391 lines
15 KiB
Python
391 lines
15 KiB
Python
"""
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AI Agent 服务类 - 优化版本：双协程 + 结束哨兵 + 完整的取消和异常处理
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接收外部传入的 checkpointer，不负责管理连接生命周期
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"""
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import json
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import asyncio
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from typing import AsyncGenerator, Dict, Any, Optional, Tuple
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# LangGraph 序列化器（修复 checkpoint 反序列化警告）
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from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
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# 本地模块
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from ..model_services import get_cached_chat_services
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from ..main_graph.main_graph_builder import build_agent_graph
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from backend.app.logger import debug, info, warning, error
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from ..main_graph.state import AgentState
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from .stream_context import token_queue_var
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class AIAgentService:
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def __init__(self, checkpointer):
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self.checkpointer = checkpointer
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self.graph = None
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self.chat_services = None
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# Mem0 客户端
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self.mem0_client = None
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async def initialize(self):
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# 0. 初始化 Mem0 客户端
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from ..memory.mem0_client import Mem0Client
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self.mem0_client = Mem0Client()
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# 1. 获取缓存的模型字典
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self.chat_services = get_cached_chat_services()
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info(f"✅ 加载了 {len(self.chat_services)} 个可用模型: {list(self.chat_services.keys())}")
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# 2. 构建图
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info(f"🔄 构建 Agent 图...")
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graph_builder = build_agent_graph(
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chat_services=self.chat_services,
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mem0_client=self.mem0_client
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)
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# 编译图
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self.graph = graph_builder.compile(checkpointer=self.checkpointer)
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info(f"✅ Agent 图初始化完成")
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return self
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def _resolve_model(self, model: str) -> str:
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"""
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解析并验证模型名称，不可用时回退到第一个可用模型
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Args:
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model: 目标模型名称
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Returns:
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实际使用的模型名称
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"""
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if not model or model not in self.chat_services:
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fallback = next(iter(self.chat_services.keys()))
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warning(f"模型 '{model}' 不可用，回退到 '{fallback}'")
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return fallback
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return model
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def _build_invocation(
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self, message: str, thread_id: str, model: str, user_id: str
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""
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构建图调用所需的 config 和 input_state
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Args:
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message: 用户消息
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thread_id: 会话 ID
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model: 模型名称
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user_id: 用户 ID
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Returns:
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(config, input_state) 元组
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"""
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from langchain_core.messages import HumanMessage
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config = {
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"configurable": {
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"thread_id": thread_id,
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},
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"metadata": {"user_id": user_id}
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}
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input_state = {
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"messages": [HumanMessage(content=message)],
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"user_id": user_id,
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}
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return config, input_state
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async def process_message(
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self, message: str, thread_id: str, model: str = "", user_id: str = "default_user"
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) -> dict:
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"""处理用户消息，返回包含回复、token统计和耗时的字典"""
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# 解析模型名称
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resolved_model = self._resolve_model(model)
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# 构建调用参数
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config, input_state = self._build_invocation(message, thread_id, resolved_model, user_id)
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result = await self.graph.ainvoke(input_state, config=config)
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reply = ""
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if result.get("messages"):
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reply = result["messages"][-1].content
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token_usage = result.get("last_token_usage", {})
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elapsed_time = result.get("last_elapsed_time", 0.0)
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return {
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"reply": reply,
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"token_usage": token_usage,
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"elapsed_time": elapsed_time,
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"model_used": resolved_model
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}
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def _serialize_value(self, value):
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"""递归将 LangChain 对象转换为可 JSON 序列化的格式"""
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if hasattr(value, 'content'):
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msg_type = getattr(value, 'type', 'message')
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return {
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"role": msg_type,
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"content": getattr(value, 'content', ''),
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"additional_kwargs": getattr(value, 'additional_kwargs', {}),
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"tool_calls": getattr(value, 'tool_calls', [])
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}
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elif isinstance(value, dict):
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return {k: self._serialize_value(v) for k, v in value.items()}
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elif isinstance(value, (list, tuple)):
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return [self._serialize_value(item) for item in value]
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else:
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try:
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json.dumps(value)
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return value
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except (TypeError, ValueError):
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return str(value)
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async def _handle_message_chunk(
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self, chunk: Dict[str, Any], current_node: Optional[str], tool_calls_in_progress: Dict[str, Any]
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) -> AsyncGenerator[Dict[str, Any], None]:
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"""处理 messages 类型的 chunk"""
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message_chunk, metadata = chunk["data"]
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node_name = metadata.get("langgraph_node", "unknown")
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new_current_node = current_node
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# 检测节点变化，发送节点开始事件
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if node_name != current_node:
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if current_node:
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yield {"type": "node_end", "node": current_node}
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yield {"type": "node_start", "node": node_name}
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new_current_node = node_name
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# 处理消息内容
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token_content = getattr(message_chunk, 'content', str(message_chunk))
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reasoning_token = ""
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if hasattr(message_chunk, 'additional_kwargs'):
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reasoning_token = message_chunk.additional_kwargs.get("reasoning_content", "")
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# 处理思考过程
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if reasoning_token:
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yield {
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"type": "llm_token",
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"node": node_name,
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"reasoning_token": reasoning_token
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}
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# 处理工具调用
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elif hasattr(message_chunk, 'tool_calls') and message_chunk.tool_calls:
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for tool_call in message_chunk.tool_calls:
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tool_call_id = tool_call.get("id", "")
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tool_name = tool_call.get("name", "")
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tool_args = tool_call.get("args", {})
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# 记录工具调用开始，避免重复
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if tool_call_id and tool_call_id not in tool_calls_in_progress:
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tool_calls_in_progress[tool_call_id] = {
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"name": tool_name,
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"args": tool_args
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}
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yield {
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"type": "tool_call_start",
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"tool": tool_name,
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"args": tool_args,
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"id": tool_call_id
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}
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# 处理普通 token
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elif token_content:
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yield {
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"type": "llm_token",
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"node": node_name,
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"token": token_content,
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"reasoning_token": reasoning_token
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}
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# 返回更新后的 current_node
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yield {"type": "_update_state", "current_node": new_current_node}
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async def _handle_updates_chunk(
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self, chunk: Dict[str, Any], tool_calls_in_progress: Dict[str, Any], actual_model_used: str
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) -> AsyncGenerator[Dict[str, Any], None]:
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"""处理 updates 类型的 chunk"""
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updates_data = chunk["data"]
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new_actual_model = actual_model_used
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serialized_data = self._serialize_value(updates_data)
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# 检查是否有工具结果
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if "messages" in serialized_data:
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for msg in serialized_data["messages"]:
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# 检测工具结果消息
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if msg.get("role") == "tool":
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tool_call_id = msg.get("tool_call_id", "")
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tool_name = msg.get("name", "")
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tool_result = msg.get("content", "")
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if tool_call_id and tool_call_id in tool_calls_in_progress:
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yield {
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"type": "tool_call_end",
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"tool": tool_name,
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"id": tool_call_id,
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"result": tool_result
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}
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del tool_calls_in_progress[tool_call_id]
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yield {
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"type": "state_update",
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"data": serialized_data
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}
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# 返回更新后的模型
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yield {"type": "_update_state", "actual_model_used": new_actual_model}
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async def process_message_stream(
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self, message: str, thread_id: str, model: str = "", user_id: str = "default_user"
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) -> AsyncGenerator[Dict[str, Any], None]:
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"""流式处理消息 - 双协程 + 结束哨兵 + 完整取消和异常处理"""
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# 解析模型名称
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resolved_model = self._resolve_model(model)
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# 构建调用参数
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config, input_state = self._build_invocation(message, thread_id, resolved_model, user_id)
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info(f"🚀 开始执行 Agent 图，指定模型: {resolved_model}")
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current_node = None
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tool_calls_in_progress: Dict[str, Any] = {}
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actual_model_used = resolved_model
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full_message_content = ""
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# 创建 token 队列
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token_queue = asyncio.Queue()
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# 结束哨兵
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SENTINEL = object()
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# 设置上下文变量
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token_queue_var.set(token_queue)
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# 事件和错误跟踪
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graph_error = None
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graph_done = asyncio.Event()
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async def run_graph_task():
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"""后台任务：运行 graph.astream()"""
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nonlocal current_node, actual_model_used, full_message_content, graph_error
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try:
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info(f"📡 开始调用 graph.astream()...")
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event_count = 0
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async for chunk in self.graph.astream(
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input_state,
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config=config,
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stream_mode=["messages", "updates"],
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version="v2",
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subgraphs=True
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):
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chunk_count = 0
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chunk_count += 1
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chunk_type = chunk["type"]
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# 记录原始 chunk 信息（前 10 个和后 10 个）
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if chunk_count <= 10 or chunk_count % 50 == 0:
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info(f" [{chunk_count}] chunk_type={chunk_type}, data={type(chunk.get('data'))}")
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if chunk_type == "messages":
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async for event in self._handle_message_chunk(
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chunk, current_node, tool_calls_in_progress
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):
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if event.get("type") == "_update_state":
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current_node = event.get("current_node", current_node)
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else:
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event_count += 1
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# 记录前 10 个事件
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if event_count <= 10:
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info(f" → yield event #{event_count}: {event.get('type')}")
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# 如果是 agent 节点的 token，收集完整消息
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if (
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event.get("type") == "llm_token"
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and event.get("node") == "agent"
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and "token" in event
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):
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full_message_content += event["token"]
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await token_queue.put(event)
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elif chunk_type == "updates":
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async for event in self._handle_updates_chunk(
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chunk, tool_calls_in_progress, actual_model_used
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):
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if event.get("type") == "_update_state":
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actual_model_used = event.get("actual_model_used", actual_model_used)
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else:
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event_count += 1
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if event_count <= 10:
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info(f" → yield event #{event_count}: {event.get('type')}")
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await token_queue.put(event)
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# 完整消息集合完成后，一次性打印
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info(f"✅ graph.astream() 完成，共 {event_count} 个 events")
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if full_message_content:
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info(f"📄 完整消息内容: {repr(full_message_content)}")
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except Exception as e:
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error(f"❌ 执行图时出错: {e}")
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import traceback
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error(f"📋 堆栈: {traceback.format_exc()}")
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graph_error = e
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await token_queue.put({
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"type": "error",
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"message": str(e)
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})
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finally:
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# 发送结束哨兵
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await token_queue.put(SENTINEL)
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graph_done.set()
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# 启动后台任务
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graph_task = asyncio.create_task(run_graph_task())
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try:
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# 主协程：从队列里取事件并 yield
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while True:
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try:
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# 等待队列中的事件，带超时检查任务是否完成
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event = await asyncio.wait_for(token_queue.get(), timeout=0.5)
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# 检查是否是结束哨兵
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if event is SENTINEL:
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break
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yield event
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except asyncio.TimeoutError:
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# 超时检查任务是否完成
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if graph_task.done():
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# 检查任务是否抛出异常
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if graph_task.exception():
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exc = graph_task.exception()
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error(f"❌ 后台任务异常: {exc}")
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break
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except asyncio.CancelledError:
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info("⚠️ 流式生成被取消")
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raise
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finally:
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# 无论成功或失败，都清理资源
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# 取消后台任务
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if not graph_task.done():
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info("⏹️ 取消后台任务")
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graph_task.cancel()
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try:
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await graph_task
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except asyncio.CancelledError:
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info("✅ 后台任务已取消")
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# 发送结束事件，保证前端平稳关闭
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if current_node:
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yield {
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"type": "node_end",
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"node": current_node
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}
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yield {
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"type": "done",
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"model_used": actual_model_used
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}
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