248 lines
12 KiB
Python
248 lines
12 KiB
Python
from __future__ import annotations
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import asyncio, json
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import uuid
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from typing import AsyncIterable, List, Optional, Literal
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from fastapi import Body, Request
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from fastapi.concurrency import run_in_threadpool
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from sse_starlette.sse import EventSourceResponse
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from langchain.prompts.chat import ChatPromptTemplate
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from chatchat.settings import Settings
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from chatchat.server.agent.tools_factory.search_internet import search_engine
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from chatchat.server.api_server.api_schemas import OpenAIChatOutput
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from chatchat.server.chat.utils import History
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from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
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from chatchat.server.knowledge_base.kb_doc_api import search_docs, search_temp_docs
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from chatchat.server.knowledge_base.utils import format_reference
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from chatchat.server.utils import (wrap_done, get_ChatOpenAI, get_default_llm,
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BaseResponse, get_prompt_template, build_logger,
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check_embed_model, api_address
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)
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import time
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logger = build_logger()
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async def kb_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
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mode: Literal["local_kb", "temp_kb", "search_engine"] = Body("local_kb", description="知识来源"),
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kb_name: str = Body("", description="mode=local_kb时为知识库名称；temp_kb时为临时知识库ID，search_engine时为搜索引擎名称", examples=["samples"]),
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top_k: int = Body(Settings.kb_settings.VECTOR_SEARCH_TOP_K, description="匹配向量数"),
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score_threshold: float = Body(
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Settings.kb_settings.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(True, description="流式输出"),
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model: str = Body(get_default_llm(), description="LLM 模型名称。"),
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temperature: float = Body(Settings.model_settings.TEMPERATURE, description="LLM 采样温度", ge=0.0, le=2.0),
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max_tokens: Optional[int] = Body(
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Settings.model_settings.MAX_TOKENS,
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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prompt_settings.yaml中配置)"
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),
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return_direct: bool = Body(False, description="直接返回检索结果，不送入 LLM"),
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request: Request = None,
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):
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logger.info(f"kb_chat:,mode {mode}")
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start_time = time.time()
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if mode == "local_kb":
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kb = KBServiceFactory.get_service_by_name(kb_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 {kb_name}")
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async def knowledge_base_chat_iterator() -> AsyncIterable[str]:
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try:
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logger.info(f"***********************************knowledge_base_chat_iterator:,mode {mode}")
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start_time1 = time.time()
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nonlocal history, prompt_name, max_tokens
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history = [History.from_data(h) for h in history]
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if mode == "local_kb":
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kb = KBServiceFactory.get_service_by_name(kb_name)
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ok, msg = kb.check_embed_model()
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logger.info(f"***********************************knowledge_base_chat_iterator:,mode {mode}，kb_name：{kb_name}")
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if not ok:
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raise ValueError(msg)
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# docs = search_docs( query = query,knowledge_base_name = kb_name,top_k = top_k, score_threshold = 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=kb_name,
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top_k=top_k,
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score_threshold=score_threshold,
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file_name="",
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metadata={})
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source_documents = format_reference(kb_name, docs, api_address(is_public=True))
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logger.info(
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f"***********************************knowledge_base_chat_iterator:,after format_reference:{docs}")
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end_time1 = time.time()
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execution_time1 = end_time1 - start_time1
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logger.info(f"kb_chat Execution time检索完成: {execution_time1:.6f} seconds")
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elif mode == "temp_kb":
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ok, msg = check_embed_model()
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if not ok:
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raise ValueError(msg)
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docs = await run_in_threadpool(search_temp_docs,
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kb_name,
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query=query,
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top_k=top_k,
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score_threshold=score_threshold)
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source_documents = format_reference(kb_name, docs, api_address(is_public=True))
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elif mode == "search_engine":
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result = await run_in_threadpool(search_engine, query, top_k, kb_name)
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docs = [x.dict() for x in result.get("docs", [])]
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source_documents = [f"""出处 [{i + 1}] [{d['metadata']['filename']}]({d['metadata']['source']}) \n\n{d['page_content']}\n\n""" for i,d in enumerate(docs)]
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else:
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docs = []
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source_documents = []
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# import rich
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# rich.print(dict(
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# mode=mode,
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# query=query,
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# knowledge_base_name=kb_name,
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# top_k=top_k,
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# score_threshold=score_threshold,
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# ))
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# rich.print(docs)
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if return_direct:
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yield OpenAIChatOutput(
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id=f"chat{uuid.uuid4()}",
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model=None,
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object="chat.completion",
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content="",
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role="assistant",
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finish_reason="stop",
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docs=source_documents,
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) .model_dump_json()
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return
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callback = AsyncIteratorCallbackHandler()
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callbacks = [callback]
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# Enable langchain-chatchat to support langfuse
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import os
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langfuse_secret_key = os.environ.get('LANGFUSE_SECRET_KEY')
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langfuse_public_key = os.environ.get('LANGFUSE_PUBLIC_KEY')
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langfuse_host = os.environ.get('LANGFUSE_HOST')
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if langfuse_secret_key and langfuse_public_key and langfuse_host :
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from langfuse import Langfuse
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from langfuse.callback import CallbackHandler
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langfuse_handler = CallbackHandler()
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callbacks.append(langfuse_handler)
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if max_tokens in [None, 0]:
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max_tokens = Settings.model_settings.MAX_TOKENS
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start_time1 = time.time()
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llm = get_ChatOpenAI(
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model_name=model,
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=callbacks,
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)
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# TODO： 视情况使用 API
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# # 加入reranker
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# if Settings.kb_settings.USE_RERANKER:
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# reranker_model_path = get_model_path(Settings.kb_settings.RERANKER_MODEL)
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# reranker_model = LangchainReranker(top_n=top_k,
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# device=embedding_device(),
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# max_length=Settings.kb_settings.RERANKER_MAX_LENGTH,
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# model_name_or_path=reranker_model_path
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# )
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# print("-------------before rerank-----------------")
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# print(docs)
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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\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_name = "empty"
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prompt_template = get_prompt_template("rag", 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 = chat_prompt | llm
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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.ainvoke({"context": context, "question": query}),
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callback.done),
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)
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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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# yield documents first
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ret = OpenAIChatOutput(
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id=f"chat{uuid.uuid4()}",
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object="chat.completion.chunk",
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content="",
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role="assistant",
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model=model,
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docs=source_documents,
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)
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yield ret.model_dump_json()
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async for token in callback.aiter():
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ret = OpenAIChatOutput(
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id=f"chat{uuid.uuid4()}",
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object="chat.completion.chunk",
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content=token,
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role="assistant",
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model=model,
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)
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yield ret.model_dump_json()
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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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ret = OpenAIChatOutput(
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id=f"chat{uuid.uuid4()}",
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object="chat.completion",
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content=answer,
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role="assistant",
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model=model,
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)
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yield ret.model_dump_json()
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await task
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except asyncio.exceptions.CancelledError:
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logger.warning("streaming progress has been interrupted by user.")
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return
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except Exception as e:
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logger.error(f"error in knowledge chat: {e}")
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yield {"data": json.dumps({"error": str(e)})}
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return
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if stream:
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eventSource = EventSourceResponse(knowledge_base_chat_iterator())
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# 记录结束时间
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end_time = time.time()
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# 计算执行时间
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execution_time = end_time - start_time
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logger.info(f"final kb_chat Execution time: {execution_time:.6f} seconds")
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return eventSource
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else:
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return await knowledge_base_chat_iterator().__anext__()
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