#!/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()