from fastapi import Body, Request from fastapi.responses import StreamingResponse from configs import (LLM_MODEL, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, TEMPERATURE) from server.utils import wrap_done, get_ChatOpenAI from server.utils import BaseResponse, get_prompt_template from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, List, Optional import asyncio from langchain.prompts.chat import ChatPromptTemplate from server.chat.utils import History from server.knowledge_base.kb_service.base import KBService, KBServiceFactory import json import os from urllib.parse import urlencode from server.knowledge_base.kb_doc_api import search_docs from langchain.prompts import PromptTemplate async def knowledge_base_chat(query: str = Body(..., description="用户输入", examples=["你好"]), knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]), top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"), score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=2), history: List[History] = Body([], description="历史对话", examples=[[ {"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"}, {"role": "assistant", "content": "虎头虎脑"}]] ), stream: bool = Body(False, description="流式输出"), model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: int = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): kb = KBServiceFactory.get_service_by_name(knowledge_base_name) if kb is None: return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}") history = [History.from_data(h) for h in history] print(f"server/chat/knowledge_base_chat function, history:{history}") async def knowledge_base_chat_iterator(query: str, top_k: int, history: Optional[List[History]], model_name: str = LLM_MODEL, prompt_name: str = prompt_name, ) -> AsyncIterable[str]: print(f"knowledge_base_chat_iterator,query:{query},model_name:{model_name},prompt_name:{prompt_name}") model1 = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[], ) # augment_prompt_template = get_prompt_template("data_augment", "default") # input_msg1 = History(role="user", content=augment_prompt_template).to_msg_template(False) # chat_prompt1 = ChatPromptTemplate.from_messages( # [i.to_msg_template() for i in history] + [input_msg1]) # chain1 = LLMChain(prompt=chat_prompt1, llm=model1) # print(f"knowledge_base_chat_iterator,prompt_template:{chat_prompt1}") # result = chain1._call({ "question": query}) # print(f"chain1._call, result:{result}") callback = AsyncIteratorCallbackHandler() model = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], ) #augment_prompt_template = get_prompt_template("data_augment", "default") #input_msg = History(role="user", content=augment_prompt_template).to_msg_template(False) #chat_prompt = ChatPromptTemplate.from_messages( # [i.to_msg_template() for i in history] + [input_msg]) #chain = LLMChain(prompt=chat_prompt, llm=model) #print(f"knowledge_base_chat_iterator,prompt_template:{chat_prompt}") ##chain = LLMChain(prompt=PromptTemplate.from_template(augment_prompt_template), llm=model) ##print(f"knowledge_base_chat_iterator,prompt_template:{augment_prompt_template}") #task = asyncio.create_task(wrap_done( # chain.acall({ "question": query}), # callback.done), #) #prompt_template = "请找出和{question}最相似的一句话" #llm_chain = LLMChain(prompt=PromptTemplate.from_template(prompt_template), llm=model) #result = llm_chain(query) #print(f"请找出和question 最相似的一句话：{result}") docs = search_docs(query, knowledge_base_name, top_k, score_threshold, model1) context = "\n".join([doc.page_content for doc in docs]) #print(f"knowledge_base_chat_iterator,search docs context:{context}") prompt_template = get_prompt_template("knowledge_base_chat", prompt_name) input_msg = History(role="user", content=prompt_template).to_msg_template(False) print(f"knowledge_base_chat_iterator,input_msg:{input_msg}") chat_prompt = ChatPromptTemplate.from_messages( [i.to_msg_template() for i in history] + [input_msg]) #print(f"knowledge_base_chat_iterator,chat_prompt:{chat_prompt}") chain = LLMChain(prompt=chat_prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"context": context, "question": query}), callback.done), ) print(f"task call end") source_documents = [] for inum, doc in enumerate(docs): filename = os.path.split(doc.metadata["source"])[-1] parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name":filename}) url = f"/knowledge_base/download_doc?" + parameters text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n""" source_documents.append(text) print(f"knowledge_base_chat_iterator, stream:{stream}") if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield json.dumps({"answer": token}, ensure_ascii=False) yield json.dumps({"docs": source_documents}, ensure_ascii=False) else: answer = "" async for token in callback.aiter(): answer += token yield json.dumps({"answer": answer, "docs": source_documents}, ensure_ascii=False) await task return StreamingResponse(knowledge_base_chat_iterator(query=query, top_k=top_k, history=history, model_name=model_name, prompt_name=prompt_name), media_type="text/event-stream")