""" Agent 记忆管理：包装已有 persistent_memory_service，提供会话级和长期记忆。 支持 LLM 自动压缩总结对话历史。 """ from __future__ import annotations import json import logging from typing import Any, Dict, List, Optional from sqlalchemy.orm import Session from app.core.database import SessionLocal from app.services.persistent_memory_service import ( load_persistent_memory, save_persistent_memory, persist_enabled, ) logger = logging.getLogger(__name__) class AgentMemory: """ 分层记忆管理器： - 工作记忆：当前会话消息列表（由 AgentRuntime 直接管理） - 长期记忆：从 MySQL 加载/保存的用户画像和关键事实 - 记忆压缩：LLM 自动总结对话历史，提取关键信息存入长期记忆 """ def __init__( self, scope_kind: str = "agent", scope_id: Optional[str] = None, session_key: Optional[str] = None, persist: bool = True, max_history: int = 20, ): self.scope_kind = scope_kind self.scope_id = scope_id or "default" self.session_key = session_key or "default_session" self.persist = persist and persist_enabled() self.max_history = max_history # 从长期记忆加载的上下文（启动时加载） self._long_term_context: Dict[str, Any] = {} # 记录已压缩的消息数，避免重复压缩 self._last_compressed_msg_count = 0 async def initialize(self) -> str: """ 初始化记忆：从 DB/Redis 加载长期记忆，构造初始上下文文本。 返回注入 system prompt 的记忆文本块。 """ if not self.persist or not self.scope_id: return "" db: Optional[Session] = None try: db = SessionLocal() payload = load_persistent_memory( db, self.scope_kind, self.scope_id, self.session_key ) if payload and isinstance(payload, dict): self._long_term_context = payload # 构建注入 system prompt 的记忆文本 parts = [] profile = payload.get("user_profile") if profile and isinstance(profile, dict): profile_text = json.dumps(profile, ensure_ascii=False) parts.append(f"## 用户画像\n{profile_text}") context = payload.get("context") if context and isinstance(context, dict): ctx_text = json.dumps(context, ensure_ascii=False) parts.append(f"## 上下文\n{ctx_text}") history = payload.get("conversation_history") if history and isinstance(history, list) and len(history) > 0: summary = self._summarize_history(history) parts.append(f"## 历史对话摘要\n{summary}") if parts: return "\n\n".join(parts) except Exception as e: logger.warning("加载长期记忆失败: %s", e) finally: if db: db.close() return "" async def save_context( self, user_message: str, assistant_reply: str, messages: Optional[List[Dict[str, Any]]] = None, ) -> None: """将单轮对话保存到长期记忆。如有消息列表，LLM 自动压缩总结。""" if not self.persist or not self.scope_id: return # 更新上下文 ctx = self._long_term_context.get("context", {}) ctx["last_user_message"] = user_message[:500] ctx["last_assistant_reply"] = assistant_reply[:500] self._long_term_context["context"] = ctx # 如果有完整消息列表且新增了足够多的消息，运行 LLM 压缩总结 if messages and len(messages) > self._last_compressed_msg_count + 2: await self._compress_and_summarize(messages) self._last_compressed_msg_count = len(messages) db: Optional[Session] = None try: db = SessionLocal() save_persistent_memory( db, self.scope_kind, self.scope_id, self.session_key, self._long_term_context, ) except Exception as e: logger.warning("保存长期记忆失败: %s", e) finally: if db: db.close() async def _compress_and_summarize( self, messages: List[Dict[str, Any]] ) -> None: """ 使用 LLM 压缩总结对话历史，提取用户画像和关键事实。 只处理非 system 消息。 """ from openai import AsyncOpenAI from app.core.config import settings # 提取对话消息（去掉 system 和 tool 消息） conversation = [] for m in messages: role = m.get("role", "") if role == "system": continue if role == "tool": # 工具结果精简后加入 content = m.get("content", "") name = m.get("name", "tool") conversation.append({"role": "user" if role == "tool" else role, "content": f"[工具 {name} 执行结果]\n{content[:200]}"}) else: conversation.append({"role": role, "content": m.get("content", "")[:500]}) if len(conversation) < 2: return # 构建总结 prompt summary_prompt = ( "你是一个记忆管理助手。请分析以下对话历史，提取关于用户的关键信息。\n\n" "请返回 JSON 格式（不要 markdown 包裹），包含以下字段：\n" "1. user_profile: 用户画像对象，包含用户的偏好、角色、关键需求等\n" "2. key_facts: 从对话中提取的关键事实列表（字符串数组）\n" "3. summary: 对话的简要总结（100字以内）\n" "4. topics: 讨论过的话题列表（字符串数组）\n\n" "如果没有足够信息，相应字段设为空对象或空数组。" ) summary_messages = [ {"role": "system", "content": summary_prompt}, *conversation[-10:], # 只取最近 10 条消息 ] try: api_key = settings.DEEPSEEK_API_KEY or settings.OPENAI_API_KEY or "" base_url = settings.DEEPSEEK_BASE_URL or settings.OPENAI_BASE_URL or "https://api.deepseek.com" if api_key == "your-openai-api-key": api_key = settings.DEEPSEEK_API_KEY or "" base_url = settings.DEEPSEEK_BASE_URL or "https://api.deepseek.com" if not api_key: logger.warning("记忆压缩：未配置 API Key，跳过") return client = AsyncOpenAI(api_key=api_key, base_url=base_url) resp = await client.chat.completions.create( model="deepseek-v4-flash", messages=summary_messages, temperature=0.3, max_tokens=1024, timeout=30, ) raw = resp.choices[0].message.content or "" # 解析 JSON result = json.loads(raw.strip().removeprefix("```json").removesuffix("```").strip()) # 合并到长期记忆 existing_profile = self._long_term_context.get("user_profile", {}) new_profile = result.get("user_profile", {}) if isinstance(new_profile, dict) and new_profile: # 合并画像（新信息覆盖旧信息） existing_profile.update(new_profile) self._long_term_context["user_profile"] = existing_profile # 合并关键事实 existing_facts = self._long_term_context.get("key_facts", []) new_facts = result.get("key_facts", []) if isinstance(new_facts, list): all_facts = list(dict.fromkeys(existing_facts + new_facts)) # 去重 self._long_term_context["key_facts"] = all_facts[-20:] # 最多保留 20 条 # 更新摘要 summary = result.get("summary", "") if summary: ctx = self._long_term_context.get("context", {}) ctx["compressed_summary"] = summary self._long_term_context["context"] = ctx # 记录话题 topics = result.get("topics", []) if isinstance(topics, list) and topics: existing_topics = self._long_term_context.get("topics", []) all_topics = list(dict.fromkeys(existing_topics + topics)) self._long_term_context["topics"] = all_topics[-20:] logger.info("记忆压缩总结完成: profile=%s facts=%d topics=%d", "updated" if new_profile else "unchanged", len(new_facts), len(topics)) except json.JSONDecodeError: logger.warning("记忆压缩：LLM 返回非 JSON 格式，跳过") except Exception as e: logger.warning("记忆压缩失败: %s", e) def trim_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ 裁剪消息列表：保留最近的 N 条，但始终保留第一条 system 消息。 """ if len(messages) <= self.max_history: return messages system_msgs = [m for m in messages if m.get("role") == "system"] other_msgs = [m for m in messages if m.get("role") != "system"] trimmed = other_msgs[-(self.max_history - len(system_msgs)):] return system_msgs + trimmed @staticmethod def _summarize_history(history: List[Dict[str, Any]]) -> str: """汇总历史对话。""" turns = 0 for m in history: if m.get("role") == "user": turns += 1 return f"共 {turns} 轮历史对话（详情已存入长期记忆）"