185 lines
8.7 KiB
Python
185 lines
8.7 KiB
Python
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from langchain.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
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from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
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LLM_MODEL, SEARCH_ENGINE_TOP_K, TEMPERATURE,
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OVERLAP_SIZE)
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from fastapi import Body
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from fastapi.responses import StreamingResponse
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from fastapi.concurrency import run_in_threadpool
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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
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from typing import List, Optional, Dict
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from server.chat.utils import History
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from langchain.docstore.document import Document
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import json
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def bing_search(text, result_len=SEARCH_ENGINE_TOP_K):
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if not (BING_SEARCH_URL and BING_SUBSCRIPTION_KEY):
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return [{"snippet": "please set BING_SUBSCRIPTION_KEY and BING_SEARCH_URL in os ENV",
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"title": "env info is not found",
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"link": "https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html"}]
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search = BingSearchAPIWrapper(bing_subscription_key=BING_SUBSCRIPTION_KEY,
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bing_search_url=BING_SEARCH_URL)
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return search.results(text, result_len)
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def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K):
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search = DuckDuckGoSearchAPIWrapper()
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return search.results(text, result_len)
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def metaphor_search(
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text: str,
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result_len: int = SEARCH_ENGINE_TOP_K,
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splitter_name: str = "SpacyTextSplitter",
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chunk_size: int = 500,
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chunk_overlap: int = OVERLAP_SIZE,
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) -> List[Dict]:
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from metaphor_python import Metaphor
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from server.knowledge_base.kb_cache.faiss_cache import memo_faiss_pool
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from server.knowledge_base.utils import make_text_splitter
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if not METAPHOR_API_KEY:
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return []
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client = Metaphor(METAPHOR_API_KEY)
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search = client.search(text, num_results=result_len, use_autoprompt=True)
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contents = search.get_contents().contents
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# metaphor 返回的内容都是长文本，需要分词再检索
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docs = [Document(page_content=x.extract,
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metadata={"link": x.url, "title": x.title})
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for x in contents]
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text_splitter = make_text_splitter(splitter_name=splitter_name,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap)
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splitted_docs = text_splitter.split_documents(docs)
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# 将切分好的文档放入临时向量库，重新筛选出TOP_K个文档
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if len(splitted_docs) > result_len:
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vs = memo_faiss_pool.new_vector_store()
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vs.add_documents(splitted_docs)
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splitted_docs = vs.similarity_search(text, k=result_len, score_threshold=1.0)
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docs = [{"snippet": x.page_content,
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"link": x.metadata["link"],
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"title": x.metadata["title"]}
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for x in splitted_docs]
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return docs
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SEARCH_ENGINES = {"bing": bing_search,
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"duckduckgo": duckduckgo_search,
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"metaphor": metaphor_search,
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}
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def search_result2docs(search_results):
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docs = []
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for result in search_results:
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doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "",
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metadata={"source": result["link"] if "link" in result.keys() else "",
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"filename": result["title"] if "title" in result.keys() else ""})
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docs.append(doc)
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return docs
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async def lookup_search_engine(
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query: str,
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search_engine_name: str,
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top_k: int = SEARCH_ENGINE_TOP_K,
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):
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search_engine = SEARCH_ENGINES[search_engine_name]
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results = await run_in_threadpool(search_engine, query, result_len=top_k)
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docs = search_result2docs(results)
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return docs
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async def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
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search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
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top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
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history: List[History] = Body([],
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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(False, description="流式输出"),
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model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
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temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
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max_tokens: int = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
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prompt_name: str = Body("default",description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
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):
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if search_engine_name not in SEARCH_ENGINES.keys():
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return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
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if search_engine_name == "bing" and not BING_SUBSCRIPTION_KEY:
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return BaseResponse(code=404, msg=f"要使用Bing搜索引擎，需要设置 `BING_SUBSCRIPTION_KEY`")
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history = [History.from_data(h) for h in history]
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async def search_engine_chat_iterator(query: str,
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search_engine_name: str,
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top_k: int,
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history: Optional[List[History]],
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model_name: str = LLM_MODEL,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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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=temperature,
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max_tokens=max_tokens,
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callbacks=[callback],
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)
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docs = await lookup_search_engine(query, search_engine_name, top_k)
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context = "\n".join([doc.page_content for doc in docs])
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prompt_template = get_prompt_template("search_engine_chat", prompt_name)
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input_msg = History(role="user", content=prompt_template).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_template() for i in history] + [input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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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({"context": context, "question": query}),
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callback.done),
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)
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source_documents = [
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f"""出处 [{inum + 1}] [{doc.metadata["source"]}]({doc.metadata["source"]}) \n\n{doc.page_content}\n\n"""
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for inum, doc in enumerate(docs)
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]
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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({"answer": token}, ensure_ascii=False)
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yield json.dumps({"docs": source_documents}, 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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yield json.dumps({"answer": answer,
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"docs": source_documents},
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ensure_ascii=False)
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await task
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return StreamingResponse(search_engine_chat_iterator(query=query,
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search_engine_name=search_engine_name,
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top_k=top_k,
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history=history,
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model_name=model_name,
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prompt_name=prompt_name),
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media_type="text/event-stream")
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