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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m31s
- 重构 fast_paths.py，让 fast_chitchat 和 fast_rag 都进入 llm_call 而不是直接设置 final_result - 修改 check_fast_path_success 函数返回 'llm_call' 而不是 'success' - 更新 main_graph_builder.py 的条件边配置，支持路由到 llm_call - 在快速路径节点中添加清除 state.final_result 的逻辑，避免复用旧结果 - 重构 RAG 工具初始化方式，使用模块级变量管理 - 修改 finalize.py 让它返回 final_result - 更新 agent_service.py 的 RAG 工具注入方式 - 简化 hybrid_router.py 的代码结构 - 清理 rag_nodes.py 的全局变量相关代码 - 更新相关测试文件
345 lines
15 KiB
Python
345 lines
15 KiB
Python
"""
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AI Agent 服务类 - 支持多模型动态切换
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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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# 本地模块
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from ..main_graph.utils.main_graph_builder import build_react_main_graph
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from ..main_graph.tools.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
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from ..main_graph.config import set_stream_writer
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from ..model_services.chat_services import get_all_chat_services, LocalVLLMChatProvider
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from ..main_graph.utils.rag_initializer import init_rag_tool
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from ..core.intent_classifier import get_intent_classifier
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from ..logger import info, warning, error
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from ..main_graph.state import MainGraphState, CurrentAction
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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.graphs = {}
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self.tools = AVAILABLE_TOOLS.copy()
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self.tools_by_name = TOOLS_BY_NAME.copy()
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# 添加：意图分类器
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self.intent_classifier = get_intent_classifier()
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# RAG 管道（可选，需要时设置）
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self.rag_pipeline = 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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# 创建一个临时的 LLM 用于 Mem0（用第一个可用的）
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chat_services = get_all_chat_services()
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temp_llm = None
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if chat_services:
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temp_llm = list(chat_services.values())[0]
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self.mem0_client = Mem0Client(temp_llm)
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# 1. 初始化 RAG 工具（如果需要）
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def create_local_llm():
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provider = LocalVLLMChatProvider()
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return provider.get_service()
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rag_tool = await init_rag_tool(create_local_llm)
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if rag_tool:
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self.tools.append(rag_tool)
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self.tools_by_name[rag_tool.name] = rag_tool
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self.rag_tool = rag_tool # 保存到实例变量，供 config 注入
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# 2. 构建各模型的 Graph（使用新版 React 模式）
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for name, llm in chat_services.items():
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try:
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info(f"🔄 初始化模型 '{name}'...")
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graph = build_react_main_graph(
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llm=llm,
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tools=self.tools,
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mem0_client=self.mem0_client
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).compile(checkpointer=self.checkpointer)
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self.graphs[name] = graph
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info(f"✅ 模型 '{name}' 初始化成功")
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except Exception as e:
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warning(f"⚠️ 模型 '{name}' 初始化失败: {e}")
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if not self.graphs:
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raise RuntimeError("没有可用的模型")
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return self
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async def process_message(self, message: str, thread_id: str, model: str = "local", user_id: str = "default_user") -> 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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if model not in self.graphs:
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# 回退到第一个可用模型
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available = list(self.graphs.keys())
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if not available:
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raise RuntimeError("没有可用的模型")
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model = available[0]
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warning(f"模型 '{model}' 不可用，已回退到 '{model}'")
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graph = self.graphs[model]
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config = {
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"configurable": {
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"thread_id": thread_id,
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"rag_tool": getattr(self, "rag_tool", None), # 注入 RAG 工具
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},
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"metadata": {"user_id": user_id}
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}
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# 新版状态输入：传入完整的 MainGraphState，关键是 user_query
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from ..main_graph.state import MainGraphState, CurrentAction
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input_state = {
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"user_query": message,
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"messages": [{"role": "user", "content": message}],
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"user_id": user_id,
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"current_action": CurrentAction.NONE
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}
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result = await graph.ainvoke(input_state, config=config)
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reply = result.get("final_result", "")
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if not reply and result.get("messages"):
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reply = result["messages"][-1].content
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token_usage = result.get("debug_info", {}).get("token_usage", {})
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elapsed_time = result.get("debug_info", {}).get("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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}
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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 process_message_stream(self, message: str, thread_id: str, model_name: str, user_id: str = "default_user"):
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"""流式处理消息，返回异步生成器（全部走 React 模式）"""
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graph = self.graphs.get(model_name)
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if not graph:
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raise ValueError(f"模型 '{model_name}' 未找到或未初始化")
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config = {
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"configurable": {
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"thread_id": thread_id,
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"rag_tool": getattr(self, "rag_tool", None), # 注入 RAG 工具
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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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"user_query": message,
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"messages": [{"role": "user", "content": message}],
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"user_id": user_id,
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"current_action": CurrentAction.NONE
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}
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# ========== 意图识别（保留用于日志）==========
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intent_result = await self.intent_classifier.classify(message)
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info(f"🧠 意图识别: {intent_result.intent_type} (置信度: {intent_result.confidence:.2f})")
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info(f"📝 推理: {intent_result.reasoning}")
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# 发送意图分类事件
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yield {
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"type": "intent_classified",
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"intent": intent_result.intent_type.value,
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"confidence": intent_result.confidence,
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"reasoning": intent_result.reasoning
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}
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# 发送路径决策事件（现在都是 react_loop）
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yield {
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"type": "path_decision",
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"path": "react_loop",
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"intent": intent_result.intent_type.value
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}
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# ========================================
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# ========== React 循环路径 ==========
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info(f"🚀 开始执行 React 图，模型: {model_name}")
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current_node = None
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tool_calls_in_progress = {}
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try:
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info(f"📡 开始调用 graph.astream()...")
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chunk_count = 0
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full_message_content = "" # 收集完整消息内容
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async for chunk in graph.astream(
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input_state,
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config=config,
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stream_mode=["messages", "updates", "custom"],
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version="v2",
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subgraphs=True
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):
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chunk_count += 1
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chunk_type = chunk["type"]
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processed_event = {}
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if chunk_type == "messages":
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message_chunk, metadata = chunk["data"]
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node_name = metadata.get("langgraph_node", "unknown")
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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 {
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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": "node_start",
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"node": node_name
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}
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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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processed_event = {
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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
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if 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 - 只收集，不打印单个 token
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elif token_content:
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processed_event = {
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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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if node_name == "llm_call":
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full_message_content += token_content
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elif chunk_type == "updates":
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updates_data = chunk["data"]
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info(f"[Stream] updates 数据: {list(updates_data.keys()) if isinstance(updates_data, dict) else type(updates_data)}")
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# 特别检查 final_result
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if isinstance(updates_data, dict) and "final_result" in updates_data:
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info(f"[Stream] 收到 final_result: {str(updates_data['final_result'])[:100]}...")
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serialized_data = self._serialize_value(updates_data)
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if "review_pending" in serialized_data and serialized_data["review_pending"]:
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review_id = serialized_data.get("review_id", "")
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content_to_review = serialized_data.get("content_to_review", "")
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yield {
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"type": "human_review_request",
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"review_id": review_id,
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"content": content_to_review
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}
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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_output = msg.get("content", "")
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if 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_output
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}
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del tool_calls_in_progress[tool_call_id]
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processed_event = {
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"type": "state_update",
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"data": serialized_data
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}
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elif chunk_type == "custom":
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custom_data = chunk["data"]
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# 处理我们从 react_reason_node 发送的自定义推理事件
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if isinstance(custom_data, dict):
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# 检查是否是我们的推理事件
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if "action" in custom_data and "reasoning" in custom_data:
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yield {
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"type": "react_reasoning",
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"step": custom_data.get("step", 1),
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"action": custom_data.get("action", "unknown"),
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"confidence": custom_data.get("confidence", 0),
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"reasoning": custom_data.get("reasoning", "")
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}
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else:
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# 处理其他自定义事件
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serialized_data = self._serialize_value(custom_data)
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processed_event = {
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"type": "custom",
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"data": serialized_data
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}
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else:
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# 处理其他自定义事件
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serialized_data = self._serialize_value(custom_data)
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processed_event = {
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"type": "custom",
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"data": serialized_data
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}
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if processed_event:
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yield processed_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 {chunk_count} 个chunks")
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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 React 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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raise
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# 发送结束事件
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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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} |