每日备份 2026-03-27
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OpenClaw Backup
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# Memory Vector Skill
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> OpenClaw 向量记忆系统集成
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## 功能
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- 语义搜索记忆
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- 添加新记忆
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- 查看最近记忆
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- 记忆统计
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## 环境变量
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- `SILICONFLOW_API_KEY`: 硅基流动 API Key（可选，默认使用配置）
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## 命令
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### 搜索记忆
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```
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向量搜索 [关键词]
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搜索记忆 [关键词]
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找找 [关键词]
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```
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### 添加记忆
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```
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添加记忆 [内容] --importance [1-5] --tags [标签]
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记住 [内容]
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```
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### 查看状态
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```
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记忆数量
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最近记忆
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记忆统计
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```
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## 代码位置
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```
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~/openclaw-memory-vector/
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├── vector_memory.py # 核心引擎
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├── memory_cli.py # CLI 工具
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└── data/memory/ # 数据目录
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```
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## 使用示例
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**搜索**：
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```
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用户：帮我搜一下之前关于股票的记录
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AI：好的，搜一下记忆库...
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```
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**添加**：
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```
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用户：把这个记下来，铜陵有色成本7.9元
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AI：✅ 已添加到记忆库
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```
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## 注意事项
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1. 需要先安装依赖：`pip3 install chromadb openai sqlalchemy`
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2. 需要设置 `SILICONFLOW_API_KEY` 环境变量
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3. 数据存储在 `~/openclaw-memory-vector/data/`
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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OpenClaw 向量记忆系统 Skill
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用于 OpenClaw 主系统调用
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"""
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import os
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import sys
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import json
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# 添加项目路径
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VECTOR_DIR = os.path.expanduser("~/openclaw-memory-vector")
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sys.path.insert(0, VECTOR_DIR)
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from vector_memory import VectorMemorySystem
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from memory_backup import MemoryBackup
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class MemoryVectorSkill:
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"""向量记忆 skill"""
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def __init__(self):
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self.api_key = os.getenv("SILICONFLOW_API_KEY", "sk-fpjdtxbxrhtekshircjhegstloxaodriekotjdyzzktyegcl")
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self.vm = None
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def _get_vm(self):
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"""获取向量记忆系统实例"""
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if not self.vm:
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self.vm = VectorMemorySystem(
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api_key=self.api_key,
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persist_dir=os.path.join(VECTOR_DIR, "data/memory")
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)
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return self.vm
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def search(self, query: str) -> str:
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"""搜索记忆"""
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vm = self._get_vm()
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results = vm.search(query, top_k=5)
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if not results:
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return f"没有找到与「{query}」相关的记忆"
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lines = [f"🔍 搜索「{query}」找到 {len(results)} 条相关记忆：\n"]
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for i, r in enumerate(results, 1):
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similarity = r['similarity'] * 100
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lines.append(f"{i}. {r['content'][:60]}...")
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lines.append(f" 相似度: {similarity:.1f}%")
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lines.append("")
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return "\n".join(lines)
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def add(self, content: str, importance: int = 3, tags: list = None) -> str:
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"""添加记忆"""
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vm = self._get_vm()
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metadata = {"tags": tags or []} if tags else {}
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vm.add_memory(content, metadata, importance)
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stars = "⭐" * importance
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return f"✅ 已添加记忆 [{stars}]：{content[:40]}..."
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def recent(self, limit: int = 10) -> str:
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"""查看最近记忆"""
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vm = self._get_vm()
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results = vm.get_recent(limit)
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if not results:
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return "还没有任何记忆"
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lines = [f"📅 最近 {len(results)} 条记忆：\n"]
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for i, r in enumerate(results, 1):
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stars = "⭐" * r['importance']
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lines.append(f"{i}. [{stars}] {r['content'][:50]}...")
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lines.append(f" 时间: {r['created_at']}")
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lines.append("")
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return "\n".join(lines)
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def count(self) -> str:
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"""记忆统计"""
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vm = self._get_vm()
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total = vm.count()
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return f"📊 当前记忆总数: **{total}** 条"
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def backup(self) -> str:
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"""手动备份"""
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backup = MemoryBackup()
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vm = self._get_vm()
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result = backup.backup_all(vm)
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return f"""✅ 备份完成！
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- JSON: {result['json']}
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- Markdown: {result['markdown']}
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- 向量库: {result['vector']}"""
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def main():
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"""CLI 入口"""
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if len(sys.argv) < 2:
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print("用法: python3 skill.py <command> [args...]")
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print("命令: search <query>")
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print(" add <content> [--importance N] [--tags tag1,tag2]")
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print(" recent [N]")
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print(" count")
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print(" backup")
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sys.exit(1)
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skill = MemoryVectorSkill()
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command = sys.argv[1]
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if command == "search":
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query = " ".join(sys.argv[2:])
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print(skill.search(query))
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elif command == "add":
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content = sys.argv[2]
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importance = 3
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tags = []
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# 解析参数
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for i, arg in enumerate(sys.argv[3:]):
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if arg == "--importance" and i+3 < len(sys.argv):
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importance = int(sys.argv[i+4])
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elif arg == "--tags" and i+3 < len(sys.argv):
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tags = sys.argv[i+4].split(",")
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print(skill.add(content, importance, tags))
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elif command == "recent":
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limit = int(sys.argv[2]) if len(sys.argv) > 2 else 10
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print(skill.recent(limit))
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elif command == "count":
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print(skill.count())
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elif command == "backup":
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print(skill.backup())
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else:
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print(f"未知命令: {command}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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