ailine/backend/app/agent/agent_service.py

"""
AI Agent 服务类 - 极简 LangGraph Agent 架构
接收外部传入的 checkpointer，不负责管理连接生命周期
"""

import json
import asyncio
from typing import AsyncGenerator, Dict, Any, Optional, Tuple

# LangGraph 序列化器（修复 checkpoint 反序列化警告）
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer

# 本地模块
from ..model_services import get_cached_chat_services
from ..main_graph.main_graph_builder import build_agent_graph
from backend.app.logger import debug, info, warning, error
from ..main_graph.state import AgentState


class AIAgentService:
    def __init__(self, checkpointer):
        self.checkpointer = checkpointer
        self.graph = None
        self.chat_services = None
        # Mem0 客户端
        self.mem0_client = None

    async def initialize(self):
        # 0. 初始化 Mem0 客户端
        from ..memory.mem0_client import Mem0Client
        self.mem0_client = Mem0Client()
        
        # 1. 获取缓存的模型字典
        self.chat_services = get_cached_chat_services()
        info(f"✅ 加载了 {len(self.chat_services)} 个可用模型: {list(self.chat_services.keys())}")
        
        # 2. 构建图
        info(f"🔄 构建 Agent 图...")
        graph_builder = build_agent_graph(
            chat_services=self.chat_services,
            mem0_client=self.mem0_client
        )
        
        # 编译图
        self.graph = graph_builder.compile(checkpointer=self.checkpointer)
        info(f"✅ Agent 图初始化完成")
        
        return self

    def _resolve_model(self, model: str) -> str:
        """
        解析并验证模型名称，不可用时回退到第一个可用模型
        
        Args:
            model: 目标模型名称
        
        Returns:
            实际使用的模型名称
        """
        if not model or model not in self.chat_services:
            fallback = next(iter(self.chat_services.keys()))
            warning(f"模型 '{model}' 不可用，回退到 '{fallback}'")
            return fallback
        return model

    def _build_invocation(
        self, message: str, thread_id: str, model: str, user_id: str
    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """
        构建图调用所需的 config 和 input_state
        
        Args:
            message: 用户消息
            thread_id: 会话 ID
            model: 模型名称
            user_id: 用户 ID
        
        Returns:
            (config, input_state) 元组
        """
        from langchain_core.messages import HumanMessage
        
        config = {
            "configurable": {
                "thread_id": thread_id,
            },
            "metadata": {"user_id": user_id}
        }
        
        input_state = {
            "messages": [HumanMessage(content=message)],
            "user_id": user_id,
        }
        return config, input_state

    async def process_message(
        self, message: str, thread_id: str, model: str = "", user_id: str = "default_user"
    ) -> dict:
        """处理用户消息，返回包含回复、token统计和耗时的字典"""
        # 解析模型名称
        resolved_model = self._resolve_model(model)
        
        # 构建调用参数
        config, input_state = self._build_invocation(message, thread_id, resolved_model, user_id)

        result = await self.graph.ainvoke(input_state, config=config)
        
        reply = ""
        if result.get("messages"):
            reply = result["messages"][-1].content
        
        token_usage = result.get("last_token_usage", {})
        elapsed_time = result.get("last_elapsed_time", 0.0)

        return {
            "reply": reply,
            "token_usage": token_usage,
            "elapsed_time": elapsed_time,
            "model_used": resolved_model
        }

    def _serialize_value(self, value):
        """递归将 LangChain 对象转换为可 JSON 序列化的格式"""
        if hasattr(value, 'content'):
            msg_type = getattr(value, 'type', 'message')
            return {
                "role": msg_type,
                "content": getattr(value, 'content', ''),
                "additional_kwargs": getattr(value, 'additional_kwargs', {}),
                "tool_calls": getattr(value, 'tool_calls', [])
            }
        elif isinstance(value, dict):
            return {k: self._serialize_value(v) for k, v in value.items()}
        elif isinstance(value, (list, tuple)):
            return [self._serialize_value(item) for item in value]
        else:
            try:
                json.dumps(value)
                return value
            except (TypeError, ValueError):
                return str(value)

    async def _handle_message_chunk(
        self, chunk: Dict[str, Any], current_node: Optional[str], tool_calls_in_progress: Dict[str, Any]
    ) -> AsyncGenerator[Dict[str, Any], None]:
        """处理 messages 类型的 chunk"""
        message_chunk, metadata = chunk["data"]
        node_name = metadata.get("langgraph_node", "unknown")
        new_current_node = current_node

        # 检测节点变化，发送节点开始事件
        if node_name != current_node:
            if current_node:
                yield {"type": "node_end", "node": current_node}
            yield {"type": "node_start", "node": node_name}
            new_current_node = node_name

        # 处理消息内容
        token_content = getattr(message_chunk, 'content', str(message_chunk))
        reasoning_token = ""
        if hasattr(message_chunk, 'additional_kwargs'):
            reasoning_token = message_chunk.additional_kwargs.get("reasoning_content", "")

        # 处理思考过程
        if reasoning_token:
            yield {
                "type": "llm_token",
                "node": node_name,
                "reasoning_token": reasoning_token
            }
        # 处理工具调用
        elif hasattr(message_chunk, 'tool_calls') and message_chunk.tool_calls:
            for tool_call in message_chunk.tool_calls:
                tool_call_id = tool_call.get("id", "")
                tool_name = tool_call.get("name", "")
                tool_args = tool_call.get("args", {})

                # 记录工具调用开始，避免重复
                if tool_call_id and tool_call_id not in tool_calls_in_progress:
                    tool_calls_in_progress[tool_call_id] = {
                        "name": tool_name,
                        "args": tool_args
                    }
                    yield {
                        "type": "tool_call_start",
                        "tool": tool_name,
                        "args": tool_args,
                        "id": tool_call_id
                    }
        # 处理普通 token
        elif token_content:
            yield {
                "type": "llm_token",
                "node": node_name,
                "token": token_content,
                "reasoning_token": reasoning_token
            }

        # 返回更新后的 current_node
        yield {"type": "_update_state", "current_node": new_current_node}

    async def _handle_updates_chunk(
        self, chunk: Dict[str, Any], tool_calls_in_progress: Dict[str, Any], actual_model_used: str
    ) -> AsyncGenerator[Dict[str, Any], None]:
        """处理 updates 类型的 chunk"""
        updates_data = chunk["data"]
        new_actual_model = actual_model_used
        
        serialized_data = self._serialize_value(updates_data)
        
        # 检查是否有工具结果
        if "messages" in serialized_data:
            for msg in serialized_data["messages"]:
                # 检测工具结果消息
                if msg.get("role") == "tool":
                    tool_call_id = msg.get("tool_call_id", "")
                    tool_name = msg.get("name", "")
                    tool_result = msg.get("content", "")
                    
                    if tool_call_id and tool_call_id in tool_calls_in_progress:
                        yield {
                            "type": "tool_call_end",
                            "tool": tool_name,
                            "id": tool_call_id,
                            "result": tool_result
                        }
                        del tool_calls_in_progress[tool_call_id]
        
        yield {
            "type": "state_update",
            "data": serialized_data
        }
        
        # 返回更新后的模型
        yield {"type": "_update_state", "actual_model_used": new_actual_model}

    async def process_message_stream(
        self, message: str, thread_id: str, model: str = "", user_id: str = "default_user"
    ) -> AsyncGenerator[Dict[str, Any], None]:
        """流式处理消息，返回异步生成器"""
        # 解析模型名称
        resolved_model = self._resolve_model(model)
        
        # 构建调用参数
        config, input_state = self._build_invocation(message, thread_id, resolved_model, user_id)

        info(f"🚀 开始执行 Agent 图，指定模型: {resolved_model}")
        current_node = None
        tool_calls_in_progress: Dict[str, Any] = {}
        actual_model_used = resolved_model
        chunk_count = 0
        full_message_content = ""

        try:
            info(f"📡 开始调用 graph.astream()...")
            
            event_count = 0
            
            async for chunk in self.graph.astream(
                input_state,
                config=config,
                stream_mode=["messages", "updates"],
                version="v2",
                subgraphs=True
            ):
                chunk_count += 1
                chunk_type = chunk["type"]
                
                # 记录原始 chunk 信息（前 10 个和后 10 个）
                if chunk_count <= 10 or chunk_count % 50 == 0:
                    info(f"  [{chunk_count}] chunk_type={chunk_type}, data={type(chunk.get('data'))}")

                if chunk_type == "messages":
                    async for event in self._handle_message_chunk(
                        chunk, current_node, tool_calls_in_progress
                    ):
                        if event.get("type") == "_update_state":
                            current_node = event.get("current_node", current_node)
                        else:
                            event_count += 1
                            # 记录前 10 个事件
                            if event_count <= 10:
                                info(f"    → yield event #{event_count}: {event.get('type')}")
                            
                            # 如果是 agent 节点的 token，收集完整消息
                            if (
                                event.get("type") == "llm_token"
                                and event.get("node") == "agent"
                                and "token" in event
                            ):
                                full_message_content += event["token"]
                            yield event

                elif chunk_type == "updates":
                    async for event in self._handle_updates_chunk(
                        chunk, tool_calls_in_progress, actual_model_used
                    ):
                        if event.get("type") == "_update_state":
                            actual_model_used = event.get("actual_model_used", actual_model_used)
                        else:
                            event_count += 1
                            if event_count <= 10:
                                info(f"    → yield event #{event_count}: {event.get('type')}")
                            yield event

            # 完整消息集合完成后，一次性打印
            info(f"✅ graph.astream() 完成，共 {chunk_count} 个 chunks, {event_count} 个 events")
            if full_message_content:
                info(f"📄 完整消息内容: {repr(full_message_content)}")

        except Exception as e:
            error(f"❌ 执行图时出错: {e}")
            import traceback
            error(f"📋 堆栈: {traceback.format_exc()}")
            yield {
                "type": "error",
                "message": str(e)
            }
        finally:
            # 无论成功或失败，都发送结束事件，保证前端平稳关闭
            if current_node:
                yield {
                    "type": "node_end",
                    "node": current_node
                }
            yield {
                "type": "done",
                "model_used": actual_model_used
            }