#!/usr/bin/env python3 """ collect_to_inbox.py —— 扫描知识库各分类目录，提取主要内容生成标准化 MD 放入收件箱 用法： python3 collect_to_inbox.py --root ~/workbuddy-agent-os/agent-sync [--dry-run] [--force] 参数： --root agent-os 根目录（默认 ~/workbuddy-agent-os/agent-sync） --dry-run 只预览不写入 --force 忽略 collected 标记，重新收集 """ import argparse import os import re import sys from datetime import datetime from pathlib import Path # ─── 角色检查：所有机器均可执行汇聚 ─── try: sys.path.insert(0, str(Path.home() / "workbuddy-agent-os" / "agent-sync" / "05_tools" / "01_system")) from role_check import check_capability if not check_capability("knowledge_collection"): print("⛔ 能力 knowledge_collection 未启用，跳过 collect_to_inbox") sys.exit(0) except ImportError as e: print(f"⚠️ 角色检查模块不可用 ({e})，继续执行") # 扫描的源目录配置：(相对路径, 提取策略) SCAN_DIRS = [ ("03_knowledge/50_resources/视频笔记", "extract_summary"), ("03_knowledge/50_resources/字幕存档", "extract_head"), ("03_knowledge/50_resources/阅读笔记", "extract_summary"), ("03_knowledge/50_resources/全文存档", "extract_head"), ("03_knowledge/50_resources/翻译存档", "extract_summary"), ("03_knowledge/50_resources/灵感素材", "extract_summary"), ("03_knowledge/50_resources/语音转写", "extract_key_points"), ("03_knowledge/20_methods", "extract_method"), ("03_knowledge/01_daily/闪念笔记", "extract_key_points"), ("03_knowledge/40_references", "extract_summary"), ] # 不扫描的目录 SKIP_DIRS = {"00_inbox", "99_system", "90_archive", "10_concepts", "30_facts", "60_opinions", "50_resources/资源索引"} def parse_frontmatter(content: str) -> tuple[dict, str]: """解析 Frontmatter，返回 (metadata_dict, body_text)""" if not content.startswith("---"): return {}, content end = content.find("---", 3) if end == -1: return {}, content fm_text = content[3:end].strip() body = content[end + 3:].strip() metadata = {} for line in fm_text.split("\n"): line = line.strip() if ":" in line: key, _, val = line.partition(":") key = key.strip() val = val.strip().strip('"').strip("'") if val: metadata[key] = val return metadata, body def extract_summary(body: str, max_chars: int = 500) -> str: """提取摘要：取前 max_chars 字符""" if len(body) <= max_chars: return body return body[:max_chars] + "\n\n...（内容已截断，完整内容见源文件）" def extract_head(body: str, max_chars: int = 500) -> str: """提取头部内容""" return extract_summary(body, max_chars) def extract_key_points(body: str, max_chars: int = 500) -> str: """提取关键要点：优先提取列表项""" lines = body.split("\n") points = [] for line in lines: stripped = line.strip() if stripped.startswith(("- ", "* ", "1.", "2.", "3.", "4.", "5.")): points.append(stripped) if len("\n".join(points)) > max_chars: break if points: result = "\n".join(points[:20]) if len(result) > max_chars: result = result[:max_chars] + "\n\n...（内容已截断）" return result return extract_summary(body, max_chars) def extract_method(body: str, max_chars: int = 500) -> str: """提取方法论：方法名 + 步骤""" lines = body.split("\n") # 提取标题和编号列表 method_lines = [] for line in lines: stripped = line.strip() if stripped.startswith("#") or stripped.startswith(("-", "*", "1.", "2.", "3.", "4.", "5.")): method_lines.append(stripped) if len("\n".join(method_lines)) > max_chars: break if method_lines: return "\n".join(method_lines[:30]) return extract_summary(body, max_chars) EXTRACT_FUNCS = { "extract_summary": extract_summary, "extract_head": extract_head, "extract_key_points": extract_key_points, "extract_method": extract_method, } def infer_nature(source_dir: str, metadata: dict) -> str: """根据源目录和元数据推断 nature""" dir_lower = source_dir.lower() if "方法" in dir_lower or "method" in dir_lower: return "method" if "视频笔记" in dir_lower: return "resource" if "字幕" in dir_lower or "转写" in dir_lower: return "reference" if "阅读" in dir_lower or "全文" in dir_lower: return "reference" if "翻译" in dir_lower: return "reference" if "灵感" in dir_lower: return "opinion" if "闪念" in dir_lower: return "opinion" if "references" in dir_lower: return "reference" # 从 tags 推断 tags = metadata.get("tags", "") if "方法论" in tags or "方法" in tags: return "method" if "概念" in tags: return "concept" return "resource" def infer_domain(metadata: dict) -> str: """从标签推断领域""" tags = metadata.get("tags", "").lower() domain_keywords = { "技术": ["编程", "代码", "开发", "tech", "python", "javascript", "ai", "llm", "机器学习"], "金融": ["投资", "股票", "基金", "finance", "交易", "财经"], "产品": ["产品", "设计", "ux", "product"], "效率": ["效率", "工具", "workflow", "自动化"], "阅读": ["读书", "阅读", "书评", "book"], "生活": ["生活", "旅行", "美食", "健身"], } for domain, keywords in domain_keywords.items(): if any(kw in tags for kw in keywords): return domain return "general" def collect_to_inbox(root_dir: str, dry_run: bool = False, force: bool = False): """主函数：扫描各目录，提取内容到收件箱""" root = Path(root_dir).expanduser() inbox_dir = root / "03_knowledge" / "00_inbox" stats = {"scanned": 0, "collected": 0, "skipped_collected": 0, "skipped_duplicate": 0, "errors": 0} # 确保收件箱存在 if not dry_run: inbox_dir.mkdir(parents=True, exist_ok=True) # 获取收件箱已有文件名集合（用于去重） existing_inbox = set() if inbox_dir.exists(): for f in inbox_dir.glob("*.md"): existing_inbox.add(f.name) collected_date = datetime.now().strftime("%Y-%m-%d") for rel_dir, extract_strategy in SCAN_DIRS: scan_dir = root / rel_dir if not scan_dir.exists(): continue # 递归扫描子目录（灵感素材下有平台子目录） for md_file in sorted(scan_dir.rglob("*.md")): stats["scanned"] += 1 try: content = md_file.read_text(encoding="utf-8") except Exception as e: print(f" ❌ 读取失败: {md_file.name} - {e}") stats["errors"] += 1 continue metadata, body = parse_frontmatter(content) # 检查是否已收集 if not force and metadata.get("collected") == "true": stats["skipped_collected"] += 1 continue # 去重检查 dest_name = md_file.name if dest_name in existing_inbox: stats["skipped_duplicate"] += 1 continue # 提取内容 extract_func = EXTRACT_FUNCS.get(extract_strategy, extract_summary) extracted = extract_func(body) # 推断属性 nature = infer_nature(rel_dir, metadata) domain = infer_domain(metadata) title = metadata.get("title", md_file.stem) source_date = metadata.get("date", collected_date) tags = metadata.get("tags", "") source_rel = str(md_file.relative_to(root)) # 生成收件箱文件内容 inbox_content = f"""--- title: "{title}" source_dir: {rel_dir} source_file: {md_file.name} date: {source_date} collected_date: {collected_date} tags: {tags} nature: {nature} domain: {domain} status: inbox --- # {title} > 来源：{rel_dir} {extracted} """ if dry_run: print(f" 📥 [预览] {md_file.name} → 00_inbox/{dest_name}") print(f" nature={nature}, domain={domain}, 策略={extract_strategy}") else: # 写入收件箱 dest_path = inbox_dir / dest_name dest_path.write_text(inbox_content, encoding="utf-8") print(f" 📥 {md_file.name} → 00_inbox/{dest_name}") # 标记原文件为已收集 if content.startswith("---"): # 在 Frontmatter 末尾添加 collected 标记 end = content.find("---", 3) if end != -1: fm = content[3:end] if "collected:" not in fm: fm += f"\ncollected: true\ncollected_date: {collected_date}" new_content = "---" + fm + content[end:] md_file.write_text(new_content, encoding="utf-8") else: # 没有 Frontmatter，添加一个简单的 new_content = f"---\ncollected: true\ncollected_date: {collected_date}\n---\n\n{content}" md_file.write_text(new_content, encoding="utf-8") existing_inbox.add(dest_name) stats["collected"] += 1 # 输出统计 print(f"\n{'[预览] ' if dry_run else ''}收集统计：") print(f" 扫描文件：{stats['scanned']}") print(f" 提取到收件箱：{stats['collected']}") print(f" 跳过（已收集）：{stats['skipped_collected']}") print(f" 跳过（重复）：{stats['skipped_duplicate']}") print(f" 错误：{stats['errors']}") return stats def main(): parser = argparse.ArgumentParser(description="收集各分类目录内容到收件箱") parser.add_argument("--root", default="~/workbuddy-agent-os/agent-sync", help="agent-os 根目录") parser.add_argument("--dry-run", action="store_true", help="只预览不写入") parser.add_argument("--force", action="store_true", help="忽略 collected 标记，重新收集") args = parser.parse_args() print(f"=== Collect to Inbox ===") print(f"根目录：{args.root}") print(f"模式：{'预览' if args.dry_run else '执行'}") print() collect_to_inbox(args.root, dry_run=args.dry_run, force=args.force) if __name__ == "__main__": main()