87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
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"""
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记忆存储节点模块
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负责将对话历史提交给 Mem0 进行事实提取和存储
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"""
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from typing import Any, Dict
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from langgraph.runtime import Runtime
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# 本地模块
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from app.state import MessagesState, GraphContext
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from app.memory.mem0_client import Mem0Client
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from app.utils.logging import log_state_change
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from app.logger import debug, info, error, warning
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def create_summarize_node(mem0_client: Mem0Client):
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"""
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工厂函数：创建记忆存储节点
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Args:
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mem0_client: Mem0 客户端实例
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Returns:
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异步节点函数
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"""
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async def summarize_conversation(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
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"""
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记忆存储节点 - 使用 Mem0
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Args:
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state: 当前对话状态
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runtime: LangGraph 运行时上下文
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Returns:
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重置计数器的状态更新
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"""
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log_state_change("summarize", state, "进入")
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messages = state["messages"]
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if len(messages) < 4:
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debug("📝 [记忆添加] 对话过短，跳过")
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return {"turns_since_last_summary": 0}
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user_id = runtime.context.user_id
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# 确保 Mem0 已初始化（懒加载）
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if not mem0_client._initialized:
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await mem0_client.initialize()
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# 将整个对话历史转换为 Mem0 需要的消息格式
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mem0_messages = []
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for msg in messages:
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# 兼容 dict 和对象两种格式
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if isinstance(msg, dict):
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msg_type = msg.get("type", "")
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msg_content = msg.get("content", "")
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else:
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msg_type = getattr(msg, 'type', '')
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msg_content = getattr(msg, 'content', '')
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if msg_type == "human":
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mem0_messages.append({"role": "user", "content": msg_content})
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elif msg_type == "ai":
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mem0_messages.append({"role": "assistant", "content": msg_content})
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elif msg_type == "tool":
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mem0_messages.append({"role": "system", "content": f"[工具返回] {msg_content}"})
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if mem0_client.mem0:
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try:
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# 异步调用 Mem0 自动提取并存储事实
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success = await mem0_client.add_memories(
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mem0_messages,
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user_id=user_id
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)
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if success:
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info(f"📝 [记忆添加] 已提交给 Mem0 进行事实提取")
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except Exception as e:
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error(f"❌ Mem0 记忆添加失败: {e}")
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else:
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warning("⚠️ Mem0 未初始化，跳过记忆添加")
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log_state_change("summarize", state, "离开")
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return {"turns_since_last_summary": 0}
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return summarize_conversation
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