70 lines
3.0 KiB
Python
70 lines
3.0 KiB
Python
from langchain.utilities import BingSearchAPIWrapper
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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 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 import PromptTemplate
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from langchain.docstore.document import Document
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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 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 bing_search_chat(query: str = Body(..., description="用户输入", example="你好"),
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top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
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):
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async def bing_search_chat_iterator(query: str,
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top_k: int,
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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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results = bing_search(query, result_len=top_k)
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docs = search_result2docs(results)
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context = "\n".join([doc.page_content for doc in docs])
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prompt = PromptTemplate(template=PROMPT_TEMPLATE, input_variables=["context", "question"])
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chain = LLMChain(prompt=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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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield token
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await task
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return StreamingResponse(bing_search_chat_iterator(query, top_k), media_type="text/event-stream")
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