from langchain.utilities.bing_search import BingSearchAPIWrapper from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY, LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE) from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from langchain.prompts.chat import ChatPromptTemplate from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.docstore.document import Document from fastapi import Body from fastapi.concurrency import run_in_threadpool from sse_starlette import EventSourceResponse from server.utils import wrap_done, get_ChatOpenAI from server.utils import BaseResponse, get_prompt_template from server.chat.utils import History from typing import AsyncIterable import asyncio import json from typing import List, Optional, Dict from strsimpy.normalized_levenshtein import NormalizedLevenshtein from markdownify import markdownify def bing_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs): if not (BING_SEARCH_URL and BING_SUBSCRIPTION_KEY): return [{"snippet": "please set BING_SUBSCRIPTION_KEY and BING_SEARCH_URL in os ENV", "title": "env info is not found", "link": "https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html"}] search = BingSearchAPIWrapper(bing_subscription_key=BING_SUBSCRIPTION_KEY, bing_search_url=BING_SEARCH_URL) return search.results(text, result_len) def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs): search = DuckDuckGoSearchAPIWrapper() return search.results(text, result_len) def metaphor_search( text: str, result_len: int = SEARCH_ENGINE_TOP_K, split_result: bool = False, chunk_size: int = 500, chunk_overlap: int = OVERLAP_SIZE, ) -> List[Dict]: from metaphor_python import Metaphor if not METAPHOR_API_KEY: return [] client = Metaphor(METAPHOR_API_KEY) search = client.search(text, num_results=result_len, use_autoprompt=True) contents = search.get_contents().contents for x in contents: x.extract = markdownify(x.extract) # metaphor 返回的内容都是长文本，需要分词再检索 if split_result: docs = [Document(page_content=x.extract, metadata={"link": x.url, "title": x.title}) for x in contents] text_splitter = RecursiveCharacterTextSplitter(["\n\n", "\n", ".", " "], chunk_size=chunk_size, chunk_overlap=chunk_overlap) splitted_docs = text_splitter.split_documents(docs) # 将切分好的文档放入临时向量库，重新筛选出TOP_K个文档 if len(splitted_docs) > result_len: normal = NormalizedLevenshtein() for x in splitted_docs: x.metadata["score"] = normal.similarity(text, x.page_content) splitted_docs.sort(key=lambda x: x.metadata["score"], reverse=True) splitted_docs = splitted_docs[:result_len] docs = [{"snippet": x.page_content, "link": x.metadata["link"], "title": x.metadata["title"]} for x in splitted_docs] else: docs = [{"snippet": x.extract, "link": x.url, "title": x.title} for x in contents] return docs SEARCH_ENGINES = {"bing": bing_search, "duckduckgo": duckduckgo_search, "metaphor": metaphor_search, } def search_result2docs(search_results): docs = [] for result in search_results: doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "", metadata={"source": result["link"] if "link" in result.keys() else "", "filename": result["title"] if "title" in result.keys() else ""}) docs.append(doc) return docs async def lookup_search_engine( query: str, search_engine_name: str, top_k: int = SEARCH_ENGINE_TOP_K, split_result: bool = False, ): search_engine = SEARCH_ENGINES[search_engine_name] results = await run_in_threadpool(search_engine, query, result_len=top_k, split_result=split_result) docs = search_result2docs(results) return docs async def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]), search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]), top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"), 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_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[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)"), split_result: bool = Body(False, description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）") ): if search_engine_name not in SEARCH_ENGINES.keys(): return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}") if search_engine_name == "bing" and not BING_SUBSCRIPTION_KEY: return BaseResponse(code=404, msg=f"要使用Bing搜索引擎，需要设置 `BING_SUBSCRIPTION_KEY`") history = [History.from_data(h) for h in history] async def search_engine_chat_iterator(query: str, search_engine_name: str, top_k: int, history: Optional[List[History]], model_name: str = LLM_MODELS[0], prompt_name: str = prompt_name, ) -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], ) docs = await lookup_search_engine(query, search_engine_name, top_k, split_result=split_result) context = "\n".join([doc.page_content for doc in docs]) prompt_template = get_prompt_template("search_engine_chat", prompt_name) input_msg = History(role="user", content=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) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"context": context, "question": query}), callback.done), ) source_documents = [ f"""出处 [{inum + 1}] [{doc.metadata["source"]}]({doc.metadata["source"]}) \n\n{doc.page_content}\n\n""" for inum, doc in enumerate(docs) ] if len(source_documents) == 0: # 没有找到相关资料（不太可能） source_documents.append(f"""未找到相关文档,该回答为大模型自身能力解答！""") 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 EventSourceResponse(search_engine_chat_iterator(query=query, search_engine_name=search_engine_name, top_k=top_k, history=history, model_name=model_name, prompt_name=prompt_name), )