116 lines
5.3 KiB
Python
116 lines
5.3 KiB
Python
"""CLI 入口: python -m skill_search"""
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from __future__ import annotations
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import argparse, json, sys
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from .engine import SearchResult, SkillSearchError, detect_environment, search, get_stats
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# ── 格式化 ───────────────────────────────────────────────
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def format_results(results: list[SearchResult], env: dict, query: str) -> str:
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lines = [f'🔍 搜索: "{query}"',
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f"🖥️ 环境: {env.get('os','?')} / {env.get('shell','?')} / {', '.join(env.get('runtimes',[]))}",
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f"📊 找到 {len(results)} 个匹配结果\n"]
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if not results:
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lines.append("未找到匹配的 skill。试试其他关键词？")
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return "\n".join(lines)
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for i, r in enumerate(results, 1):
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s = r.skill
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safe_icon = "🟢" if s.autonomous_safe else "🔴"
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score_bar = "█" * int(r.final_score * 10) + "░" * (10 - int(r.final_score * 10))
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lines += [
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f"{'─'*60}",
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f"#{i} {safe_icon} {s.name}",
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f" 路径: {s.key}",
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f" 类别: {s.category} | 标签: {', '.join(s.tags[:5])}",
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f" 摘要: {s.one_line_summary}",
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f" 评分: [{score_bar}] {r.final_score:.2f} (相关={r.relevance:.2f} 质量={r.quality:.1f})",
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f" 清晰={s.clarity} 完整={s.completeness} 可操作={s.actionability} | 形式={s.form}",
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]
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if r.match_reasons:
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lines.append(f" 匹配: {' | '.join(r.match_reasons[:3])}")
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if r.warnings:
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lines.extend(f" {w}" for w in r.warnings)
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lines.append("")
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lines.append(f"{'─'*60}")
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return "\n".join(lines)
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def format_results_json(results: list[SearchResult]) -> list[dict]:
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out = []
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for r in results:
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s = r.skill
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out.append({
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"rank": len(out) + 1, "key": s.key, "name": s.name,
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"category": s.category, "tags": s.tags,
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"description": s.description, "one_line_summary": s.one_line_summary,
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"scores": {"final": round(r.final_score, 3), "relevance": round(r.relevance, 3),
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"quality": round(r.quality, 1), "clarity": s.clarity,
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"completeness": s.completeness, "actionability": s.actionability},
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"safety": {"autonomous_safe": s.autonomous_safe, "blast_radius": s.blast_radius,
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"requires_credentials": s.requires_credentials,
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"data_exposure": s.data_exposure, "effect_scope": s.effect_scope},
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"platform": {"os": s.os, "runtimes": s.runtimes, "tools": s.tools, "services": s.services},
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"warnings": r.warnings, "match_reasons": r.match_reasons,
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})
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return out
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# ── CLI ──────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(prog="skill_search",
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description="Skill 检索系统 — 根据环境和需求智能推荐 skill（API 客户端）")
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parser.add_argument("query", nargs="?", help="搜索关键词（如: 'python testing'）")
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parser.add_argument("--category", "-cat", help="限定类别")
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parser.add_argument("--top", "-k", type=int, default=10, help="返回结果数（默认 10）")
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parser.add_argument("--json", action="store_true", help="JSON 格式输出")
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parser.add_argument("--env", action="store_true", help="仅显示检测到的环境信息")
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parser.add_argument("--stats", action="store_true", help="显示索引统计信息")
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parser.add_argument("--api-url", help="指定 API 地址（也可用 SKILL_SEARCH_API 环境变量）")
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args = parser.parse_args()
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if args.api_url:
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import os; os.environ["SKILL_SEARCH_API"] = args.api_url
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env = detect_environment()
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if args.env:
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print("🖥️ 当前环境:")
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print(f" OS: {env['os']}")
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print(f" Shell: {env['shell']}")
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print(f" 运行时: {', '.join(env['runtimes'])}")
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print(f" 工具: {', '.join(env['tools'])}")
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print(f" 模型能力: tool_calling={env['model']['tool_calling']}, "
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f"reasoning={env['model']['reasoning']}, context={env['model']['context_window']}")
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return
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if args.stats:
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try:
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stats = get_stats(env)
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print(f"📊 索引统计:")
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print(f" 总计: {stats.get('total', '?')} 个 skills")
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print(f" 自动安全: {stats.get('safe_count', '?')} 个")
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if 'categories' in stats:
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print(f" 类别分布:")
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for cat, cnt in sorted(stats['categories'].items(), key=lambda x: -x[1]):
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print(f" {cat:15s} {cnt:4d}")
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except SkillSearchError as e:
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print(f"❌ {e}", file=sys.stderr); sys.exit(1)
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return
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if not args.query:
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parser.print_help(); return
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try:
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results = search(query=args.query, env=env, category=args.category, top_k=args.top)
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except SkillSearchError as e:
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print(f"❌ {e}", file=sys.stderr); sys.exit(1)
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if args.json:
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print(json.dumps(format_results_json(results), indent=2, ensure_ascii=False))
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else:
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print(format_results(results, env, args.query))
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if __name__ == "__main__":
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main() |