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个人知识库：存笔记、搜内容。用自然语言搜本地文档，不用记文件名。跟 AI 说'把这段存到知识库'自动保存并建索引。推荐搭配 Bilibili Auto Transcript：视频转录自动入库，转完即搜。\n\nTags: 54lynnn:1.1.0, chinese:1.5.4, knowledge:1.5.4, latest:1.6.1, rag:1.5.4, search:1.5.4\n\nVersion history:\n\nv1.6.1 | 2026-06-03T08:51:06.513Z | auto\n\n**Virtual environment support and installation process improvement**\n\n- Added virtual environment (.venv) support; all dependencies now installed into a dedicated environment.\n- Updated installation commands and documentation to use .venv/bin/python3 and .venv/bin/pip.\n- Modified all scripts and documentation to match the new environment setup.\n- Removed the old skill-card.md file and added a new README.md.\n- Improved installation reliability and isolated dependency management.\n\nv1.6.0 | 2026-06-01T03:33:41.245Z | user\n\nSQLite存储引擎升级 + 混合打分优化 + 8B默认模型\n\nv1.5.7 | 2026-06-01T01:31:03.742Z | user\n\n默认模型降回0.6B(CPU友好)，8B改为进阶选项/ 标题智能提取+关键词加分\n\nv1.5.6 | 2026-06-01T01:29:41.252Z | user\n\n标题智能提取(H1)→可读标题 / 关键词搜索加成+0.15→精准匹配天然靠前\n\nv1.5.5 | 2026-05-31T18:13:19.937Z | user\n\n添加GitHub开源链接、QQ群信息，文档更新\n\nv1.5.4 | 2026-05-31T02:47:29.854Z | user\n\n新增自动索引功能文档+安装指引（可选）。别再说'半小时内自动扫描'这种skill没有的功能了。\n\nv1.5.3 | 2026-05-31T02:28:58.187Z | user\n\n解耦两个skill：移除转录脚本中的索引调用；新增定时自动扫描（每30分钟），发现新文件自动索引，无变化不操作\n\nv1.5.2 | 2026-05-31T01:19:09.090Z | user\n\n文档优化\n\nv1.5.1 | 2026-05-31T01:11:54.085Z | user\n\n小版本修复\n\nv1.5.0 | 2026-05-31T00:46:07.527Z | user\n\n架构重构：FastAPI + TypeScript Express 替代 http.server；混合搜索（向量+关键词）；新增千问3 Embedding 模型支持；统一启动器 start.py；网页界面全面升级；保存知识规范流程。\n\nv1.2.0 | 2026-05-30T12:35:22.965Z | user\n\n新增「保存知识的规范流程」，描述更新为存搜一体。任何 agent 按 SKILL.md 指引即可完成'把这段存到知识库'操作。\n\nv1.1.2 | 2026-05-29T23:44:08.287Z | user\n\n描述更新：推荐搭配 Bilibili Auto Transcript 使用。\n\nv1.1.1 | 2026-05-29T23:43:08.351Z | user\n\n还原干净显示名称。\n\nv1.1.0 | 2026-05-29T23:34:30.795Z | user\n\n品牌更新：显示名称加入 54Lynnn 个人标识；推荐搭配 bilibili-auto-transcript。\n\nv1.0.0 | 2026-05-29T23:29:12.414Z | user\n\n首个版本：个人知识库搜索与管理系统，支持语义搜索、网页管理界面、自动索引。\n\nArchive index:\n\nArchive v1.6.1: 13 files, 47190 bytes\n\nFiles: README.md (6679b), scripts/index_knowledge.py (18634b), scripts/knowledge_api.py (11039b), scripts/query_knowledge.py (10076b), skill-card.md (2022b), SKILL.md (6244b), start.py (9838b), web/package-lock.json (49574b), web/package.json (498b), web/public/index.html (27213b), web/src/server.ts (7781b), web/tsconfig.json (451b), _meta.json (132b)\n\nFile v1.6.1:SKILL.md\n\n---\nname: knowledge-rag\nversion: \"1.6.0\"\ndescription: \"个人知识库：存笔记、搜内容。用自然语言搜本地文档，不用记文件名。跟 AI 说'把这段存到知识库'自动保存并建索引。推荐搭配 Bilibili Auto Transcript：视频转录自动入库，转完即搜。\"\nmetadata:\n openclaw:\n emoji: \"📖\"\n requires:\n bins: [\"python3\", \"ollama\"]\n install:\n - id: \"ollama\"\n kind: \"system\"\n label: \"安装 Ollama（向量引擎）\"\n url: \"https://ollama.com/download\"\n - id: \"embed-model\"\n kind: \"shell\"\n label: \"下载推荐向量模型（千问3 Embedding 8B，约 4.7GB）\"\n command: \"ollama pull qwen3-embedding:8b\"\n - id: \"pip-deps\"\n kind: \"shell\"\n label: \"创建虚拟环境并安装 Python 依赖\"\n command: \"cd {{SKILL_DIR}} && python3 -m venv .venv && .venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\"\n - id: \"auto-index-cron\"\n kind: \"shell\"\n optional: true\n label: \"（可选）开启自动索引——每30分钟扫描新文件\"\n command: \"echo '提示：安装后可运行 openclaw cron add 定时运行 index_knowledge.py 实现自动索引'\"\n---\n\n# 📖 Knowledge RAG\n\n用自然语言搜索你的笔记和文档，像问 ChatGPT 一样问你的私人知识库。\n\n---\n\n## 🚀 安装启动\n\n```bash\n# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding，中文最优）\nollama pull qwen3-embedding:8b\n\n# 3. 创建虚拟环境并安装依赖\npython3 -m venv .venv\n.venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\n\n# 4. 启动\n.venv/bin/python3 start.py\n```\n\n打开 `http://localhost:5777` → 文件丢到 `~/workspace/knowledge/` → 管理页点「重新索引」→ 搜索页开搜。\n\n### 可选：开启自动索引\n新增文件后不用每次都手动点重新索引，设个定时任务每30分钟自动扫描：\n```bash\nopenclaw cron add \\\n --name \"knowledge-rag 自动索引\" \\\n --every 1800000 \\\n --message \"运行索引检查：cd ~/.openclaw/workspace/skills/knowledge-rag && .venv/bin/python3 scripts/index_knowledge.py\" \\\n --silent\n```\n\n---\n\n## 📂 知识库目录\n\n```\n~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）\n```\n\n支持 `.txt` / `.md` / `.pdf`（文字版）/ `.docx` 文件。\n\n---\n\n## 🔍 搜索方式\n\n### 直接问我\n> \"帮我搜笔记里关于 Docker 的部分\" \n> \"知识库里有没有讲 Transformer 的文章？\"\n\n我会自动调搜索，带原文回答。\n\n### 命令行\n```bash\n.venv/bin/python3 scripts/query_knowledge.py \"你的问题\"\n.venv/bin/python3 scripts/query_knowledge.py \"微服务\" --source bilibili --top 5\n.venv/bin/python3 scripts/query_knowledge.py --stats\n```\n\n新增文件后运行索引即可：\n```bash\n.venv/bin/python3 scripts/index_knowledge.py # 增量\n.venv/bin/python3 scripts/index_knowledge.py --force # 全量重建\n```\n\n---\n\n## 📝 Agent 存知识规范\n\n当用户说\"把这段存到知识库\"，按以下规则操作：\n\n| 内容类型 | 存放目录 | 文件名 |\n|---------|---------|--------|\n| 技术笔记、概念解释 | `notes/` | 中文描述.md |\n| B站视频转录 | `bilibili/` | 自动处理 |\n| 公众号、网页文章 | `wechat-articles/` | 文章标题.md |\n| 其他 | `other/` | 内容摘要.md |\n\n保存后运行 `.venv/bin/python3 scripts/index_knowledge.py` 更新索引。\n\n---\n\n## 🔧 切换模型\n\n在设置页修改 `embed_model` 字段：\n\n| 场景 | 模型 | 大小 |\n|------|------|------|\n| ✅ 推荐（质量最高） | `qwen3-embedding:8b` | 4.7GB | 中文优秀，检索质量最高 |\n| 轻量（CPU友好） | `qwen3-embedding:0.6b` | 640MB | 对 CPU 友好，中文不错 |\n| 多语言 | `bge-m3` | 1.2GB |\n| 最小体积 | `nomic-embed-text` | 274MB |\n\n改完后点「重新索引」，系统自动检测并全量重建。\n\n---\n\n## 🧑‍💻 环境检测（给 AI 助手的指引）\n\n用户首次使用或出问题时，按顺序排查：\n\n1. **Ollama 是否安装？** → `ollama --version`，否则引导去 https://ollama.com/download\n2. **千问3 模型是否下载？** → `ollama list | grep qwen3-embedding`，否则 `ollama pull qwen3-embedding:8b`\n3. **索引模型是否匹配？** → 读 `~/workspace/knowledge/.rag_data/model_meta.json`，不匹配则提示重建\n\n快速诊断：\n```bash\ncurl -s http://localhost:8768/api/stats | .venv/bin/python3 -c \"\nimport sys,json; d=json.load(sys.stdin)\nem = d.get('embed_model',{})\nprint(f'配置: {em.get(\\\"current\\\",\\\"?\\\")} | 索引: {em.get(\\\"stored\\\",\\\"?\\\")} ({em.get(\\\"stored_dim\\\",\\\"?\\\")}维)')\nprint(f'状态: {\\\"✅ 正常\\\" if not em.get(\\\"mismatch\\\") else \\\"❌ 不匹配\\\"}')\"\n```\n\n---\n\n## ⚠️ 注意事项\n\n- 依赖 Ollama，首次下载模型约 640MB 需联网\n- PDF 支持**文字版** PDF（PyPDF2 优先提取，失败自动降级到 pdfplumber），纯图像版 PDF 暂不支持\n- DOCX 支持通过 python-docx 提取段落文字和表格内容\n- PDF/DOCX 解析需先创建虚拟环境：`python3 -m venv .venv && .venv/bin/pip install PyPDF2 pdfplumber python-docx`\n- 仅支持纯文本内容，不支持图片和 PDF 中的图像\n- 删除文件后运行「重新索引」即可从搜索结果移除（增量索引自动清理已删除文件，无需 --force）\n- 数据全部在 `~/workspace/knowledge/`，卸载不丢\n\n## 🎯 推荐搭配\n\n**Bilibili Auto Transcript** — 装了这个 skill 后，B站视频转录完自动存到知识库，不用手动操作：\n\n```bash\nclawhub install bilibili-auto-transcript\n```\n\n转录的文件自动进 `~/workspace/knowledge/bilibili/`，转完即搜。\n\n---\n\n## 📦 开源 & 交流\n\n- **GitHub**：[github.com/54Lynnn/knowledge-rag](https://github.com/54Lynnn/knowledge-rag)（⭐️ Star 支持）\n- **ClawHub**：[clawhub.ai/54lynnn/knowledge-rag](https://clawhub.ai/54lynnn/knowledge-rag)\n- **QQ 群**：120363664（欢迎扫码加入交流）\n\nFile v1.6.1:README.md\n\n

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📖 Knowledge RAG

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把你的笔记和文档变成 AI 知识库，像聊天一样搜索

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\n \"ClawHub\"\n \"GitHub\n

\n

\n\n---\n\n## 这是什么\n\n一个**本地运行的 RAG（检索增强生成）知识库系统**。把你的 `.txt` / `.md` / `.pdf` / `.docx` 文件丢进来，用自然语言搜索，像问 ChatGPT 一样问你的私人知识库。\n\n**跟大多数 RAG 工具不同：**\n\n- **极简部署** — 不需要 Docker，不需要数据库，一台机器一个 Ollama 就够了\n- **Trae/OpenClaw Agent 集成** — 在 IDE 里直接让 AI 搜你的知识库并带原文回答\n- **B站转录自动入库** — 配合 bilibili-auto-transcript，视频转录完自动进知识库\n\n---\n\n## 快速开始\n\n```bash\n# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding 8B）\nollama pull qwen3-embedding:8b\n\n# 3. 创建虚拟环境并安装依赖\npython3 -m venv .venv\n.venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\n\n# 4. 启动\n.venv/bin/python3 start.py\n```\n\n打开 `http://localhost:5777` → 把文件丢到 `~/workspace/knowledge/` → 点「重新索引」→ 开搜。\n\n---\n\n## 架构\n\n```\n┌─────────────────────────────────────────────────┐\n│ 浏览器 (index.html) │\n└──────────────────┬──────────────────────────────┘\n │ HTTP\n┌──────────────────▼──────────────────────────────┐\n│ TypeScript Express (server.ts) │\n│ Web 层：静态文件、配置读写、请求路由 │\n└──────────────────┬──────────────────────────────┘\n │ HTTP Proxy\n┌──────────────────▼──────────────────────────────┐\n│ Python FastAPI (knowledge_api.py) │\n│ RAG 层：搜索、索引、管理、统计 API │\n└──────┬─────────────────────────────┬────────────┘\n │ │\n┌──────▼──────┐ ┌────────▼──────────┐\n│ index_ │ │ query_ │\n│ knowledge.py│ │ knowledge.py │\n│ 索引构建 │ │ 向量搜索+混合打分 │\n└──────┬──────┘ └────────┬──────────┘\n │ │\n └──────────┬─────────────────┘\n │ SQLite\n ┌──────────▼──────────┐\n │ chunks.db │\n │ (文本 + 向量 BLOB) │\n └─────────────────────┘\n```\n\n## 知识库目录结构\n\n```\n~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录（配合 bilibili-auto-transcript）\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）\n```\n\n支持的文件格式：`.txt` / `.md` / `.pdf`（文字版）/ `.docx`\n\n---\n\n## 核心特性\n\n| 特性 | 说明 |\n|------|------|\n| **SQLite 存储** | 文本 + 向量存同一张表，原子写入，不会崩溃后数据不一致 |\n| **增量索引** | hash 比对 + mtime 快速跳过，只处理变更文件，删除的文件自动清理 |\n| **混合搜索** | 向量余弦相似度 + 查询扩展 + 标题/文本关键词加成 |\n| **标题提取** | Markdown H1、PDF/DOCX 首行自动识别为标题，提高匹配精度 |\n| **文件解析** | PDF（PyPDF2 + pdfplumber 降级）、DOCX（含表格内容） |\n| **删除单条** | API 支持按文件名删除索引，无需全量重建 |\n| **Web 界面** | 搜索 + 管理（统计、索引、配置）一体 |\n| **多模型支持** | 可切换 embedding 模型，切换后自动全量重建 |\n\n---\n\n## 切换模型\n\n在 `~/workspace/knowledge/.knowledge-config.json` 中修改 `embed_model` 字段：\n\n| 场景 | 模型 | 大小 |\n|------|------|------|\n| 推荐（质量最高） | `qwen3-embedding:8b` | 4.7GB |\n| 轻量（CPU 友好） | `qwen3-embedding:0.6b` | 640MB |\n| 多语言 | `bge-m3` | 1.2GB |\n| 最小体积 | `nomic-embed-text` | 274MB |\n\n改完后重新索引，系统自动检测并全量重建。\n\n---\n\n## CLI 用法\n\n```bash\n# 索引\n.venv/bin/python3 scripts/index_knowledge.py # 增量更新\n.venv/bin/python3 scripts/index_knowledge.py --force # 全量重建\n\n# 搜索\n.venv/bin/python3 scripts/query_knowledge.py \"你的问题\"\n.venv/bin/python3 scripts/query_knowledge.py \"微服务\" --source bilibili --top 5\n.venv/bin/python3 scripts/query_knowledge.py --stats\n```\n\n---\n\n## 在 OpenClaw/Trae 中使用\n\n本项目同时也是 **OpenClaw 生态的 Skill**。如果你在用 OpenClaw 或 Trae IDE：\n\n```bash\n# 直接从 ClawHub 安装\nclawhub install knowledge-rag\n```\n\n安装后，你的 AI Agent 会自动获得「搜索知识库」的能力，还可以配合 bilibili-auto-transcript 实现「转录完自动入库」。\n\n---\n\n## 注意事项\n\n- 依赖 [Ollama](https://ollama.com/download)，首次下载模型约 4.7GB（8B）或 640MB（0.6B）需联网\n- PDF 支持**文字版** PDF，纯图像版 PDF 暂不支持\n- PDF/DOCX 解析需先创建虚拟环境：`python3 -m venv .venv && .venv/bin/pip install PyPDF2 pdfplumber python-docx`\n- 数据全部在 `~/workspace/knowledge/`，卸载不丢\n\n---\n\n## 交流\n\n- [GitHub Issues](https://github.com/54Lynnn/knowledge-rag/issues) — 反馈问题\n- QQ 群：120363664\n\n如果这个项目对你有帮助，欢迎 ⭐️ Star 支持！\n\nFile v1.6.1:_meta.json\n\n{\n \"ownerId\": \"kn742cdrc476d1kbhpsvnrnznx81x4r7\",\n \"slug\": \"knowledge-rag\",\n \"version\": \"1.6.1\",\n \"publishedAt\": 1780476666513\n}\n\nFile v1.6.1:skill-card.md\n\n## Description:\n\nPersonal knowledge base skill for saving notes, indexing local documents, and searching them with natural-language queries.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[54lynnn](https://clawhub.ai/user/54lynnn)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, agents, and individual users use this skill to create a local searchable knowledge base from notes and documents, then retrieve relevant source text through chat-style prompts or CLI commands.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Private document search and admin APIs may be exposed too broadly.\n\nMitigation: Run only on a trusted single-user machine and do not expose ports 5777 or 8768 to a LAN or untrusted browser context.\n\nRisk: The skill can browse or index outside the intended knowledge folder.\n\nMitigation: Avoid placing sensitive documents in reachable paths until API binding, authentication, and path limits are fixed.\n\nRisk: Installation depends on external Ollama and package installation steps.\n\nMitigation: Use verified Ollama and package sources before installing or pulling embedding models.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/54lynnn/skills/knowledge-rag)\n- [Ollama Download](https://ollama.com/download)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown guidance with inline shell commands and local API or CLI outputs]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Produces local search guidance and retrieval results backed by a local SQLite index and Ollama embeddings.]\n\n## Skill Version(s):\n\n1.6.1 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.6.1:web/package-lock.json\n\n{\n \"name\": \"knowledge-server-ts\",\n \"version\": \"1.0.0\",\n 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\"integrity\": \"sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==\",\n \"license\": \"ISC\"\n }\n }\n}\n\nFile v1.6.1:web/package.json\n\n{\n \"name\": \"knowledge-server-ts\",\n \"version\": \"1.0.0\",\n \"private\": true,\n \"description\": \"知识仓库搜索界面（TypeScript 版）\",\n \"type\": \"module\",\n \"scripts\": {\n \"dev\": \"tsx watch src/server.ts\",\n \"start\": \"tsx src/server.ts\",\n \"build\": \"tsc\",\n \"serve\": \"node dist/server.js\"\n },\n \"dependencies\": {\n \"express\": \"^5.1.0\"\n },\n \"devDependencies\": {\n \"@types/express\": \"^5.0.1\",\n \"@types/node\": \"^22.14.1\",\n \"tsx\": \"^4.19.4\",\n \"typescript\": \"^5.8.3\"\n }\n}\n\nFile v1.6.1:web/tsconfig.json\n\n{\n \"compilerOptions\": {\n \"target\": \"ES2022\",\n \"module\": \"ESNext\",\n \"moduleResolution\": \"bundler\",\n \"outDir\": \"dist\",\n \"rootDir\": \"src\",\n \"strict\": true,\n \"esModuleInterop\": true,\n \"skipLibCheck\": true,\n \"forceConsistentCasingInFileNames\": true,\n \"resolveJsonModule\": true,\n \"declaration\": true,\n \"declarationMap\": true,\n \"sourceMap\": true\n },\n \"include\": [\"src/**/*\"],\n \"exclude\": [\"node_modules\", \"dist\"]\n}\n\nArchive v1.6.0: 12 files, 44059 bytes\n\nFiles: scripts/index_knowledge.py (18549b), scripts/knowledge_api.py (10981b), scripts/query_knowledge.py (9896b), skill-card.md (2232b), SKILL.md (5916b), start.py (9592b), web/package-lock.json (49574b), web/package.json (498b), web/public/index.html (27213b), web/src/server.ts (7781b), web/tsconfig.json (451b), _meta.json (132b)\n\nFile v1.6.0:SKILL.md\n\n---\nname: knowledge-rag\nversion: \"1.5.5\"\ndescription: \"个人知识库：存笔记、搜内容。用自然语言搜本地文档，不用记文件名。跟 AI 说'把这段存到知识库'自动保存并建索引。推荐搭配 Bilibili Auto Transcript：视频转录自动入库，转完即搜。\"\nmetadata:\n openclaw:\n emoji: \"📖\"\n requires:\n bins: [\"python3\", \"ollama\"]\n install:\n - id: \"ollama\"\n kind: \"system\"\n label: \"安装 Ollama（向量引擎）\"\n url: \"https://ollama.com/download\"\n - id: \"embed-model\"\n kind: \"shell\"\n label: \"下载推荐向量模型（千问3 Embedding 8B，约 4.7GB）\"\n command: \"ollama pull qwen3-embedding:8b\"\n - id: \"pip-deps\"\n kind: \"shell\"\n label: \"安装 PDF/DOCX 解析依赖\"\n command: \"pip install --break-system-packages PyPDF2 pdfplumber python-docx\"\n - id: \"auto-index-cron\"\n kind: \"shell\"\n optional: true\n label: \"（可选）开启自动索引——每30分钟扫描新文件\"\n command: \"echo '提示：安装后可运行 openclaw cron add 定时运行 index_knowledge.py 实现自动索引'\"\n---\n\n# 📖 Knowledge RAG\n\n用自然语言搜索你的笔记和文档，像问 ChatGPT 一样问你的私人知识库。\n\n---\n\n## 🚀 安装启动\n\n```bash\n# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding，中文最优）\nollama pull qwen3-embedding:8b\n\n# 3. 启动（自动完成环境检测、依赖安装、打开浏览器）\npython3 start.py\n```\n\n打开 `http://localhost:5777` → 文件丢到 `~/workspace/knowledge/` → 管理页点「重新索引」→ 搜索页开搜。\n\n### 可选：开启自动索引\n新增文件后不用每次都手动点重新索引，设个定时任务每30分钟自动扫描：\n```bash\nopenclaw cron add \\\n --name \"knowledge-rag 自动索引\" \\\n --every 1800000 \\\n --message \"运行索引检查：python3 scripts/index_knowledge.py\" \\\n --silent\n```\n\n---\n\n## 📂 知识库目录\n\n```\n~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）\n```\n\n支持 `.txt` / `.md` / `.pdf`（文字版）/ `.docx` 文件。\n\n---\n\n## 🔍 搜索方式\n\n### 直接问我\n> \"帮我搜笔记里关于 Docker 的部分\" \n> \"知识库里有没有讲 Transformer 的文章？\"\n\n我会自动调搜索，带原文回答。\n\n### 命令行\n```bash\npython3 scripts/query_knowledge.py \"你的问题\"\npython3 scripts/query_knowledge.py \"微服务\" --source bilibili --top 5\npython3 scripts/query_knowledge.py --stats\n```\n\n新增文件后运行索引即可：\n```bash\npython3 scripts/index_knowledge.py # 增量\npython3 scripts/index_knowledge.py --force # 全量重建\n```\n\n---\n\n## 📝 Agent 存知识规范\n\n当用户说\"把这段存到知识库\"，按以下规则操作：\n\n| 内容类型 | 存放目录 | 文件名 |\n|---------|---------|--------|\n| 技术笔记、概念解释 | `notes/` | 中文描述.md |\n| B站视频转录 | `bilibili/` | 自动处理 |\n| 公众号、网页文章 | `wechat-articles/` | 文章标题.md |\n| 其他 | `other/` | 内容摘要.md |\n\n保存后运行 `python3 scripts/index_knowledge.py` 更新索引。\n\n---\n\n## 🔧 切换模型\n\n在设置页修改 `embed_model` 字段：\n\n| 场景 | 模型 | 大小 |\n|------|------|------|\n| ✅ 推荐（质量最高） | `qwen3-embedding:8b` | 4.7GB | 中文优秀，检索质量最高 |\n| 轻量（CPU友好） | `qwen3-embedding:0.6b` | 640MB | 对 CPU 友好，中文不错 |\n| 多语言 | `bge-m3` | 1.2GB |\n| 最小体积 | `nomic-embed-text` | 274MB |\n\n改完后点「重新索引」，系统自动检测并全量重建。\n\n---\n\n## 🧑‍💻 环境检测（给 AI 助手的指引）\n\n用户首次使用或出问题时，按顺序排查：\n\n1. **Ollama 是否安装？** → `ollama --version`，否则引导去 https://ollama.com/download\n2. **千问3 模型是否下载？** → `ollama list | grep qwen3-embedding`，否则 `ollama pull qwen3-embedding:8b`\n3. **索引模型是否匹配？** → 读 `~/workspace/knowledge/.rag_data/model_meta.json`，不匹配则提示重建\n\n快速诊断：\n```bash\ncurl -s http://localhost:8768/api/stats | python3 -c \"\nimport sys,json; d=json.load(sys.stdin)\nem = d.get('embed_model',{})\nprint(f'配置: {em.get(\\\"current\\\",\\\"?\\\")} | 索引: {em.get(\\\"stored\\\",\\\"?\\\")} ({em.get(\\\"stored_dim\\\",\\\"?\\\")}维)')\nprint(f'状态: {\\\"✅ 正常\\\" if not em.get(\\\"mismatch\\\") else \\\"❌ 不匹配\\\"}')\"\n```\n\n---\n\n## ⚠️ 注意事项\n\n- 依赖 Ollama，首次下载模型约 640MB 需联网\n- PDF 支持**文字版** PDF（PyPDF2 优先提取，失败自动降级到 pdfplumber），纯图像版 PDF 暂不支持\n- DOCX 支持通过 python-docx 提取段落文字和表格内容\n- PDF/DOCX 解析需安装额外依赖：`pip install --break-system-packages PyPDF2 pdfplumber python-docx`\n- 仅支持纯文本内容，不支持图片和 PDF 中的图像\n- 删除文件后运行「重新索引」即可从搜索结果移除（增量索引自动清理已删除文件，无需 --force）\n- 数据全部在 `~/workspace/knowledge/`，卸载不丢\n\n## 🎯 推荐搭配\n\n**Bilibili Auto Transcript** — 装了这个 skill 后，B站视频转录完自动存到知识库，不用手动操作：\n\n```bash\nclawhub install bilibili-auto-transcript\n```\n\n转录的文件自动进 `~/workspace/knowledge/bilibili/`，转完即搜。\n\n---\n\n## 📦 开源 & 交流\n\n- **GitHub**：[github.com/54Lynnn/knowledge-rag](https://github.com/54Lynnn/knowledge-rag)（⭐️ Star 支持）\n- **ClawHub**：[clawhub.ai/54lynnn/knowledge-rag](https://clawhub.ai/54lynnn/knowledge-rag)\n- **QQ 群**：120363664（欢迎扫码加入交流）\n\nFile v1.6.0:_meta.json\n\n{\n \"ownerId\": \"kn742cdrc476d1kbhpsvnrnznx81x4r7\",\n \"slug\": \"knowledge-rag\",\n \"version\": \"1.6.0\",\n \"publishedAt\": 1780284821245\n}\n\nFile v1.6.0:skill-card.md\n\n## Description:
\nKnowledge Rag helps an agent save, index, and search a user's local notes and documents with natural-language queries.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[54lynnn](https://clawhub.ai/user/54lynnn)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nExternal users and developers use this skill to build a local personal knowledge base, index text, Markdown, PDF, and DOCX files, and retrieve relevant passages through a web UI, CLI commands, or agent-guided search.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Unauthenticated local services can browse and reconfigure access to local folders.
\nMitigation: Run the services only on a trusted local network and avoid exposing the web or API ports beyond the local machine.
\nRisk: The knowledge directory may include private notes, PDFs, or documents.
\nMitigation: Do not configure broad locations such as the home directory, and confirm before saving or indexing sensitive content.
\nRisk: Installation uses local packages and Ollama model setup.
\nMitigation: Prefer verified Ollama and package installations over curl-to-shell commands.
\n\n\n## Reference(s):
\n- [ClawHub Skill Page](https://clawhub.ai/54lynnn/knowledge-rag)
\n- [Publisher Profile](https://clawhub.ai/user/54lynnn)
\n- [Ollama Download](https://ollama.com/download)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]
\n**Output Format:** [Markdown guidance with shell commands, local configuration, and search results from indexed documents.]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Uses local Ollama embeddings and stores index data under the configured local knowledge directory.]
\n\n## Skill Version(s):
\n1.6.0 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
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\"integrity\": \"sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==\",\n \"license\": \"ISC\"\n }\n }\n}\n\nFile v1.6.0:web/package.json\n\n{\n \"name\": \"knowledge-server-ts\",\n \"version\": \"1.0.0\",\n \"private\": true,\n \"description\": \"知识仓库搜索界面（TypeScript 版）\",\n \"type\": \"module\",\n \"scripts\": {\n \"dev\": \"tsx watch src/server.ts\",\n \"start\": \"tsx src/server.ts\",\n \"build\": \"tsc\",\n \"serve\": \"node dist/server.js\"\n },\n \"dependencies\": {\n \"express\": \"^5.1.0\"\n },\n \"devDependencies\": {\n \"@types/express\": \"^5.0.1\",\n \"@types/node\": \"^22.14.1\",\n \"tsx\": \"^4.19.4\",\n \"typescript\": \"^5.8.3\"\n }\n}\n\nFile v1.6.0:web/tsconfig.json\n\n{\n \"compilerOptions\": {\n \"target\": \"ES2022\",\n \"module\": \"ESNext\",\n \"moduleResolution\": \"bundler\",\n \"outDir\": \"dist\",\n \"rootDir\": \"src\",\n \"strict\": true,\n \"esModuleInterop\": true,\n \"skipLibCheck\": true,\n \"forceConsistentCasingInFileNames\": true,\n \"resolveJsonModule\": true,\n \"declaration\": true,\n \"declarationMap\": true,\n \"sourceMap\": true\n },\n \"include\": [\"src/**/*\"],\n \"exclude\": [\"node_modules\", \"dist\"]\n}\n\nArchive v1.5.7: 12 files, 42768 bytes\n\nFiles: scripts/index_knowledge.py (16056b), scripts/knowledge_api.py (10332b), scripts/query_knowledge.py (8550b), skill-card.md (2228b), SKILL.md (5854b), start.py (9594b), web/package-lock.json (49574b), web/package.json (498b), web/public/index.html (27213b), web/src/server.ts (7643b), web/tsconfig.json (451b), _meta.json (132b)\n\nFile v1.5.7:SKILL.md\n\n---\nname: knowledge-rag\nversion: \"1.5.5\"\ndescription: \"个人知识库：存笔记、搜内容。用自然语言搜本地文档，不用记文件名。跟 AI 说'把这段存到知识库'自动保存并建索引。推荐搭配 Bilibili Auto Transcript：视频转录自动入库，转完即搜。\"\nmetadata:\n openclaw:\n emoji: \"📖\"\n requires:\n bins: [\"python3\", \"ollama\"]\n install:\n - id: \"ollama\"\n kind: \"system\"\n label: \"安装 Ollama（向量引擎）\"\n url: \"https://ollama.com/download\"\n - id: \"embed-model\"\n kind: \"shell\"\n label: \"下载推荐向量模型（千问3 Embedding 0.6B，约 640MB）\"\n command: \"ollama pull qwen3-embedding:0.6b\"\n - id: \"pip-deps\"\n kind: \"shell\"\n label: \"安装 PDF/DOCX 解析依赖\"\n command: \"pip install --break-system-packages PyPDF2 pdfplumber python-docx\"\n - id: \"auto-index-cron\"\n kind: \"shell\"\n optional: true\n label: \"（可选）开启自动索引——每30分钟扫描新文件\"\n command: \"echo '提示：安装后可运行 openclaw cron add 定时运行 index_knowledge.py 实现自动索引'\"\n---\n\n# 📖 Knowledge RAG\n\n用自然语言搜索你的笔记和文档，像问 ChatGPT 一样问你的私人知识库。\n\n---\n\n## 🚀 安装启动\n\n```bash\n# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding，中文最优）\nollama pull qwen3-embedding:0.6b\n\n# 3. 启动（自动完成环境检测、依赖安装、打开浏览器）\npython3 start.py\n```\n\n打开 `http://localhost:5777` → 文件丢到 `~/workspace/knowledge/` → 管理页点「重新索引」→ 搜索页开搜。\n\n### 可选：开启自动索引\n新增文件后不用每次都手动点重新索引，设个定时任务每30分钟自动扫描：\n```bash\nopenclaw cron add \\\n --name \"knowledge-rag 自动索引\" \\\n --every 1800000 \\\n --message \"运行索引检查：python3 scripts/index_knowledge.py\" \\\n --silent\n```\n\n---\n\n## 📂 知识库目录\n\n```\n~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）\n```\n\n支持 `.txt` / `.md` / `.pdf`（文字版）/ `.docx` 文件。\n\n---\n\n## 🔍 搜索方式\n\n### 直接问我\n> \"帮我搜笔记里关于 Docker 的部分\" \n> \"知识库里有没有讲 Transformer 的文章？\"\n\n我会自动调搜索，带原文回答。\n\n### 命令行\n```bash\npython3 scripts/query_knowledge.py \"你的问题\"\npython3 scripts/query_knowledge.py \"微服务\" --source bilibili --top 5\npython3 scripts/query_knowledge.py --stats\n```\n\n新增文件后运行索引即可：\n```bash\npython3 scripts/index_knowledge.py # 增量\npython3 scripts/index_knowledge.py --force # 全量重建\n```\n\n---\n\n## 📝 Agent 存知识规范\n\n当用户说\"把这段存到知识库\"，按以下规则操作：\n\n| 内容类型 | 存放目录 | 文件名 |\n|---------|---------|--------|\n| 技术笔记、概念解释 | `notes/` | 中文描述.md |\n| B站视频转录 | `bilibili/` | 自动处理 |\n| 公众号、网页文章 | `wechat-articles/` | 文章标题.md |\n| 其他 | `other/` | 内容摘要.md |\n\n保存后运行 `python3 scripts/index_knowledge.py` 更新索引。\n\n---\n\n## 🔧 切换模型\n\n在设置页修改 `embed_model` 字段：\n\n| 场景 | 模型 | 大小 |\n|------|------|------|\n| ✅ 推荐（CPU友好） | `qwen3-embedding:0.6b` | 640MB | 中文最优，CPU 流畅\n| 进阶（需 GPU） | `qwen3-embedding:8b` | 4.7GB | 质量最高 |\n| 多语言 | `bge-m3` | 1.2GB |\n| 最小体积 | `nomic-embed-text` | 274MB |\n\n改完后点「重新索引」，系统自动检测并全量重建。\n\n---\n\n## 🧑‍💻 环境检测（给 AI 助手的指引）\n\n用户首次使用或出问题时，按顺序排查：\n\n1. **Ollama 是否安装？** → `ollama --version`，否则引导去 https://ollama.com/download\n2. **千问3 模型是否下载？** → `ollama list | grep qwen3-embedding`，否则 `ollama pull qwen3-embedding:0.6b`（如有独显可升级到 8b）\n3. **索引模型是否匹配？** → 读 `~/workspace/knowledge/.rag_data/model_meta.json`，不匹配则提示重建\n\n快速诊断：\n```bash\ncurl -s http://localhost:8768/api/stats | python3 -c \"\nimport sys,json; d=json.load(sys.stdin)\nem = d.get('embed_model',{})\nprint(f'配置: {em.get(\\\"current\\\",\\\"?\\\")} | 索引: {em.get(\\\"stored\\\",\\\"?\\\")} ({em.get(\\\"stored_dim\\\",\\\"?\\\")}维)')\nprint(f'状态: {\\\"✅ 正常\\\" if not em.get(\\\"mismatch\\\") else \\\"❌ 不匹配\\\"}')\"\n```\n\n---\n\n## ⚠️ 注意事项\n\n- 依赖 Ollama，首次下载模型约 640MB 需联网\n- PDF 支持**文字版** PDF（PyPDF2 优先提取，失败自动降级到 pdfplumber），纯图像版 PDF 暂不支持\n- DOCX 支持通过 python-docx 提取段落文字和表格内容\n- PDF/DOCX 解析需安装额外依赖：`pip install --break-system-packages PyPDF2 pdfplumber python-docx`\n- 仅支持纯文本内容，不支持图片和 PDF 中的图像\n- 删除文件后需重新索引才能从搜索结果移除\n- 数据全部在 `~/workspace/knowledge/`，卸载不丢\n\n## 🎯 推荐搭配\n\n**Bilibili Auto Transcript** — 装了这个 skill 后，B站视频转录完自动存到知识库，不用手动操作：\n\n```bash\nclawhub install bilibili-auto-transcript\n```\n\n转录的文件自动进 `~/workspace/knowledge/bilibili/`，转完即搜。\n\n---\n\n## 📦 开源 & 交流\n\n- **GitHub**：[github.com/54Lynnn/knowledge-rag](https://github.com/54Lynnn/knowledge-rag)（⭐️ Star 支持）\n- **ClawHub**：[clawhub.ai/54lynnn/knowledge-rag](https://clawhub.ai/54lynnn/knowledge-rag)\n- **QQ 群**：120363664（欢迎扫码加入交流）\n\nFile v1.5.7:_meta.json\n\n{\n \"ownerId\": \"kn742cdrc476d1kbhpsvnrnznx81x4r7\",\n \"slug\": \"knowledge-rag\",\n \"version\": \"1.5.7\",\n \"publishedAt\": 1780277463742\n}\n\nFile v1.5.7:skill-card.md\n\n## Description:
\nKnowledge Rag helps agents store notes and local documents, index supported text-based files, and search a private knowledge base with natural-language queries.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[54lynnn](https://clawhub.ai/user/54lynnn)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nExternal users, developers, and agent operators use this skill to maintain a local personal knowledge base, save new notes into organized folders, reindex content, and retrieve source text through a local web UI or command-line workflow.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: The local web services expose filesystem browsing, knowledge-base settings, reindexing, and search over private indexed content.
\nMitigation: Run the service only on trusted machines and networks, stop it when not in use, and review folder selection before indexing.
\nRisk: Sensitive files placed in the configured knowledge directory may be indexed and returned in search results.
\nMitigation: Avoid storing secrets in the knowledge directory and review indexed folders before use.
\n\n\n## Reference(s):
\n- [ClawHub skill listing](https://clawhub.ai/54lynnn/knowledge-rag)
\n- [ClawHub publisher profile](https://clawhub.ai/user/54lynnn)
\n- [Ollama download](https://ollama.com/download)
\n\n\n## Skill Output:
\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]
\n**Output Format:** [Markdown guidance with inline shell commands and local search result text]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Requires Python 3, Ollama, a local embedding model, and local knowledge files under the configured knowledge directory.]
\n\n## Skill Version(s):
\n1.5.7 (source: server release evidence; artifact frontmatter reports 1.5.5)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
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virtual environment (.venv) support; all depend","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"curl -fsSL https://ollama.com/install.sh | sh"},{"language":"bash","snippet":"# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding，中文最优）\nollama pull qwen3-embedding:8b\n\n# 3. 创建虚拟环境并安装依赖\npython3 -m venv .venv\n.venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\n\n# 4. 启动\n.venv/bin/python3 start.py"},{"language":"bash","snippet":"openclaw cron add \\\n --name \"knowledge-rag 自动索引\" \\\n --every 1800000 \\\n --message \"运行索引检查：cd ~/.openclaw/workspace/skills/knowledge-rag && .venv/bin/python3 scripts/index_knowledge.py\" \\\n --silent"},{"language":"text","snippet":"~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）"},{"language":"bash","snippet":".venv/bin/python3 scripts/query_knowledge.py \"你的问题\"\n.venv/bin/python3 scripts/query_knowledge.py \"微服务\" --source bilibili --top 5\n.venv/bin/python3 scripts/query_knowledge.py --stats"},{"language":"bash","snippet":".venv/bin/python3 scripts/index_knowledge.py # 增量\n.venv/bin/python3 scripts/index_knowledge.py --force # 全量重建"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: knowledge-rag\nversion: \"1.6.0\"\ndescription: \"个人知识库：存笔记、搜内容。用自然语言搜本地文档，不用记文件名。跟 AI 说'把这段存到知识库'自动保存并建索引。推荐搭配 Bilibili Auto Transcript：视频转录自动入库，转完即搜。\"\nmetadata:\n openclaw:\n emoji: \"📖\"\n requires:\n bins: [\"python3\", \"ollama\"]\n install:\n - id: \"ollama\"\n kind: \"system\"\n label: \"安装 Ollama（向量引擎）\"\n url: \"https://ollama.com/download\"\n - id: \"embed-model\"\n kind: \"shell\"\n label: \"下载推荐向量模型（千问3 Embedding 8B，约 4.7GB）\"\n command: \"ollama pull qwen3-embedding:8b\"\n - id: \"pip-deps\"\n kind: \"shell\"\n label: \"创建虚拟环境并安装 Python 依赖\"\n command: \"cd {{SKILL_DIR}} && python3 -m venv .venv && .venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\"\n - id: \"auto-index-cron\"\n kind: \"shell\"\n optional: true\n label: \"（可选）开启自动索引——每30分钟扫描新文件\"\n command: \"echo '提示：安装后可运行 openclaw cron add 定时运行 index_knowledge.py 实现自动索引'\"\n---\n\n# 📖 Knowledge RAG\n\n用自然语言搜索你的笔记和文档，像问 ChatGPT 一样问你的私人知识库。\n\n---\n\n## 🚀 安装启动\n\n```bash\n# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding，中文最优）\nollama pull qwen3-embedding:8b\n\n# 3. 创建虚拟环境并安装依赖\npython3 -m venv .venv\n.venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\n\n# 4. 启动\n.venv/bin/python3 start.py\n```\n\n打开 `http://localhost:5777` → 文件丢到 `~/workspace/knowledge/` → 管理页点「重新索引」→ 搜索页开搜。\n\n### 可选：开启自动索引\n新增文件后不用每次都手动点重新索引，设个定时任务每30分钟自动扫描：\n```bash\nopenclaw cron add \\\n --name \"knowledge-rag 自动索引\" \\\n --every 1800000 \\\n --message \"运行索引检查：cd ~/.openclaw/workspace/skills/knowledge-rag && .venv/bin/python3 scripts/index_knowledge.py\" \\\n --silent\n```\n\n---\n\n## 📂 知识库目录\n\n```\n~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）\n```\n\n支持 `.txt` / `.md` / `.pdf`（文字版）/ `.docx` 文件。\n\n---\n\n## 🔍 搜索方式\n\n### 直接问我\n> \"帮我搜笔记里关于 Docker 的部分\" \n> \"知识库里有没有讲 Transformer 的文章？\"\n\n我会自动调搜索，带原文回答。\n\n### 命令行\n```bash\n.venv/bin/python3 scripts/query_knowledge.py \"你的问题\"\n.venv/bin/python3 scripts/query_knowledge.py \"微服务\" --source bilibili --top 5\n.venv/bin/python3 scripts/query_knowledge.py --stats\n```\n\n新增文件后运行索引即可：\n```bash\n.venv/bin/python3 scripts/index_knowledge.py # 增量\n.venv/bin/python3 scripts/index_knowledge.py --force # 全量重建\n```\n\n---\n\n## 📝 Agent 存知识规范\n\n当用户说\"把这段存到知识库\"，按以下规则操作：\n\n| 内容类型 | 存放目录 | 文件名 |\n|---------|---------|--------|\n| 技术笔记、概念解释 | `notes/` | 中文描述.md |\n| B站视频转录 | `bilibili/` | 自动处理 |\n| 公众号、网页文章 | `wechat-articles/` | 文章标题.md |\n| 其他 | `other/` | 内容摘要.md |\n\n保存后运行 `.venv/bin/python3 scripts/index_knowledge.py` 更新索引。\n\n---\n\n## 🔧 切换模型\n\n在设置页修改 `embed_model` 字段：\n\n| 场景 | 模型 | 大小 |\n|------|------|------|\n| ✅ 推荐（质量最高） | `qwen3-embedding:8b` | 4.7GB | 中文优秀，检索质量最高 |\n| 轻量（CPU友好） | `qwen3-embedding:0.6b` | 640MB | 对 CPU 友好，中文不错 |\n| 多语言 | `bge-m3` | 1.2GB |\n| 最小体积 | `nomic-embed-text` | 274MB |\n\n改完后点「重新索引」，系统自动检测并全量重建。\n\n---\n\n## \ud83e"},{"path":"README.md","content":"

\n

📖 Knowledge RAG

\n

把你的笔记和文档变成 AI 知识库，像聊天一样搜索

\n

\n \"ClawHub\"\n \"GitHub\n

\n

\n\n---\n\n## 这是什么\n\n一个**本地运行的 RAG（检索增强生成）知识库系统**。把你的 `.txt` / `.md` / `.pdf` / `.docx` 文件丢进来，用自然语言搜索，像问 ChatGPT 一样问你的私人知识库。\n\n**跟大多数 RAG 工具不同：**\n\n- **极简部署** — 不需要 Docker，不需要数据库，一台机器一个 Ollama 就够了\n- **Trae/OpenClaw Agent 集成** — 在 IDE 里直接让 AI 搜你的知识库并带原文回答\n- **B站转录自动入库** — 配合 bilibili-auto-transcript，视频转录完自动进知识库\n\n---\n\n## 快速开始\n\n```bash\n# 1. 安装 Ollama\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# 2. 拉取推荐模型（千问3 Embedding 8B）\nollama pull qwen3-embedding:8b\n\n# 3. 创建虚拟环境并安装依赖\npython3 -m venv .venv\n.venv/bin/pip install numpy requests fastapi pydantic uvicorn PyPDF2 pdfplumber python-docx\n\n# 4. 启动\n.venv/bin/python3 start.py\n```\n\n打开 `http://localhost:5777` → 把文件丢到 `~/workspace/knowledge/` → 点「重新索引」→ 开搜。\n\n---\n\n## 架构\n\n```\n┌─────────────────────────────────────────────────┐\n│ 浏览器 (index.html) │\n└──────────────────┬──────────────────────────────┘\n │ HTTP\n┌──────────────────▼──────────────────────────────┐\n│ TypeScript Express (server.ts) │\n│ Web 层：静态文件、配置读写、请求路由 │\n└──────────────────┬──────────────────────────────┘\n │ HTTP Proxy\n┌──────────────────▼──────────────────────────────┐\n│ Python FastAPI (knowledge_api.py) │\n│ RAG 层：搜索、索引、管理、统计 API │\n└──────┬─────────────────────────────┬────────────┘\n │ │\n┌──────▼──────┐ ┌────────▼──────────┐\n│ index_ │ │ query_ │\n│ knowledge.py│ │ knowledge.py │\n│ 索引构建 │ │ 向量搜索+混合打分 │\n└──────┬──────┘ └────────┬──────────┘\n │ │\n └──────────┬─────────────────┘\n │ SQLite\n ┌──────────▼──────────┐\n │ chunks.db │\n │ (文本 + 向量 BLOB) │\n └─────────────────────┘\n```\n\n## 知识库目录结构\n\n```\n~/workspace/knowledge/\n ├── notes/ ← 技术笔记、读书笔记\n ├── bilibili/ ← B站转录（配合 bilibili-auto-transcript）\n ├── wechat-articles/ ← 公众号文章\n ├── other/ ← 其他文档\n └── （可建任意子目录，去设置页添加）\n```\n\n支持的文件格式：`.txt` / `.md` / `.pdf`（文字版）/ `.docx`\n\n---\n\n## 核心特性\n\n| 特性 | 说明 |\n|------|------|\n| **SQLite 存储** | 文本 + 向量存同一张表，原子写入，不会崩溃后数据不一致 |\n| **增量索引** | hash 比对 + mtime 快速跳过，只处理变更文件，删除的文件自动清理 |\n| **混合搜索** | 向量余弦相似度 + 查询扩展 + 标题/文本关键词加成 |\n| **标题提取** | Markdown H1、PDF/DOCX 首行自动识别为标题，提高匹配精度 |\n| **文件解析** | PDF（PyPDF2 + pdfplumber 降级）、DOCX（含表格内容） |\n| **删除单条** | API 支持按文件名删除索引，无需全量重建 |\n| **Web 界面** | 搜索 + 管理（统计、索引、配置）一体 |\n| **多模型支持** | 可切换 embedding 模型，切换后自动全量重建 |\n\n---\n\n## 切换模型\n\n在 `~/workspace/knowledge/.knowledge-config"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn742cdrc476d1kbhpsvnrnznx81x4r7\",\n \"slug\": \"knowledge-rag\",\n \"version\": \"1.6.1\",\n \"publishedAt\": 1780476666513\n}"},{"path":"skill-card.md","content":"## Description:\n\nPersonal knowledge base skill for saving notes, indexing local documents, and searching them with natural-language queries.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[54lynnn](https://clawhub.ai/user/54lynnn)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, agents, and individual users use this skill to create a local searchable knowledge base from notes and documents, then retrieve relevant source text through chat-style prompts or CLI commands.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Private document search and admin APIs may be exposed too broadly.\n\nMitigation: Run only on a trusted single-user machine and do not expose ports 5777 or 8768 to a LAN or untrusted browser context.\n\nRisk: The skill can browse or index outside the intended knowledge folder.\n\nMitigation: Avoid placing sensitive documents in reachable paths until API binding, authentication, and path limits are fixed.\n\nRisk: Installation depends on external Ollama and package installation steps.\n\nMitigation: Use verified Ollama and package sources before installing or pulling embedding models.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/54lynnn/skills/knowledge-rag)\n- [Ollama Download](https://ollama.com/download)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown guidance with inline shell commands and local API or CLI outputs]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Produces local search guidance and retrieval results backed by a local SQLite index and Ollama embeddings.]\n\n## Skill Version(s):\n\n1.6.1 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"web/package-lock.json","content":"{\n \"name\": \"knowledge-server-ts\",\n \"version\": \"1.0.0\",\n \"lockfileVersion\": 3,\n \"requires\": true,\n \"packages\": {\n \"\": {\n \"name\": \"knowledge-server-ts\",\n \"version\": \"1.0.0\",\n \"dependencies\": {\n \"express\": \"^5.1.0\"\n },\n \"devDependencies\": {\n \"@types/express\": \"^5.0.1\",\n 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