DESKTOP-72TV0V4\caoxiaozhu
5c435ab21e
- Add run_stream method to AgentCore for streaming output - Add base_url parameter to LLM clients for OpenRouter support - Add xbot module for new agent implementation - Refactor Chat.vue into composable + components (ChatHeader, ChatMessage, ChatInput, ChatSidebar, ChatAgentSelector) - Add ChatStream handler for SSE streaming in Go server - Add UseXBot field to chat request Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
"""
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Memory Manager - 记忆管理器
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"""
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from typing import Dict, List, Optional
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from app.agent.memory.working import WorkingMemory
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from app.agent.memory.session import SessionMemory
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from app.agent.memory.persistent import PersistentMemory
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class MemoryManager:
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"""记忆管理器 - 统一接口"""
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def __init__(self, agent_id: int):
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self.agent_id = agent_id
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self.working = WorkingMemory()
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self.session = SessionMemory(agent_id)
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self.persistent = PersistentMemory(agent_id)
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async def load_context(self, query: str, user_id: int, session_id: str) -> Dict[str, str]:
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"""
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加载上下文记忆
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优化：跳过耗时的向量搜索，提升响应速度
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生产环境可以加回来
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Args:
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query: 查询内容
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user_id: 用户 ID
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session_id: 会话 ID
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Returns:
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Dict: 包含 summary, knowledge 等
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"""
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# 1. Working Memory (内存，最快)
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working_context = self.working.get()
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# 2. Session Memory (Redis) - 暂时跳过，减少延迟
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# session_context = await self.session.get_summary(user_id, session_id)
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session_context = ""
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# 3. Persistent Memory (向量库) - 暂时跳过，减少延迟
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# persistent_context = await self.persistent.search(query, user_id, top_k=3)
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persistent_context = []
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return {
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'working': working_context.get('recent_messages', []),
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'session': session_context,
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'persistent': persistent_context,
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'summary': "", # 简化
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'knowledge': ""
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}
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async def save(self, user_input: str, response: str, user_id: int, session_id: str):
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"""
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保存记忆
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Args:
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user_input: 用户输入
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response: 智能体回复
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user_id: 用户 ID
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session_id: 会话 ID
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"""
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# 1. 写入 Working
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self.working.add(user_input, response)
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# 2. 写入 Session (定期摘要)
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await self.session.add(user_input, response, user_id, session_id)
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# 3. 提取关键信息写入 Persistent (定期)
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if self.working.size() >= 5:
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await self._extract_and_persist(user_input, response, user_id)
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def _build_summary(self, session_context: str, persistent_context: List[str]) -> str:
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"""构建记忆摘要"""
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parts = []
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if session_context:
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parts.append(f"会话记忆: {session_context}")
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if persistent_context:
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parts.append(f"长期记忆: {'; '.join(persistent_context[:3])}")
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return "\n".join(parts) if parts else "无相关记忆"
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async def _extract_and_persist(self, user_input: str, response: str, user_id: int):
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"""提取并持久化关键信息"""
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# 提取关键信息（简化版：取前100字符作为摘要）
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key_points = []
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# 简化：直接保存重要交互
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if len(response) > 50: # 只保存有意义的回复
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summary = response[:100] + "..."
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key_points.append(summary)
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for point in key_points:
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await self.persistent.add(point, user_id, memory_type="conversation")
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# 重置 Working Memory
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self.working.clear()
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