76 lines
3.5 KiB
Python
76 lines
3.5 KiB
Python
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, PROMPT_TEMPLATE,
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VECTOR_SEARCH_TOP_K)
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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 server.knowledge_base.kb_service.base import KBService, KBServiceFactory
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import json
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def knowledge_base_chat(query: str = Body(..., description="用户输入", example="你好"),
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knowledge_base_name: str = Body(..., description="知识库名称", example="samples"),
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top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
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history: List[History] = Body([],
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description="历史对话",
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example=[
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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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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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async def knowledge_base_chat_iterator(query: str,
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kb: KBService,
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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 = kb.search_docs(query, 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})
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await task
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return StreamingResponse(knowledge_base_chat_iterator(query, kb, top_k, history),
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media_type="text/event-stream")
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