[add] app chat service
This commit is contained in:
Mark
@@ -475,11 +475,298 @@ class AppChatService:
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# 发送错误事件
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yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
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async def workflow_chat(
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: AgentConfig,
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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web_search: bool = False,
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memory: bool = True,
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storage_type: Optional[str] = None,
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user_rag_memory_id: Optional[str] = None,
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) -> Dict[str, Any]:
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"""聊天（非流式）"""
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start_time = time.time()
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config_id = None
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if variables is None:
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variables = {}
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# 获取模型配置ID
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model_config_id = config.default_model_config_id
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api_key_obj = ModelApiKeyService.get_a_api_key(model_config_id)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.get("system_prompt", "")
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 添加知识库检索工具
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knowledge_retrieval = config.get("knowledge_retrieval")
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if knowledge_retrieval:
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knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
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kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
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if kb_ids:
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kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id)
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tools.append(kb_tool)
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# 添加长期记忆工具
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memory_flag = False
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if memory == True:
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memory_config = config.get("memory", {})
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if memory_config.get("enabled") and user_id:
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memory_flag = True
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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web_tools = config.get("tools")
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web_search_choice = web_tools.get("web_search", {})
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web_search_enable = web_search_choice.get("enabled", False)
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if web_search == True:
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if web_search_enable == True:
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search_tool = create_web_search_tool({})
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tools.append(search_tool)
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logger.debug(
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"已添加网络搜索工具",
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extra={
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"tool_count": len(tools)
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}
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)
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# 获取模型参数
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model_parameters = config.get("model_parameters", {})
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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)
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# 加载历史消息
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history = []
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memory_config = {"enabled": True, 'max_history': 10}
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if memory_config.get("enabled"):
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messages = self.conversation_service.get_messages(
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conversation_id=conversation_id,
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limit=memory_config.get("max_history", 10)
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)
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history = [
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{"role": msg.role, "content": msg.content}
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for msg in messages
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]
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# 调用 Agent
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result = await agent.chat(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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config_id=config_id,
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memory_flag=memory_flag
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)
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# 保存消息
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self.conversation_service.save_conversation_messages(
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conversation_id=conversation_id,
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user_message=message,
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assistant_message=result["content"]
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)
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elapsed_time = time.time() - start_time
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return {
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"conversation_id": conversation_id,
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"message": result["content"],
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"usage": result.get("usage", {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}),
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"elapsed_time": elapsed_time
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}
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async def workflow_chat_stream(
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: AgentConfig,
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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web_search: bool = False,
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memory: bool = True,
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storage_type: Optional[str] = None,
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user_rag_memory_id: Optional[str] = None,
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) -> AsyncGenerator[str, None]:
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"""聊天（流式）"""
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try:
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start_time = time.time()
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config_id = None
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if variables is None:
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variables = {}
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# 获取模型配置ID
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model_config_id = config.default_model_config_id
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api_key_obj = ModelApiKeyService.get_a_api_key(model_config_id)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.get("system_prompt", "")
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 添加知识库检索工具
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knowledge_retrieval = config.get("knowledge_retrieval")
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if knowledge_retrieval:
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knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
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kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
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if kb_ids:
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kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id)
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tools.append(kb_tool)
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# 添加长期记忆工具
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memory_flag = False
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if memory:
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memory_config = config.get("memory", {})
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if memory_config.get("enabled") and user_id:
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memory_flag = True
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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web_tools = config.get("tools")
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web_search_choice = web_tools.get("web_search", {})
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web_search_enable = web_search_choice.get("enabled", False)
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if web_search == True:
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if web_search_enable == True:
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search_tool = create_web_search_tool({})
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tools.append(search_tool)
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logger.debug(
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"已添加网络搜索工具",
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extra={
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"tool_count": len(tools)
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}
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)
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# 获取模型参数
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model_parameters = config.get("model_parameters", {})
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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streaming=True
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)
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# 加载历史消息
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history = []
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memory_config = {"enabled": True, 'max_history': 10}
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if memory_config.get("enabled"):
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messages = self.conversation_service.get_messages(
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conversation_id=conversation_id,
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limit=memory_config.get("max_history", 10)
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)
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history = [
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{"role": msg.role, "content": msg.content}
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for msg in messages
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]
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# 发送开始事件
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yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n"
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# 流式调用 Agent
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full_content = ""
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async for chunk in agent.chat_stream(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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config_id=config_id,
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memory_flag=memory_flag
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):
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full_content += chunk
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# 发送消息块事件
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yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
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elapsed_time = time.time() - start_time
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# 保存消息
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="user",
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content=message
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)
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=full_content,
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meta_data={
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"model": api_key_obj.model_name,
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"usage": {}
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}
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)
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# 发送结束事件
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end_data = {"elapsed_time": elapsed_time, "message_length": len(full_content)}
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yield f"event: end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n"
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logger.info(
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"流式聊天完成",
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extra={
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"conversation_id": str(conversation_id),
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"elapsed_time": elapsed_time,
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"message_length": len(full_content)
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}
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)
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except (GeneratorExit, asyncio.CancelledError):
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# 生成器被关闭或任务被取消，正常退出
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logger.debug("流式聊天被中断")
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raise
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except Exception as e:
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logger.error(f"流式聊天失败: {str(e)}", exc_info=True)
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# 发送错误事件
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yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
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# ==================== 依赖注入函数 ====================
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def get_app_chat_service(
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db: Annotated[Session, Depends(get_db)]
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) -> ChatService:
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) -> AppChatService:
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"""获取工作流服务（依赖注入）"""
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return ChatService(db)
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return AppChatService(db)
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