diff --git a/libs/chatchat-server/chatchat/server/chat/kb_chat.py b/libs/chatchat-server/chatchat/server/chat/kb_chat.py index 3aa1ce4..09fe58a 100644 --- a/libs/chatchat-server/chatchat/server/chat/kb_chat.py +++ b/libs/chatchat-server/chatchat/server/chat/kb_chat.py @@ -22,7 +22,7 @@ from chatchat.server.utils import (wrap_done, get_ChatOpenAI, get_default_llm, BaseResponse, get_prompt_template, build_logger, check_embed_model, api_address ) - +import time logger = build_logger() @@ -60,6 +60,8 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=[" return_direct: bool = Body(False, description="直接返回检索结果，不送入 LLM"), request: Request = None, ): + logger.info(f"kb_chat:,mode {mode}") + start_time = time.time() if mode == "local_kb": kb = KBServiceFactory.get_service_by_name(kb_name) if kb is None: @@ -67,6 +69,8 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=[" async def knowledge_base_chat_iterator() -> AsyncIterable[str]: try: + logger.info(f"***********************************knowledge_base_chat_iterator:,mode {mode}") + start_time1 = time.time() nonlocal history, prompt_name, max_tokens history = [History.from_data(h) for h in history] @@ -74,8 +78,10 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=[" if mode == "local_kb": kb = KBServiceFactory.get_service_by_name(kb_name) ok, msg = kb.check_embed_model() + logger.info(f"***********************************knowledge_base_chat_iterator:,mode {mode}，kb_name：{kb_name}") if not ok: raise ValueError(msg) + # docs = search_docs( query = query,knowledge_base_name = kb_name,top_k = top_k, score_threshold = score_threshold,) docs = await run_in_threadpool(search_docs, query=query, knowledge_base_name=kb_name, @@ -83,7 +89,13 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=[" score_threshold=score_threshold, file_name="", metadata={}) + source_documents = format_reference(kb_name, docs, api_address(is_public=True)) + logger.info( + f"***********************************knowledge_base_chat_iterator:,after format_reference:{docs}") + end_time1 = time.time() + execution_time1 = end_time1 - start_time1 + logger.info(f"kb_chat Execution time检索完成: {execution_time1:.6f} seconds") elif mode == "temp_kb": ok, msg = check_embed_model() if not ok: @@ -139,6 +151,7 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=[" if max_tokens in [None, 0]: max_tokens = Settings.model_settings.MAX_TOKENS + start_time1 = time.time() llm = get_ChatOpenAI( model_name=model, temperature=temperature, @@ -223,6 +236,12 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=[" return if stream: - return EventSourceResponse(knowledge_base_chat_iterator()) + eventSource = EventSourceResponse(knowledge_base_chat_iterator()) + # 记录结束时间 + end_time = time.time() + # 计算执行时间 + execution_time = end_time - start_time + logger.info(f"final kb_chat Execution time: {execution_time:.6f} seconds") + return eventSource else: return await knowledge_base_chat_iterator().__anext__() diff --git a/libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py b/libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py index 5949afe..56b35e0 100644 --- a/libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py +++ b/libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py @@ -72,10 +72,11 @@ def search_docs( if kb is not None: if query: docs = kb.search_docs(query, top_k, score_threshold) - logger.info(f"search_docs, query:{query},top_k:{top_k},score_threshold:{score_threshold}") - # data = [DocumentWithVSId(**x[0].dict(), score=x[1], id=x[0].metadata.get("id")) for x in docs] + # logger.info(f"search_docs, query:{query},top_k:{top_k},len(docs):{len(docs)},docs:{docs}") docs_key = kb.search_content_internal(query,2) + # logger.info(f"before merge_and_deduplicate docs_key:{docs_key}") docs = merge_and_deduplicate(docs, docs_key) + logger.info(f"after merge_and_deduplicate docs:{docs}") data = [DocumentWithVSId(**{"id": x.metadata.get("id"), **x.dict()}) for x in docs] elif file_name or metadata: data = kb.list_docs(file_name=file_name, metadata=metadata) @@ -84,26 +85,16 @@ def search_docs( del d.metadata["vector"] return [x.dict() for x in data] -def merge_and_deduplicate(list1: List[Tuple[Document, float]], list2: List[Tuple[Document, float]]) -> List[Tuple[Document, float]]: - # 使用字典来存储 page_content 和对应的元组 (Document, float) - merged_dict = {} - # 遍历第一个列表 - for item in list1: - document, value = item - page_content = document.page_content - # 如果 page_content 不在字典中，将其加入字典 - if page_content not in merged_dict: - merged_dict[page_content] = item +def merge_and_deduplicate(list1: List[Document], list2: List[Document]) -> List[Document]: + # 使用字典存储唯一的 Document + merged_dict = {doc.page_content: doc for doc in list1} - # 遍历第二个列表 - for item in list2: - document, value = item - page_content = document.page_content - # 如果 page_content 不在字典中，将其加入字典 - if page_content not in merged_dict: - merged_dict[page_content] = item + # 遍历 list2，将新的 Document 添加到字典 + for doc in list2: + if doc.page_content not in merged_dict: + merged_dict[doc.page_content] = doc - # 将字典的值转换为列表并返回 + # 返回去重后的列表 return list(merged_dict.values()) def list_files(knowledge_base_name: str) -> ListResponse: diff --git a/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/base.py b/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/base.py index 5be3efb..4b6730b 100644 --- a/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/base.py +++ b/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/base.py @@ -213,7 +213,7 @@ class KBService(ABC): def search_content_internal(self, query: str, top_k: int, - )->List[Tuple[Document, float]]: + )->List[Document]: docs = self.searchbyContentInternal(query,top_k) return docs def get_doc_by_ids(self, ids: List[str]) -> List[Document]: diff --git a/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/es_kb_service.py b/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/es_kb_service.py index 0d918cb..7aef928 100644 --- a/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/es_kb_service.py +++ b/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/es_kb_service.py @@ -37,9 +37,12 @@ class ESKBService(KBService): self.client_cert = kb_config.get("client_cert", None) self.dims_length = kb_config.get("dims_length", None) self.embeddings_model = get_Embeddings(self.embed_model) + logger.info(f"self.kb_path:{self.kb_path },self.index_name:{self.index_name}, self.scheme:{self.scheme},self.IP:{self.IP}," + f"self.PORT:{self.PORT},self.user:{self.user},self.password:{self.password},self.verify_certs:{self.verify_certs}," + f"self.client_cert:{self.client_cert},self.client_key:{self.client_key},self.dims_length:{self.dims_length}") try: connection_info = dict( - host=f"{self.scheme}://{self.IP}:{self.PORT}" + hosts=f"{self.scheme}://{self.IP}:{self.PORT}" ) if self.user != "" and self.password != "": connection_info.update(basic_auth=(self.user, self.password)) @@ -53,7 +56,9 @@ class ESKBService(KBService): connection_info.update(client_key=self.client_key) connection_info.update(client_cert=self.client_cert) # ES python客户端连接（仅连接） + logger.info(f"connection_info:{connection_info}") self.es_client_python = Elasticsearch(**connection_info) + # logger.info(f"after Elasticsearch connection_info:{connection_info}") except ConnectionError: logger.error("连接到 Elasticsearch 失败！") raise ConnectionError @@ -181,15 +186,17 @@ class ESKBService(KBService): search_results = self.es_client_python.search(index=self.index_name, body=tem_query, size=top_k) hits = [hit for hit in search_results["hits"]["hits"]] docs_and_scores = [ - ( + # ( Document( page_content=hit["_source"]["context"], metadata=hit["_source"]["metadata"], - ), - 1.3, - ) + ) + # , + # 1.3, + # ) for hit in hits ] + # logger.info(f"docs_and_scores:{docs_and_scores}") return docs_and_scores def get_doc_by_ids(self, ids: List[str]) -> List[Document]: results = [] @@ -227,7 +234,7 @@ class ESKBService(KBService): }, "track_total_hits": True, } - print(f"***do_delete_doc: kb_file.filepath:{kb_file.filepath}, base_file_name:{base_file_name}") + print(f"***do_delete_doc: kb_file.filepath:{kb_file.filepath}") # 注意设置size，默认返回10个。 search_results = self.es_client_python.search(index=self.index_name, body=query,size=200) delete_list = [hit["_id"] for hit in search_results['hits']['hits']]