feat: mem version1
This commit is contained in:
ai-business-hql
@@ -0,0 +1,190 @@
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'''
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Author: ai-business-hql ai.bussiness.hql@gmail.com
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Date: 2025-11-19
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LastEditors: ai-business-hql ai.bussiness.hql@gmail.com
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LastEditTime: 2025-11-19
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FilePath: /ComfyUI-Copilot/backend/dao/session_message_table.py
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Description: Session message table for conversation memory management with compression
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'''
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import os
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import json
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from typing import Dict, Any, Optional, List
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from sqlalchemy import create_engine, Column, Integer, String, DateTime, Text
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker
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from datetime import datetime
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# 创建数据库基类
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Base = declarative_base()
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# 定义session_message表模型
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class SessionMessage(Base):
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__tablename__ = 'session_message'
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id = Column(Integer, primary_key=True, autoincrement=True)
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session_id = Column(String(255), nullable=False, unique=True) # 每个session只有一条记录
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messages = Column(Text, nullable=False) # JSON字符串，存储完整消息列表
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index = Column(Integer, default=0) # 已压缩的消息数量，messages[:index]已被摘要
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summary = Column(Text, nullable=True) # 压缩后的历史摘要
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attributes = Column(Text, nullable=True) # JSON字符串，存储额外属性
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created_at = Column(DateTime, default=datetime.utcnow)
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def to_dict(self):
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return {
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'id': self.id,
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'session_id': self.session_id,
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'messages': json.loads(self.messages) if self.messages else [],
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'index': self.index,
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'summary': self.summary,
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'attributes': json.loads(self.attributes) if self.attributes else None,
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'created_at': self.created_at.isoformat() if self.created_at else None
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}
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class SessionMessageManager:
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"""会话消息管理器"""
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def __init__(self, db_path: str = None):
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if db_path is None:
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# 默认数据库路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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db_dir = os.path.join(current_dir, '..', 'data')
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os.makedirs(db_dir, exist_ok=True)
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db_path = os.path.join(db_dir, 'session_message.db')
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self.db_path = db_path
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self.engine = create_engine(f'sqlite:///{db_path}', echo=False)
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self.SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=self.engine)
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# 创建表
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Base.metadata.create_all(bind=self.engine)
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def get_session(self):
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"""获取数据库会话"""
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return self.SessionLocal()
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def get_session_message(self, session_id: str) -> Optional[Dict[str, Any]]:
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"""获取指定session的消息记录"""
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session = self.get_session()
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try:
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record = session.query(SessionMessage)\
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.filter(SessionMessage.session_id == session_id)\
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.first()
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if record:
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return record.to_dict()
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return None
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finally:
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session.close()
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def save_session_message(
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self,
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session_id: str,
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messages: List[Dict[str, Any]],
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index: int = 0,
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summary: str = None,
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attributes: Optional[Dict[str, Any]] = None
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) -> int:
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"""保存或更新会话消息记录"""
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session = self.get_session()
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try:
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# 查找是否已存在
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record = session.query(SessionMessage)\
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.filter(SessionMessage.session_id == session_id)\
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.first()
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if record:
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# 更新已有记录
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record.messages = json.dumps(messages, ensure_ascii=False)
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record.index = index
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record.summary = summary
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if attributes:
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record.attributes = json.dumps(attributes, ensure_ascii=False)
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session.commit()
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session.refresh(record)
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return record.id
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else:
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# 创建新记录
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new_record = SessionMessage(
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session_id=session_id,
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messages=json.dumps(messages, ensure_ascii=False),
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index=index,
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summary=summary,
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attributes=json.dumps(attributes, ensure_ascii=False) if attributes else None
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)
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session.add(new_record)
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session.commit()
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session.refresh(new_record)
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return new_record.id
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except Exception as e:
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session.rollback()
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raise e
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finally:
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session.close()
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def update_summary(self, session_id: str, summary: str, index: int) -> bool:
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"""更新指定session的摘要和索引"""
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session = self.get_session()
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try:
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record = session.query(SessionMessage)\
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.filter(SessionMessage.session_id == session_id)\
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.first()
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if record:
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record.summary = summary
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record.index = index
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session.commit()
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return True
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return False
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except Exception as e:
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session.rollback()
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raise e
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finally:
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session.close()
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def delete_session_message(self, session_id: str) -> bool:
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"""删除指定session的消息记录"""
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session = self.get_session()
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try:
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record = session.query(SessionMessage)\
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.filter(SessionMessage.session_id == session_id)\
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.first()
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if record:
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session.delete(record)
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session.commit()
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return True
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return False
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except Exception as e:
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session.rollback()
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raise e
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finally:
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session.close()
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# 全局会话消息管理器实例
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session_message_manager = SessionMessageManager()
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def get_session_message(session_id: str) -> Optional[Dict[str, Any]]:
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"""获取会话消息的便捷函数"""
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return session_message_manager.get_session_message(session_id)
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def save_session_message(
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session_id: str,
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messages: List[Dict[str, Any]],
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index: int = 0,
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summary: str = None,
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attributes: Optional[Dict[str, Any]] = None
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) -> int:
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"""保存会话消息的便捷函数"""
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return session_message_manager.save_session_message(
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session_id, messages, index, summary, attributes
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)
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def update_summary(session_id: str, summary: str, index: int) -> bool:
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"""更新摘要的便捷函数"""
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return session_message_manager.update_summary(session_id, summary, index)
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def delete_session_message(session_id: str) -> bool:
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"""删除会话消息的便捷函数"""
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return session_message_manager.delete_session_message(session_id)
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@@ -2,7 +2,7 @@
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Author: ai-business-hql qingli.hql@alibaba-inc.com
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Date: 2025-06-16 16:50:17
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LastEditors: ai-business-hql ai.bussiness.hql@gmail.com
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LastEditTime: 2025-11-18 19:18:30
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LastEditTime: 2025-11-19 20:16:03
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FilePath: /comfyui_copilot/backend/service/mcp-client.py
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Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
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'''
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@@ -34,6 +34,7 @@ except Exception:
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from ..agent_factory import create_agent
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from ..service.workflow_rewrite_agent import create_workflow_rewrite_agent
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from ..service.message_memory import message_memory_optimize
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from ..utils.request_context import get_rewrite_context, get_session_id, get_config
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from ..utils.logger import log
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from openai.types.responses import ResponseTextDeltaEvent
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@@ -117,6 +118,11 @@ async def comfyui_agent_invoke(messages: List[Dict[str, Any]], images: List[Imag
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if not config:
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raise ValueError("No config found in request context")
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# Optimize messages with memory compression
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log.info(f"[MCP] Original messages count: {len(messages)}")
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messages = message_memory_optimize(session_id, messages)
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log.info(f"[MCP] Optimized messages count: {len(messages)}, messages: {messages}")
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# Create MCP server instances
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mcp_server = MCPServerSse(
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params= {
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@@ -0,0 +1,261 @@
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'''
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Author: ai-business-hql ai.bussiness.hql@gmail.com
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Date: 2025-11-19
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LastEditors: ai-business-hql ai.bussiness.hql@gmail.com
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LastEditTime: 2025-11-19
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FilePath: /ComfyUI-Copilot/backend/service/message_memory.py
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Description: Message memory manager with automatic compression
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'''
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from typing import List, Dict, Any, Tuple
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from ..dao.session_message_table import (
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get_session_message,
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save_session_message,
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update_summary
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)
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from .summary_agent import generate_summary
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from ..utils.logger import log
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# 配置参数
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COMPRESSION_THRESHOLD = 8 # 超过10条未压缩消息时触发压缩（4个来回）
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KEEP_RECENT_MESSAGES = 4 # 保留最新4条消息不压缩（2个来回）
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def message_memory_optimize(
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session_id: str,
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messages: List[Dict[str, Any]]
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) -> List[Dict[str, Any]]:
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"""
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优化消息内存，自动压缩历史消息
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工作流程：
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1. 加载 session 数据（如果不存在则创建）
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2. 更新完整的消息列表
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3. 检查是否需要压缩（未压缩消息 > 10条）
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4. 如果需要，执行压缩并更新数据库
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5. 返回优化后的消息列表（summary + 未压缩的消息）
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Args:
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session_id: 会话ID
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messages: 当前的完整消息列表
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Returns:
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优化后的消息列表，可以直接传给 LLM
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- 如果有压缩：[{"role": "system", "content": "Previous summary: ..."}, ...recent_messages]
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- 如果无压缩：原始 messages
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"""
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try:
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log.info(f"[Memory] Starting memory optimization for session: {session_id}")
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log.info(f"[Memory] Input messages count: {len(messages)}")
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# 1. 加载或创建 session 记录
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record = get_session_message(session_id)
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if record is None:
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# 首次访问，创建新记录
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log.info(f"[Memory] New session detected, creating record")
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save_session_message(
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session_id=session_id,
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messages=messages,
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index=0,
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summary=None
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)
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# 新会话不需要压缩，直接返回原始消息
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return messages
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# 2. 更新消息列表（保存当前完整的消息）
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current_index = record['index']
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current_summary = record['summary']
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log.info(f"[Memory] Existing session - index: {current_index}, summary length: {len(current_summary) if current_summary else 0}")
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# 保存更新后的完整消息列表
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save_session_message(
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session_id=session_id,
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messages=messages,
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index=current_index, # 暂时保持不变
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summary=current_summary
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)
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# 3. 检查是否需要压缩
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uncompressed_count = len(messages) - current_index
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log.info(f"[Memory] Uncompressed messages: {uncompressed_count} (threshold: {COMPRESSION_THRESHOLD})")
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if uncompressed_count <= COMPRESSION_THRESHOLD:
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# 不需要压缩，构建返回消息
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log.info(f"[Memory] No compression needed")
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return _build_optimized_messages(current_summary, messages, current_index)
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# 4. 执行压缩
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log.info(f"[Memory] Compression triggered!")
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# 计算压缩范围
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# 待压缩：messages[current_index : len(messages) - KEEP_RECENT_MESSAGES]
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compress_end_index = len(messages) - KEEP_RECENT_MESSAGES
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messages_to_compress = messages[current_index:compress_end_index]
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log.info(f"[Memory] Compressing messages from index {current_index} to {compress_end_index}")
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log.info(f"[Memory] Messages to compress: {len(messages_to_compress)}")
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# 生成新的摘要（整合历史摘要）
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new_summary = generate_summary(
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messages=messages_to_compress,
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previous_summary=current_summary
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)
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log.info(f"[Memory] Generated new summary: {new_summary[:100]}...")
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# 更新数据库
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update_summary(
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session_id=session_id,
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summary=new_summary,
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index=compress_end_index
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)
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log.info(f"[Memory] Updated index to: {compress_end_index}")
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# 5. 返回优化后的消息
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optimized_messages = _build_optimized_messages(new_summary, messages, compress_end_index)
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log.info(f"[Memory] Returning {len(optimized_messages)} optimized messages")
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return optimized_messages
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except Exception as e:
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log.error(f"[Memory] Error in message_memory_optimize: {str(e)}")
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log.error(f"[Memory] Falling back to original messages")
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# 发生错误时返回原始消息，确保系统继续运行
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return messages
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def _build_optimized_messages(
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summary: str,
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full_messages: List[Dict[str, Any]],
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index: int
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) -> List[Dict[str, Any]]:
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"""
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构建优化后的消息列表
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Args:
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summary: 压缩后的历史摘要
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full_messages: 完整的消息列表
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index: 已压缩的消息索引
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Returns:
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优化后的消息列表
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"""
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# 获取未压缩的消息
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recent_messages = full_messages[index:]
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if summary and len(summary.strip()) > 0 and len(recent_messages) > 0:
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# 有摘要且有未压缩消息，将 summary 作为上下文信息添加到第一条消息前
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# 注意：不能使用 role="system"，因为 create_agent 已经有 instructions 了
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# 将 summary 作为一条独立的 user 消息添加在开头
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summary_context = {
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"role": "user",
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"content": f"[Context from previous conversation]: {summary}"
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}
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return [summary_context] + recent_messages
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else:
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# 无摘要或无消息，直接返回未压缩的消息
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return recent_messages
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def get_optimized_messages(session_id: str) -> List[Dict[str, Any]]:
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"""
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获取优化后的消息列表（不触发新的压缩）
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Args:
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session_id: 会话ID
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Returns:
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优化后的消息列表
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"""
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try:
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record = get_session_message(session_id)
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if record is None:
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return []
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return _build_optimized_messages(
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summary=record['summary'],
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full_messages=record['messages'],
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index=record['index']
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)
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except Exception as e:
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log.error(f"[Memory] Error in get_optimized_messages: {str(e)}")
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return []
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def get_compression_stats(session_id: str) -> Dict[str, Any]:
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"""
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获取压缩统计信息
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Args:
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session_id: 会话ID
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Returns:
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压缩统计信息字典
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"""
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try:
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record = get_session_message(session_id)
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if record is None:
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return {
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"exists": False,
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"total_messages": 0,
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"compressed_messages": 0,
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"uncompressed_messages": 0,
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"has_summary": False,
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"summary_length": 0
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}
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total = len(record['messages'])
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compressed = record['index']
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uncompressed = total - compressed
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return {
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"exists": True,
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"total_messages": total,
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"compressed_messages": compressed,
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"uncompressed_messages": uncompressed,
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"has_summary": bool(record['summary']),
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"summary_length": len(record['summary']) if record['summary'] else 0,
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"compression_ratio": f"{compressed}/{total}" if total > 0 else "0/0"
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}
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except Exception as e:
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log.error(f"[Memory] Error in get_compression_stats: {str(e)}")
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return {"error": str(e)}
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# 测试函数
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def test_message_memory():
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"""测试消息内存管理"""
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test_session_id = "test_session_123"
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|
||||
# 模拟对话
|
||||
messages = []
|
||||
for i in range(15):
|
||||
messages.append({"role": "user", "content": f"User message {i+1}"})
|
||||
messages.append({"role": "assistant", "content": f"Assistant response {i+1}"})
|
||||
|
||||
print(f"Total messages: {len(messages)}")
|
||||
|
||||
# 第一次优化
|
||||
optimized = message_memory_optimize(test_session_id, messages)
|
||||
print(f"Optimized messages count: {len(optimized)}")
|
||||
|
||||
# 获取统计信息
|
||||
stats = get_compression_stats(test_session_id)
|
||||
print(f"Compression stats: {stats}")
|
||||
|
||||
# 打印优化后的消息
|
||||
for msg in optimized[:3]:
|
||||
print(f"{msg['role']}: {msg['content'][:50]}...")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_message_memory()
|
||||
|
||||
@@ -0,0 +1,151 @@
|
||||
'''
|
||||
Author: ai-business-hql ai.bussiness.hql@gmail.com
|
||||
Date: 2025-11-19
|
||||
LastEditors: ai-business-hql ai.bussiness.hql@gmail.com
|
||||
LastEditTime: 2025-11-19
|
||||
FilePath: /ComfyUI-Copilot/backend/service/summary_agent.py
|
||||
Description: Summary agent for compressing conversation history
|
||||
'''
|
||||
|
||||
import json
|
||||
from typing import List, Dict, Any
|
||||
from pydantic import BaseModel
|
||||
from openai import OpenAI
|
||||
|
||||
from ..utils.key_utils import workflow_config_adapt
|
||||
from ..utils.globals import WORKFLOW_MODEL_NAME, get_comfyui_copilot_api_key, LLM_DEFAULT_BASE_URL
|
||||
from ..utils.request_context import get_config
|
||||
from ..utils.logger import log
|
||||
|
||||
|
||||
class SummaryResponse(BaseModel):
|
||||
"""
|
||||
对话历史摘要响应，必须是简洁的英文文本
|
||||
"""
|
||||
summary: str
|
||||
|
||||
|
||||
def generate_summary(messages: List[Dict[str, Any]], previous_summary: str = None) -> str:
|
||||
"""
|
||||
生成对话历史摘要
|
||||
|
||||
Args:
|
||||
messages: 需要摘要的消息列表，格式为 [{"role": "user/assistant", "content": "..."}]
|
||||
previous_summary: 之前的摘要（如果有），新摘要会整合历史信息
|
||||
|
||||
Returns:
|
||||
生成的摘要文本，不超过 200 words
|
||||
"""
|
||||
try:
|
||||
# 构建消息内容文本
|
||||
messages_text = "\n\n".join([
|
||||
f"**{msg['role'].upper()}**: {msg['content']}"
|
||||
for msg in messages
|
||||
if isinstance(msg.get('content'), str) # 只处理文本内容
|
||||
])
|
||||
|
||||
# 构建用户提示
|
||||
if previous_summary:
|
||||
user_prompt = f"""Please generate a concise summary of the following conversation history, integrating it with the previous summary.
|
||||
|
||||
## Previous Summary
|
||||
{previous_summary}
|
||||
|
||||
## New Conversation Messages
|
||||
{messages_text}
|
||||
|
||||
## Requirements
|
||||
- Maximum 200 words
|
||||
- Focus on key topics, decisions, and important context
|
||||
- Use clear, objective language
|
||||
- Integrate previous summary seamlessly with new information
|
||||
- Avoid redundancy
|
||||
"""
|
||||
else:
|
||||
user_prompt = f"""Please generate a concise summary of the following conversation history.
|
||||
|
||||
## Conversation Messages
|
||||
{messages_text}
|
||||
|
||||
## Requirements
|
||||
- Maximum 200 words
|
||||
- Focus on key topics, decisions, and important context
|
||||
- Use clear, objective language
|
||||
"""
|
||||
|
||||
# 系统提示词
|
||||
system_prompt = """You are an expert conversation summarizer. Your task is to create concise, informative summaries of conversation histories that preserve the most important information while reducing token usage.
|
||||
|
||||
Focus on:
|
||||
- Main topics discussed
|
||||
- Key decisions or conclusions
|
||||
- Important technical details or requirements
|
||||
- User's goals and preferences
|
||||
- Context that would be needed for future interactions
|
||||
|
||||
Keep summaries factual, objective, and efficient."""
|
||||
|
||||
# 获取配置
|
||||
config = get_config()
|
||||
|
||||
# 创建OpenAI客户端
|
||||
client = OpenAI(
|
||||
base_url=config.get("openai_base_url") or LLM_DEFAULT_BASE_URL,
|
||||
api_key=config.get("openai_api_key") or get_comfyui_copilot_api_key() or ""
|
||||
)
|
||||
|
||||
# 调用LLM生成摘要
|
||||
completion = client.chat.completions.parse(
|
||||
model=config.get("model_select"),
|
||||
messages=[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt}
|
||||
],
|
||||
max_tokens=8192, # 限制输出长度
|
||||
temperature=0.3, # 较低的温度确保摘要一致性
|
||||
response_format=SummaryResponse
|
||||
)
|
||||
|
||||
result = completion.choices[0].message.parsed
|
||||
log.info(f"Generated summary: {result.summary[:100]}...")
|
||||
|
||||
if result.summary:
|
||||
return result.summary
|
||||
else:
|
||||
log.warning("Summary generation returned empty result")
|
||||
return ""
|
||||
|
||||
except Exception as e:
|
||||
log.error(f"Failed to generate summary: {str(e)}")
|
||||
# 返回一个简单的回退摘要
|
||||
fallback = f"Conversation with {len(messages)} messages"
|
||||
if previous_summary:
|
||||
fallback = f"{previous_summary} | {fallback}"
|
||||
return fallback
|
||||
|
||||
|
||||
def test_summary_agent():
|
||||
"""测试摘要生成功能"""
|
||||
test_messages = [
|
||||
{"role": "user", "content": "I want to create an image generation workflow using Stable Diffusion"},
|
||||
{"role": "assistant", "content": "I can help you with that. Let me search for relevant workflows..."},
|
||||
{"role": "user", "content": "I need it to support LoRA models"},
|
||||
{"role": "assistant", "content": "I've found some workflows with LoRA support. Here are the options..."},
|
||||
]
|
||||
|
||||
summary = generate_summary(test_messages)
|
||||
print("Generated Summary:", summary)
|
||||
|
||||
# 测试增量摘要
|
||||
new_messages = [
|
||||
{"role": "user", "content": "Can you also add upscaling?"},
|
||||
{"role": "assistant", "content": "Sure, I'll modify the workflow to include upscaling nodes..."},
|
||||
]
|
||||
|
||||
updated_summary = generate_summary(new_messages, previous_summary=summary)
|
||||
print("Updated Summary:", updated_summary)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_summary_agent()
|
||||
|
||||
Reference in New Issue
Block a user