py @@ -92,6 +92,7 @@ def initialize_by_component_configer(self, component_configer: LLMConfiger) -> ' Returns: LLM: the LLM object """ + super().initialize_by_component_configer(component_configer) if component_configer.name: self.name = component_configer.name if component_configer.description: diff --git a/docs/guidebook/_picture/knowledge_structure.png b/docs/guidebook/_picture/knowledge_structure.png new file mode 100644 index 00000000..8a2c6a20 Binary files /dev/null and b/docs/guidebook/_picture/knowledge_structure.png differ diff --git a/docs/guidebook/_picture/law_agent_demo.png b/docs/guidebook/_picture/law_agent_demo.png index 2f67ec55..89bc730a 100644 Binary files a/docs/guidebook/_picture/law_agent_demo.png and b/docs/guidebook/_picture/law_agent_demo.png differ diff --git a/docs/guidebook/_picture/rag_structure.png b/docs/guidebook/_picture/rag_structure.png new file mode 100644 index 00000000..2d9a2095 Binary files /dev/null and b/docs/guidebook/_picture/rag_structure.png differ diff --git a/docs/guidebook/en/2_2_4_DocProcessor.md b/docs/guidebook/en/2_2_4_DocProcessor.md new file mode 100644 index 00000000..195f1c73 --- /dev/null +++ b/docs/guidebook/en/2_2_4_DocProcessor.md @@ -0,0 +1,172 @@ +# DocProcessor + +The DocProcessor is responsible for various processing tasks on Document, such as text splitting, keyword extraction, etc. The input and output of DocProcessor are both List[Document], ensuring that multiple DocProcessors can be stacked to form a processing pipeline for the Document. + +The Document is defined as follows: +```python +import uuid +from typing import Dict, Any, Optional, List, Set + +from pydantic import BaseModel, Field, model_validator + + +class Document(BaseModel): + class Config: + arbitrary_types_allowed = True + + id: str = None + text: Optional[str] = "" + metadata: Optional[Dict[str, Any]] = None + embedding: List[float] = Field(default_factory=list) + keywords: Set[str] = Field(default_factory=set) + + @model_validator(mode='before') + def create_id(cls, values): + text: str = values.get('text', '') + if not values.get('id'): + values['id'] = str(uuid.uuid5(uuid.NAMESPACE_URL, text)) + return values +``` +- id: A unique identifier for a specific document, generated by default using uuid. +- text: The text content of the document. +- metadata: Metadata information of the document, typically including the original filename, the position in the original file, etc. +- embedding: The vectorized form of the document, which could be a text vector, or in a subclass like ImageDocument, it could be the vectorized result of an image. +- keywords: Keywords extracted from the document, which can also serve as tags for the text. + +The DocProcessor is defined as follows: +```python +from abc import abstractmethod +from typing import List, Optional + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + +class DocProcessor(ComponentBase): + component_type: ComponentEnum = ComponentEnum.DOC_PROCESSOR + name: Optional[str] = None + description: Optional[str] = None + + def process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + return self._process_docs(origin_docs, query) + + @abstractmethod + def _process_docs(self, origin_docs: List[Document], + query: Query = None) -> \ + List[Document]: + pass +``` +Users need to mainly override the _process_docs function in their custom DocProcessor to implement the specific logic for processing Document. + +After writing the corresponding code, you can refer to the following YAML configuration to register your DocProcessor as an aU component: +```yaml +name: 'dashscope_reranker' +description: 'reranker use dashscope api' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.dashscope_reranker' + class: 'DashscopeReranker' +``` +The type in the metadata must be DOC_PROCESSOR. + +### Pay Attention to the Package Path of Your Custom DocProcessor +In the config.toml file of the agentUniverse project, you need to configure the package path corresponding to your DocProcessor. Please double-check whether the path of the file you created is under the doc_processor path or its subdirectories in CORE_PACKAGE. + +For example, the configuration in the sample project is as follows: +```yaml +[CORE_PACKAGE] +doc_processor = ['sample_standard_app.app.core.doc_processor'] +``` + + +## The following DocProcessors are built into agentUniverse: +### [CharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml) +This component splits the original text based on the number of characters. +The component definition file is as follows: +```yaml +name: 'character_text_splitter' +description: 'langchain character text splitter' +chunk_size: 200 +chunk_overlap: 20 +separators: "/n/n" +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.character_text_splitter' + class: 'CharacterTextSplitter' +``` +- chunk_size: The size of the text chunks after splitting. +- chunk_overlap: The length of the overlapping part between adjacent chunks. +- separators: The specified separator. + +### [TokenTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml) +This component splits the text based on the specified tokenizer, splitting the text into multiple segments according to the set chunk_size and chunk_overlap, where each segment contains a specified number of tokens. + +The component definition file is as follows: + +```yaml +name: 'token_text_splitter' +description: 'langchain token text splitter' +chunk_size: 200 +chunk_overlap: 20 +tokenizer: 'default_tokenizer' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.token_text_splitter' + class: 'TokenTextSplitter' +``` +- chunk_size: The number of tokens in the text after splitting. +- chunk_overlap: The number of overlapping tokens between adjacent chunks. +- tokenizer: The specified tokenizer used to split the text into tokens. + +### [RecursiveCharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml) + +This component recursively splits the original text based on the specified separators. It first tries to split the text using the separator with the highest priority. If it fails to meet the chunk_size requirement, it will recursively use the next separator until the text is successfully split. + +The component definition file is as follows: +```yaml +name: 'recursive_character_text_splitter' +description: 'langchain recursive character text splitter' +chunk_size: 200 +chunk_overlap: 20 +separators: + - "\n\n" + - "\n" +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.recursive_character_text_splitter' + class: 'RecursiveCharacterTextSplitter' +``` +- chunk_size: The size of the text chunks after splitting. +- chunk_overlap: The length of the overlapping part between adjacent chunks. +- separators: A list of specified separators. The component will try to split using the separators in order. If the first separator cannot meet the conditions, it will recursively use the next separator. + +### [JiebaKeywordExtractor](../../../agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml) +This component uses the Jieba segmentation library to extract keywords from the text. It can extract the most important keywords based on the set top_k parameter, which can be used later to build an inverted index. +The component definition file is as follows: +```yaml +name: 'jieba_keyword_extractor' +description: 'extract keywords from text' +top_k: 3 +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.jieba_keyword_extractor' + class: 'JiebaKeywordExtractor' +``` +- top_k: The number of keywords to extract from the text, meaning the top_k keywords will be extracted. + +### [DashscopeReranker](../../../agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml) + +This component uses the DashScope API to rerank texts, sorting the content recalled by the Store based on the relevance to the Query content. + +The component definition file is as follows: +```yaml +name: 'dashscope_reranker' +description: 'reranker use dashscope api' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.dashscope_reranker' + class: 'DashscopeReranker' +``` +This component requires configuring DASHSCOPE_API_KEY in the environment variables. \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md b/docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md new file mode 100644 index 00000000..b8617abe --- /dev/null +++ b/docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md @@ -0,0 +1,110 @@ +# How to Build a RAG Agent + +This tutorial provides a quick guide to building a RAG (Retrieval-Augmented Generation) agent within agentUniverse. The full structure and content of the case can be found in [this document](7_1_1_Legal_Consultation_Case.md). This document focuses on the construction process, so some non-essential content has been omitted. + +## Case Description +This case is based on RagPlanner and sets up a simple legal consultation agent that provides relevant legal advice by retrieving applicable articles from the Civil Code and Criminal Code and considering the case background. +The case uses the Qianwen large model and DashScope's embedding and rerank functionalities. Before using it, you need to configure the `DASHSCOPE_API_KEY` in your environment variables. + +The knowledge file is defined as follows: +```yaml +name: "law_knowledge" +description: "中国民法与刑法相关的知识库" +stores: + - "civil_law_chroma_store" + - "criminal_law_chroma_store" + - "civil_law_sqlite_store" + - "criminal_law_sqlite_store" +insert_processors: + - "recursive_character_text_splitter" +post_processors: + - "dashscope_reranker" +readers: + pdf: "default_pdf_reader" + +metadata: + type: 'KNOWLEDGE' + module: 'sample_standard_app.app.core.knowledge.law_knowledge' + class: 'LawKnowledge' +``` + +## Building the Knowledge Index + +Origin docs： +- [民法典.pdf](../../../sample_standard_app/app/resources/民法典.pdf) +- [刑法.pdf](../../../sample_standard_app/app/resources/刑法.pdf) + +### Extracting Text from PDFs +Since the original documents in this case are in PDF format, we configured the Knowledge component as follows: +```yaml +readers: + pdf: "default_pdf_reader" +``` +This allows the extraction of text from the PDF. If you want to read more file formats, you can refer to the [Reader component](2_2_4_Reader.md). + +### Splitting Long Text +Since the text content in the original documents is very long, we need to split it into smaller chunks. Here we use `recursive_character_text_splitter` for splitting, configured as follows:： +```yaml +insert_processors: + - "recursive_character_text_splitter" +``` +This configuration is a list format, allowing multiple document processors to be configured. The only specified processor in this case, recursive_character_text_splitter, recursively splits the original document according to a specified delimiter until it meets the required length. For more details, refer to [DocProcessor](2_2_4_DocProcessor.md). This document also includes other document processors that you can use or customize. + +### Configuring the Store +This case includes four Stores: the Civil Law and Criminal Law are stored separately in SQLite and ChromaDB. We will take `civil_law_chroma_store` as an example, with other Stores being similar. +```yaml +name: 'civil_law_chroma_store' +description: '保存了中国民法典的所有内容，以文本向量形式存储' +persist_path: '../../DB/civil_law.db' +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.chroma_store' + class: 'ChromaStore' +``` + +The `persist_path` specifies the local storage location of the database file and designates `dashscope_embedding` as the component for vectorizing the text in the database. `similarity_top_k` indicates the number of documents to be retrieved. For more details on Store, refer to [this document](2_2_4_Store.md). + +### Executing the Insertion Process + +After completing the above configurations, you can execute the following code to build the knowledge base: +```python +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager + + +if __name__ == '__main__': + AgentUniverse().start(config_path='../../config/config.toml', core_mode=True) + law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge") + law_knowledge.insert_knowledge( + source_path="../resources/刑法.pdf", + stores=["civil_law_sqlite_store"] + ) +``` +We specify the data to be inserted using the `source_path` parameter in the `insert_knowledge` method and designate different Stores for different documents using the `stores` parameter. The `stores` parameter is optional; if not specified, the data is inserted into all stores by default. + + +## Building the Retrieval Process + +### Re-ranking +We configured the post-processing flow of the knowledge as follows: +```yaml +post_processors: + - "dashscope_reranker" +``` +This indicates that we will use Dashscope’s re-ranking service to re-rank the documents. You can also add other post-processing flows here. + +## Using RAG in the Agent + +```yaml +info: + name: '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' +action: + knowledge: + - 'law_knowledge' +``` +We configure the knowledge like this in the YAML file of the Agent. + +For the complete case and how to invoke it, please refer to [this document](7_1_1_Legal_Consultation_Case.md). \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_Knowledge.md b/docs/guidebook/en/2_2_4_Knowledge.md index 95bea78c..9622389b 100644 --- a/docs/guidebook/en/2_2_4_Knowledge.md +++ b/docs/guidebook/en/2_2_4_Knowledge.md @@ -7,5 +7,18 @@ By loading additional knowledge content, agents can learn and master parts of th agentUniverse defines a standard knowledge format, which includes various knowledge data loading methods, and connections to diverse knowledge storage systems. You can define any form of knowledge data into standard knowledge components for agents and other components to use. +## Knowledge Structure + +In agentUniverse, the overall architecture of Knowledge and its related domain objects is illustrated in the following diagram: +![agentUniverse Knowledge architecture](../_picture/knowledge_structure.png) +The upper part of the diagram, from Reader to Store, represents the Knowledge injection process, while the lower part represents the Knowledge query process. + +### Knowledge Injection Process + +In the Knowledge injection process, `Reader` is used to read raw data and convert it into the `Document` format used within agentUniverse. A Document can contain various types of content such as text, images, vectors, etc., and can be extended to include more forms such as audio or video by inheriting this type. After that, the `DocProcessor` performs a series of processing operations on the `Document`. As shown in the diagram, the input and output of the `DocProcessor` are both in the form of a `Document`, meaning you can stack multiple `DocProcessors` during this process. Finally, the processed `Document` is stored in different Stores, which can be any type of data storage, including but not limited to relational databases, vector databases, and graph databases. Therefore, the same Document can be stored in different Stores in various forms, but they will share the same ID in their metadata, indicating that they originate from the same Document. This is used to avoid retrieving duplicate content during subsequent queries. + +### Knowledge Query Process +When querying Knowledge, the user needs to construct a `Query`. Like `Document`, the content of a Query can also be diverse, ranging from simple strings to vectors or images——as long as the Store containing the Knowledge supports that query format. After passing the Query into the Knowledge component, similar to `DocProcessor`, the `QueryParaphraser` is used to process the Query. You can extract keywords from the original query string to query paragraphs with specific tags, or split the original query into multiple sub-queries that are easier to search, among other possibilities. After that, the RagRouter component is responsible for pairing the Query with the appropriate `Store`, generating multiple [Query, Store] query tasks. This pairing can be based on the LLM's understanding of the match between the query text and the Store descriptions, or it can be based on specific rules to select certain Stores. If resources allow, queries can even be performed on all Stores directly. The content returned from `Store` queries is still in the Document format, so we can continue to use `DocProcessor` for a series of post-processing tasks on the retrieved Documents. The difference here is that the `Query` will also be passed as a default parameter to the `DocProcessor`, allowing for processing tasks like reranking that require comparing the retrieved content with the `Query`. Finally, you can convert the retrieved Documents into a string format that is more understandable by the LLM using the default or custom to_llm method, which serves as the final output. + # Conclusion By now, you should have a basic understanding of the design principles behind knowledge components. In the next section, we will introduce you to the standard definitions of knowledge components, how to customize and create your own knowledge, and how to utilize knowledge. \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md b/docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md index d2729ab1..283bbbc1 100644 --- a/docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md +++ b/docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md @@ -7,149 +7,71 @@ Based on the design features of agentUniverse domain components, creating a know The `knowledge_xx.yaml` file contains important information about the knowledge component such as its name, description, loading method, storage method, etc., while the `knowledge_xx.py` file contains the specific definitions of the knowledge. Understanding this principle, let's delve into how to create these two parts. ## Creating the Knowledge Configuration - knowledge_xx.yaml -We will detail the various components in the configuration. - -### Setting Basic Attributes of Knowledge -* `name`: The name of the knowledge, which you can set to any name you prefer. -* `description`: A description of the knowledge, filled according to your actual needs. -* `store`: The storage medium for the knowledge (e.g., ChromaStore, RedisStore, FaissStore, etc.) -* `reader`: Represents the method of reading the knowledge (e.g., FileReader, UrlReader, etc.) -* `ext_info`: Extended parameters - -### Setting Knowledge Component Metadata -**`metadata` - Component Metadata** -* `type` : Component type, 'KNOWLEDGE' -* `module`: The package path of the knowledge entity -* `class`: The class name of the knowledge entity - -### A Sample Knowledge Configuration +### An Example of a Knowledge Definition Configuration ```yaml -name: 'demo_knowledge' -description: 'demo knowledge' +name: "sample_knowledge" +description: "a knowledge sample" +stores: + - "a_store" + - "another_store" +query_paraphrasers: + - "a_query_paraphraser" +insert_processors: + - "a_doc_processor" +rag_router: "a_rag_router" +post_processors: + - "another_doc_processor" +readers: + pdf: "default_pdf_reader" + metadata: type: 'KNOWLEDGE' - module: 'sample_standard_app.app.core.knowledge.demo_knowledge' - class: 'DemoKnowledge' -``` - -The above is an actual sample of a Knowledge configuration. - -Besides, the standard configuration items introduced above, you can find more knowledge configuration YAML samples in our sample project under the path `sample_standard_app.app.core.knowledge`. - -Additionally, agentUniverse does not restrict users from extending the knowledge YAML configuration content. You can create any custom configuration keys according to your requirements, but please be mindful not to use the same names as the default configuration keywords mentioned above. - -## Creating the Knowledge Domain Behavior Definition - knowledge_xx.py - -### Creating a Knowledge Class Object -Create the corresponding Knowledge class object and inherit the base class Knowledge from the agentUniverse framework. - -### Setting Up the Reader Module -The Reader module is responsible for reading the knowledge, and this part is optional. agentUniverse currently has built-in Readers for the following data formats: - -* DocxReader -* PdfReader -* PptxReader -* WebPdfReader -* FileReader - -Their paths are located under `agentuniverse.agent.action.knowledge.reader.file`. The Reader module supports customization, and you can write your own Reader's loading methods. - -### Setting Up the Store Module -The Store module is responsible for the persistence interaction of the knowledge, performing operations like insertion, reading, deletion, and updating of the knowledge. This part is optional as well. agentUniverse currently has built-in Stores for the following data formats: - -* ChromaStore -* MilvusStore - -Their paths are located under `agentuniverse.agent.action.knowledge.store`. The Store module supports customization, and you can write your own Store's loading methods. - -### Defining the query_knowledge Method -This method is responsible for querying the corresponding knowledge. Its default implementation uses the Store function queries, but users can override it based on their actual needs. - -```python -def query_knowledge(self, **kwargs) -> List[Document]: - """Query the knowledge. - - Query documents from the store and return the results. - """ - query = Query(**kwargs) - return self.store.query(query) -``` - -### Defining the insert_knowledge Method -This method is responsible for adding new pieces of knowledge, with its default implementation utilizing the insertion operation of the Store function. Users have the flexibility to override this behavior based on their specific requirements. - -```python -def insert_knowledge(self, **kwargs) -> None: - """Insert the knowledge. - - Load data by the reader and insert the documents into the store. - """ - document_list: List[Document] = self.reader.load_data() - self.store.insert_documents(document_list, **kwargs) + module: 'sample_standard_app.app.core.knowledge.sample_knowledge' + class: 'SampleKnowledge' ``` +- stores: All associated Stores. A list of strings, where each string represents the name of a Store component. +- query_paraphrasers: Query paraphrasers that convert the input query into a form more suitable for retrieval. A list of strings, where each string represents the name of a QueryParaphraser component. +- insert_processors: Insert processors that process text when inserting it into the knowledge base, such as recursive character splitting. A list of strings, where each string represents the name of a DocProcessor component. +- rag_router: The Retrieval-Augmented Generation (RAG) router that controls how queries are routed within the knowledge base. +- post_processors: Post-processors that optimize and rank retrieval results to enhance the relevance of the returned results. A list of strings, where each string represents the name of a DocProcessor component. +- readers: A dictionary where the key represents the file type, and the value represents the corresponding `Reader` component's name. -#### A Sample Knowledge Object Definition -```python -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.reader.file.web_pdf_reader import WebPdfReader -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore -from agentuniverse.agent.action.knowledge.store.document import Document -from langchain.text_splitter import TokenTextSplitter - -SPLITTER = TokenTextSplitter(chunk_size=800, chunk_overlap=100) +## Creating Knowledge Domain Behavior Definition - knowledge_xx.py +agentUniverse provides a standard Knowledge class that you can use directly in the YAML definition file or extend by overriding some of its methods. +### [Knowledge Class Definition:](../../../agentuniverse/agent/action/knowledge/knowledge.py) -class DemoKnowledge(Knowledge): - """The demo knowledge.""" +- _load_data(self, *args: Any, **kwargs: Any) -> List[Document] +: Loads the data source and selects an appropriate reader based on the type of data source (file or URL) to load the document data. - def __init__(self, **kwargs): - """The __init__ method. +- _insert_process(self, origin_docs: List[Document]) -> List[Document] +: Processes the original documents to be inserted by sequentially applying the configured document processors for preprocessing. - Some parameters, such as name and description, - are injected into this class by the demo_knowledge.yaml configuration. +- _rag_post_process(self, origin_docs: List[Document], query: Query) +: Post-processes the retrieved original documents by applying the configured post-processors based on the query conditions. +- _paraphrase_query(self, origin_query: Query) -> Query +: Rewrites the original query by applying the configured query paraphrasers. - Args: - name (str): Name of the knowledge. +- insert_knowledge(self, **kwargs) -> None +: Inserts knowledge data. This method calls `_load_data` to load document data, preprocesses the documents through `_insert_process`, and then inserts the documents into the storage in parallel. - description (str): Description of the knowledge. +- _route_rag(self, query: Query) +: Routes the query to the appropriate storage using the specified RAG router based on the query conditions. - store (Store): Store of the knowledge, store class is used to store knowledge - and provide retrieval capabilities, such as ChromaDB store or Redis Store, - demo knowledge uses ChromaDB as the knowledge storage. +- query_knowledge(self, **kwargs) -> List[Document] +: Queries knowledge data. This method first rewrites the query by calling `_paraphrase_query`, then routes it to storage via `_route_rag`, performs the query in parallel, and finally post-processes the query results via `_rag_post_process`. - reader (Reader): Reader is used to load data, - the demo knowledge uses WebPdfReader to load pdf files from web. - """ - super().__init__(**kwargs) - self.store = ChromaStore(collection_name="chroma_store", embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-3-small'), dimensions=1056) - self.reader = WebPdfReader() - # initialize the knowledge - # self.insert_knowledge() +- to_llm(self, retrieved_docs: List[Document]) -> Any +: Converts the retrieved documents into a format suitable for input to a Large Language Model (LLM), concatenating all document texts for further processing. - def insert_knowledge(self, **kwargs) -> None: - """Insert the knowledge into the knowledge store. - Step1: Load data from the web using WebPdfReader. - Step2: Split the data into chunks using TokenTextSplitter(). - Step3: Insert the data into the ChromaStore. +## Pay Attention to the Package Path of Your Defined Knowledge +With the above Knowledge configuration and definition, you have mastered all the steps required to create Knowledge. Next, before using these Knowledge components, ensure that they are in the correct package scan path. - Note: - To avoid that the token in the embedding process exceeds the limit, the document needs to be split. - """ - doc_list = self.reader.load_data('https://www.sfu.ca/~poitras/BUFFET.pdf') - lc_doc_list = SPLITTER.split_documents(Document.as_langchain_list(doc_list)) - self.store.insert_documents(Document.from_langchain_list(lc_doc_list)) -``` +In the config.toml file of the agentUniverse project, you need to configure the package path for the Knowledge component. Ensure that the package path of the file you created is under the `CORE_PACKAGE` in the `knowledge` path or its subpaths. -## Take Note of the Path Where Your Knowledge Is Located -With the above Knowledge configuration and definition steps, you have mastered all the steps for creating knowledge. Next, we will use this knowledge. Before use, please ensure that the created Knowledge is in the correct package scan path. - -In the config.toml file of the agentUniverse project, you need to configure the package corresponding to the Knowledge configuration. Please reconfirm whether the package path of the created file is under the `CORE_PACKAGE`'s `knowledge` path or its subpaths. - -For example, in the configuration of the sample project, it is as follows: +For example, in the configuration of the sample project: ```yaml [CORE_PACKAGE] # Scan and register knowledge components for all paths under this list, with priority over the default. @@ -157,21 +79,19 @@ knowledge = ['sample_standard_app.app.core.knowledge'] ``` # How to Use the Knowledge Component -## Configuring Use in the Agent +## Configuring Knowledge in an Agent Based on the content in [Creating and Using Agents](2_2_1_Agent_Create_And_Use.md), you can set any created knowledge under the knowledge action in the agent. ## Using the Knowledge Manager You can obtain the Knowledge instance corresponding to the name via the `.get_instance_obj(xx_knowledge_name)` method in the Knowledge Manager, and call it using the `query_knowledge` method. ```python -from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManage +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager -knowledge = KnowledgeManager().get_instance_obj(knowledge_name) -knowledge.query_knowledge(**query_input) +knowledge = KnowledgeManager().get_instance_obj("knowledge_name") +knowledge.insert_knowledge(source_path="source_file") +knowledge.query_knowledge() ``` -# Learn More About Existing Knowledge -For more knowledge examples provided by the framework, you can check the `sample_standard_app.app.core.knowledge` package path. - -# Conclusion -Now you have mastered the definition and use of the Knowledge component. Go ahead and try creating and using your knowledge. \ No newline at end of file +## Learn More About Existing Knowledge +[Legal Consultation Case](7_1_1_Legal_Consultation_Case.md) \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md b/docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md index 574c12e2..abd510af 100644 --- a/docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md +++ b/docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md @@ -1,17 +1,28 @@ -# Knowledge and Related Domain Objects -In this chapter, we will focus on listing the Knowledge and related core domain objects. We have omitted their corresponding code parts. If you would like to learn more, please refer to the [API Reference Section](4_1_API_Reference.md). +# Knowledge Related Domain Objects +In this section, we will list the knowledge and its related core domain objects. The corresponding code portions have been omitted. If you wish to learn more, please refer to the source code based on the package path. -## Knowledge Base Class -Package Path:`agentuniverse.agent.action.knowledge.knowledge.Knowledge` -## Knowledge Manager -Package Path:`agentuniverse.agent.action.knowledge.knowledge_manager.KnowledgeManager` +### Knowledge +Package Path: `agentuniverse.agent.action.knowledge.knowledge.Knowledge` +Documentation Link: [Knowledge](2_2_4_知识定义与使用.md) -## Embedding Object Base Class -Package Path:`agentuniverse.agent.action.knowledge.embedding.embedding.Embedding` +### Reader +Package Path: `agentuniverse.agent.action.knowledge.reader.reader.Reader` +Documentation Link: [Reader](2_2_4_Reader.md) -## Reader Object Base Class -Package Path:`agentuniverse.agent.action.knowledge.reader.reader.Reader` +### DocProcessor +Package Path: `agentuniverse.agent.action.knowledge.doc_processor.doc_processor.DocProcessor` +Documentation Link: [DocProcessor](2_2_4_DocProcessor.md) -## Store Object Base Class -Package Path:`agentuniverse.agent.action.knowledge.store.store.Store` \ No newline at end of file +### Store +Package Path: `agentuniverse.agent.action.knowledge.store.store.Store` +Documentation Link: [Store](2_2_4_Store.md) + +The following new components are involved in this process: +### QueryParaphraser +Package Path: `agentuniverse.agent.action.knowledge.reader.reader.Reader` +Documentation Link: [QueryParaphraser](2_2_4_QueryParaphraser.md) + +### RagRouter +Package Path: `agentuniverse.agent.action.knowledge.reader.reader.Reader` +Documentation Link: [RagRouter](2_2_4_RagRouter.md) diff --git a/docs/guidebook/en/2_2_4_QueryParaphraser.md b/docs/guidebook/en/2_2_4_QueryParaphraser.md new file mode 100644 index 00000000..3e9fdaea --- /dev/null +++ b/docs/guidebook/en/2_2_4_QueryParaphraser.md @@ -0,0 +1,84 @@ +# QueryParaphraser + +The QueryParaphraser is responsible for processing and refining the original Query, including rewriting, splitting, and keyword extraction. This allows the Query to retrieve more accurate and richer content from the Store. The input and output of the QueryParaphraser are both in the form of a Query, enabling the Query to undergo multiple layers of paraphrasing. + + +The Query is defined as follows: +```python +from PIL.Image import Image +from typing import Optional, List, Set + +from pydantic import BaseModel, Field + + +class Query(BaseModel): + query_str: Optional[str] = None + query_text_bundles: Optional[List[str]] = Field(default_factory=list) + query_image_bundles: Optional[List[Image]] = Field(default_factory=list) + keywords: Optional[Set[str]] = Field(default_factory=set) + embeddings: List[List[float]] = Field(default_factory=list) + ext_info: dict = {} + similarity_top_k: Optional[int] = None +``` +- query_str: An optional string field used to store the text content of the original query. +- query_text_bundles: An optional list of strings used to store multiple rewritten query text segments. +- query_image_bundles: An optional list of images used to store multiple query images. +- keywords: An optional set field used to store the keywords of the query. +- embeddings: A list of embedding vectors used to store the query's embedding representations for similarity matching. +- ext_info: A dictionary field used to store additional information related to the query, supporting arbitrary extensions. +- similarity_top_k: An optional integer field used to specify the number of most similar results to return in similarity searches. + +The QueryParaphraser is defined as follows: +```python +from abc import abstractmethod +from typing import Optional + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + + +class QueryParaphraser(ComponentBase): + + component_type: ComponentEnum = ComponentEnum.QUERY_PARAPHRASER + name: Optional[str] = None + description: Optional[str] = None + + @abstractmethod + def query_paraphrase(self, origin_query: Query) -> Query: + """Paraphrase the origin query string to different style.""" +``` +When creating a custom subclass of QueryParaphraser, you need to override the query_paraphrase method, with both the input and output in the form of a Query. + +After writing the corresponding code, you can refer to the following YAML configuration to register the QueryParaphraser as an aU component: +```yaml +name: 'query_keyword_extractor' +description: 'extract keywords from query origin str' +metadata: + type: 'QUERY_PARAPHRASER' + module: 'agentuniverse.agent.action.knowledge.query_paraphraser.query_keyword_extractor' + class: 'QueryKeywordExtractor' +``` +The type in the metadata must be QUERY_PARAPHRASER。 +### Pay Attention to the Package Path of Your Custom QueryParaphrase: + +In the config.toml file of the agentUniverse project, you need to configure the package path corresponding to your QueryParaphraser. Please double-check whether the path of the file you created is under the query_paraphraser path or its subdirectories in CORE_PACKAGE. + +For example, the configuration in the sample project is as follows: +```yaml +[CORE_PACKAGE] +query_paraphraser = ['sample_standard_app.app.core.query_paraphraser'] +``` + +## The following QueryParaphraser are built into agentUniverse: +### [query_keyword_extractor](../../../agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml) +This component extracts keywords from the original text in the Query and saves them into the keywords field of the Query. +The component definition file is as follows: +```yaml +name: 'query_keyword_extractor' +description: 'extract keywords from query origin str' +metadata: + type: 'QUERY_PARAPHRASER' + module: 'agentuniverse.agent.action.knowledge.query_paraphraser.query_keyword_extractor' + class: 'QueryKeywordExtractor' +``` \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_RAG.md b/docs/guidebook/en/2_2_4_RAG.md new file mode 100644 index 00000000..4e57aa64 --- /dev/null +++ b/docs/guidebook/en/2_2_4_RAG.md @@ -0,0 +1,22 @@ +# RAG + +Retrieval-Augmented Generation (RAG) is a cutting-edge natural language processing (NLP) technique that significantly enhances the accuracy and diversity of text generation tasks by combining information retrieval and generative models. The core idea of RAG is to first retrieve the most relevant content from a large document corpus based on the input query and then pass this retrieved information as context to the generative model. This allows the generative model to not only rely on its training data but also dynamically access up-to-date external knowledge, enabling the agent to generate responses that are more contextually appropriate. + +## General RAG Workflow +![rag_structure](../_picture/rag_structure.png) + +According to [this review](https://arxiv.org/pdf/2312.10997), the majority of current RAG workflows can be summarized into three parts: + +1. Document Index Construction: This involves transforming the documents from a large-scale corpus into a form that is easy to retrieve, usually by splitting the text into smaller chunks and converting them into vector embeddings stored in a vector database. +2. Retrieval of Relevant Knowledge: After a user provides a query, the system retrieves a set of relevant documents from the index constructed in the first step. This process may include query reformulation (to ensure the query retrieves more relevant content), re-ranking of documents (to select the most relevant subset from a large number of retrieved items), and other steps—all aimed at filtering out the documents most relevant to the original query. +3. Final Response Generation: In this step, the documents retrieved in the second step are fed into a large model, which, combined with the prompt and original query, generates the final response. + +## RAG in agentUniverse +In contrast to the general RAG workflow described above, agentUniverse breaks down RAG into two parts: + +1. Knowledge Component: This includes the ability to build a knowledge base from documents and retrieve knowledge based on a query. You can refer to [Knowledge](2_2_4_知识.md) for detailed usage instructions. +2. Agent-Driven Response Generation Using Knowledge: Once the knowledge component is built in the first step, the agent can use it to retrieve documents relevant to the query and then pass these, along with the prompt and context, to a large model to generate the final response. For specific usage methods, you can refer to [Agent Creation and Use ](2_2_1_Agent_Create_And_Use.md) and set any knowledge you’ve created under the agent's action knowledge section. + +If you want to quickly build a RAG workflow using your own documents and use it in an agent, you can refer to [How to Build a Knowledge-Based RAG Agent](2_2_4_How_To_Build_A_RAG_Agent). + + diff --git a/docs/guidebook/en/2_2_4_RagRouter.md b/docs/guidebook/en/2_2_4_RagRouter.md new file mode 100644 index 00000000..58ddfeb3 --- /dev/null +++ b/docs/guidebook/en/2_2_4_RagRouter.md @@ -0,0 +1,81 @@ +# RagRouter + +The RagRouter is responsible for routing the original Query to different Stores, preventing unnecessary queries in unrelated databases, which could otherwise introduce irrelevant information and waste computational resources. + +The RagRouter is defined as follows: +```python +from typing import List, Optional, Tuple + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + + +class RagRouter(ComponentBase): + + component_type: ComponentEnum = ComponentEnum.RAG_ROUTER + name: Optional[str] = None + description: Optional[str] = None + + def rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + return self._rag_route(query, store_list) + + def _rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + pass +``` +When creating a custom subclass of RagRouter, you need to override the `_rag_route` function. This function takes a Query and a list of strings as input, where each string corresponds to the name of a Store component. The output is a list of [Query, str] tuples, where each element represents a query task for a specific store. + +After writing code, you can refer to the following YAML configuration to register the RagRouter as an aU component: +```yaml +name: 'base_router' +description: 'base rag router map query to all store' +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.base_router' + class: 'BaseRouter' +``` +The `metadata.type` must be set to RAG_ROUTER. + +### Pay Attention to the Package Path of Your Custom RagRouter: +In the config.toml file of the agentUniverse project, you need to configure the package path corresponding to your RagRouter. Please double-check whether the path of the file you created is under the rag_router path or its subdirectories in CORE_PACKAGE. + +For example, the configuration in the sample project is as follows: +```yaml +[CORE_PACKAGE] +rag_router = ['sample_standard_app.app.core.rag_router'] +``` + + +## The following RagRouters are built into agentUniverse: +### [base_router](../../../agentuniverse/agent/action/knowledge/rag_router/base_router.yaml) +This component's primary function is to route the query to all the stores in the system, ensuring that the query can find answers in all possible resources. It is also the default RagRouter in Knowledge. +```yaml +name: 'base_router' +description: 'base rag router map query to all store' +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.base_router' + class: 'BaseRouter' +``` + +### [nlu_rag_router](../../../agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py) +This component selects the most relevant stores by analyzing the correlation between the description of all stores and the original query text using a configured language model (LLM). + +Users need to create their own YAML file for this component, with a format similar to the following: +```yaml +name: 'nlu_rag_router' +description: 'base rag router map query to all store' +store_amount: 2 +llm: + name: demo_llm + model_name: gpt-4o +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.nlu_rag_router' + class: 'NluRagRouter' +``` +The fields that users need to fill out include: +- store_amount: Specifies the number of stores to which the query will be routed, controlling the distribution range of the query. +- llm: Contains the configuration of the large language model used to filter relevant databases based on the Store descriptions and the query_str in the Query. The name refers to the name of the model component, and model_name specifies the specific model used. \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_Reader.md b/docs/guidebook/en/2_2_4_Reader.md new file mode 100644 index 00000000..c677e315 --- /dev/null +++ b/docs/guidebook/en/2_2_4_Reader.md @@ -0,0 +1,55 @@ +# Reader + +The Reader is responsible for extracting information from various sources into the Document format used within agentUniverse. These sources can range from local files to web pages or even an I/O interface. + +The Reader is defined as follows: +```python +from abc import abstractmethod +from typing import List, Any, Optional + +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + +class Reader(ComponentBase): + """The basic class for the knowledge reader.""" + component_type: ComponentEnum = ComponentEnum.READER + name: Optional[str] = None + description: Optional[str] = None + + def load_data(self, *args: Any, **kwargs: Any) -> List[Document]: + """Load data from the input params.""" + return self._load_data(*args, **kwargs) + + @abstractmethod + def _load_data(self, *args: Any, **kwargs: Any) -> List[Document]: + """Load data from the input params.""" +``` +When creating a custom subclass of Reader, you need to override the `_load_data` function, which is responsible for reading data and outputting a List[Document]. + +After writing the corresponding code, you can refer to the following YAML configuration to register your Reader as an aU component: +```yaml +name: 'default_txt_reader' +description: 'default txt reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.txt_reader' + class: 'TxtReader' +``` +The `metadata.type` must be set to READER. + +### Pay Attention to the Package Path of Your Defined Reader: +In the config.toml file of the agentUniverse project, you must configure the package path for the Reader. Ensure that the package path of the file you created is under the `CORE_PACKAGE` in the `reader` path or its subpaths. + +For example, in the configuration of the sample project: +```yaml +[CORE_PACKAGE] +reader = ['sample_standard_app.app.core.reader'] +``` + +## Prebuilt Readers in agentUniverse: +- [default_docx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml): Reads text content from local Docx files. +- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml): Reads text content from local PDF files. +- [default_pptx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml): Reads text content from local PPTX files. +- [default_txt_reader](../../../agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml): Reads text content from TXT files. +- [default_web_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml): Reads text content from PDF files found on the web. \ No newline at end of file diff --git a/docs/guidebook/en/2_2_4_Store.md b/docs/guidebook/en/2_2_4_Store.md new file mode 100644 index 00000000..6618226b --- /dev/null +++ b/docs/guidebook/en/2_2_4_Store.md @@ -0,0 +1,82 @@ +# Store + +`Store` is responsible for storing `Document` objects and providing query capabilities during the knowledge retrieval phase. The specific form of a `Store` can vary, including relational databases, vector databases, graph databases, and more. Therefore, the same `Document` can be stored in different Stores in various formats, and the retrieval methods are tied to the capabilities of the specific `Store`. + +The Store is defined as follows: +```python +from typing import Any, List, Optional + +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent_serve.web.post_fork_queue import add_post_fork + +class Store(ComponentBase): + component_type: ComponentEnum = ComponentEnum.STORE + name: Optional[str] = None + description: Optional[str] = None + client: Any = None + async_client: Any = None + + def _new_client(self) -> Any: + pass + + def _new_async_client(self) -> Any: + pass + + def _initialize_by_component_configer(self, + store_configer: ComponentConfiger) \ + -> 'Store': + if store_configer.name: + self.name = store_configer.name + if store_configer.description: + self.description = store_configer.description + add_post_fork(self._new_client) + add_post_fork(self._new_async_client) + return self + + def query(self, query: Query, **kwargs) -> List[Document]: + raise NotImplementedError + + def insert_document(self, documents: List[Document], **kwargs): + raise NotImplementedError + + def delete_document(self, document_id: str, **kwargs): + raise NotImplementedError + + def upsert_document(self, documents: List[Document], **kwargs): + raise NotImplementedError + + def update_document(self, documents: List[Document], **kwargs): + raise NotImplementedError +``` +- `_new_client` and `_new_async_client` are used to create database connections. These methods are added to the [post_fork](2_4_1_Web_Server.md) execution list during the component registration phase to ensure that the database connections created are independent in Gunicorn mode child processes. +- The `query`function is called by the knowledge component during a query, responsible for searching the store for relevant content based on the passed Query instance and returning the results in the form of Document objects. +- The `Store` also includes CRUD (Create, Read, Update, Delete) operations for Document data, serving as the management interface for knowledge storage. + +After writing the code, you can refer to the following YAML configuration to register your Store as an aU component: +```yaml +name: 'sample_store' +description: 'a sample store' +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.sample_store' + class: 'SampleStore' +``` +The `metadata.type` must be set to STORE. + +### Pay Attention to the Package Path of Your Defined Store +In the config.toml file of the agentUniverse project, you must configure the package path for the Store. Ensure that the package path of the file you created is under the CORE_PACKAGE in the store path or its subpaths. + +For example, in the configuration of the sample project: +```yaml +[CORE_PACKAGE] +store = ['sample_standard_app.app.core.store'] +``` + +## Prebuilt Stores in agentUniverse: +- [Chroma](3_3_2_ChromaDB.md) +- [Milvus](3_3_1_Milvus.md) +- [Sqlite](3_3_3_Sqlite.md) \ No newline at end of file diff --git a/docs/guidebook/en/3_3_1_Milvus.md b/docs/guidebook/en/3_3_1_Milvus.md index cc23c7ae..1bb8bee9 100644 --- a/docs/guidebook/en/3_3_1_Milvus.md +++ b/docs/guidebook/en/3_3_1_Milvus.md @@ -17,26 +17,35 @@ These two commands will pull the Milvus image and start a container, providing d pip install pymilvus ``` -### What can I do with Milvus - -You can use Milvus in the [Knowledge component](2_2_4_Knowledge.md) to store and query knowledge. You can create a storage component using Milvus as follows: - -```python -from agentuniverse.agent.action.knowledge.store.milvus_store import MilvusStore -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge - -init_params = {} -init_params['name'] = 'test_knowledge' -init_params['description'] = 'test_knowledge_description' -init_params['store'] = MilvusStore( - connection_args={"host": "localhost", "port": "19530"}, - collection_name="test_knowledge", - embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-ada-002' - ) -) -knowledge = Knowledge(**init_params) +### How to Configure Milvus Components +```yaml +name: 'milvus_store' +description: 'a store based on milvus' +connection_args: + host: '127.0.0.1' + port: '19530' +search_args: + metric_type: "L2" + params: + nprobe: 10 +index_params: + metric_type: "L2" + index_type: "HNSW" + params: + M: 8 + efConstruction: 32 +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.milvus_store' + class: 'MilvusStore' ``` +- connection_args: Parameters for connecting to the Milvus database, including the host address (host) and port number (port). +- search_args: Search parameters, defining the distance metric type (metric_type) used during searches and related parameters such as nprobe. +- index_params: Indexing parameters, specifying the index type (index_type), distance metric type (metric_type), and specific parameters for building the index, such as M and efConstruction. +- embedding_model: The model used to generate embedding vectors, specified here as dashscope_embedding. +- similarity_top_k: The number of most similar results returned in similarity search. +### Usage +[Knowledge_Define_And_Use](2_2_4_Knowledge_Define_And_Use.md) -The above code will create a Milvus-based Knowledge instance. For detailed usage of Knowledge, you can refer to the [Knowledge component](2_2_4_Knowledge.md) or the code `tests/test_agentuniverse/unit/agent/action/knowledge/test_knowledge_with_milvus.py`. diff --git a/docs/guidebook/en/3_3_2_ChromaDB.md b/docs/guidebook/en/3_3_2_ChromaDB.md index d48ffc2b..44526af0 100644 --- a/docs/guidebook/en/3_3_2_ChromaDB.md +++ b/docs/guidebook/en/3_3_2_ChromaDB.md @@ -3,22 +3,21 @@ The agentUniverse already integrates ChromaDB-related dependencies, so you do not need to install additional packages to use ChromaDB's features. If you want to learn about the underlying principles of ChromaDB, you can visit the [official ChromaDB website](https://www.trychroma.com/). -### What can I do with ChromaDB? - - -You can use ChromaDB in the [Knowledge component](2_2_4_Knowledge.md) to store and query knowledge. You can create a storage component using ChromaDB with the following method: -```python -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore - - -init_params = dict() -init_params['name'] = 'test_knowledge' -init_params['description'] = 'test_knowledge_description' -init_params['store'] = ChromaStore(collection_name="test_knowledge", embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-ada-002')) -knowledge = Knowledge(**init_params) +### How to Configure ChromaDB Components +```yaml +name: 'chroma_store' +description: 'store based on chroma db' +persist_path: '../../DB/criminal_law.db' +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.chroma_store' + class: 'ChromaStore' ``` +- persist_path: The persistence storage path for the database, used for storing and loading vector data. +- embedding_model: The model used to generate embedding vectors, specified here as dashscope_embedding. +- similarity_top_k: The number of most similar results returned in similarity search. -The above code will create a Knowledge component based on ChromaDB. For more details on how to use the Knowledge component, you can refer to the [Knowledge component](2_2_4_Knowledge.md)，or check the code in `tests/test_agentuniverse/unit/agent/action/knowledge/test_knowledge.py`。 +### Usage +[Knowledge_Define_And_Use](2_2_4_Knowledge_Define_And_Use.md) \ No newline at end of file diff --git a/docs/guidebook/en/3_3_3_Sqlite.md b/docs/guidebook/en/3_3_3_Sqlite.md new file mode 100644 index 00000000..9007a574 --- /dev/null +++ b/docs/guidebook/en/3_3_3_Sqlite.md @@ -0,0 +1,28 @@ +## Sqlite + +The SQLite component in the Store module is used to store the text content of Document and build a corresponding inverted index based on Jieba's keyword extraction feature. During the retrieval phase, relevant documents are recalled through keywords, and the relevance between the query and the recalled texts is calculated using the BM25 algorithm, ultimately returning the most similar Top k documents. + +### How to Configure the SQLite Component + +You can use ChromaDB to store and query knowledge in the [Knowledge Components](2_2_4_Knowledge.md). You can create a storage component using SQLite with the following configuration: +```yaml +name: 'sqlite_store' +description: 'a store based on sqlite' +db_path: '../../DB/civil_law_sqlite.db' +k1: 1.5 +b: 0.75 +keyword_extractor: 'jieba_keyword_extractor' +similarity_top_k: 10 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.sqlite_store' + class: 'SQLiteStore' +``` +- db_path: The path to the SQLite database file, used for storing and managing text data. +- k1: The k1 parameter in the BM25 algorithm, which controls the influence of term frequency on the score. +- b: The b parameter in the BM25 algorithm, which controls the influence of document length on the score. +- keyword_extractor: The tool used for keyword extraction, specified here as jieba_keyword_extractor. +- similarity_top_k: Return top k similar documents based on bm25 score. + +### Usage +[Knowledge_Define_And_Use](2_2_4_Knowledge_Define_And_Use.md) \ 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 index 14782290..7cca9dd8 100644 --- a/docs/guidebook/en/7_1_1_Legal_Consultation_Case.md +++ b/docs/guidebook/en/7_1_1_Legal_Consultation_Case.md @@ -1,26 +1,80 @@ # Legal Consultation Case ## Case Description -This case is based on RagPlanner and builds a simple legal consultation agent. It provides relevant legal advice by retrieving related articles from the Civil Code and Criminal Law and combining them with the case background. +This case demonstrates a simple legal consultation agent built using `RagPlanner`. The agent provides legal advice by retrieving relevant provisions from the Civil Law and the Criminal Law, and combining them with the case background. -This case utilizes the Qianwen large model and the DashScope embedding feature. Before using it, you need to configure the DASHSCOPE_API_KEY in your environment variables. +The case leverages the DashScope embedding and rerank features with the Qwen llm. Before using this, you need to configure the `DASHSCOPE_API_KEY` in your environment variables. ## Components ### Legal Knowledge Base -The legal knowledge base is based on the [Knowledge Component ](2_2_4_知识.md) in agentUniverse. By embedding the original text of legal articles and storing them in the ChromaDB vector database, it creates a knowledge base that the agent can easily reference and retrieve. -Original legal texts: -- [Civil Code.pdf](../../../sample_standard_app/app/resources/民法典.pdf) -- [Criminal Law.pdf](../../../sample_standard_app/app/resources/刑法.pdf) +The legal knowledge base is constructed using [Knowledge Components](2_2_4_Knowledge_Related_Domain_Objects.md) from agentUniverse. By storing the original legal provisions in the ChromaDB and Sqlite database, the knowledge base facilitates efficient retrieval and consultation for the agent. +Original legal documents: +- [民法典.pdf](../../../sample_standard_app/app/resources/民法典.pdf) +- [刑法.pdf](../../../sample_standard_app/app/resources/刑法.pdf) -You can refer to the following code: -- [Civil Code Knowledge Base](../../../sample_standard_app/app/core/knowledge/civil_law_knowledge.py) -- [Criminal Law Knowledge Base](../../../sample_standard_app/app/core/knowledge/criminal_law_knowledge.py) +### [Knowledge Definition](../../../sample_standard_app/app/core/knowledge/law_knowledge.yaml) +```yaml +name: "law_knowledge" +description: "中国民法与刑法相关的知识库" +stores: + - "civil_law_chroma_store" + - "criminal_law_chroma_store" + - "civil_law_sqlite_store" + - "criminal_law_sqlite_store" +query_paraphrasers: + - "custom_query_keyword_extractor" +insert_processors: + - "recursive_character_text_splitter" +rag_router: "nlu_rag_router" +post_processors: + - "dashscope_reranker" +readers: + pdf: "default_pdf_reader" -To make it convenient for you, we have placed the database files with relevant information in [this directory](../../../sample_standard_app/DB/), If you want to build the knowledge base from scratch, you can modify the following part of the code: +metadata: + type: 'KNOWLEDGE' + module: 'sample_standard_app.app.core.knowledge.law_knowledge' + class: 'LawKnowledge' +``` + +### Reader Component +- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml) + +### DocProcessor Component +- [custom_query_keyword_extractor](../../../sample_standard_app/app/core/doc_processor/query_keyword_extractor.yaml) +- [recursive_character_text_splitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml) + +### QueryParaphraser Component +- [custom_query_keyword_extractor](../../../sample_standard_app/app/core/query_paraphraser/custom_query_keyword_extractor.yaml) + +### RagRouter Component +- [nlu_rag_router](../../../sample_standard_app/app/core/rag_router/nlu_rag_router.yaml) + +### Store Component +- [civil_law_chroma_store](../../../sample_standard_app/app/core/store/civil_law_chroma_store.yaml) +- [criminal_law_chroma_store](../../../sample_standard_app/app/core/store/criminal_law_chroma_store.yaml) +- [civil_law_sqlite_store](../../../sample_standard_app/app/core/store/civil_law_sqlite_store.yaml) +- [criminal_law_sqlite_store](../../../sample_standard_app/app/core/store/criminal_law_sqlite_store.yaml) + +For your convenience, we have placed the databases containing the relevant information in [this directory](../../../sample_standard_app/DB/). If you want to build the knowledge base from scratch, you can run the following code: ```python -# Initialize the knowledge -# self.insert_knowledge() +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager + + +if __name__ == '__main__': + AgentUniverse().start(config_path='../../config/config.toml', core_mode=True) + civil_store_list = ["civil_law_sqlite_store", "civil_law_chroma_store"] + criminal_store_list = ["criminal_law_sqlite_store", "criminal_law_chroma_store"] + law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge") + law_knowledge.insert_knowledge( + source_path="../resources/刑法.pdf", + stores=criminal_store_list + ) + law_knowledge.insert_knowledge( + source_path="../resources/民法典.pdf", + stores=civil_store_list + ) ``` -Remove the comment from self.insert_knowledge() to rebuild the database. ### Law Agent This agent involves the following two files: diff --git a/docs/guidebook/zh/2_2_4_DocProcessor.md b/docs/guidebook/zh/2_2_4_DocProcessor.md new file mode 100644 index 00000000..458d2f29 --- /dev/null +++ b/docs/guidebook/zh/2_2_4_DocProcessor.md @@ -0,0 +1,172 @@ +# DocProcessor + +DocProcessor负责对Document进行各种处理，如文本拆分、关键词提取等等。DocProcessor的输入输出均为List[Document]，这保证了多个DocProcessor可以叠加形成对Document的一个加工流。 + +Document定义如下： +```python +import uuid +from typing import Dict, Any, Optional, List, Set + +from pydantic import BaseModel, Field, model_validator + + +class Document(BaseModel): + class Config: + arbitrary_types_allowed = True + + id: str = None + text: Optional[str] = "" + metadata: Optional[Dict[str, Any]] = None + embedding: List[float] = Field(default_factory=list) + keywords: Set[str] = Field(default_factory=set) + + @model_validator(mode='before') + def create_id(cls, values): + text: str = values.get('text', '') + if not values.get('id'): + values['id'] = str(uuid.uuid5(uuid.NAMESPACE_URL, text)) + return values +``` +- id：用于标识一段特定文档的唯一标识，默认通过uuid生成。 +- text：文档中的文本内容 +- metadata：文档的元数据信息，通常包含原始文件名、原始文件中的位置等。 +- embedding：文档向量化后的形式，可以是文本向量，在Document的子类ImageDocument中，也可以是图像向量化后的结果。 +- keywords：文档中的关键词，也可以是这段文本的tag。 + +DocProcessor定义如下： +```python +from abc import abstractmethod +from typing import List, Optional + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + +class DocProcessor(ComponentBase): + component_type: ComponentEnum = ComponentEnum.DOC_PROCESSOR + name: Optional[str] = None + description: Optional[str] = None + + def process_docs(self, origin_docs: List[Document], query: Query = None) -> \ + List[Document]: + return self._process_docs(origin_docs, query) + + @abstractmethod + def _process_docs(self, origin_docs: List[Document], + query: Query = None) -> \ + List[Document]: + pass +``` +用户在自定义的DocProcessor中主要完成对`_process_docs`函数的重写，实现具体的Document处理逻辑。 + +在编写完对应代码后，可以参考下面的yaml将你的DocProcessor注册为aU组件： +```yaml +name: 'dashscope_reranker' +description: 'reranker use dashscope api' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.dashscope_reranker' + class: 'DashscopeReranker' +``` +其中metadata的type必须为DOC_PROCESSOR。 + +### 关注您定义的DocProcessor所在的包路径 +在agentUniverse项目的config.toml中需要配置DocProcessor配置对应的package, 请再次确认您创建的文件所在的包路径是否在`CORE_PACKAGE`中`doc_processor`路径或其子路径下。 + +以示例工程中的配置为例，如下： +```yaml +[CORE_PACKAGE] +doc_processor = ['sample_standard_app.app.core.doc_processor'] +``` + + +## agentUniverse内置有以下DocProcessor: +### [CharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml) +该组件根据字符数对原始文本进行拆分。 +组件定义文件如下： +```yaml +name: 'character_text_splitter' +description: 'langchain character text splitter' +chunk_size: 200 +chunk_overlap: 20 +separators: "/n/n" +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.character_text_splitter' + class: 'CharacterTextSplitter' +``` +- chunk_size: 切分后文本长度大小。 +- chunk_overlap: 相邻切分文本重合部分的长度。 +- separators: 指定的分隔符 + +### [TokenTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml) +该组件根据指定的 tokenizer 对文本进行切分，按照设定的 chunk_size 和 chunk_overlap 将文本拆分为多个片段，每个片段包含指定数量的tokens。 + +组件定义文件如下： + +```yaml +name: 'token_text_splitter' +description: 'langchain token text splitter' +chunk_size: 200 +chunk_overlap: 20 +tokenizer: 'default_tokenizer' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.token_text_splitter' + class: 'TokenTextSplitter' +``` +- chunk_size: 切分后文本的token数量。 +- chunk_overlap: 相邻切分文本重合部分的token数量。 +- tokenizer: 指定的tokenizer，用于将文本切分为tokens + +### [RecursiveCharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml) + +该组件根据指定的分隔符递归地对原始文本进行切分。它首先尝试使用优先级最高的分隔符进行切分，如果无法满足 chunk_size 的要求，则会递归地使用下一个分隔符进行切分，直到文本被成功分割。 + +组件定义文件如下： +```yaml +name: 'recursive_character_text_splitter' +description: 'langchain recursive character text splitter' +chunk_size: 200 +chunk_overlap: 20 +separators: + - "\n\n" + - "\n" +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.recursive_character_text_splitter' + class: 'RecursiveCharacterTextSplitter' +``` +- chunk_size: 切分后文本长度大小。 +- chunk_overlap: 相邻切分文本重合部分的长度。 +- separators: 指定的分隔符列表，按顺序尝试使用分隔符进行切分。如果第一个分隔符不能满足条件，则递归地使用下一个分隔符。 + +### [JiebaKeywordExtractor](../../../agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml) +该组件使用结巴（Jieba）分词库从文本中提取关键词。它可以根据设定的 top_k 参数提取出最重要的几个关键词，用于后续作为倒排索引。 +组件定义文件如下： +```yaml +name: 'jieba_keyword_extractor' +description: 'extract keywords from text' +top_k: 3 +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.jieba_keyword_extractor' + class: 'JiebaKeywordExtractor' +``` +- top_k: 从文本中提取的关键词数量，即排名前 top_k 的关键词会被提取。 + +### [DashscopeReranker](../../../agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml) + +该组件使用 DashScope API 对文本进行重新排序（rerank），对Store召回的内容按照Query内容进行相关性排序。 + +组件定义文件如下： +```yaml +name: 'dashscope_reranker' +description: 'reranker use dashscope api' +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.dashscope_reranker' + class: 'DashscopeReranker' +``` +该组件需要在环境变量中配置`DASHSCOPE_API_KEY`。 \ No newline at end of file diff --git a/docs/guidebook/zh/2_2_4_QueryParaphraser.md b/docs/guidebook/zh/2_2_4_QueryParaphraser.md new file mode 100644 index 00000000..ec34db1c --- /dev/null +++ b/docs/guidebook/zh/2_2_4_QueryParaphraser.md @@ -0,0 +1,82 @@ +# QueryParaphraser + +QueryParaphraser负责对原始的Query进行加工处理，包括改写、拆分、提取关键词等，使得Query在Store中能召回更丰富且精准的内容。QueryParaphraser的输入和输出均为Query，因此query能经过多个QueryParaphraser叠加处理。 + +Query定义如下： +```python +from PIL.Image import Image +from typing import Optional, List, Set + +from pydantic import BaseModel, Field + + +class Query(BaseModel): + query_str: Optional[str] = None + query_text_bundles: Optional[List[str]] = Field(default_factory=list) + query_image_bundles: Optional[List[Image]] = Field(default_factory=list) + keywords: Optional[Set[str]] = Field(default_factory=set) + embeddings: List[List[float]] = Field(default_factory=list) + ext_info: dict = {} + similarity_top_k: Optional[int] = None +``` +- query_str: 一个可选的字符串字段，用于存储原始查询的文本内容。 +- query_text_bundles: 一个可选的字符串列表字段，用于存储多个改写后的查询文本片段。 +- query_image_bundles: 一个可选的图像列表字段，用于存储多个查询图像。 +- keywords: 一个可选的集合字段，用于存储查询的关键词。 +- embeddings: 一个嵌入向量列表字段，用于存储查询的嵌入表示，用于相似度匹配。 +- ext_info: 一个字典字段，用于存储与查询相关的额外信息，支持任意扩展。 +- similarity_top_k: 一个可选的整数字段，用于指定相似度搜索中返回的最相似结果的数量。 + +QueryParaphraser定义如下： +```python +from abc import abstractmethod +from typing import Optional + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + + +class QueryParaphraser(ComponentBase): + + component_type: ComponentEnum = ComponentEnum.QUERY_PARAPHRASER + name: Optional[str] = None + description: Optional[str] = None + + @abstractmethod + def query_paraphrase(self, origin_query: Query) -> Query: + """Paraphrase the origin query string to different style.""" +``` +用户在自定义的QueryParaphraser子类中，需要重写`query_paraphrase`函数，函数的出入参均为Query形式。 + +在编写完对应代码后，可以参考下面的yaml将QueryParaphraser注册为aU组件： +```yaml +name: 'query_keyword_extractor' +description: 'extract keywords from query origin str' +metadata: + type: 'QUERY_PARAPHRASER' + module: 'agentuniverse.agent.action.knowledge.query_paraphraser.query_keyword_extractor' + class: 'QueryKeywordExtractor' +``` +其中metadata的type必须为QUERY_PARAPHRASER。 +### 关注您定义的QueryParaphraser所在的包路径 +在agentUniverse项目的config.toml中需要配置QueryParaphraser配置对应的package, 请再次确认您创建的文件所在的包路径是否在`CORE_PACKAGE`中`query_paraphraser`路径或其子路径下。 + +以示例工程中的配置为例，如下： +```yaml +[CORE_PACKAGE] +query_paraphraser = ['sample_standard_app.app.core.query_paraphraser'] +``` + +## agentUniverse目前内置有以下QueryParaphraser: +### [query_keyword_extractor](../../../agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml) +该组件会根据Query中的原始文本提取关键词，并保存至Query的keywords字段 +组件定义文件如下： +```yaml +name: 'query_keyword_extractor' +description: 'extract keywords from query origin str' +metadata: + type: 'QUERY_PARAPHRASER' + module: 'agentuniverse.agent.action.knowledge.query_paraphraser.query_keyword_extractor' + class: 'QueryKeywordExtractor' +``` \ No newline at end of file diff --git a/docs/guidebook/zh/2_2_4_RAG.md b/docs/guidebook/zh/2_2_4_RAG.md new file mode 100644 index 00000000..737f7485 --- /dev/null +++ b/docs/guidebook/zh/2_2_4_RAG.md @@ -0,0 +1,20 @@ +# RAG + +Retrieval-Augmented Generation (RAG) 是一种前沿的自然语言处理（NLP）技术，通过结合信息检索和生成模型，显著提升了文本生成任务的准确性和多样性。RAG 的核心思想在于，在生成文本之前，先从大规模文档库中检索出与输入查询最相关的内容，并将这些检索到的信息作为上下文，传递给生成模型。这样，生成模型不仅仅依赖于训练数据，还可以动态获取最新的外部知识，从而让智能体生成更加符合语境的响应。 + +## 通用RAG流程 +![rag_structure](../_picture/rag_structure.png) + +参考[这篇综述](https://arxiv.org/pdf/2312.10997)，我们可以将现在大多数的RAG流程概括为三个部分： +1. 文档的索引构建：从原始的大规模文档库中的Documents构建出便于检索召回的形式，一般是将文本拆分为小段的chunks后通过embedding转为向量索引的形式存储在向量数据库中。 +2. 相关知识的检索：用户在提供Query后，根据Query的内容从第一步中的文档索引里找出相关的一批文档。这个过程会涉及到对原始Query的改写（使得Query能召回更相关的内容）、文档的重排序（从大量召回内容里选取更加相关的一小部分）等步骤，但所有步骤的目的都是为了筛选出和原始Query最相关的文档内容。 +3. 最终结果的生成：这一步需要将步骤2中召回的文档送入大模型，结合提示词和原始Query的内容，生成最后的回答。 + +## agentUniverse中的RAG +对比上述的通用RAG流程，agentUniverse将RAG拆分为了两部分： +1. 知识组件：包含从文档构建知识库以及根据Query检索知识的能力。您可以参考[知识](2_2_4_知识.md)了解详细的使用方式。 +2. 智能体利用知识生成回答：在第一步构建完成知识组件后，智能体可以利用知识组件召回和Query相关的文档，和提示词以及上下文一同传递给大模型，生成最终的回答。具体使用方法您可以参考[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的action下的knowledge中设置您创建的任意知识。 + +如果您想快速利用自己的文档构建RAG流程并在智能体中使用的话，您可以参考[如何构建基于知识的RAG智能体](2_2_4_如何构建RAG智能体.md)。 + + diff --git a/docs/guidebook/zh/2_2_4_RagRouter.md b/docs/guidebook/zh/2_2_4_RagRouter.md new file mode 100644 index 00000000..1ec15b46 --- /dev/null +++ b/docs/guidebook/zh/2_2_4_RagRouter.md @@ -0,0 +1,81 @@ +# RagRouter + +RagRouter负责将原始Query路由至不同的Store，避免在和原始问题无关的数据库中进行查询召回干扰信息，同时也可以节约计算资源。 + +RagRouter定义如下： +```python +from typing import List, Optional, Tuple + +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + + +class RagRouter(ComponentBase): + + component_type: ComponentEnum = ComponentEnum.RAG_ROUTER + name: Optional[str] = None + description: Optional[str] = None + + def rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + return self._rag_route(query, store_list) + + def _rag_route(self, query: Query, store_list: List[str]) \ + -> List[Tuple[Query, str]]: + pass +``` +用户在自定义的RagRouter子类中，需要重写`_rag_route`函数，其中入参为Query和一个string list，每一个string均为一个Store组件的名称，出参是一个[Query, str]的list，list中的每个元素对应一个query在某一个store上的查询任务。 + +在编写完对应代码后，可以参考下面的yaml将QueryParaphraser注册为aU组件： +```yaml +name: 'base_router' +description: 'base rag router map query to all store' +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.base_router' + class: 'BaseRouter' +``` +其中metadata的type必须为RAG_ROUTER。 + +### 关注您定义的RagRouter所在的包路径 +在agentUniverse项目的config.toml中需要配置RagRouter配置对应的package, 请再次确认您创建的文件所在的包路径是否在`CORE_PACKAGE`中`rag_router`路径或其子路径下。 + +以示例工程中的配置为例，如下： +```yaml +[CORE_PACKAGE] +rag_router = ['sample_standard_app.app.core.rag_router'] +``` + + +## agentUniverse目前内置有以下RagRouter: +### [base_router](../../../agentuniverse/agent/action/knowledge/rag_router/base_router.yaml) +该组件的的主要功能是将查询传递给系统中的所有存储库，以确保查询能够在所有可能的资源中找到答案，它也是Knowledge中默认的RagRouter。 +```yaml +name: 'base_router' +description: 'base rag router map query to all store' +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.base_router' + class: 'BaseRouter' +``` + +### [nlu_rag_router](../../../agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py) +该组件的通过配置的llm分析所有store的描述信息与原始查询文本的关联程度，从所有Store中选出最相关的N个进行查询。 + +该组件需要用户自己编写yaml文件，格式可参考如下： +```yaml +name: 'nlu_rag_router' +description: 'base rag router map query to all store' +store_amount: 2 +llm: + name: demo_llm + model_name: gpt-4o +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.nlu_rag_router' + class: 'NluRagRouter' +``` +其中需要用户填写的内容包括： +- store_amount: 指定查询会被路由到的存储库数量，控制查询的分发范围。 +- llm: 包含大语言模型的配置，用于根据Store的描述信息和Query中的query_str筛选相关的数据库。name 表示模型组件的名称，model_name 指定使用的具体模型。 \ No newline at end of file diff --git a/docs/guidebook/zh/2_2_4_Reader.md b/docs/guidebook/zh/2_2_4_Reader.md new file mode 100644 index 00000000..8f67f3ed --- /dev/null +++ b/docs/guidebook/zh/2_2_4_Reader.md @@ -0,0 +1,55 @@ +# Reader + +Reader负责从各式各样的信息源中将信息抽取成agentUniverse中的Document形式。这些信息源可以是各种本地文件，也可以是网页或者一个IO接口， + +Reader的定义如下: +```python +from abc import abstractmethod +from typing import List, Any, Optional + +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase + +class Reader(ComponentBase): + """The basic class for the knowledge reader.""" + component_type: ComponentEnum = ComponentEnum.READER + name: Optional[str] = None + description: Optional[str] = None + + def load_data(self, *args: Any, **kwargs: Any) -> List[Document]: + """Load data from the input params.""" + return self._load_data(*args, **kwargs) + + @abstractmethod + def _load_data(self, *args: Any, **kwargs: Any) -> List[Document]: + """Load data from the input params.""" +``` +用户在自定义的Reader子类中，需要重写`_load_data`函数，读取数据并输出List[Document]。 + +在编写完对应代码后，可以参考下面的yaml将你的Reader注册为aU组件： +```yaml +name: 'default_txt_reader' +description: 'default txt reader' +metadata: + type: 'READER' + module: 'agentuniverse.agent.action.knowledge.reader.file.txt_reader' + class: 'TxtReader' +``` +其中metadata的type固定为READER + +### 关注您定义的Reader所在的包路径 +在agentUniverse项目的config.toml中需要配置Reader配置对应的package, 请再次确认您创建的文件所在的包路径是否在`CORE_PACKAGE`中`reader`路径或其子路径下。 + +以示例工程中的配置为例，如下： +```yaml +[CORE_PACKAGE] +reader = ['sample_standard_app.app.core.reader'] +``` + +## agentUniverse目前内置有以下Reader: +- [default_docx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml)：读取本地Docx文件中的文本内容 +- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)：读取本地Pdf文件中的文本内容 +- [default_pptx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml)：读取本地Pptx文件中的文本内容 +- [default_txt_reader](../../../agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml)：读取txt文件中的文本内容 +- [default_web_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml)：读取本地Docx文件中的文本内容 \ No newline at end of file diff --git a/docs/guidebook/zh/2_2_4_Store.md b/docs/guidebook/zh/2_2_4_Store.md new file mode 100644 index 00000000..ac048aa3 --- /dev/null +++ b/docs/guidebook/zh/2_2_4_Store.md @@ -0,0 +1,82 @@ +# Store + +`Store`负责对`Document`进行存储，并在知识的检索阶段提供查询能力。`Store`的具体形式可以是多样的，包括关系型数据库、向量数据库、图数据库等形式。因此同一份`Document`也能在不同的`Store`中以不同的形式存储，而具体的检索方式也和`Store`的能力相绑定。 + +Store定义如下： +```python +from typing import Any, List, Optional + +from agentuniverse.base.component.component_base import ComponentEnum +from agentuniverse.base.component.component_base import ComponentBase +from agentuniverse.base.config.component_configer.component_configer import ComponentConfiger +from agentuniverse.agent.action.knowledge.store.document import Document +from agentuniverse.agent.action.knowledge.store.query import Query +from agentuniverse.agent_serve.web.post_fork_queue import add_post_fork + +class Store(ComponentBase): + component_type: ComponentEnum = ComponentEnum.STORE + name: Optional[str] = None + description: Optional[str] = None + client: Any = None + async_client: Any = None + + def _new_client(self) -> Any: + pass + + def _new_async_client(self) -> Any: + pass + + def _initialize_by_component_configer(self, + store_configer: ComponentConfiger) \ + -> 'Store': + if store_configer.name: + self.name = store_configer.name + if store_configer.description: + self.description = store_configer.description + add_post_fork(self._new_client) + add_post_fork(self._new_async_client) + return self + + def query(self, query: Query, **kwargs) -> List[Document]: + raise NotImplementedError + + def insert_document(self, documents: List[Document], **kwargs): + raise NotImplementedError + + def delete_document(self, document_id: str, **kwargs): + raise NotImplementedError + + def upsert_document(self, documents: List[Document], **kwargs): + raise NotImplementedError + + def update_document(self, documents: List[Document], **kwargs): + raise NotImplementedError +``` +- `_new_client`和`_new_async_client`用于创建数据库链接，在组件注册阶段会被添加到[post_fork](2_4_1_Web_Server.md)执行列表中，保证创建的数据库连接在Gunicorn模式下的子进程中是独立的。 +- `query`函数是知识组件在查询时调用的函数，负责根据传入的Query实例在store中查找相关的内容并以document的形式返回 +- `Store`的还包括对`Docuemnt`类型数据的增删改查，作为知识存储的管理接口。 + +在编写完对应代码后，可以参考下面的yaml将你的Store注册为aU组件： +```yaml +name: 'sample_store' +description: 'a sample store' +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.sample_store' + class: 'SampleStore' +``` +其中metadata的type固定为STORE. + +### 关注您定义的Store所在的包路径 +在agentUniverse项目的config.toml中需要配置Store配置对应的package, 请再次确认您创建的文件所在的包路径是否在`CORE_PACKAGE`中`store`路径或其子路径下。 + +以示例工程中的配置为例，如下： +```yaml +[CORE_PACKAGE] +store = ['sample_standard_app.app.core.store'] +``` + +## agentUniverse目前内置Store： +- [Chroma](3_3_2_ChromaDB.md) +- [Milvus](3_3_1_Milvus.md) +- [Sqlite](3_3_3_Sqlite.md) \ No newline at end of file diff --git "a/docs/guidebook/zh/2_2_4_\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/2_2_4_\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md" new file mode 100644 index 00000000..27e8ecaf --- /dev/null +++ "b/docs/guidebook/zh/2_2_4_\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md" @@ -0,0 +1,110 @@ +# 如何构建RAG智能体 + +本教程提供一个在agentUniverse中快速构建一个RAG智能体。案例的完整结构和内容可在[该文档](7_1_1_法律咨询案例.md)中查看，本文档侧重在构建过程的教学，因此省略了部分非必要的内容。 + +## 案例说明 +本案例基于RagPlanner，搭建了一个简单的法律咨询智能体，通过检索民法典和刑法中的相关条例并结合案件背景给出相关的法律建议。 +该案例基于千问大模型和DashScope的embedding和rerank功能，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 + +知识文件的定义如下： +```yaml +name: "law_knowledge" +description: "中国民法与刑法相关的知识库" +stores: + - "civil_law_chroma_store" + - "criminal_law_chroma_store" + - "civil_law_sqlite_store" + - "criminal_law_sqlite_store" +insert_processors: + - "recursive_character_text_splitter" +post_processors: + - "dashscope_reranker" +readers: + pdf: "default_pdf_reader" + +metadata: + type: 'KNOWLEDGE' + module: 'sample_standard_app.app.core.knowledge.law_knowledge' + class: 'LawKnowledge' +``` + +## 构建知识索引 + +法律书籍原本： +- [民法典.pdf](../../../sample_standard_app/app/resources/民法典.pdf) +- [刑法.pdf](../../../sample_standard_app/app/resources/刑法.pdf) + +### 抽取PDF中的文本内容 +因为本案例中，原始文档是pdf格式，因此我们在Knowledge中进行了如下配置： +```yaml +readers: + pdf: "default_pdf_reader" +``` +这样就可以将pdf中的文本抽取出来。如果您想要读取更多格式的文件，您可以参考[Reader组件](2_2_4_Reader.md)中的内容 + +### 拆分长文本 +因为原始文档中的文本内容非常长，因此我们需要将其拆分为小的片段。这里我们选用`recursive_character_text_splitter`对其进行拆分，配置如下： +```yaml +insert_processors: + - "recursive_character_text_splitter" +``` +该配置项是一个list形式，可以配置多种处理文档的处理器，这个案例中指定的唯一处理器`recursive_character_text_splitter`会根据指定的分隔符递归的拆分原始文档直到满足指定长度，具体内容可以参考[DocProcessor](2_2_4_DocProcessor.md)。该文档中也包含了其它的文档处理器，您可以直接使用或是编写自己的处理器。 + +### 配置Store +本案例中包含四个Store：民法和刑法分别存储至sqlite以及chromadb中。我们仅以`civil_law_chroma_store`作为例子，其它Store类似。 +`civil_law_chroma_store`配置如下： +```yaml +name: 'civil_law_chroma_store' +description: '保存了中国民法典的所有内容，以文本向量形式存储' +persist_path: '../../DB/civil_law.db' +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.chroma_store' + class: 'ChromaStore' +``` + +其中`persist_path`指定了数据库本地文件的存储位置，并指定`dashscope_embedding`作为数据库向量化文本的组件，`similarity_top_k`表示召回的文档数量。关于`Store`的具体内容您可以在[该文档](2_2_4_Store.md)中查看。 + +### 执行插入流程 +在完成上述的配置后，我们可以执行下面的代码完成知识库的构建： +```python +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager + + +if __name__ == '__main__': + AgentUniverse().start(config_path='../../config/config.toml', core_mode=True) + law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge") + law_knowledge.insert_knowledge( + source_path="../resources/刑法.pdf", + stores=["civil_law_sqlite_store"] + ) +``` +我们通过调用`insert_knowledge`方法中的`source_path`指定了需要插入的数据，并通过`stores`参数分别为不同的文档指定了不同的Store，`stores`非必要参数，如果不指定则默认插入所有的store。 + + +## 构建检索流程 + +### 重排序 +我们对知识的后处理流程进行了如下配置： +```yaml +post_processors: + - "dashscope_reranker" +``` +表示我们会调用Dashscope的重排序服务对文档进行重排序。您也可以在这里加入其它的后处理流程。 + +## 在智能体中使用RAG + +```yaml +info: + name: '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' +action: + knowledge: + - 'law_knowledge' +``` +我们将knowledge像这样配置在Agent中的yaml中即可。 + +完整的案例以及调用方法请参考[文档](7_1_1_法律咨询案例.md)。 \ No newline at end of file diff --git "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md" "b/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md" index 6fbf6fb4..e02d5b26 100644 --- "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md" +++ "b/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md" @@ -7,5 +7,17 @@ agentUniverse定义了标准的知识定义，其包含了各类知识数据的加载方式，也包含了去连接那些各种各样的知识存储，您可以将任何任何形式的知识数据定义成标准的知识组件供agent与其他组件使用。 +## 知识架构 +agentUniverse中，知识的整体架构图如下所示： +![agentUniverse知识架构](../_picture/knowledge_structure.png) +其中上半部分从Reader到Store为知识的注入流程，而下半部分为知识的查询流程。 + +### 知识注入流程 + +在知识注入流程中，通过`Reader`读取原始的数据转化为agentUniverse内部`Document`的形式，`Document`可以包含文本、图像、向量等等中的一种或多种内容，也可以继承该类型自定义拓展为更多的形式，如音频、视频。在此之后，`DocProcessor`会对`Document`进行一系列的处理操作, 如图上所示，`DocProcessor`的输入与输出均为Document，意味着您可以在这一过程中叠加使用多个不同的`DocProcessor`。最后，经过处理的`Document`会被存储至不同的`Store`中，`Store`可以是任意的数据存储方式，包括但不限于：关系型数据库、向量数据库、图数据库。因此同一份`Document`可以在不同的`Store`中保存为不同的形式，但他们的元信息中会包含相同的id，表示它们来源于同一个`Document`,用于在后续查询时避免召回相同的内容。 + +### 知识查询流程 +在查询知识时，用户需要构建一个`Query`。类似`Document`,`Query`的内容也可以是多样的：从简单的字符串，到向量、图像都可以——只要知识包含的`Store`支持这样的查询方式。在传入`Knowledge`组件后，和`DocProcessor`相似，`QueryParaphraser`用于对`Query`进行处理，您可以对原始查询字符串提取关键词用于查询包含特定tag的段落，也可以将原始查询问题拆分为多个更易于查询的子问题，等等。在此之后，`RagRouter`组件会负责将`Query`与合适的`Store`进行配对，生成多个[Query, Store]的查询任务，这种配对可以是基于LLM对于查询文本和`Store`描述匹配程度的理解，也可以是基于特定规则选取特定的`Store`，如果资源允许，也可以直接在所有`Store`上进行查询。`Store`查询返回的内容依旧是`Document`类型，因此在后续流程中我们依旧可以使用`DocProcessor`对召回的`Document`进行一系列后处理的流程，不同之处在于，此时`Query`也会作为默认参数传递给`DocProcessor`，因此在该阶段可以对召回的`Document`进行类似Rerank等需要比较召回内容和Query内容的处理。最后，您还可以通过默认或是自定义的to_llm方法将召回的`Document`转为方便llm理解的字符串形式，作为最终的输出。 + # 总结 至此您已初步了解知识组件的设计原理，在下一节我们将具体向您介绍知识组件的标准定义、如何自定义创建知识、如何使用知识等。 diff --git "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" index a5b81160..6e40ae23 100644 --- "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" +++ "b/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" @@ -1,17 +1,26 @@ # 知识与相关领域对象 -在本章节我们会重点列出Knowledge与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](4_1_API参考.md)。 +在本章节我们会列举知识与其相关的核心领域对象, 我们省略了对应的代码部分，若您希望进一步了解请根据包路径阅读对应的源码。 -## Knowledge基础类 -包路径：`agentuniverse.agent.action.knowledge.knowledge.Knowledge` +### Knowledge +包路径：`agentuniverse.agent.action.knowledge.knowledge.Knowledge` +文档链接：[Knowledge](2_2_4_知识定义与使用.md) -## Knowledge管理器 -包路径：`agentuniverse.agent.action.knowledge.knowledge_manager.KnowledgeManager` +### Reader +包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader` +文档链接：[Reader](2_2_4_Reader.md) -## Embedding对象基础类 -包路径：`agentuniverse.agent.action.knowledge.embedding.embedding.Embedding` +### DocProcessor +包路径：`agentuniverse.agent.action.knowledge.doc_processor.doc_processor.DocProcessor` +文档链接：[DocProcessor](2_2_4_DocProcessor.md) -## Reader对象基础类 -包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader` +### Store +包路径：`agentuniverse.agent.action.knowledge.store.store.Store` +文档链接：[Store](2_2_4_Store.md) -## Store对象基础类 -包路径：`agentuniverse.agent.action.knowledge.store.store.Store` \ No newline at end of file +### QueryParaphraser +包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader` +文档链接：[QueryParaphraser](2_2_4_QueryParaphraser.md) + +### RagRouter +包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader` +文档链接：[RagRouter](2_2_4_RagRouter.md) diff --git "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" index 6a17c889..b2be3538 100644 --- "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" +++ "b/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" @@ -6,144 +6,65 @@ 其中`knowledge_xx.yaml`包含了Knowledge组件的名称、描述、加载方式、存储方式等重要信息；`knowledge_xx.py`包含了的知识具体定义。理解这一原理后，让我们具体看看该如何创建这两部分内容。 ## 创建Knowledge配置 - knowledge_xx.yaml -我们将详细介绍配置中的各组成部分。 - -### 设置Knowledge的基本属性 -* `name`: 知识名称，您可以按照自己的期望设置任何名字 -* `description`: 知识描述, 按照您的实际需求填写 -* `store`: 知识的存储媒介（比如ChromaStore/RedisStore/FaissStore等） -* `reader`: 代表知识的读取方式（比如FileReader/UrlReader等） -* `ext_info`: 扩展参数 - -### 设置Knowledge组件元信息 -**`metadata` - 组件元信息** -* `type` : 组件类型，'KNOWLEDGE' -* `module`: 知识实体包路径 -* `class`: 知识实体类名 ### 一个Knowledge定义配置的实际样例 ```yaml -name: 'demo_knowledge' -description: 'demo knowledge' +name: "sample_knowledge" +description: "a knowledge sample" +stores: + - "a_store" + - "another_store" +query_paraphrasers: + - "a_query_paraphraser" +insert_processors: + - "a_doc_processor" +rag_router: "a_rag_router" +post_processors: + - "another_doc_processor" +readers: + pdf: "default_pdf_reader" + metadata: type: 'KNOWLEDGE' - module: 'sample_standard_app.app.core.knowledge.demo_knowledge' - class: 'DemoKnowledge' + module: 'sample_standard_app.app.core.knowledge.sample_knowledge' + class: 'SampleKnowledge' ``` - -上述是一个实际的Knowledge配置的样例。除了上述介绍的标准配置项。 - -您可以在我们的样例工程中的`sample_standard_app.app.core.knowledge`路径下更多的knowledge配置yaml样例。 - -除此之外agentuniverse不限制用户对knowledge yaml配置内容进行扩展，您可以根据自己的要求创建任何自定义配置key,但请注意不要与上述默认的配置关键字重名。 +- stores: 所有关联的Store。一个string的list，每个string表示一个Store组件的名称。 +- query_paraphrasers: 查询改写器，用于将输入的查询转换为更加适合检索的形式。一个string的list，每个string表示一个QueryParaphraser组件的名称。 +- insert_processors: 插入处理器，用于在将文本插入知识库时对其进行处理，例如递归字符切分，一个string的list，每个string表示一个DocProcessor组件的名称。 +- rag_router: 检索增强生成（RAG）路由器，控制查询在知识库中的路由方式。 +- post_processors: 后处理器，用于对检索结果进行优化和排序，提升返回结果的相关性。一个string的list，每个string表示一个DocProcessor组件的名称。 +- readers: Dict形式，其中key表示文件类型，value表示对应的Reader组件名称。 ## 创建Knowledge领域行为定义 - knowledge_xx.py +agentUniverse提供了一个标准的Knowledge类，您可以直接在yaml定义文件中使用该类或是继承它并改写其中的部分方法。 -### 创建Knowledge类对象 -创建对应的Knowledge类对象并继承agentUniverse框架Knowledge基础类 `Knowledge`; - -### 设定Reader模块 -Reader模块的作用是做知识的读取，这部分是可选的。 - -agentUniverse目前内置了如下数据格式的Reader： -* DocxReader -* PdfReader -* PptxReader -* WebPdfReader -* FileReader - -其路径位于`agentuniverse.agent.action.knowledge.reader.file`包路径下，Reader模块是支持定制的，您可以自行编写Reader的加载方式。 - -### 设定Store模块 -Store模块的作用负责知识持久化交互，做知识的插入/读取/删除/更新等操作，这部分是可选的。。 - -agentUniverse目前内置了如下数据格式的Store： -* ChromaStore -* MilvusStore - -其路径位于`agentuniverse.agent.action.knowledge.store`包路径下，Store模块是支持定制的，您可以自行编写Store的加载方式。 - -### 定义query_knowledge方法 -该方法负责对应知识的查询，其默认实现为使用Store函数的查询，用户可以根据实际情况进行覆写。 - -```python -def query_knowledge(self, **kwargs) -> List[Document]: - """Query the knowledge. - - Query documents from the store and return the results. - """ - query = Query(**kwargs) - return self.store.query(query) -``` - -### 定义insert_knowledge方法 -该方法负责对应知识的新增，其默认实现为使用Store函数的插入，用户可以根据实际情况进行覆写。 - -```python -def insert_knowledge(self, **kwargs) -> None: - """Insert the knowledge. - - Load data by the reader and insert the documents into the store. - """ - document_list: List[Document] = self.reader.load_data() - self.store.insert_documents(document_list, **kwargs) -``` +### [Knowledge类的定义:](../../../agentuniverse/agent/action/knowledge/knowledge.py) -#### 一个实际的Knowledge对象定义样例 -```python -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.reader.file.web_pdf_reader import WebPdfReader -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore -from agentuniverse.agent.action.knowledge.store.document import Document -from langchain.text_splitter import TokenTextSplitter +- _load_data(self, *args: Any, **kwargs: Any) -> List[Document] +: 加载数据源，根据数据源的类型（文件或URL）选择合适的`Reader`，并加载文档数据。 -SPLITTER = TokenTextSplitter(chunk_size=800, chunk_overlap=100) +- _insert_process(self, origin_docs: List[Document]) -> List[Document] +: 处理插入的原始文档，依次应用配置的`DocProcessor`进行文档的预处理。 +- _rag_post_process(self, origin_docs: List[Document], query: Query) +: 对检索到的原始文档进行后处理，根据查询条件应用配置的后处理器`DocProcessor`对文档进行进一步处理。 -class DemoKnowledge(Knowledge): - """The demo knowledge.""" +- _paraphrase_query(self, origin_query: Query) -> Query +: 对原始查询进行改写，应用配置的`QueryParaphraser`对查询进行改写处理。 - def __init__(self, **kwargs): - """The __init__ method. +- insert_knowledge(self, **kwargs) -> None +: 插入知识数据。该方法调用`_load_data`加载文档数据，经过`_insert_process`预处理后，将文档并行插入到存储中。 - Some parameters, such as name and description, - are injected into this class by the demo_knowledge.yaml configuration. +- _route_rag(self, query: Query) +: 通过指定的`RagRouter`，根据查询条件选择合适的存储来进行查询操作。 +- query_knowledge(self, **kwargs) -> List[Document] +: 查询知识数据。该方法首先调用`_paraphrase_query`对查询进行改写，然后通过`_route_rag`选择存储，并行执行查询操作，最后调用`_rag_post_process`对查询结果进行后处理。 - Args: - name (str): Name of the knowledge. +- to_llm(self, retrieved_docs: List[Document]) -> Any +: 将检索到的文档转换为LLM输入格式，拼接所有文档文本以供后续处理。 - description (str): Description of the knowledge. - - store (Store): Store of the knowledge, store class is used to store knowledge - and provide retrieval capabilities, such as ChromaDB store or Redis Store, - demo knowledge uses ChromaDB as the knowledge storage. - - reader (Reader): Reader is used to load data, - the demo knowledge uses WebPdfReader to load pdf files from web. - """ - super().__init__(**kwargs) - self.store = ChromaStore(collection_name="chroma_store", embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-3-small'), dimensions=1056) - self.reader = WebPdfReader() - # initialize the knowledge - # self.insert_knowledge() - - def insert_knowledge(self, **kwargs) -> None: - """Insert the knowledge into the knowledge store. - - Step1: Load data from the web using WebPdfReader. - Step2: Split the data into chunks using TokenTextSplitter(). - Step3: Insert the data into the ChromaStore. - - Note: - To avoid that the token in the embedding process exceeds the limit, the document needs to be split. - """ - doc_list = self.reader.load_data('https://www.sfu.ca/~poitras/BUFFET.pdf') - lc_doc_list = SPLITTER.split_documents(Document.as_langchain_list(doc_list)) - self.store.insert_documents(Document.from_langchain_list(lc_doc_list)) -``` ## 关注您定义的Knowledge所在的包路径 通过上面的Knowledge配置与定义，您已经掌握了知识创建的所有步骤；接下去我们将使用这些Knowledge，在使用前请关注创建的Knowledge是否在正确的包扫描路径内。 @@ -165,14 +86,12 @@ knowledge = ['sample_standard_app.app.core.knowledge'] 通过Knowledge管理器中的`.get_instance_obj(xx_knowledge_name)` 方法可以获取对应名称的Knowledge实例, 使用`query_knowledge`方法进行调用。 ```python -from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManage +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager -knowledge = KnowledgeManager().get_instance_obj(knowledge_name) -knowledge.query_knowledge(**query_input) +knowledge = KnowledgeManager().get_instance_obj("knowledge_name") +knowledge.insert_knowledge(source_path="source_file") +knowledge.query_knowledge() ``` -# 了解更多已有知识Knowledge -框架提供的更多Knowledge示例在`sample_standard_app.app.core.knowledge`包路径下，您可以进一步查看对应的知识。 - -# 总结 -至此您已经掌握了知识Knowledge的定义与使用，赶快去尝试创建与使用知识吧。 \ No newline at end of file +## 了解更多已有知识Knowledge +[Knowledge法律咨询案例](7_1_1_法律咨询案例.md) \ No newline at end of file diff --git a/docs/guidebook/zh/3_3_1_Milvus.md b/docs/guidebook/zh/3_3_1_Milvus.md index f898f8c5..fdc584c8 100644 --- a/docs/guidebook/zh/3_3_1_Milvus.md +++ b/docs/guidebook/zh/3_3_1_Milvus.md @@ -18,25 +18,36 @@ $ bash standalone_embed.sh start pip install pymilvus ```` -### 我可以用Milvus做些什么 - -您可以在[知识组件](2_2_4_知识.md)中使用Milvus来存储和查询知识，你可以使用以下方式来创建一个使用Milvus的存储组件: -```python -from agentuniverse.agent.action.knowledge.store.milvus_store import MilvusStore -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge - -init_params = {} -init_params['name'] = 'test_knowledge' -init_params['description'] = 'test_knowledge_description' -init_params['store'] = MilvusStore( - connection_args={"host": "localhost", "port": "19530"}, - collection_name="test_knowledge", - embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-ada-002' - ) -) -knowledge = Knowledge(**init_params) +### 如何配置Milvus组件 +```yaml +name: 'milvus_store' +description: 'a store based on milvus' +connection_args: + host: '127.0.0.1' + port: '19530' +search_args: + metric_type: "L2" + params: + nprobe: 10 +index_params: + metric_type: "L2" + index_type: "HNSW" + params: + M: 8 + efConstruction: 32 +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.milvus_store' + class: 'MilvusStore' ``` +- connection_args: 连接 Milvus 数据库的参数，包括主机地址 (host) 和端口号 (port)。 +- search_args: 搜索参数，定义了搜索时使用的距离度量类型 (metric_type) 和相关参数（如 nprobe）。 +- index_params: 索引参数，定义了使用的索引类型 (index_type)、距离度量类型 (metric_type) 以及构建索引时的具体参数（如 M 和 efConstruction）。 +- embedding_model: 用于生成嵌入向量的模型名称，这里指定为 dashscope_embedding。 +- similarity_top_k: 在相似度搜索中返回最相似结果的数量。 + +### 使用方式 +[知识定义与使用](2_2_4_知识定义与使用.md) -上面的代码会创建一个基于Milvus的Knowledge，关于Knowledge的具体用法您可以参考[知识组件](2_2_4_知识.md)，或是参考代码`tests/test_agentuniverse/unit/agent/action/knowledge/test_knowledge_with_milvus.py`。 diff --git a/docs/guidebook/zh/3_3_2_ChromaDB.md b/docs/guidebook/zh/3_3_2_ChromaDB.md index 8ea84f0f..488fec66 100644 --- a/docs/guidebook/zh/3_3_2_ChromaDB.md +++ b/docs/guidebook/zh/3_3_2_ChromaDB.md @@ -3,22 +3,21 @@ agentUniverse中已集成ChromaDB相关依赖，您无需额外安装包即可使用ChromaDB的相关功能。 如果您想学习ChromaDB相关的底层原理，您可以查阅ChromaDB的[官方网站](https://www.trychroma.com/)。 -### 我可以用ChromaDB做些什么 - - -您可以在[知识组件](2_2_4_知识.md)中使用ChromaDB来存储和查询知识，你可以使用以下方式来创建一个使用ChromaDB的存储组件: -```python -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore - - -init_params = dict() -init_params['name'] = 'test_knowledge' -init_params['description'] = 'test_knowledge_description' -init_params['store'] = ChromaStore(collection_name="test_knowledge", embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-ada-002')) -knowledge = Knowledge(**init_params) +### 如何配置Chromadb组件 +```yaml +name: 'chroma_store' +description: 'store based on chroma db' +persist_path: '../../DB/criminal_law.db' +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.chroma_store' + class: 'ChromaStore' ``` +- persist_path: 数据库的持久化存储路径，用于存储和加载向量数据。 +- embedding_model: 用于生成嵌入向量的模型名称，这里指定为 dashscope_embedding。 +- similarity_top_k: 在相似度搜索中返回最相似结果的数量。 -上面的代码会创建一个基于ChromaDB的Knowledge，关于Knowledge的具体用法您可以参考[知识组件](2_2_4_知识.md)，或是参考代码`tests/test_agentuniverse/unit/agent/action/knowledge/test_knowledge.py`。 \ No newline at end of file +### 使用方式 +[知识定义与使用](2_2_4_知识定义与使用.md) diff --git a/docs/guidebook/zh/3_3_3_Sqlite.md b/docs/guidebook/zh/3_3_3_Sqlite.md new file mode 100644 index 00000000..cf5d9506 --- /dev/null +++ b/docs/guidebook/zh/3_3_3_Sqlite.md @@ -0,0 +1,28 @@ +## Sqlite + +Store组件中的sqlite用于保存`Document`中的文本内容，并基于Jieba的关键词提取功能构建对应的倒排索引，在检索阶段通过关键词召回相关文档，并通过BM25算法计算Query与召回文本的相关度，最终返回最相似的Top k文档。 + +### 如何配置Sqlite组件 + +您可以在[知识组件](2_2_4_知识.md)中使用ChromaDB来存储和查询知识，你可以使用以下方式来创建一个使用ChromaDB的存储组件: +```yaml +name: 'sqlite_store' +description: 'a store based on sqlite' +db_path: '../../DB/civil_law_sqlite.db' +k1: 1.5 +b: 0.75 +keyword_extractor: 'jieba_keyword_extractor' +similarity_top_k: 10 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.sqlite_store' + class: 'SQLiteStore' +``` +- db_path: SQLite 数据库文件的路径，用于存储和管理文本数据。 +- k1: BM25 算法中的参数 k1，控制词频对得分的影响。 +- b: BM25 算法中的参数 b，控制文档长度对得分的影响。 +- keyword_extractor: 用于提取关键词的工具名称，这里指定为 jieba_keyword_extractor。 +- similarity_top_k: 根据BM25分数返回的最相关的top k。 + +### 使用方式 +[知识定义与使用](2_2_4_知识定义与使用.md) \ No newline at end of file diff --git "a/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md" index c7d8b7c7..d88528ee 100644 --- "a/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md" +++ "b/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md" @@ -2,25 +2,79 @@ ## 案例说明 本案例基于RagPlanner，搭建了一个简单的法律咨询智能体，通过检索民法典和刑法中的相关条例并结合案件背景给出相关的法律建议。 -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`。 +该案例基于千问大模型和DashScope的embedding和rerank功能，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。 ## 构成 ### 法律知识库 -法律知识库基于agentUniverse中的[知识组件](2_2_4_知识.md)，通过将法律条款原文embedding后存储至ChromaDB的向量数据库中，构建方便智能体查阅检索的知识库。 +法律知识库基于agentUniverse中的[知识组件](2_2_4_知识及相关领域对象.md)，通过将法律条款原文存储至ChromaDB和Sqlite中，构建方便智能体查阅检索的知识库。 法律书籍原本： - [民法典.pdf](../../../sample_standard_app/app/resources/民法典.pdf) - [刑法.pdf](../../../sample_standard_app/app/resources/刑法.pdf) -具体代码可以参考： -- [民法典知识库](../../../sample_standard_app/app/core/knowledge/civil_law_knowledge.py) -- [刑法知识库](../../../sample_standard_app/app/core/knowledge/criminal_law_knowledge.py) +### [知识定义](../../../sample_standard_app/app/core/knowledge/law_knowledge.yaml) +```yaml +name: "law_knowledge" +description: "中国民法与刑法相关的知识库" +stores: + - "civil_law_chroma_store" + - "criminal_law_chroma_store" + - "civil_law_sqlite_store" + - "criminal_law_sqlite_store" +query_paraphrasers: + - "custom_query_keyword_extractor" +insert_processors: + - "recursive_character_text_splitter" +rag_router: "nlu_rag_router" +post_processors: + - "dashscope_reranker" +readers: + pdf: "default_pdf_reader" -为了方便您使用，我们已经将存储有相关信息的数据库文件放在[该目录下](../../../sample_standard_app/DB/)，如果您想从头构建知识库的话，您可以修改代码中的如下部分： +metadata: + type: 'KNOWLEDGE' + module: 'sample_standard_app.app.core.knowledge.law_knowledge' + class: 'LawKnowledge' +``` + +### Reader组件 +- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml) + +### DocProcessor组件 +- [custom_query_keyword_extractor](../../../sample_standard_app/app/core/doc_processor/query_keyword_extractor.yaml) +- [recursive_character_text_splitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml) + +### QueryParaphraser组件 +- [custom_query_keyword_extractor](../../../sample_standard_app/app/core/query_paraphraser/custom_query_keyword_extractor.yaml) + +### RagRouter组件 +- [nlu_rag_router](../../../sample_standard_app/app/core/rag_router/nlu_rag_router.yaml) + +### Store组件 +- [civil_law_chroma_store](../../../sample_standard_app/app/core/store/civil_law_chroma_store.yaml) +- [criminal_law_chroma_store](../../../sample_standard_app/app/core/store/criminal_law_chroma_store.yaml) +- [civil_law_sqlite_store](../../../sample_standard_app/app/core/store/civil_law_sqlite_store.yaml) +- [criminal_law_sqlite_store](../../../sample_standard_app/app/core/store/criminal_law_sqlite_store.yaml) + +为了方便您使用，我们已经将存储有相关信息的数据库文件放在[该目录下](../../../sample_standard_app/DB/)，如果您想从头构建知识库的话，您可以运行如下代码： ```python -# Initialize the knowledge -# self.insert_knowledge() +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager + + +if __name__ == '__main__': + AgentUniverse().start(config_path='../../config/config.toml', core_mode=True) + civil_store_list = ["civil_law_sqlite_store", "civil_law_chroma_store"] + criminal_store_list = ["criminal_law_sqlite_store", "criminal_law_chroma_store"] + law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge") + law_knowledge.insert_knowledge( + source_path="../resources/刑法.pdf", + stores=criminal_store_list + ) + law_knowledge.insert_knowledge( + source_path="../resources/民法典.pdf", + stores=civil_store_list + ) ``` -将`self.insert_knowledge()`前的注释去掉，即可重新构建数据库。 ### 法律智能体 该智能体涉及如下两个文件： diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index ea772093..00000000 --- a/poetry.lock +++ /dev/null @@ -1,4530 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - -[[package]] -name = "aiohttp" -version = "3.9.4" -description = "Async http client/server framework (asyncio)" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohttp-3.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = 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[tool.poetry.extras] log_ext = ["aliyun-log-python-sdk"] diff --git a/sample_standard_app/DB/civil_law.db/373b58cb-6652-4697-8788-5e639678e409/data_level0.bin b/sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/data_level0.bin similarity index 100% rename from sample_standard_app/DB/civil_law.db/373b58cb-6652-4697-8788-5e639678e409/data_level0.bin rename to sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/data_level0.bin diff --git a/sample_standard_app/DB/civil_law.db/373b58cb-6652-4697-8788-5e639678e409/header.bin b/sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/header.bin similarity index 100% rename from sample_standard_app/DB/civil_law.db/373b58cb-6652-4697-8788-5e639678e409/header.bin rename to sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/header.bin diff --git a/sample_standard_app/DB/civil_law.db/373b58cb-6652-4697-8788-5e639678e409/length.bin b/sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/length.bin similarity index 100% rename from sample_standard_app/DB/civil_law.db/373b58cb-6652-4697-8788-5e639678e409/length.bin rename to sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/length.bin diff --git a/sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/link_lists.bin b/sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/link_lists.bin similarity index 100% rename from sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/link_lists.bin rename to sample_standard_app/DB/civil_law.db/a132b5c0-0bdd-46a1-aa05-7d5646c07a93/link_lists.bin diff --git a/sample_standard_app/DB/civil_law.db/chroma.sqlite3 b/sample_standard_app/DB/civil_law.db/chroma.sqlite3 index a107ddd5..412382a6 100644 Binary files a/sample_standard_app/DB/civil_law.db/chroma.sqlite3 and b/sample_standard_app/DB/civil_law.db/chroma.sqlite3 differ diff --git a/sample_standard_app/DB/civil_law_sqlite.db b/sample_standard_app/DB/civil_law_sqlite.db new file mode 100644 index 00000000..3e087b1a Binary files /dev/null and b/sample_standard_app/DB/civil_law_sqlite.db differ diff --git a/sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/data_level0.bin b/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/data_level0.bin similarity index 100% rename from sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/data_level0.bin rename to sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/data_level0.bin diff --git a/sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/header.bin b/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/header.bin similarity index 100% rename from sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/header.bin rename to sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/header.bin diff --git a/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/length.bin b/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/length.bin new file mode 100644 index 00000000..e40369cc Binary files /dev/null and b/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/length.bin differ diff --git a/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/link_lists.bin b/sample_standard_app/DB/criminal_law.db/0a7fb2e4-b0e8-4ab1-8922-f0e5bf4b2efa/link_lists.bin new file mode 100644 index 00000000..e69de29b diff --git a/sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/length.bin b/sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/length.bin deleted file mode 100644 index 73385e9b..00000000 Binary files a/sample_standard_app/DB/criminal_law.db/c93232cd-8eec-4ac4-a4a2-e64315265207/length.bin and /dev/null differ diff --git a/sample_standard_app/DB/criminal_law.db/chroma.sqlite3 b/sample_standard_app/DB/criminal_law.db/chroma.sqlite3 index 37142723..d65c8f1d 100644 Binary files a/sample_standard_app/DB/criminal_law.db/chroma.sqlite3 and b/sample_standard_app/DB/criminal_law.db/chroma.sqlite3 differ diff --git a/sample_standard_app/DB/criminal_law_sqlite.db b/sample_standard_app/DB/criminal_law_sqlite.db new file mode 100644 index 00000000..c79eeb11 Binary files /dev/null and b/sample_standard_app/DB/criminal_law_sqlite.db differ 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 03ced866..f3376267 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 @@ -25,8 +25,7 @@ plan: name: 'rag_planner' action: knowledge: - - 'civil_law_knowledge' - - 'criminal_law_knowledge' + - 'law_knowledge' metadata: type: 'AGENT' module: 'sample_standard_app.app.core.agent.rag_agent_case.law_rag_agent' diff --git a/sample_standard_app/app/core/doc_processor/__init__.py b/sample_standard_app/app/core/doc_processor/__init__.py new file mode 100644 index 00000000..57356490 --- /dev/null +++ b/sample_standard_app/app/core/doc_processor/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/14 15:58 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/sample_standard_app/app/core/doc_processor/query_keyword_extractor.yaml b/sample_standard_app/app/core/doc_processor/query_keyword_extractor.yaml new file mode 100644 index 00000000..ec1c105e --- /dev/null +++ b/sample_standard_app/app/core/doc_processor/query_keyword_extractor.yaml @@ -0,0 +1,7 @@ +name: 'query_keyword_extractor' +description: 'extract keywords from query' +top_k: 6 +metadata: + type: 'DOC_PROCESSOR' + module: 'agentuniverse.agent.action.knowledge.doc_processor.jieba_keyword_extractor' + class: 'JiebaKeywordExtractor' \ No newline at end of file diff --git a/sample_standard_app/app/core/knowledge/civil_law_knowledge.py b/sample_standard_app/app/core/knowledge/civil_law_knowledge.py deleted file mode 100644 index fa38fac7..00000000 --- a/sample_standard_app/app/core/knowledge/civil_law_knowledge.py +++ /dev/null @@ -1,44 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/3/28 19:28 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: demo_knowledge.py - -from agentuniverse.agent.action.knowledge.embedding.dashscope_embedding import DashscopeEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.reader.file.pdf_reader import PdfReader -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore -from agentuniverse.agent.action.knowledge.store.document import Document -from langchain.text_splitter import TokenTextSplitter -from pathlib import Path - -SPLITTER = TokenTextSplitter(chunk_size=600, chunk_overlap=100) - - -class CivilLawKnowledge(Knowledge): - """The demo knowledge.""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.store = ChromaStore( - collection_name="law_store", - persist_path="../../DB/civil_law.db", - embedding_model=DashscopeEmbedding( - embedding_model_name='text-embedding-v2' - ), - dimensions=1536) - self.reader = PdfReader() - # Initialize the knowledge - # self.insert_knowledge() - - def insert_knowledge(self, **kwargs) -> None: - """ - Load civil law pdf and save into vector database. - """ - civil_law_docs = self.reader.load_data(Path("../resources/民法典.pdf")) - lc_doc_list = SPLITTER.split_documents(Document.as_langchain_list( - civil_law_docs - )) - self.store.insert_documents(Document.from_langchain_list(lc_doc_list)) diff --git a/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml b/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml deleted file mode 100644 index 87e6eb1d..00000000 --- a/sample_standard_app/app/core/knowledge/civil_law_knowledge.yaml +++ /dev/null @@ -1,6 +0,0 @@ -name: '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' - class: 'CivilLawKnowledge' \ No newline at end of file diff --git a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.py b/sample_standard_app/app/core/knowledge/criminal_law_knowledge.py deleted file mode 100644 index 124baa83..00000000 --- a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.py +++ /dev/null @@ -1,62 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/3/28 19:28 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: demo_knowledge.py - -from agentuniverse.agent.action.knowledge.embedding.dashscope_embedding import DashscopeEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.reader.file.pdf_reader import PdfReader -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore -from agentuniverse.agent.action.knowledge.store.document import Document -from langchain.text_splitter import TokenTextSplitter -from pathlib import Path - -SPLITTER = TokenTextSplitter(chunk_size=600, chunk_overlap=100) - - -class CriminalLawKnowledge(Knowledge): - """The demo knowledge.""" - - def __init__(self, **kwargs): - """The __init__ method. - - Some parameters, such as name and description, - are injected into this class by the demo_knowledge.yaml configuration. - - - Args: - name (str): Name of the knowledge. - - description (str): Description of the knowledge. - - store (Store): Store of the knowledge, store class is used to store knowledge - and provide retrieval capabilities, such as ChromaDB store or Redis Store, - demo knowledge uses ChromaDB as the knowledge storage. - - reader (Reader): Reader is used to load data, - the demo knowledge uses WebPdfReader to load pdf files from web. - """ - super().__init__(**kwargs) - self.store = ChromaStore( - collection_name="law_store", - persist_path="../../DB/criminal_law.db", - embedding_model=DashscopeEmbedding( - embedding_model_name='text-embedding-v2' - ), - dimensions=1536) - self.reader = PdfReader() - # Initialize the knowledge - # self.insert_knowledge() - - def insert_knowledge(self, **kwargs) -> None: - """ - Load criminal law pdf and save into vector database. - """ - criminal_law_docs = self.reader.load_data(Path("../resources/刑法.pdf")) - lc_doc_list = SPLITTER.split_documents(Document.as_langchain_list( - criminal_law_docs - )) - self.store.insert_documents(Document.from_langchain_list(lc_doc_list)) diff --git a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml b/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml deleted file mode 100644 index 184b16f9..00000000 --- a/sample_standard_app/app/core/knowledge/criminal_law_knowledge.yaml +++ /dev/null @@ -1,6 +0,0 @@ -name: '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' - class: 'CriminalLawKnowledge' \ No newline at end of file diff --git a/sample_standard_app/app/core/knowledge/demo_knowledge.py b/sample_standard_app/app/core/knowledge/demo_knowledge.py deleted file mode 100644 index 592b9ec1..00000000 --- a/sample_standard_app/app/core/knowledge/demo_knowledge.py +++ /dev/null @@ -1,60 +0,0 @@ -# !/usr/bin/env python3 -# -*- coding:utf-8 -*- - -# @Time : 2024/3/28 19:28 -# @Author : wangchongshi -# @Email : wangchongshi.wcs@antgroup.com -# @FileName: demo_knowledge.py - -from agentuniverse.agent.action.knowledge.embedding.openai_embedding import OpenAIEmbedding -from agentuniverse.agent.action.knowledge.knowledge import Knowledge -from agentuniverse.agent.action.knowledge.reader.file.web_pdf_reader import WebPdfReader -from agentuniverse.agent.action.knowledge.store.chroma_store import ChromaStore -from agentuniverse.agent.action.knowledge.store.document import Document -from langchain.text_splitter import TokenTextSplitter - -SPLITTER = TokenTextSplitter(chunk_size=800, chunk_overlap=100) - - -class DemoKnowledge(Knowledge): - """The demo knowledge.""" - - def __init__(self, **kwargs): - """The __init__ method. - - Some parameters, such as name and description, - are injected into this class by the demo_knowledge.yaml configuration. - - - Args: - name (str): Name of the knowledge. - - description (str): Description of the knowledge. - - store (Store): Store of the knowledge, store class is used to store knowledge - and provide retrieval capabilities, such as ChromaDB store or Redis Store, - demo knowledge uses ChromaDB as the knowledge storage. - - reader (Reader): Reader is used to load data, - the demo knowledge uses WebPdfReader to load pdf files from web. - """ - super().__init__(**kwargs) - self.store = ChromaStore(collection_name="chroma_store", embedding_model=OpenAIEmbedding( - embedding_model_name='text-embedding-3-small'), dimensions=1056) - self.reader = WebPdfReader() - # initialize the knowledge - # self.insert_knowledge() - - def insert_knowledge(self, **kwargs) -> None: - """Insert the knowledge into the knowledge store. - - Step1: Load data from the web using WebPdfReader. - Step2: Split the data into chunks using TokenTextSplitter(). - Step3: Insert the data into the ChromaStore. - - Note: - To avoid that the token in the embedding process exceeds the limit, the document needs to be split. - """ - doc_list = self.reader.load_data('https://www.sfu.ca/~poitras/BUFFET.pdf') - lc_doc_list = SPLITTER.split_documents(Document.as_langchain_list(doc_list)) - self.store.insert_documents(Document.from_langchain_list(lc_doc_list)) diff --git a/sample_standard_app/app/core/knowledge/demo_knowledge.yaml b/sample_standard_app/app/core/knowledge/demo_knowledge.yaml deleted file mode 100644 index fafc720c..00000000 --- a/sample_standard_app/app/core/knowledge/demo_knowledge.yaml +++ /dev/null @@ -1,6 +0,0 @@ -name: 'demo_knowledge' -description: 'demo knowledge' -metadata: - type: 'KNOWLEDGE' - module: 'sample_standard_app.app.core.knowledge.demo_knowledge' - class: 'DemoKnowledge' \ No newline at end of file diff --git a/sample_standard_app/app/core/knowledge/law_knowledge.py b/sample_standard_app/app/core/knowledge/law_knowledge.py new file mode 100644 index 00000000..a9534ee0 --- /dev/null +++ b/sample_standard_app/app/core/knowledge/law_knowledge.py @@ -0,0 +1,23 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- +from typing import List, Any + +# @Time : 2024/8/14 15:54 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: law_knowledge.py +import json + +from agentuniverse.agent.action.knowledge.knowledge import Knowledge +from agentuniverse.agent.action.knowledge.store.document import Document + + +class LawKnowledge(Knowledge): + def to_llm(self, retrieved_docs: List[Document]) -> Any: + + retrieved_texts = [json.dumps({ + "text": doc.text, + "from": doc.metadata["file_name"] + },ensure_ascii=False) for doc in retrieved_docs] + return '\n=========================================\n'.join( + retrieved_texts) diff --git a/sample_standard_app/app/core/knowledge/law_knowledge.yaml b/sample_standard_app/app/core/knowledge/law_knowledge.yaml new file mode 100644 index 00000000..09c714c9 --- /dev/null +++ b/sample_standard_app/app/core/knowledge/law_knowledge.yaml @@ -0,0 +1,21 @@ +name: "law_knowledge" +description: "中国民法与刑法相关的知识库" +stores: + - "civil_law_chroma_store" + - "criminal_law_chroma_store" + - "civil_law_sqlite_store" + - "criminal_law_sqlite_store" +query_paraphrasers: + - "custom_query_keyword_extractor" +insert_processors: + - "recursive_character_text_splitter" +rag_router: "nlu_rag_router" +post_processors: + - "dashscope_reranker" +readers: + pdf: "default_pdf_reader" + +metadata: + type: 'KNOWLEDGE' + module: 'sample_standard_app.app.core.knowledge.law_knowledge' + class: 'LawKnowledge' \ No newline at end of file diff --git a/sample_standard_app/app/core/query_paraphraser/__init__.py b/sample_standard_app/app/core/query_paraphraser/__init__.py new file mode 100644 index 00000000..afb8eef8 --- /dev/null +++ b/sample_standard_app/app/core/query_paraphraser/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/14 15:59 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/sample_standard_app/app/core/query_paraphraser/custom_query_keyword_extractor.yaml b/sample_standard_app/app/core/query_paraphraser/custom_query_keyword_extractor.yaml new file mode 100644 index 00000000..91010189 --- /dev/null +++ b/sample_standard_app/app/core/query_paraphraser/custom_query_keyword_extractor.yaml @@ -0,0 +1,7 @@ +name: 'custom_query_keyword_extractor' +description: 'extract keywords from query origin str' +keyword_extractor: 'query_keyword_extractor' +metadata: + type: 'QUERY_PARAPHRASER' + module: 'agentuniverse.agent.action.knowledge.query_paraphraser.query_keyword_extractor' + class: 'QueryKeywordExtractor' \ No newline at end of file diff --git a/sample_standard_app/app/core/rag_router/__init__.py b/sample_standard_app/app/core/rag_router/__init__.py new file mode 100644 index 00000000..c4051841 --- /dev/null +++ b/sample_standard_app/app/core/rag_router/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/13 17:07 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/sample_standard_app/app/core/rag_router/nlu_rag_router.yaml b/sample_standard_app/app/core/rag_router/nlu_rag_router.yaml new file mode 100644 index 00000000..cb082e1d --- /dev/null +++ b/sample_standard_app/app/core/rag_router/nlu_rag_router.yaml @@ -0,0 +1,10 @@ +name: 'nlu_rag_router' +description: 'base rag router map query to all store' +store_amount: 2 +llm: + name: demo_llm + model_name: gpt-4o +metadata: + type: 'RAG_ROUTER' + module: 'agentuniverse.agent.action.knowledge.rag_router.nlu_rag_router' + class: 'NluRagRouter' \ No newline at end of file diff --git a/sample_standard_app/app/core/store/__init__.py b/sample_standard_app/app/core/store/__init__.py new file mode 100644 index 00000000..72e19321 --- /dev/null +++ b/sample_standard_app/app/core/store/__init__.py @@ -0,0 +1,7 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/8/6 14:55 +# @Author : fanen.lhy +# @Email : fanen.lhy@antgroup.com +# @FileName: __init__.py.py diff --git a/sample_standard_app/app/core/store/civil_law_chroma_store.yaml b/sample_standard_app/app/core/store/civil_law_chroma_store.yaml new file mode 100644 index 00000000..01993454 --- /dev/null +++ b/sample_standard_app/app/core/store/civil_law_chroma_store.yaml @@ -0,0 +1,9 @@ +name: 'civil_law_chroma_store' +description: '保存了中国民法典的所有内容，以文本向量形式存储' +persist_path: '../../DB/civil_law.db' +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.chroma_store' + class: 'ChromaStore' \ No newline at end of file diff --git a/sample_standard_app/app/core/store/civil_law_sqlite_store.yaml b/sample_standard_app/app/core/store/civil_law_sqlite_store.yaml new file mode 100644 index 00000000..9fb14ae5 --- /dev/null +++ b/sample_standard_app/app/core/store/civil_law_sqlite_store.yaml @@ -0,0 +1,11 @@ +name: 'civil_law_sqlite_store' +description: '保存了中国民法的所有内容，以文本形式存储' +db_path: '../../DB/civil_law_sqlite.db' +k1: 1.5 +b: 0.75 +keyword_extractor: 'jieba_keyword_extractor' +similarity_top_k: 10 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.sqlite_store' + class: 'SQLiteStore' \ No newline at end of file diff --git a/sample_standard_app/app/core/store/criminal_law_chroma_store.yaml b/sample_standard_app/app/core/store/criminal_law_chroma_store.yaml new file mode 100644 index 00000000..e4480c45 --- /dev/null +++ b/sample_standard_app/app/core/store/criminal_law_chroma_store.yaml @@ -0,0 +1,9 @@ +name: 'criminal_law_chroma_store' +description: '保存了中国刑法的所有内容，以文本向量形式存储' +persist_path: '../../DB/criminal_law.db' +embedding_model: 'dashscope_embedding' +similarity_top_k: 100 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.chroma_store' + class: 'ChromaStore' \ No newline at end of file diff --git a/sample_standard_app/app/core/store/criminal_law_sqlite_store.yaml b/sample_standard_app/app/core/store/criminal_law_sqlite_store.yaml new file mode 100644 index 00000000..1c174340 --- /dev/null +++ b/sample_standard_app/app/core/store/criminal_law_sqlite_store.yaml @@ -0,0 +1,11 @@ +name: 'criminal_law_sqlite_store' +description: '保存了中国刑法的所有内容，以文本形式存储' +db_path: '../../DB/criminal_law_sqlite.db' +k1: 1.5 +b: 0.75 +keyword_extractor: 'jieba_keyword_extractor' +similarity_top_k: 10 +metadata: + type: 'STORE' + module: 'agentuniverse.agent.action.knowledge.store.sqlite_store' + class: 'SQLiteStore' \ No newline at end of file diff --git a/sample_standard_app/config/config.toml b/sample_standard_app/config/config.toml index d3369c6b..b561863c 100644 --- a/sample_standard_app/config/config.toml +++ b/sample_standard_app/config/config.toml @@ -25,12 +25,20 @@ prompt = ['sample_standard_app.app.core.prompt'] sqldb_wrapper = ['sample_standard_app.app.core.sqldb_wrapper'] # Scan and register product components for all paths under this list, with priority over the default. product = ['sample_standard_app.app.core.product'] +# Scan and register store components for all paths under this list, with priority over the default. +store = ['sample_standard_app.app.core.store'] +# Scan and register rag_router components for all paths under this list, with priority over the default. +rag_router = ['sample_standard_app.app.core.rag_router'] +# Scan and register doc_processor components for all paths under this list, with priority over the default. +doc_processor = ['sample_standard_app.app.core.doc_processor'] +# Scan and register query_paraphraser components for all paths under this list, with priority over the default. +query_paraphraser = ['sample_standard_app.app.core.query_paraphraser'] [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. 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