09467568ec
- agent_runtime 模块与 agent_chat API，前端 AgentChat 视图与路由对接 - workflow_engine: code 节点命名空间与 json 引用修复 - llm_service: 工具调用 extra_body（如 DeepSeek） - create_homework_manager_agent / _3 脚本与测试脚本扩展 - frontend: WORKFLOW_EXECUTION_HTTP_TIMEOUT_MS、AgentChatPreview/MainLayout 等 - 文档：架构说明与自主 Agent 改造完成情况 Made-with: Cursor
136 lines
4.7 KiB
Python
136 lines
4.7 KiB
Python
"""
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Agent 记忆管理：包装已有 persistent_memory_service，提供会话级和长期记忆。
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"""
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from __future__ import annotations
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import json
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import logging
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from typing import Any, Dict, List, Optional
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from sqlalchemy.orm import Session
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from app.core.database import SessionLocal
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from app.services.persistent_memory_service import (
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load_persistent_memory,
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save_persistent_memory,
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persist_enabled,
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)
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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class AgentMemory:
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"""
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分层记忆管理器：
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- 工作记忆：当前会话消息列表（由 AgentRuntime 直接管理）
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- 长期记忆：从 MySQL 加载/保存的用户画像和关键事实
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- 上下文压缩：对话过长时自动裁剪或总结
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"""
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def __init__(
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self,
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scope_kind: str = "agent",
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scope_id: Optional[str] = None,
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session_key: Optional[str] = None,
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persist: bool = True,
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max_history: int = 20,
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):
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self.scope_kind = scope_kind
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self.scope_id = scope_id or "default"
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self.session_key = session_key or "default_session"
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self.persist = persist and persist_enabled()
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self.max_history = max_history
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# 从长期记忆加载的上下文（启动时加载）
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self._long_term_context: Dict[str, Any] = {}
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async def initialize(self) -> str:
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"""
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初始化记忆：从 DB/Redis 加载长期记忆，构造初始上下文文本。
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返回注入 system prompt 的记忆文本块。
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"""
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if not self.persist or not self.scope_id:
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return ""
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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payload = load_persistent_memory(
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db, self.scope_kind, self.scope_id, self.session_key
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)
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if payload and isinstance(payload, dict):
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self._long_term_context = payload
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# 构建注入 system prompt 的记忆文本
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parts = []
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profile = payload.get("user_profile")
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if profile and isinstance(profile, dict):
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profile_text = json.dumps(profile, ensure_ascii=False)
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parts.append(f"## 用户画像\n{profile_text}")
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context = payload.get("context")
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if context and isinstance(context, dict):
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ctx_text = json.dumps(context, ensure_ascii=False)
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parts.append(f"## 上下文\n{ctx_text}")
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history = payload.get("conversation_history")
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if history and isinstance(history, list) and len(history) > 0:
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summary = self._summarize_history(history)
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parts.append(f"## 历史对话摘要\n{summary}")
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if parts:
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return "\n\n".join(parts)
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except Exception as e:
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logger.warning("加载长期记忆失败: %s", e)
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finally:
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if db:
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db.close()
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return ""
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async def save_context(
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self, user_message: str, assistant_reply: str
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) -> None:
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"""将单轮对话保存到长期记忆。"""
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if not self.persist or not self.scope_id:
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return
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# 更新上下文
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ctx = self._long_term_context.get("context", {})
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ctx["last_user_message"] = user_message[:500]
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ctx["last_assistant_reply"] = assistant_reply[:500]
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self._long_term_context["context"] = ctx
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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save_persistent_memory(
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db, self.scope_kind, self.scope_id,
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self.session_key, self._long_term_context,
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)
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except Exception as e:
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logger.warning("保存长期记忆失败: %s", e)
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finally:
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if db:
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db.close()
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def trim_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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裁剪消息列表：保留最近的 N 条，但始终保留第一条 system 消息。
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"""
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if len(messages) <= self.max_history:
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return messages
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system_msgs = [m for m in messages if m.get("role") == "system"]
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other_msgs = [m for m in messages if m.get("role") != "system"]
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trimmed = other_msgs[-(self.max_history - len(system_msgs)):]
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return system_msgs + trimmed
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@staticmethod
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def _summarize_history(history: List[Dict[str, Any]]) -> str:
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"""简单汇总历史对话（不做 LLM 压缩，仅计数）。"""
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turns = 0
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for m in history:
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if m.get("role") == "user":
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turns += 1
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return f"共 {turns} 轮历史对话（详情已存入长期记忆）"
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