162 lines
8.0 KiB
Python
162 lines
8.0 KiB
Python
from fastapi import Body, Request
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from fastapi.responses import StreamingResponse
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from sse_starlette.sse import EventSourceResponse
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from fastapi.concurrency import run_in_threadpool
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from configs import (LLM_MODELS,
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FIRST_VECTOR_SEARCH_TOP_K,
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VECTOR_SEARCH_TOP_K,
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SCORE_THRESHOLD,
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TEMPERATURE,
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USE_RERANKER,
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RERANKER_MODEL,
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RERANKER_MAX_LENGTH,
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MODEL_PATH,
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DOWNLOAD_BASE_URL)
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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, List, Optional
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from server.chat.utils import History
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from server.knowledge_base.kb_service.base import KBServiceFactory
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import json
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from urllib.parse import urlencode
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from server.knowledge_base.kb_doc_api import search_docs
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from server.reranker.reranker import LangchainReranker
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from server.utils import embedding_device
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async 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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score_threshold: float = Body(
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SCORE_THRESHOLD,
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description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右",
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ge=0,
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le=2
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),
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history: List[History] = Body(
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[],
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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_MODELS[0], 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: Optional[int] = Body(
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None,
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description="限制LLM生成Token数量，默认None代表模型最大值"
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),
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prompt_name: str = Body(
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"default",
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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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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.from_data(h) for h in history]
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async def knowledge_base_chat_iterator(
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query: str,
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top_k: int,
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history: Optional[List[History]],
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model_name: str = model_name,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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nonlocal max_tokens
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callback = AsyncIteratorCallbackHandler()
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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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 = search_docs(query, knowledge_base_name, top_k, score_threshold)
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# docs = await run_in_threadpool(search_docs,
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# query=query,
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# knowledge_base_name=knowledge_base_name,
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# top_k=top_k,
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# score_threshold=score_threshold)
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# 加入reranker
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if USE_RERANKER:
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reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL,"BAAI/bge-reranker-large")
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print("-----------------model path------------------")
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print(reranker_model_path)
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reranker_model = LangchainReranker(top_n=top_k,
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device=embedding_device(),
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max_length=RERANKER_MAX_LENGTH,
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model_name_or_path=reranker_model_path
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)
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docs = reranker_model.compress_documents(documents=docs,
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query=query)
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print("---------after rerank------------------")
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print(docs)
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context = "\n".join([doc.page_content for doc in docs])
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if len(docs) == 0: # 如果没有找到相关文档，使用empty模板
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prompt_template = get_prompt_template("knowledge_base_chat", "empty")
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else:
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prompt_template = get_prompt_template("knowledge_base_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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for inum, doc in enumerate(docs):
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filename = doc.metadata.get("source")
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parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
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#base_url = request.base_url
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#base_url = "http://10.138.20.199:17861/"
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base_url = DOWNLOAD_BASE_URL
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print(f"base_url:{base_url}")
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url = f"{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 len(source_documents) == 0: # 没有找到相关文档
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source_documents.append(f"<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>")
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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(knowledge_base_chat_iterator(query=query,
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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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#return EventSourceResponse(knowledge_base_chat_iterator(query, top_k, history,model_name,prompt_name))
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