OpenClaw 完整备份 - 2026-03-21

openclaw/skills/memory-vector/SKILL.md

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+# Memory Vector Skill
+
+> OpenClaw 向量记忆系统集成
+
+## 功能
+
+- 语义搜索记忆
+- 添加新记忆
+- 查看最近记忆
+- 记忆统计
+
+## 环境变量
+
+- `SILICONFLOW_API_KEY`: 硅基流动 API Key（可选，默认使用配置）
+
+## 命令
+
+### 搜索记忆
+
+```
+向量搜索 [关键词]
+搜索记忆 [关键词]
+找找 [关键词]
+```
+
+### 添加记忆
+
+```
+添加记忆 [内容] --importance [1-5] --tags [标签]
+记住 [内容]
+```
+
+### 查看状态
+
+```
+记忆数量
+最近记忆
+记忆统计
+```
+
+## 代码位置
+
+```
+~/openclaw-memory-vector/
+├── vector_memory.py    # 核心引擎
+├── memory_cli.py       # CLI 工具
+└── data/memory/       # 数据目录
+```
+
+## 使用示例
+
+**搜索**：
+```
+用户：帮我搜一下之前关于股票的记录
+AI：好的，搜一下记忆库...
+```
+
+**添加**：
+```
+用户：把这个记下来，铜陵有色成本7.9元
+AI：✅ 已添加到记忆库
+```
+
+## 注意事项
+
+1. 需要先安装依赖：`pip3 install chromadb openai sqlalchemy`
+2. 需要设置 `SILICONFLOW_API_KEY` 环境变量
+3. 数据存储在 `~/openclaw-memory-vector/data/`

openclaw/skills/memory-vector/skill.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+OpenClaw 向量记忆系统 Skill
+用于 OpenClaw 主系统调用
+"""
+
+import os
+import sys
+import json
+
+# 添加项目路径
+VECTOR_DIR = os.path.expanduser("~/openclaw-memory-vector")
+sys.path.insert(0, VECTOR_DIR)
+
+from vector_memory import VectorMemorySystem
+from memory_backup import MemoryBackup
+
+
+class MemoryVectorSkill:
+    """向量记忆 skill"""
+    
+    def __init__(self):
+        self.api_key = os.getenv("SILICONFLOW_API_KEY", "sk-fpjdtxbxrhtekshircjhegstloxaodriekotjdyzzktyegcl")
+        self.vm = None
+    
+    def _get_vm(self):
+        """获取向量记忆系统实例"""
+        if not self.vm:
+            self.vm = VectorMemorySystem(
+                api_key=self.api_key,
+                persist_dir=os.path.join(VECTOR_DIR, "data/memory")
+            )
+        return self.vm
+    
+    def search(self, query: str) -> str:
+        """搜索记忆"""
+        vm = self._get_vm()
+        results = vm.search(query, top_k=5)
+        
+        if not results:
+            return f"没有找到与「{query}」相关的记忆"
+        
+        lines = [f"🔍 搜索「{query}」找到 {len(results)} 条相关记忆：\n"]
+        
+        for i, r in enumerate(results, 1):
+            similarity = r['similarity'] * 100
+            lines.append(f"{i}. {r['content'][:60]}...")
+            lines.append(f"   相似度: {similarity:.1f}%")
+            lines.append("")
+        
+        return "\n".join(lines)
+    
+    def add(self, content: str, importance: int = 3, tags: list = None) -> str:
+        """添加记忆"""
+        vm = self._get_vm()
+        metadata = {"tags": tags or []} if tags else {}
+        
+        vm.add_memory(content, metadata, importance)
+        
+        stars = "⭐" * importance
+        return f"✅ 已添加记忆 [{stars}]：{content[:40]}..."
+    
+    def recent(self, limit: int = 10) -> str:
+        """查看最近记忆"""
+        vm = self._get_vm()
+        results = vm.get_recent(limit)
+        
+        if not results:
+            return "还没有任何记忆"
+        
+        lines = [f"📅 最近 {len(results)} 条记忆：\n"]
+        
+        for i, r in enumerate(results, 1):
+            stars = "⭐" * r['importance']
+            lines.append(f"{i}. [{stars}] {r['content'][:50]}...")
+            lines.append(f"   时间: {r['created_at']}")
+            lines.append("")
+        
+        return "\n".join(lines)
+    
+    def count(self) -> str:
+        """记忆统计"""
+        vm = self._get_vm()
+        total = vm.count()
+        return f"📊 当前记忆总数: **{total}** 条"
+    
+    def backup(self) -> str:
+        """手动备份"""
+        backup = MemoryBackup()
+        vm = self._get_vm()
+        result = backup.backup_all(vm)
+        
+        return f"""✅ 备份完成！
+- JSON: {result['json']}
+- Markdown: {result['markdown']}
+- 向量库: {result['vector']}"""
+
+
+def main():
+    """CLI 入口"""
+    if len(sys.argv) < 2:
+        print("用法: python3 skill.py <command> [args...]")
+        print("命令: search <query>")
+        print("      add <content> [--importance N] [--tags tag1,tag2]")
+        print("      recent [N]")
+        print("      count")
+        print("      backup")
+        sys.exit(1)
+    
+    skill = MemoryVectorSkill()
+    command = sys.argv[1]
+    
+    if command == "search":
+        query = " ".join(sys.argv[2:])
+        print(skill.search(query))
+    
+    elif command == "add":
+        content = sys.argv[2]
+        importance = 3
+        tags = []
+        
+        # 解析参数
+        for i, arg in enumerate(sys.argv[3:]):
+            if arg == "--importance" and i+3 < len(sys.argv):
+                importance = int(sys.argv[i+4])
+            elif arg == "--tags" and i+3 < len(sys.argv):
+                tags = sys.argv[i+4].split(",")
+        
+        print(skill.add(content, importance, tags))
+    
+    elif command == "recent":
+        limit = int(sys.argv[2]) if len(sys.argv) > 2 else 10
+        print(skill.recent(limit))
+    
+    elif command == "count":
+        print(skill.count())
+    
+    elif command == "backup":
+        print(skill.backup())
+    
+    else:
+        print(f"未知命令: {command}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()