f694101747
Replace Memori with Mem0 for memory management: - Delete memori_config.py, memori_manager.py, memori_middleware.py - Add mem0_config.py, mem0_manager.py, mem0_middleware.py - Update environment variables (MEMORI_* -> MEM0_*) - Integrate Mem0 with LangGraph middleware - Add sync connection pool for Mem0 in DBPoolManager - Move checkpoint message prep to config creation Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
"""
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Mem0 配置数据类
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用于管理 Mem0 长期记忆系统的配置参数
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"""
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Optional
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# 避免循环导入
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if TYPE_CHECKING:
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from langchain_core.language_models import BaseChatModel
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@dataclass
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class Mem0Config:
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"""Mem0 长期记忆配置类"""
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# 功能开关
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enabled: bool = False
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# 语义搜索配置
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semantic_search_top_k: int = 20
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memory_prompt_template: str = (
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"\n\n=== 相关记忆 ===\n"
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"以下是从历史对话中检索到的相关信息，可以帮助你更好地回答用户问题：\n"
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"{memories}\n"
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"==================\n"
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)
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# 多租户配置
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user_id: Optional[str] = None # 用户标识
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agent_id: Optional[str] = None # Bot 标识
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session_id: Optional[str] = None # 会话标识
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# LLM 实例（用于 Mem0 的记忆提取和增强）
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llm_instance: Optional["BaseChatModel"] = None # LangChain LLM 实例
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def get_attribution_tuple(self) -> tuple[str, str]:
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"""获取 attribution 所需的元组 (user_id, agent_id)
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Returns:
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(user_id, agent_id) 元组
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"""
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if not self.user_id or not self.agent_id:
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raise ValueError("user_id and agent_id are required for attribution")
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return (self.user_id, self.agent_id)
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def is_enabled(self) -> bool:
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"""检查 Mem0 功能是否启用
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Returns:
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bool: 是否启用
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"""
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return self.enabled
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def get_memory_prompt(self, memories: list[str]) -> str:
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"""根据记忆列表生成注入提示词
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Args:
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memories: 记忆内容列表
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Returns:
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str: 格式化的记忆提示词
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"""
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if not memories:
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return ""
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memory_text = "\n".join(f"- {m}" for m in memories)
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return self.memory_prompt_template.format(memories=memory_text)
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def with_session(self, session_id: str) -> "Mem0Config":
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"""创建带有新 session_id 的配置副本
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Args:
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session_id: 新的会话 ID
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Returns:
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新的 Mem0Config 实例
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"""
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new_config = Mem0Config(**self.__dict__)
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new_config.session_id = session_id
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return new_config
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