[全量] 初始化项目代码、配置、文档及Agent协同harness
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248
langchain-chat/server/chat/self_kb_chat.py
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248
langchain-chat/server/chat/self_kb_chat.py
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from fastapi import Body, Request
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from sse_starlette.sse import EventSourceResponse
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from fastapi.concurrency import run_in_threadpool
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from configs import (LLM_MODELS,
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VECTOR_SEARCH_TOP_K,
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SCORE_THRESHOLD,
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SELF_TOP_K,
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SELF_SCORE_THRESHOLD,
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TEMPERATURE,
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SELF_TEMPERATURE,
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USE_RERANKER,
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RERANKER_MODEL,
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RERANKER_MAX_LENGTH,
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SELF_MAX_TOKENS,
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SELF_USE_RERANKER,
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MODEL_PATH)
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from server.chat.policy_fun_iast import get_llm_model_response
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from server.utils import wrap_done, get_ChatOpenAI
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from server.utils import BaseResponse, get_prompt_template
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from langchain.chains import LLMChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable, List, Optional
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from server.chat.utils import History, get_first_sentence_by_regex, get_text_by_regex
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from server.knowledge_base.kb_service.base import KBServiceFactory, TextRank
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import json
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from urllib.parse import urlencode
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from server.knowledge_base.kb_doc_api import search_self_docs
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from server.reranker.reranker import LangchainReranker
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from server.utils import embedding_device
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from configs.basic_config import *
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from langchain.memory import ConversationBufferMemory
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from langchain.chains.question_answering import load_qa_chain
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async def self_kb_chat(
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query: str = Body(..., description="用户输入", examples=["智慧科协是什么"]),
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quote: str = Body(..., description="用户引用的文段，引用问答时传该参数", examples=["今年，“智慧科协2.0”要持之以恒深入贯彻落实习近平总书记的重要指示精神"]),
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# word: str = Body(..., description="用户需要解释的名词，名词解释时传该参数", examples=["GDP"]),
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fileNames: List = Body([], description="文件名称", examples=[["孟庆海同志在“智慧科协2.0”5·30场景建设工作部署会议上的讲话.docx"]]),
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knowledge_base_name_list: list = Body(..., description="知识库列表",
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examples=[[ "p_cast0101011"]]),
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history: List[History] = Body(
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[],
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description="历史对话",
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examples=[[
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{"role": "user",
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"content": "我们来玩成语接龙，我先来，生龙活虎"},
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{"role": "assistant",
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"content": "虎头虎脑"}]]
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),
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stream: bool = Body(True, description="流式输出"),
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):
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"""
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个人知识库对话api\n
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-入参信息：\n
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query: 用户输入\n
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quote: 用户引用的文段，引用问答时传该参数\n
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fileNames: 文件名称\n
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knowledge_base_name: 知识库名称\n
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history: 历史对话\n
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stream: 是否流式输出\n
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"""
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logger.info(f"个人知识库对话入参：\nquery:{query}\nquote:{quote}\nfileNames:{fileNames}\nknowledge_base_name_list:{knowledge_base_name_list}\nhistory:{history}\nstream:{stream}")
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for knowledge_base_name in knowledge_base_name_list:
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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history = [History.to_msg_tuple(h) for h in history]
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async def knowledge_base_chat_iterator(
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query: str,
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model_name: str = LLM_MODELS[0],
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model_name1: str = LLM_MODELS[1],
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prompt_name: str = "self_default",
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) -> AsyncIterable[str]:
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nonlocal fileNames, history
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callback = AsyncIteratorCallbackHandler()
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model = get_ChatOpenAI(
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model_name=model_name,
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temperature=SELF_TEMPERATURE,
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max_tokens=SELF_MAX_TOKENS,
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callbacks=[callback],
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)
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model1 = get_ChatOpenAI(
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model_name=model_name1,
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temperature=SELF_TEMPERATURE,
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max_tokens=SELF_MAX_TOKENS,
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callbacks=[callback],
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)
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# 改写原问题
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# 遍历历史消息并收集用户消息
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user_queries = [] # 初始化列表来收集用户消息
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for message in history:
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role, content = message # 解包元组
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if role == 'user':
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user_queries.append(content)
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search_query = get_llm_model_response(
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strategy_name="self_query_rewrite",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="self_query_rewrite",
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prompt_param_dict={"query": query, "history": user_queries, "quote": quote},
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temperature=0.01,
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max_tokens=512
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)
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logger.info(f"个人知识库问答query: {query}")
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logger.info(f"个人知识库问答query_history: {user_queries}")
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json_string = search_query.strip("```json\n").strip("```")
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try: # 防止json格式错误
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# 读取改写后的query
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data = json.loads(json_string)
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query = data['query']
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search_query = ''
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for q in query:
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search_query += q
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except:
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search_query = query
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logger.info(f"个人知识库问答search_query: {search_query}")
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self_kb_route=get_llm_model_response(
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strategy_name="self_kb_route",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="self_kb_route",
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prompt_param_dict={"query": query},
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temperature=0.01,
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max_tokens=512
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)
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try:
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if self_kb_route == '0':
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logger.info(f"个人知识库问答路由结果：【全局问题】")
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docs = await run_in_threadpool(
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search_self_docs,
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query="",
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fileNames=fileNames,
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knowledge_base_name=knowledge_base_name,
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top_k=999,
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score_threshold=2
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)
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elif self_kb_route == '1':
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logger.info(f"个人知识库问答路由结果：【局部问题】")
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docs = await run_in_threadpool(
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search_self_docs,
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query=search_query,
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fileNames=fileNames,
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knowledge_base_name=knowledge_base_name,
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top_k=SELF_TOP_K,
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score_threshold=SELF_SCORE_THRESHOLD
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)
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except Exception as e:
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logger.error(f"个人知识库问答路由错误: {self_kb_route}", exc_info=True)
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docs = []
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logger.info(f"个人知识库问答source_documents: {docs}")
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# if SELF_USE_RERANKER:
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# reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL,"BAAI/bge-reranker-large")
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# print("-----------------model path------------------")
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# print(reranker_model_path)
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# reranker_model = LangchainReranker(top_n=SELF_TOP_K,
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# device=embedding_device(),
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# max_length=RERANKER_MAX_LENGTH,
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# model_name_or_path=reranker_model_path
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# )
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# for idx, doc in enumerate(docs, start=1):
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# print(f"{idx}: score={doc.score}")
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# docs = reranker_model.compress_documents(documents=docs,
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# query=query)
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# print("---------after rerank------------------")
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# for idx, doc in enumerate(docs, start=1):
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# print(f"{idx}: score={doc.score}")
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# context = "\n".join([doc.page_content for doc in docs]) #使用load_qa_chain需要送入DocumentWithVSId类型的资料
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# 判断是否找到相关文档
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if len(docs) == 0:
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prompt_name = "self_empty" # 如果没有找到相关文档，使用empty模板
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elif quote:
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# 根据quote的值选择不同的模板
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prompt_name = "self_quote" if quote else prompt_name
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# elif word:
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# 根据word的值选择不同的模板
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# prompt_name = "word_explain" if word else prompt_name
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# 获取模板并生成消息
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prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
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input_msg = History(role="system", content=prompt_template).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages([input_msg])
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if '0' in self_kb_route:
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context = "\n".join([doc.page_content for doc in docs]).strip("xa0")
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logger.info(f"个人知识库问答 context 长度：{len(context)}")
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# context_70 = context if len(context)<30000 else TextRank(context,num_sentences=70)
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context = context[:40000] if len(context)>40000 else context
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logger.info(f"截取后个人知识库问答 context 长度：{len(context)}")
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if history:
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history = history if len(history) < 20000 else TextRank(history,num_sentences=1)
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# logger.info(f"个人知识库问答 context 长度超过 30000，使用 TextRank 算法进行降维得到 context 长度：{len(context)}")
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chain = LLMChain(prompt=chat_prompt, llm=model1, verbose=True)
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task = asyncio.create_task(wrap_done(
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chain.acall({"context": context, "question": query, "history": history, "quote": quote, "fileName":fileNames}),
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callback.done),
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)
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elif '1' in self_kb_route:
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chain = load_qa_chain(
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model,
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chain_type="stuff",
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prompt=chat_prompt,
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verbose=True
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)
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# Begin a task that runs in the background.
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task = asyncio.create_task(wrap_done(
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chain.acall({"input_documents": docs, "question": query, "history": history, "quote": quote, "fileName":fileNames}),
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callback.done),
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)
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# source_documents = []
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# seen_texts = set() # 记录已出现过的 processed_text
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# counter = 1 # 初始化计数器
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# for doc in docs:
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# text = doc.metadata.get("summary", "")
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# # processed_text = get_first_sentence_by_regex(text)
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# processed_text = get_text_by_regex(text)
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# # 如果 processed_text 不在 seen_texts 中，才添加到结果中
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# if processed_text and processed_text not in seen_texts:
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# source_document = f"[{counter}] {processed_text}"
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# source_documents.append(source_document)
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# seen_texts.add(processed_text) # 标记为已出现
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# counter += 1
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if stream:
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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield json.dumps({"text": token}, ensure_ascii=False)
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else:
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answer = ""
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async for token in callback.aiter():
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answer += token
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response = {"text": answer}
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yield json.dumps(response, ensure_ascii=False)
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await task
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source_documents = []
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if len(docs) == 0: # 没有找到相关文档
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source_documents.append(f"""暂未从本篇文献中找到答案,该回答为大模型自身能力解答！""")
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else:
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# 去除文件扩展名
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# fileNames_without_ext = [name.rsplit('.', 1)[0] for name in fileNames]
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# 连接文件名（如果有多个文件名）
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# joined_fileNames = ', '.join(fileNames_without_ext)
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source_documents.append(f"""[{len(source_documents) + 1}] [{docs[0].metadata.get("source")}]()\n""")
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yield json.dumps({"docs": source_documents}, ensure_ascii=False)
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return EventSourceResponse(knowledge_base_chat_iterator(query))
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