592bca4f39
- 4.1 Fallback chain: LLM fallback_llm config in AgentLLMConfig, retry with alternate model on API failure; Agent fallback_agent in DAG nodes - 4.2 Knowledge sharing: GlobalKnowledge model with embedding-based semantic search, auto-extraction of tool names as tags after execution - 4.3 Async execution: execute_agent_task fully implemented with AgentRuntime, scheduler dual-path for workflow/non-workflow agents Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
488 lines
19 KiB
Python
488 lines
19 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,
|
||
)
|
||
from app.services.embedding_service import embedding_service, VectorEntry
|
||
|
||
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,
|
||
vector_memory_enabled: bool = True,
|
||
vector_memory_top_k: int = 5,
|
||
):
|
||
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
|
||
self.vector_memory_enabled = vector_memory_enabled
|
||
self.vector_memory_top_k = vector_memory_top_k
|
||
# 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, query: str = "") -> str:
|
||
"""
|
||
初始化记忆：从 DB/Redis 加载长期记忆 + 向量检索相关历史。
|
||
返回注入 system prompt 的记忆文本块。
|
||
"""
|
||
if not self.persist or not self.scope_id:
|
||
return ""
|
||
|
||
parts: List[str] = []
|
||
|
||
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
|
||
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}")
|
||
except Exception as e:
|
||
logger.warning("加载长期记忆失败: %s", e)
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
|
||
# 2. 向量检索：查找语义相关的历史对话
|
||
if self.vector_memory_enabled and self.scope_kind and self.scope_id:
|
||
vector_text = await self._vector_search(query)
|
||
if vector_text:
|
||
parts.append(vector_text)
|
||
|
||
# 3. 全局知识检索：从 GlobalKnowledge 表加载相关条目
|
||
global_text = await self._global_knowledge_search(query)
|
||
if global_text:
|
||
parts.append(global_text)
|
||
|
||
return "\n\n".join(parts) if parts else ""
|
||
|
||
async def _vector_search(self, query: str = "") -> str:
|
||
"""
|
||
向量检索语义相关的历史记忆，返回格式化的文本块。
|
||
若无 query 则返回最近 Top-5 条记忆。
|
||
"""
|
||
from app.models.agent_vector_memory import AgentVectorMemory
|
||
|
||
db: Optional[Session] = None
|
||
try:
|
||
db = SessionLocal()
|
||
# 查询当前 scope 的所有向量记忆（按时间倒序）
|
||
rows = (
|
||
db.query(AgentVectorMemory)
|
||
.filter(
|
||
AgentVectorMemory.scope_kind == self.scope_kind,
|
||
AgentVectorMemory.scope_id == self.scope_id,
|
||
)
|
||
.order_by(AgentVectorMemory.created_at.desc())
|
||
.limit(50) # 最多取最近 50 条做相似度计算
|
||
.all()
|
||
)
|
||
|
||
if not rows:
|
||
return ""
|
||
|
||
entries: List[VectorEntry] = []
|
||
for row in rows:
|
||
emb = embedding_service.deserialize_embedding(row.embedding) if row.embedding else []
|
||
entries.append({
|
||
"id": row.id,
|
||
"scope_kind": row.scope_kind,
|
||
"scope_id": row.scope_id,
|
||
"content_text": row.content_text,
|
||
"embedding": emb,
|
||
"metadata": row.metadata_ or {},
|
||
})
|
||
|
||
matched: List[VectorEntry] = []
|
||
|
||
if query and query.strip():
|
||
# 有 query：生成 embedding 做语义搜索
|
||
query_emb = await embedding_service.generate_embedding(query)
|
||
if query_emb:
|
||
matched = await embedding_service.similarity_search(
|
||
query_emb, entries, top_k=self.vector_memory_top_k
|
||
)
|
||
else:
|
||
# 无 query：返回最近几条
|
||
matched = entries[: self.vector_memory_top_k]
|
||
for m in matched:
|
||
m["score"] = 1.0
|
||
|
||
if not matched:
|
||
return ""
|
||
|
||
# 格式化为文本块
|
||
lines = ["## 相关历史记忆"]
|
||
for i, m in enumerate(matched, 1):
|
||
text = m.get("content_text", "")[:500]
|
||
meta = m.get("metadata", {})
|
||
entry_type = meta.get("type", "对话")
|
||
lines.append(f"{i}. [{entry_type}] {text}")
|
||
if m.get("score", 1.0) < 1.0:
|
||
lines[-1] += f" (匹配度: {m['score']:.2f})"
|
||
|
||
return "\n".join(lines)
|
||
|
||
except Exception as e:
|
||
logger.warning("向量检索失败: %s", e)
|
||
return ""
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
|
||
async def _global_knowledge_search(self, query: str = "") -> str:
|
||
"""从 GlobalKnowledge 表检索相关的全局知识条目。"""
|
||
from app.models.agent import GlobalKnowledge
|
||
|
||
db: Optional[Session] = None
|
||
try:
|
||
db = SessionLocal()
|
||
rows = (
|
||
db.query(GlobalKnowledge)
|
||
.order_by(GlobalKnowledge.created_at.desc())
|
||
.limit(50)
|
||
.all()
|
||
)
|
||
if not rows:
|
||
return ""
|
||
|
||
# 如果有 query，用向量相似度筛选；否则返回最近的条目
|
||
if query and query.strip():
|
||
entries: List[VectorEntry] = []
|
||
for row in rows:
|
||
if not row.embedding:
|
||
continue
|
||
try:
|
||
emb = embedding_service.deserialize_embedding(row.embedding)
|
||
except Exception:
|
||
emb = []
|
||
if emb:
|
||
entries.append({
|
||
"id": row.id,
|
||
"scope_kind": "global",
|
||
"scope_id": "global",
|
||
"content_text": row.content,
|
||
"embedding": emb,
|
||
"metadata": {
|
||
"source_agent_id": row.source_agent_id,
|
||
"tags": row.tags or [],
|
||
},
|
||
})
|
||
|
||
if entries:
|
||
query_emb = await embedding_service.generate_embedding(query)
|
||
if query_emb:
|
||
matched = await embedding_service.similarity_search(
|
||
query_emb, entries, top_k=min(5, len(entries)),
|
||
)
|
||
if matched:
|
||
lines = ["## 全局知识库"]
|
||
for i, m in enumerate(matched, 1):
|
||
tags = m.get("metadata", {}).get("tags", [])
|
||
tag_str = f" [{', '.join(tags[:3])}]" if tags else ""
|
||
lines.append(f"{i}.{tag_str} {m.get('content_text', '')[:500]}")
|
||
return "\n".join(lines)
|
||
else:
|
||
# 无 query，返回最近 5 条全局知识
|
||
recent = rows[:5]
|
||
if recent:
|
||
lines = ["## 全局知识库（最近）"]
|
||
for i, row in enumerate(recent, 1):
|
||
tag_str = f" [{(', '.join(row.tags[:3]))}]" if row.tags else ""
|
||
lines.append(f"{i}.{tag_str} {row.content[:500]}")
|
||
return "\n".join(lines)
|
||
|
||
return ""
|
||
except Exception as e:
|
||
logger.warning("全局知识检索失败: %s", e)
|
||
return ""
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
|
||
async def save_global_knowledge(
|
||
self, content: str, source_agent_id: str = "",
|
||
source_user_id: str = "", tags: Optional[List[str]] = None,
|
||
) -> None:
|
||
"""将知识条目写入全局知识池。"""
|
||
from app.models.agent import GlobalKnowledge
|
||
|
||
if not content or len(content) < 20:
|
||
return
|
||
|
||
db: Optional[Session] = None
|
||
try:
|
||
db = SessionLocal()
|
||
|
||
# 生成 embedding
|
||
embedding_json = ""
|
||
try:
|
||
emb = await embedding_service.generate_embedding(content)
|
||
if emb:
|
||
embedding_json = embedding_service.serialize_embedding(emb) or ""
|
||
except Exception:
|
||
pass
|
||
|
||
record = GlobalKnowledge(
|
||
content=content[:2000],
|
||
embedding=embedding_json or None,
|
||
source_agent_id=source_agent_id or "",
|
||
source_user_id=source_user_id or "",
|
||
tags=tags or [],
|
||
scope_kind=self.scope_kind,
|
||
scope_id=self.scope_id or "global",
|
||
)
|
||
db.add(record)
|
||
db.commit()
|
||
logger.info("已写入全局知识: agent=%s tags=%s", source_agent_id, tags)
|
||
except Exception as e:
|
||
logger.warning("保存全局知识失败: %s", e)
|
||
if db:
|
||
db.rollback()
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
|
||
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,
|
||
)
|
||
|
||
# 保存向量记忆（异步生成 embedding 并存储）
|
||
if self.vector_memory_enabled:
|
||
await self._save_vector_memory(
|
||
db, user_message, assistant_reply
|
||
)
|
||
except Exception as e:
|
||
logger.warning("保存长期记忆失败: %s", e)
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
|
||
async def _save_vector_memory(
|
||
self, db: Session, user_message: str, assistant_reply: str,
|
||
) -> None:
|
||
"""生成 embedding 并保存到向量记忆表。"""
|
||
from app.models.agent_vector_memory import AgentVectorMemory
|
||
|
||
content_text = f"用户: {user_message}\n助手: {assistant_reply}"
|
||
if len(content_text) > 8000:
|
||
content_text = content_text[:8000]
|
||
|
||
try:
|
||
# 生成 embedding
|
||
embedding = await embedding_service.generate_embedding(content_text)
|
||
embedding_json = embedding_service.serialize_embedding(embedding) if embedding else ""
|
||
|
||
record = AgentVectorMemory(
|
||
scope_kind=self.scope_kind,
|
||
scope_id=self.scope_id,
|
||
session_key=self.session_key,
|
||
content_text=content_text[:2000],
|
||
embedding=embedding_json or None,
|
||
metadata_={
|
||
"type": "conversation_turn",
|
||
},
|
||
)
|
||
db.add(record)
|
||
db.commit()
|
||
logger.debug("已保存向量记忆 (scope=%s/%s)", self.scope_kind, self.scope_id)
|
||
except Exception as e:
|
||
logger.warning("保存向量记忆失败: %s", e)
|
||
db.rollback()
|
||
|
||
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）"
|