9054f42cda
- GlobalKnowledge 模型新增 confidence 和 expires_at 字段 - save_global_knowledge 增加 MD5 去重、置信度评分、TTL过期 - _global_knowledge_search 增加过期过滤、置信度优先排序 - run()/run_stream() 所有完成路径补齐知识提取调用 - 新增 Alembic 迁移 010_add_global_knowledge Cl oses #9 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
535 lines
21 KiB
Python
535 lines
21 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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支持 LLM 自动压缩总结对话历史。
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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.services.embedding_service import embedding_service, VectorEntry
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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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- 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
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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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vector_memory_enabled: bool = True,
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vector_memory_top_k: int = 5,
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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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self.vector_memory_enabled = vector_memory_enabled
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self.vector_memory_top_k = vector_memory_top_k
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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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# 记录已压缩的消息数，避免重复压缩
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self._last_compressed_msg_count = 0
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async def initialize(self, query: str = "") -> str:
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"""
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初始化记忆：从 DB/Redis 加载长期记忆 + 向量检索相关历史。
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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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parts: List[str] = []
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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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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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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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# 2. 向量检索：查找语义相关的历史对话
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if self.vector_memory_enabled and self.scope_kind and self.scope_id:
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vector_text = await self._vector_search(query)
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if vector_text:
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parts.append(vector_text)
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# 3. 全局知识检索：从 GlobalKnowledge 表加载相关条目
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global_text = await self._global_knowledge_search(query)
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if global_text:
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parts.append(global_text)
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return "\n\n".join(parts) if parts else ""
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async def _vector_search(self, query: str = "") -> str:
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"""
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向量检索语义相关的历史记忆，返回格式化的文本块。
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若无 query 则返回最近 Top-5 条记忆。
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"""
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from app.models.agent_vector_memory import AgentVectorMemory
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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# 查询当前 scope 的所有向量记忆（按时间倒序）
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rows = (
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db.query(AgentVectorMemory)
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.filter(
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AgentVectorMemory.scope_kind == self.scope_kind,
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AgentVectorMemory.scope_id == self.scope_id,
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)
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.order_by(AgentVectorMemory.created_at.desc())
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.limit(50) # 最多取最近 50 条做相似度计算
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.all()
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)
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if not rows:
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return ""
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entries: List[VectorEntry] = []
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for row in rows:
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emb = embedding_service.deserialize_embedding(row.embedding) if row.embedding else []
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entries.append({
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"id": row.id,
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"scope_kind": row.scope_kind,
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"scope_id": row.scope_id,
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"content_text": row.content_text,
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"embedding": emb,
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"metadata": row.metadata_ or {},
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})
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matched: List[VectorEntry] = []
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if query and query.strip():
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# 有 query：生成 embedding 做语义搜索
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query_emb = await embedding_service.generate_embedding(query)
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if query_emb:
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matched = await embedding_service.similarity_search(
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query_emb, entries, top_k=self.vector_memory_top_k
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)
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else:
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# 无 query：返回最近几条
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matched = entries[: self.vector_memory_top_k]
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for m in matched:
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m["score"] = 1.0
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if not matched:
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return ""
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# 格式化为文本块
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lines = ["## 相关历史记忆"]
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for i, m in enumerate(matched, 1):
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text = m.get("content_text", "")[:500]
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meta = m.get("metadata", {})
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entry_type = meta.get("type", "对话")
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lines.append(f"{i}. [{entry_type}] {text}")
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if m.get("score", 1.0) < 1.0:
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lines[-1] += f" (匹配度: {m['score']:.2f})"
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return "\n".join(lines)
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except Exception as e:
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logger.warning("向量检索失败: %s", e)
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return ""
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finally:
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if db:
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db.close()
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async def _global_knowledge_search(self, query: str = "") -> str:
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"""从 GlobalKnowledge 表检索相关的全局知识条目。"""
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from datetime import datetime
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from app.models.agent import GlobalKnowledge
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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now = datetime.utcnow()
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# 查询未过期的知识（expires_at IS NULL 或 expires_at > now）
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rows = (
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db.query(GlobalKnowledge)
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.filter(
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(GlobalKnowledge.expires_at.is_(None))
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| (GlobalKnowledge.expires_at > now)
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)
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.order_by(GlobalKnowledge.created_at.desc())
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.limit(50)
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.all()
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)
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if not rows:
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return ""
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# 如果有 query，用向量相似度筛选；否则返回最近的条目
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if query and query.strip():
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entries: List[VectorEntry] = []
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for row in rows:
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if not row.embedding:
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continue
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try:
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emb = embedding_service.deserialize_embedding(row.embedding)
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except Exception:
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emb = []
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if emb:
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entries.append({
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"id": row.id,
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"scope_kind": "global",
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"scope_id": "global",
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"content_text": row.content,
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"embedding": emb,
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"metadata": {
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"source_agent_id": row.source_agent_id,
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"tags": row.tags or [],
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"confidence": row.confidence or "medium",
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},
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})
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if entries:
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query_emb = await embedding_service.generate_embedding(query)
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if query_emb:
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matched = await embedding_service.similarity_search(
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query_emb, entries, top_k=min(5, len(entries)),
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)
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if matched:
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lines = ["## 全局知识库"]
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for i, m in enumerate(matched, 1):
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tags = m.get("metadata", {}).get("tags", [])
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conf = m.get("metadata", {}).get("confidence", "medium")
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tag_str = f" [{', '.join(tags[:3])}]" if tags else ""
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conf_str = f" (置信度:{conf})" if conf != "medium" else ""
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lines.append(f"{i}.{tag_str}{conf_str} {m.get('content_text', '')[:500]}")
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return "\n".join(lines)
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else:
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# 无 query，返回最近 5 条全局知识（优先高置信度）
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recent = sorted(rows, key=lambda r: (
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0 if r.confidence == "high" else 1 if r.confidence == "medium" else 2
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))[:5]
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if recent:
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lines = ["## 全局知识库（最近）"]
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for i, row in enumerate(recent, 1):
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tag_str = f" [{(', '.join(row.tags[:3]))}]" if row.tags else ""
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conf_str = f" (置信度:{row.confidence})" if row.confidence and row.confidence != "medium" else ""
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lines.append(f"{i}.{tag_str}{conf_str} {row.content[:500]}")
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return "\n".join(lines)
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return ""
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except Exception as e:
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logger.warning("全局知识检索失败: %s", e)
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return ""
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finally:
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if db:
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db.close()
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async def save_global_knowledge(
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self, content: str, source_agent_id: str = "",
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source_user_id: str = "", tags: Optional[List[str]] = None,
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confidence: str = "medium", ttl_hours: int = 0,
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) -> None:
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"""将知识条目写入全局知识池（带去重、置信度、过期时间）。
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去重策略：对 content 取哈希，若已有相同哈希的条目则跳过。
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过期策略：ttl_hours > 0 时设置 expires_at；0 表示永不过期。
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"""
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from datetime import datetime, timedelta
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from app.models.agent import GlobalKnowledge
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if not content or len(content) < 20:
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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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# 去重：用 content 的 MD5 哈希检查是否已存在
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import hashlib
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content_hash = hashlib.md5(content[:500].encode()).hexdigest()
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# 查询最近 200 条，检查是否有相同哈希的条目
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recent = (
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db.query(GlobalKnowledge)
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.order_by(GlobalKnowledge.created_at.desc())
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.limit(200)
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.all()
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)
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for existing in recent:
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existing_hash = hashlib.md5(
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(existing.content or "")[:500].encode()
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).hexdigest()
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if existing_hash == content_hash:
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logger.info("全局知识去重：已存在相同条目，跳过写入")
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return
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# 嵌入向量
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embedding_json = ""
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try:
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emb = await embedding_service.generate_embedding(content)
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if emb:
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embedding_json = embedding_service.serialize_embedding(emb) or ""
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except Exception:
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pass
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# 过期时间
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expires_at = None
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if ttl_hours > 0:
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expires_at = datetime.utcnow() + timedelta(hours=ttl_hours)
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record = GlobalKnowledge(
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content=content[:2000],
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embedding=embedding_json or None,
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source_agent_id=source_agent_id or "",
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source_user_id=source_user_id or "",
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tags=tags or [],
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confidence=confidence or "medium",
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expires_at=expires_at,
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scope_kind=self.scope_kind,
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scope_id=self.scope_id or "global",
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)
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db.add(record)
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db.commit()
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logger.info("已写入全局知识: agent=%s tags=%s confidence=%s",
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source_agent_id, tags, confidence)
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except Exception as e:
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logger.warning("保存全局知识失败: %s", e)
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if db:
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db.rollback()
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finally:
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if db:
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db.close()
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async def save_context(
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self, user_message: str, assistant_reply: str,
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messages: Optional[List[Dict[str, Any]]] = None,
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) -> None:
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"""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。"""
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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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# 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
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if messages and len(messages) > self._last_compressed_msg_count + 2:
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await self._compress_and_summarize(messages)
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self._last_compressed_msg_count = len(messages)
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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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# 保存向量记忆（异步生成 embedding 并存储）
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if self.vector_memory_enabled:
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await self._save_vector_memory(
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db, user_message, assistant_reply
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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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async def _save_vector_memory(
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self, db: Session, user_message: str, assistant_reply: str,
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) -> None:
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"""生成 embedding 并保存到向量记忆表。"""
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from app.models.agent_vector_memory import AgentVectorMemory
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content_text = f"用户: {user_message}\n助手: {assistant_reply}"
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if len(content_text) > 8000:
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content_text = content_text[:8000]
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try:
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# 生成 embedding
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embedding = await embedding_service.generate_embedding(content_text)
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embedding_json = embedding_service.serialize_embedding(embedding) if embedding else ""
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record = AgentVectorMemory(
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scope_kind=self.scope_kind,
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scope_id=self.scope_id,
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session_key=self.session_key,
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content_text=content_text[:2000],
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embedding=embedding_json or None,
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metadata_={
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"type": "conversation_turn",
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},
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)
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db.add(record)
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db.commit()
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logger.debug("已保存向量记忆 (scope=%s/%s)", self.scope_kind, self.scope_id)
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except Exception as e:
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logger.warning("保存向量记忆失败: %s", e)
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db.rollback()
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async def _compress_and_summarize(
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self, messages: List[Dict[str, Any]]
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) -> None:
|
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"""
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使用 LLM 压缩总结对话历史，提取用户画像和关键事实。
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只处理非 system 消息。
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"""
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from openai import AsyncOpenAI
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from app.core.config import settings
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|
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# 提取对话消息（去掉 system 和 tool 消息）
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conversation = []
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for m in messages:
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role = m.get("role", "")
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if role == "system":
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continue
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if role == "tool":
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# 工具结果精简后加入
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content = m.get("content", "")
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name = m.get("name", "tool")
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conversation.append({"role": "user" if role == "tool" else role, "content": f"[工具 {name} 执行结果]\n{content[:200]}"})
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else:
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conversation.append({"role": role, "content": m.get("content", "")[:500]})
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|
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if len(conversation) < 2:
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return
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|
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# 构建总结 prompt
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summary_prompt = (
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"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"
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"请返回 JSON 格式（不要 markdown 包裹），包含以下字段：\n"
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"1. user_profile: 用户画像对象，包含用户的偏好、角色、关键需求等\n"
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"2. key_facts: 从对话中提取的关键事实列表（字符串数组）\n"
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"3. summary: 对话的简要总结（100字以内）\n"
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"4. topics: 讨论过的话题列表（字符串数组）\n\n"
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"如果没有足够信息，相应字段设为空对象或空数组。"
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)
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summary_messages = [
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{"role": "system", "content": summary_prompt},
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*conversation[-10:], # 只取最近 10 条消息
|
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]
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|
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try:
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api_key = settings.DEEPSEEK_API_KEY or settings.OPENAI_API_KEY or ""
|
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base_url = settings.DEEPSEEK_BASE_URL or settings.OPENAI_BASE_URL or "https://api.deepseek.com"
|
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if api_key == "your-openai-api-key":
|
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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）"
|