"""LangGraph Agent service with tool calling capabilities.""" import asyncio from typing import List, Dict, Any, Optional, AsyncGenerator from langchain_core.messages import HumanMessage, AIMessage from langchain_core.tools import tool # Import logger first from ...utils.logger import get_logger logger = get_logger("langgraph_agent_service") # Try to import langgraph related modules try: from langgraph.prebuilt import create_react_agent LANGGRAPH_AVAILABLE = True except ImportError: logger.warning("langgraph not available. LangGraph agent functionality will be disabled.") create_react_agent = None LANGGRAPH_AVAILABLE = False # Try to import init_chat_model from langchain_openai try: from langchain_openai import ChatOpenAI # Create a simple init_chat_model function as a replacement def init_chat_model(model_name: str, **kwargs): return ChatOpenAI(model=model_name, **kwargs) except ImportError: logger.warning("langchain_openai not available. Chat model functionality may be limited.") init_chat_model = None from pydantic import BaseModel, Field from .base import ToolRegistry from th_agenter.services.tools import WeatherQueryTool, TavilySearchTool, DateTimeTool from ..postgresql_tool_manager import get_postgresql_tool from ...core.config import get_settings from ...utils.logger import get_logger from ..agent_config import AgentConfigService logger = get_logger("langgraph_agent_service") class LangGraphAgentConfig(BaseModel): """LangGraph Agent configuration.""" model_name: str = Field(default="gpt-3.5-turbo") model_provider: str = Field(default="openai") base_url: Optional[str] = Field(default=None) api_key: Optional[str] = Field(default=None) enabled_tools: List[str] = Field(default_factory=lambda: [ "calculator", "weather", "search", "file", "image" ]) max_iterations: int = Field(default=10) temperature: float = Field(default=0.7) max_tokens: int = Field(default=1000) system_message: str = Field( default="""你是一个有用的AI助手，可以使用各种工具来帮助用户解决问题。 重要规则： 1. 工具调用失败时，必须仔细分析失败原因，特别是参数格式问题 3. 在重新调用工具前，先解释上次失败的原因和改进方案 4. 确保每个工具调用的参数格式严格符合工具的要求 """ ) verbose: bool = Field(default=True) class LangGraphAgentService: """LangGraph Agent service using create_react_agent.""" def __init__(self, db_session=None): self.settings = get_settings() self.tool_registry = ToolRegistry() self.config = LangGraphAgentConfig() # Check if langgraph is available if not LANGGRAPH_AVAILABLE: logger.warning("LangGraph is not available. Some features may be disabled.") self.tools = [] self.db_session = db_session self.config_service = AgentConfigService(db_session) if db_session else None self._initialize_tools() self._load_config() self._create_agent() def _initialize_tools(self): """Initialize available tools.""" # Use the @tool decorated functions self.tools = [ WeatherQueryTool(), TavilySearchTool(), DateTimeTool() ] def _load_config(self): """Load configuration from database if available.""" if self.config_service: try: db_config = self.config_service.get_active_config() if db_config: # Update config with database values config_dict = db_config.config_data for key, value in config_dict.items(): if hasattr(self.config, key): setattr(self.config, key, value) logger.info("Loaded configuration from database") except Exception as e: logger.warning(f"Failed to load config from database: {e}") def _create_agent(self): """Create LangGraph agent using create_react_agent.""" try: # Initialize the model llm_config = get_settings().llm.get_current_config() self.model = init_chat_model( model=llm_config['model'], model_provider='openai', temperature=llm_config['temperature'], max_tokens=llm_config['max_tokens'], base_url= llm_config['base_url'], api_key=llm_config['api_key'] ) # Create the react agent self.agent = create_react_agent( model=self.model, tools=self.tools,) logger.info("LangGraph React agent created successfully") except Exception as e: logger.error(f"Failed to create agent: {str(e)}") raise def _format_tools_info(self) -> str: """Format tools information for the prompt.""" tools_info = [] for tool_name in self.config.enabled_tools: tool = self.tool_registry.get_tool(tool_name) if tool: params_info = [] for param in tool.get_parameters(): params_info.append(f" - {param.name} ({param.type.value}): {param.description}") tool_info = f"**{tool.get_name()}**: {tool.get_description()}" if params_info: tool_info += "\n" + "\n".join(params_info) tools_info.append(tool_info) return "\n\n".join(tools_info) async def chat(self, message: str, chat_history: Optional[List[Dict[str, str]]] = None) -> Dict[str, Any]: """Process a chat message using LangGraph.""" try: logger.info(f"Starting chat with message: {message[:100]}...") # Convert chat history to messages messages = [] if chat_history: for msg in chat_history: if msg["role"] == "user": messages.append(HumanMessage(content=msg["content"])) elif msg["role"] == "assistant": messages.append(AIMessage(content=msg["content"])) # Add current message messages.append(HumanMessage(content=message)) # Use the react agent directly result = await self.agent.ainvoke({"messages": messages}, {"recursion_limit": 6},) # Extract final response final_response = "" if "messages" in result and result["messages"]: last_message = result["messages"][-1] if hasattr(last_message, 'content'): final_response = last_message.content elif isinstance(last_message, dict) and "content" in last_message: final_response = last_message["content"] return { "response": final_response, "intermediate_steps": [], "success": True, "error": None } except Exception as e: logger.error(f"LangGraph chat error: {str(e)}", exc_info=True) return { "response": f"抱歉，处理您的请求时出现错误: {str(e)}", "intermediate_steps": [], "success": False, "error": str(e) } async def chat_stream(self, message: str, chat_history: Optional[List[Dict[str, str]]] = None) -> AsyncGenerator[ Dict[str, Any], None]: """Process a chat message using LangGraph with streaming.""" try: logger.info(f"Starting streaming chat with message: {message[:100]}...") # Convert chat history to messages messages = [] if chat_history: for msg in chat_history: if msg["role"] == "user": messages.append(HumanMessage(content=msg["content"])) elif msg["role"] == "assistant": messages.append(AIMessage(content=msg["content"])) # Add current message messages.append(HumanMessage(content=message)) # Track state for streaming intermediate_steps = [] final_response_started = False accumulated_response = "" final_ai_message = None # Stream the agent execution async for event in self.agent.astream({"messages": messages}): # Handle different event types from LangGraph print('event===', event) if isinstance(event, dict): for node_name, node_output in event.items(): logger.info(f"Processing node: {node_name}, output type: {type(node_output)}") # 处理 tools 节点 if "tools" in node_name.lower(): # 提取工具信息 tool_infos = [] if isinstance(node_output, dict) and "messages" in node_output: messages_in_output = node_output["messages"] for msg in messages_in_output: # 处理 ToolMessage 对象 if hasattr(msg, 'name') and hasattr(msg, 'content'): tool_info = { "tool_name": msg.name, "tool_output": msg.content, "tool_call_id": getattr(msg, 'tool_call_id', ''), "status": "completed" } tool_infos.append(tool_info) elif isinstance(msg, dict): if 'name' in msg and 'content' in msg: tool_info = { "tool_name": msg['name'], "tool_output": msg['content'], "tool_call_id": msg.get('tool_call_id', ''), "status": "completed" } tool_infos.append(tool_info) # 返回 tools_end 事件 for tool_info in tool_infos: yield { "type": "tools_end", "content": f"工具 {tool_info['tool_name']} 执行完成", "tool_name": tool_info["tool_name"], "tool_output": tool_info["tool_output"], "node_name": node_name, "done": False } await asyncio.sleep(0.1) # 处理 agent 节点 elif "agent" in node_name.lower(): if isinstance(node_output, dict) and "messages" in node_output: messages_in_output = node_output["messages"] if messages_in_output: last_msg = messages_in_output[-1] # 获取 finish_reason finish_reason = None if hasattr(last_msg, 'response_metadata'): finish_reason = last_msg.response_metadata.get('finish_reason') elif isinstance(last_msg, dict) and 'response_metadata' in last_msg: finish_reason = last_msg['response_metadata'].get('finish_reason') # 判断是否为 thinking 或 response if finish_reason == 'tool_calls': # thinking 状态 thinking_content = "🤔 正在思考..." if hasattr(last_msg, 'content') and last_msg.content: thinking_content = f"🤔 思考: {last_msg.content[:200]}..." elif isinstance(last_msg, dict) and "content" in last_msg: thinking_content = f"🤔 思考: {last_msg['content'][:200]}..." yield { "type": "thinking", "content": thinking_content, "node_name": node_name, "raw_output": str(node_output)[:500] if node_output else "", "done": False } await asyncio.sleep(0.1) elif finish_reason == 'stop': # response 状态 if hasattr(last_msg, 'content') and hasattr(last_msg, '__class__') and 'AI' in last_msg.__class__.__name__: current_content = last_msg.content final_ai_message = last_msg if not final_response_started and current_content: final_response_started = True yield { "type": "response_start", "content": "", "intermediate_steps": intermediate_steps, "done": False } if current_content and len(current_content) > len(accumulated_response): new_content = current_content[len(accumulated_response):] for char in new_content: accumulated_response += char yield { "type": "response", "content": accumulated_response, "intermediate_steps": intermediate_steps, "done": False } await asyncio.sleep(0.03) else: # 其他 agent 状态 yield { "type": "step", "content": f"📋 执行步骤: {node_name}", "node_name": node_name, "raw_output": str(node_output)[:500] if node_output else "", "done": False } await asyncio.sleep(0.1) # 处理其他节点 else: yield { "type": "step", "content": f"📋 执行步骤: {node_name}", "node_name": node_name, "raw_output": str(node_output)[:500] if node_output else "", "done": False } await asyncio.sleep(0.1) # 最终完成事件 yield { "type": "complete", "content": accumulated_response, "intermediate_steps": intermediate_steps, "done": True } except Exception as e: logger.error(f"Error in chat_stream: {str(e)}", exc_info=True) yield { "type": "error", "content": f"处理请求时出错: {str(e)}", "done": True } # 确保最终响应包含完整内容 final_content = accumulated_response if not final_content and final_ai_message and hasattr(final_ai_message, 'content'): final_content = final_ai_message.content or "" # Final completion signal yield { "type": "response", "content": final_content, "intermediate_steps": intermediate_steps, "done": True } except Exception as e: logger.error(f"LangGraph chat stream error: {str(e)}", exc_info=True) yield { "type": "error", "content": f"抱歉，处理您的请求时出现错误: {str(e)}", "error": str(e), "done": True } def get_available_tools(self) -> List[Dict[str, Any]]: """Get list of available tools.""" tools = [] for tool in self.tools: tools.append({ "name": tool.name, "description": tool.description, "parameters": [], "enabled": True }) return tools def get_config(self) -> Dict[str, Any]: """Get current agent configuration.""" return self.config.dict() def update_config(self, config: Dict[str, Any]): """Update agent configuration.""" for key, value in config.items(): if hasattr(self.config, key): setattr(self.config, key, value) # Recreate agent with new config self._create_agent() logger.info("Agent configuration updated") # Global instance _langgraph_agent_service: Optional[LangGraphAgentService] = None def get_langgraph_agent_service(db_session=None) -> LangGraphAgentService: """Get or create LangGraph agent service instance.""" global _langgraph_agent_service if _langgraph_agent_service is None: _langgraph_agent_service = LangGraphAgentService(db_session) logger.info("LangGraph Agent service initialized") return _langgraph_agent_service