117 lines
5.1 KiB
Python
117 lines
5.1 KiB
Python
from langchain.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
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from configs.model_config import BING_SEARCH_URL, BING_SUBSCRIPTION_KEY
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from fastapi import Body
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from fastapi.responses import StreamingResponse
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from configs.model_config import (llm_model_dict, LLM_MODEL, SEARCH_ENGINE_TOP_K, PROMPT_TEMPLATE)
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from server.chat.utils import wrap_done
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from server.utils import BaseResponse
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from langchain.chat_models import ChatOpenAI
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from langchain 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
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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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SEARCH_ENGINES = {"bing": bing_search,
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"duckduckgo": duckduckgo_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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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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results = SEARCH_ENGINES[search_engine_name](query, result_len=top_k)
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docs = search_result2docs(results)
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return docs
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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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):
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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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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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) -> AsyncIterable[str]:
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callback = AsyncIteratorCallbackHandler()
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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callbacks=[callback],
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL
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)
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docs = 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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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
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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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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,
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"docs": source_documents}, ensure_ascii=False)
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await task
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return StreamingResponse(search_engine_chat_iterator(query, search_engine_name, top_k, history),
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media_type="text/event-stream")
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