036f533881
- agent_runtime: orchestrator、core/memory/schemas 调整 - agent_monitoring API、service、agent_llm_log 模型与 database 注册 - 前端 AgentDashboard、AgentConfig、Agents/MainLayout/路由与 AgentChat - 文档：(红头)项目核心文档汇总、自主AI Agent改造完成情况、AI agent改造计划 Made-with: Cursor
249 lines
9.8 KiB
Python
249 lines
9.8 KiB
Python
"""
|
||
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）"
|