修复：chat 接口默认使用 memory获取10条历史消息，导致最终拼接的prompt出错 (#2247)
修改后默认使用用户传入的history，只有同时传入conversation_id和history_len时才从数据库读取历史消息 使用memory还有一个问题，对话角色是固定的，不能适配不同的模型。 其它：注释一些无用的 print
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liunux4odoo
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@ -19,6 +19,7 @@ from server.callback_handler.conversation_callback_handler import ConversationCa
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async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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conversation_id: str = Body("", description="对话框ID"),
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history_len: int = Body(-1, description="从数据库中取历史消息的数量"),
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history: Union[int, List[History]] = Body([],
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description="历史对话，设为一个整数可以从数据库中读取历史消息",
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examples=[[
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@ -39,12 +40,14 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
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callbacks = [callback]
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memory = None
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message_id = add_message_to_db(chat_type="llm_chat", query=query, conversation_id=conversation_id)
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# 负责保存llm response到message db
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conversation_callback = ConversationCallbackHandler(conversation_id=conversation_id, message_id=message_id,
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chat_type="llm_chat",
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query=query)
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callbacks.append(conversation_callback)
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if conversation_id:
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message_id = add_message_to_db(chat_type="llm_chat", query=query, conversation_id=conversation_id)
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# 负责保存llm response到message db
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conversation_callback = ConversationCallbackHandler(conversation_id=conversation_id, message_id=message_id,
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chat_type="llm_chat",
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query=query)
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callbacks.append(conversation_callback)
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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@ -55,18 +58,24 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
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callbacks=callbacks,
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)
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if not conversation_id:
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if history: # 优先使用前端传入的历史消息
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history = [History.from_data(h) for h in history]
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prompt_template = get_prompt_template("llm_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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else:
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elif conversation_id and history_len > 0: # 前端要求从数据库取历史消息
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# 使用memory 时必须 prompt 必须含有memory.memory_key 对应的变量
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prompt = get_prompt_template("llm_chat", "with_history")
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chat_prompt = PromptTemplate.from_template(prompt)
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# 根据conversation_id 获取message 列表进而拼凑 memory
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memory = ConversationBufferDBMemory(conversation_id=conversation_id, llm=model)
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memory = ConversationBufferDBMemory(conversation_id=conversation_id,
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llm=model,
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message_limit=history_len)
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else:
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prompt_template = get_prompt_template("llm_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([input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model, memory=memory)
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@ -85,7 +85,7 @@ class ApiRequest:
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) -> Union[httpx.Response, Iterator[httpx.Response], None]:
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while retry > 0:
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try:
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print(kwargs)
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# print(kwargs)
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if stream:
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return self.client.stream("POST", url, data=data, json=json, **kwargs)
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else:
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@ -134,7 +134,7 @@ class ApiRequest:
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if as_json:
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try:
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data = json.loads(chunk)
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pprint(data, depth=1)
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# pprint(data, depth=1)
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yield data
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except Exception as e:
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msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
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@ -166,7 +166,7 @@ class ApiRequest:
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if as_json:
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try:
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data = json.loads(chunk)
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pprint(data, depth=1)
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# pprint(data, depth=1)
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yield data
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except Exception as e:
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msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
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@ -291,6 +291,7 @@ class ApiRequest:
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self,
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query: str,
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conversation_id: str = None,
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history_len: int = -1,
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history: List[Dict] = [],
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stream: bool = True,
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model: str = LLM_MODELS[0],
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@ -300,11 +301,12 @@ class ApiRequest:
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**kwargs,
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):
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'''
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对应api.py/chat/chat接口 #TODO: 考虑是否返回json
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对应api.py/chat/chat接口
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'''
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data = {
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"query": query,
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"conversation_id": conversation_id,
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"history_len": history_len,
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"history": history,
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"stream": stream,
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"model_name": model,
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@ -342,8 +344,8 @@ class ApiRequest:
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"prompt_name": prompt_name,
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}
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print(f"received input message:")
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pprint(data)
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# print(f"received input message:")
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# pprint(data)
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response = self.post("/chat/agent_chat", json=data, stream=True)
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return self._httpx_stream2generator(response)
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@ -377,8 +379,8 @@ class ApiRequest:
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"prompt_name": prompt_name,
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}
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print(f"received input message:")
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pprint(data)
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# print(f"received input message:")
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# pprint(data)
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response = self.post(
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"/chat/knowledge_base_chat",
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@ -452,8 +454,8 @@ class ApiRequest:
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"prompt_name": prompt_name,
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}
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print(f"received input message:")
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pprint(data)
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# print(f"received input message:")
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# pprint(data)
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response = self.post(
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"/chat/file_chat",
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@ -491,8 +493,8 @@ class ApiRequest:
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"split_result": split_result,
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}
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print(f"received input message:")
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pprint(data)
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# print(f"received input message:")
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# pprint(data)
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response = self.post(
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"/chat/search_engine_chat",
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