99 lines
4.6 KiB
Python
99 lines
4.6 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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import os
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from urllib.parse import urlencode
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def knowledge_base_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
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knowledge_base_name: str = Body(..., description="知识库名称", examples=["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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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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local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"),
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request: Request = None,
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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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history = [History(**h) if isinstance(h, dict) else h for h in history]
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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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for inum, doc in enumerate(docs):
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filename = os.path.split(doc.metadata["source"])[-1]
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if local_doc_url:
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url = "file://" + doc.metadata["source"]
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else:
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parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name":filename})
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url = f"{request.base_url}knowledge_base/download_doc?" + parameters
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text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n"""
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source_documents.append(text)
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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,
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"docs": source_documents},
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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(knowledge_base_chat_iterator(query, kb, top_k, history),
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media_type="text/event-stream")
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