update file format
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imClumsyPanda
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@ -48,7 +48,7 @@ PROMPT_TEMPLATES = {
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'<已知信息>{{ context }}</已知信息>\n'
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'<问题>{{ question }}</问题>\n',
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"Empty": # 搜不到知识库的时候使用
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"empty": # 搜不到知识库的时候使用
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'请你回答我的问题:\n'
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'{{ question }}\n\n',
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},
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@ -18,36 +18,49 @@ from server.knowledge_base.kb_doc_api import search_docs
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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(SCORE_THRESHOLD, 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", ge=0, le=2),
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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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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(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
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prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
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request: Request = None,
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):
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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(query: str,
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top_k: int,
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history: Optional[List[History]],
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model_name: str = LLM_MODELS[0],
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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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 = LLM_MODELS[0],
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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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@ -61,8 +74,8 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
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)
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docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
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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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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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@ -80,14 +93,14 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
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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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parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
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base_url = request.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 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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@ -108,4 +121,4 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
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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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media_type="text/event-stream")
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