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OpenClaw Markdown 记忆文件添加向量驱动的语义搜索。使用 memsearch 库，支持混合搜索（稠密向量 + BM25），SHA-256 智能去重，本地 embedding 无需 API Key。\n\nTags: latest:1.1.0\n\nVersion history:\n\nv1.1.0 | 2026-03-30T08:47:51.117Z | user\n\n索引范围扩展(MEMORY.md+根目录文件)+精简SKILL.md+修package.json\n\nv1.0.0 | 2026-03-14T08:36:47.921Z | auto\n\nInitial release of semantic-memory-search.\n\n- Adds vector-driven semantic search for OpenClaw memory files using the memsearch library\n- Supports hybrid search (dense vectors + BM25), RRF reranking, and SHA-256 based deduplication\n- Enables local embedding (all-MiniLM-L6-v2) with no API Key required\n- Stores data locally with Milvus Lite and supports offline use\n- Includes file monitoring and incremental updates for automatic reindexing\n- Integrates directly with OpenClaw\n\nArchive index:\n\nArchive v1.1.0: 8 files, 5867 bytes\n\nFiles: config/settings.json (378b), package.json (657b), README.md (2793b), scripts/index.sh (438b), scripts/search.sh (431b), skill-card.md (2249b), SKILL.md (1603b), _meta.json (141b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: semantic-memory-search\nslug: semantic-memory-search\nversion: 2.0.0\ndescription: 为 OpenClaw 记忆文件添加向量驱动的语义搜索。使用 memsearch 库，支持混合搜索（稠密向量 + BM25)，SHA-256 智能去重，本地 embedding 无需 API Key。\n本地运行，完全离线。\n每天自动索引。\nauthor: sunnyhot\nlicense: MIT\nhomepage: https://github.com/sunnyhot/semantic-memory-search\nkeywords:\n  - memory\n  - search\n  - semantic\n  - vector\n  - memsearch\nmetadata:\n  openclaw:\n    requires:\n      bins: [\"python3\"]\n    optionalBins: [\"memsearch\"]\n---\n\n# Semantic Memory Search\n\n为 OpenClaw 记忆文件提供本地语义搜索，无需 API Key。\n\n 使用 `memsearch` 命令行工具。\n\n 支持索引 memory/ 目录和根目录下的 MEMORY.md、USER.md 综文件。\n\n 混合搜索（向量 + BM25 + RRF 重排序) SHA-256 智能去重。\n\n 增量更新。\n\n## 命令令```bash\n# 篇引记忆文件（默认）\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch index \\\n  ~/.openclaw/workspace/memory/ \\\n  ~/.openclaw/workspace/MEMORY.md \\\n  ~/.openclaw/workspace/USER.md \\\n  ~/.openclaw/workspace/TOOLS.md \\\n  ~/.openclaw/workspace/AGENTS.md\n```\n\n### 语义搜索\n\n```bash\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch search \"你的搜索词\"\n```\n\n### Cron 自动索引\n\n每天 2:30 自动索引，结果推送到 Discord。\n\n 手动索引:`KMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch index` 即可。\n\n 如果没有变更，输出 `已索引 N chunks， 无需操作`。Cron ID: `2ad149da-b593-458f-8eee-5976326b0238`\n\nFile v1.1.0:README.md\n\n# 🔍 Semantic Memory Search\n\n**为 OpenClaw 记忆文件添加语义搜索能力**\n\n[![ClawHub](https://img.shields.io/badge/ClawHub-semantic--memory--search-blue)](https://clawhub.com/skills/semantic-memory-search)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n---\n\n## 痛点\n\nOpenClaw 的记忆以纯 Markdown 文件存储。这对可移植性和人类可读性很好，但：\n- ❌ 没有搜索功能\n- ❌ 只能 grep（仅关键词，会漏掉语义匹配）\n- ❌ 将整个文件加载到上下文（浪费 token）\n\n你需要一种方式来问\"我关于 X 做了什么决定？\"并获得精确的相关片段。\n\n---\n\n## ✨ 功能\n\n| 功能 | 说明 |\n|------|------|\n| 语义搜索 | 通过语义而非关键词找到相关记忆 |\n| 混合搜索 | 稠密向量 + BM25 全文检索 + RRF 重排序 |\n| 智能去重 | SHA-256 哈希，未更改文件不重新嵌入 |\n| 本地运行 | 无需 API Key，完全离线 |\n| 自动同步 | 文件变更时自动重新索引 |\n\n---\n\n## 🚀 快速开始\n\n### 1. 安装依赖\n\n```bash\npip3 install \"memsearch[local]\"\n```\n\n### 2. 索引记忆文件\n\n```bash\ncd ~/.openclaw/workspace/skills/semantic-memory-search/scripts\n./index.sh\n```\n\n### 3. 搜索记忆\n\n```bash\n./search.sh \"Discord 频道重组\"\n```\n\n---\n\n## 📊 搜索示例\n\n| 查询 | 说明 |\n|------|------|\n| \"我们选了什么缓存方案？\" | 即使没有\"缓存\"关键词也能找到 |\n| \"Discord 频道重组\" | 找到所有相关决策和过程 |\n| \"播客制作流程\" | 找到播客相关的所有记忆 |\n| \"财报跟踪配置\" | 找到财报系统的配置历史 |\n\n---\n\n## 🔧 配置\n\n配置文件：`~/.memsearch/config.toml`\n\n```toml\n[milvus]\nuri = \"~/.memsearch/milvus.db\"\n\n[embedding]\nprovider = \"local\"\nmodel = \"all-MiniLM-L6-v2\"\n\n[search]\ntop_k = 5\n```\n\n---\n\n## 📁 文件结构\n\n```\nsemantic-memory-search/\n├── SKILL.md              # 技能说明\n├── README.md             # 使用文档\n├── package.json          # 包信息\n├── LICENSE               # MIT 许可证\n├── .gitignore            # Git 忽略文件\n├── scripts/\n│   ├── index.sh          # 索引脚本\n│   └── search.sh         # 搜索脚本\n└── config/\n    └── settings.json     # 配置文件\n```\n\n---\n\n## 🔗 相关链接\n\n- [memsearch GitHub](https://github.com/zilliztech/memsearch)\n- [memsearch 文档](https://zilliztech.github.io/memsearch/)\n- [Milvus](https://milvus.io/)\n- [用例来源](https://github.com/AlexAnys/awesome-openclaw-usecases-zh/blob/main/usecases/semantic-memory-search.md)\n\n---\n\n## 📝 更新日志\n\n### v1.0.0 (2026-03-14)\n- ✅ 初始版本\n- ✅ 本地 embedding 支持\n- ✅ 语义搜索功能\n\n---\n\n## 📄 许可证\n\nMIT License\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn79e7737znkq4877n6wc93wn58292v9\",\n  \"slug\": \"semantic-memory-search\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1774860471117\n}\n\nFile v1.1.0:skill-card.md\n\n## Description:\n\nAdds vector-driven semantic search for OpenClaw Markdown memory files using memsearch with hybrid dense-vector and BM25 retrieval, SHA-256 deduplication, and local embeddings without an API key.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[sunnyhot](https://clawhub.ai/user/sunnyhot)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and OpenClaw users use this skill to index local Markdown memory files and retrieve semantically related memory snippets with memsearch.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill can index private OpenClaw memory content, and documented Discord reporting conflicts with the offline privacy claim.\n\nMitigation: Review configuration before enabling integrations, confirm exactly what is sent externally, and disable or audit Discord reporting before use.\n\nRisk: Installing or running memsearch without version controls can change retrieval behavior or dependency exposure over time.\n\nMitigation: Install in an isolated environment with pinned versions and review the generated index location before indexing sensitive files.\n\n## Reference(s):\n\n- [Semantic Memory Search on ClawHub](https://clawhub.ai/sunnyhot/skills/semantic-memory-search)\n- [memsearch GitHub](https://github.com/zilliztech/memsearch)\n- [memsearch Documentation](https://zilliztech.github.io/memsearch/)\n- [Milvus](https://milvus.io/)\n- [Semantic Memory Search Use Case](https://github.com/AlexAnys/awesome-openclaw-usecases-zh/blob/main/usecases/semantic-memory-search.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown guidance with shell commands and terminal text output]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Search results depend on local memory files, the memsearch installation, and configured top_k.]\n\n## Skill Version(s):\n\n1.1.0 (source: server release evidence)\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 v1.1.0:config/settings.json\n\n{\n  \"memory_path\": \"~/.openclaw/workspace/memory/\",\n  \"memsearch_path\": \"~/Library/Python/3.14/bin/memsearch\",\n  \"discord_channel\": \"1482296953445289985\",\n  \"milvus_uri\": \"~/.memsearch/milvus.db\",\n  \"embedding\": {\n    \"provider\": \"local\",\n    \"model\": \"all-MiniLM-L6-v2\"\n  },\n  \"search\": {\n    \"top_k\": 5\n  },\n  \"watch\": {\n    \"enabled\": false,\n    \"interval_seconds\": 60\n  }\n}\n\nFile v1.1.0:package.json\n\n{\n  \"name\": \"semantic-memory-search\",\n  \"version\": \"1.0.0\",\n  \"description\": \"为 OpenClaw Markdown 记忆文件添加向量驱动的语义搜索\",\n  \"main\": \"scripts/search.sh\",\n  \"author\": \"sunnyhot\",\n  \"license\": \"MIT\",\n  \"keywords\": [\n    \"memory\",\n    \"search\",\n    \"semantic\",\n    \"vector\",\n    \"milvus\",\n    \"memsearch\"\n  ],\n  \"repository\": {\n    \"type\": \"git\",\n    \"url\": \"https://github.com/sunnyhot/semantic-memory-search\"\n  },\n  \"engines\": {\n    \"node\": \">=18.0.0\",\n    \"python\": \">=3.10\"\n  },\n  \"openclaw\": {\n    \"requires\": {\n      \"bins\": [\"python3\"]\n    },\n    \"optionalBins\": [\"memsearch\"],\n    \"configPaths\": [\"config/settings.json\"]\n  }\n}\n\nArchive v1.0.0: 7 files, 5436 bytes\n\nFiles: config/settings.json (334b), package.json (658b), README.md (2793b), scripts/index.sh (438b), scripts/search.sh (431b), SKILL.md (3378b), _meta.json (141b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: semantic-memory-search\nslug: semantic-memory-search\nversion: 1.0.0\ndescription: 为 OpenClaw Markdown 记忆文件添加向量驱动的语义搜索。使用 memsearch 库，支持混合搜索（稠密向量 + BM25），SHA-256 智能去重，本地 embedding 无需 API Key。\nauthor: sunnyhot\nlicense: MIT\nhomepage: https://github.com/sunnyhot/semantic-memory-search\nkeywords:\n  - memory\n  - search\n  - semantic\n  - vector\n  - milvus\n  - embedding\n  - memsearch\nmetadata:\n  clawdbot:\n    emoji: 🔍\n    requires:\n      bins: [\"python3\"]\n    optionalBins: [\"memsearch\"]\n    os: [\"linux\", \"darwin\", \"win32\"]\n    configPaths:\n      - config/settings.json\n---\n\n# Semantic Memory Search\n\n**为 OpenClaw 记忆文件添加语义搜索能力**\n\n---\n\n## 🎯 核心功能\n\n### 语义搜索\n- ✅ **向量驱动** - 通过语义而非关键词找到相关记忆\n- ✅ **混合搜索** - 稠密向量 + BM25 全文检索 + RRF 重排序\n- ✅ **智能去重** - SHA-256 内容哈希，未更改文件不重新嵌入\n\n### 本地运行\n- ✅ **无需 API Key** - 使用本地 embedding（all-MiniLM-L6-v2）\n- ✅ **完全离线** - 所有数据存储在本地 Milvus Lite\n\n### 自动同步\n- ✅ **文件监视** - 记忆文件变更时自动重新索引\n- ✅ **增量更新** - 只处理新增或修改的文件\n\n---\n\n## 📦 依赖\n\n### 必需\n- Python 3.10+\n- memsearch 库\n\n### 安装\n\n```bash\npip3 install \"memsearch[local]\"\n```\n\n---\n\n## 📅 使用方法\n\n### 1. 索引记忆文件\n\n```bash\n# 使用本地 embedding（无需 API Key）\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch index ~/.openclaw/workspace/memory/\n\n# 或使用 OpenAI embedding（需要 API Key）\nexport OPENAI_API_KEY=\"your-key\"\nmemsearch index ~/.openclaw/workspace/memory/\n```\n\n### 2. 语义搜索\n\n```bash\n# 搜索记忆\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch search \"我们选了什么缓存方案？\"\n```\n\n### 3. 实时同步（可选）\n\n```bash\n# 启动文件监视器\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch watch ~/.openclaw/workspace/memory/\n```\n\n---\n\n## 🔧 配置\n\n配置文件：`~/.memsearch/config.toml`\n\n### 本地 Embedding（推荐）\n\n```toml\n[milvus]\nuri = \"~/.memsearch/milvus.db\"\n\n[embedding]\nprovider = \"local\"\nmodel = \"all-MiniLM-L6-v2\"\n\n[search]\ntop_k = 5\n```\n\n### OpenAI Embedding\n\n```toml\n[milvus]\nuri = \"~/.memsearch/milvus.db\"\n\n[embedding]\nprovider = \"openai\"\nmodel = \"text-embedding-3-small\"\n\n[search]\ntop_k = 5\n```\n\n---\n\n## 📊 搜索示例\n\n### 示例查询\n\n| 查询 | 说明 |\n|------|------|\n| \"我们选了什么缓存方案？\" | 即使没有\"缓存\"关键词也能找到 |\n| \"Discord 频道重组\" | 找到所有相关决策和过程 |\n| \"播客制作流程\" | 找到播客相关的所有记忆 |\n| \"财报跟踪配置\" | 找到财报系统的配置历史 |\n\n### 搜索结果格式\n\n```\n--- Result 1 (score: 0.0320) ---\nSource: /path/to/memory.md\nHeading: 相关标题\n# 内容摘要...\n\n--- Result 2 (score: 0.0318) ---\n...\n```\n\n---\n\n## 🔗 相关链接\n\n- [memsearch GitHub](https://github.com/zilliztech/memsearch)\n- [memsearch 文档](https://zilliztech.github.io/memsearch/)\n- [Milvus](https://milvus.io/)\n\n---\n\n## 📝 更新日志\n\n### v1.0.0 (2026-03-14)\n- ✅ 初始版本\n- ✅ 本地 embedding 支持\n- ✅ 语义搜索功能\n- ✅ OpenClaw 集成\n\n---\n\n## 📄 许可证\n\nMIT License\n\nFile v1.0.0:README.md\n\n# 🔍 Semantic Memory Search\n\n**为 OpenClaw 记忆文件添加语义搜索能力**\n\n[![ClawHub](https://img.shields.io/badge/ClawHub-semantic--memory--search-blue)](https://clawhub.com/skills/semantic-memory-search)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n---\n\n## 痛点\n\nOpenClaw 的记忆以纯 Markdown 文件存储。这对可移植性和人类可读性很好，但：\n- ❌ 没有搜索功能\n- ❌ 只能 grep（仅关键词，会漏掉语义匹配）\n- ❌ 将整个文件加载到上下文（浪费 token）\n\n你需要一种方式来问\"我关于 X 做了什么决定？\"并获得精确的相关片段。\n\n---\n\n## ✨ 功能\n\n| 功能 | 说明 |\n|------|------|\n| 语义搜索 | 通过语义而非关键词找到相关记忆 |\n| 混合搜索 | 稠密向量 + BM25 全文检索 + RRF 重排序 |\n| 智能去重 | SHA-256 哈希，未更改文件不重新嵌入 |\n| 本地运行 | 无需 API Key，完全离线 |\n| 自动同步 | 文件变更时自动重新索引 |\n\n---\n\n## 🚀 快速开始\n\n### 1. 安装依赖\n\n```bash\npip3 install \"memsearch[local]\"\n```\n\n### 2. 索引记忆文件\n\n```bash\ncd ~/.openclaw/workspace/skills/semantic-memory-search/scripts\n./index.sh\n```\n\n### 3. 搜索记忆\n\n```bash\n./search.sh \"Discord 频道重组\"\n```\n\n---\n\n## 📊 搜索示例\n\n| 查询 | 说明 |\n|------|------|\n| \"我们选了什么缓存方案？\" | 即使没有\"缓存\"关键词也能找到 |\n| \"Discord 频道重组\" | 找到所有相关决策和过程 |\n| \"播客制作流程\" | 找到播客相关的所有记忆 |\n| \"财报跟踪配置\" | 找到财报系统的配置历史 |\n\n---\n\n## 🔧 配置\n\n配置文件：`~/.memsearch/config.toml`\n\n```toml\n[milvus]\nuri = \"~/.memsearch/milvus.db\"\n\n[embedding]\nprovider = \"local\"\nmodel = \"all-MiniLM-L6-v2\"\n\n[search]\ntop_k = 5\n```\n\n---\n\n## 📁 文件结构\n\n```\nsemantic-memory-search/\n├── SKILL.md              # 技能说明\n├── README.md             # 使用文档\n├── package.json          # 包信息\n├── LICENSE               # MIT 许可证\n├── .gitignore            # Git 忽略文件\n├── scripts/\n│   ├── index.sh          # 索引脚本\n│   └── search.sh         # 搜索脚本\n└── config/\n    └── settings.json     # 配置文件\n```\n\n---\n\n## 🔗 相关链接\n\n- [memsearch GitHub](https://github.com/zilliztech/memsearch)\n- [memsearch 文档](https://zilliztech.github.io/memsearch/)\n- [Milvus](https://milvus.io/)\n- [用例来源](https://github.com/AlexAnys/awesome-openclaw-usecases-zh/blob/main/usecases/semantic-memory-search.md)\n\n---\n\n## 📝 更新日志\n\n### v1.0.0 (2026-03-14)\n- ✅ 初始版本\n- ✅ 本地 embedding 支持\n- ✅ 语义搜索功能\n\n---\n\n## 📄 许可证\n\nMIT License\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn79e7737znkq4877n6wc93wn58292v9\",\n  \"slug\": \"semantic-memory-search\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1773477407921\n}\n\nFile v1.0.0:config/settings.json\n\n{\n  \"memory_path\": \"~/.openclaw/workspace/memory/\",\n  \"memsearch_path\": \"~/Library/Python/3.14/bin/memsearch\",\n  \"milvus_uri\": \"~/.memsearch/milvus.db\",\n  \"embedding\": {\n    \"provider\": \"local\",\n    \"model\": \"all-MiniLM-L6-v2\"\n  },\n  \"search\": {\n    \"top_k\": 5\n  },\n  \"watch\": {\n    \"enabled\": false,\n    \"interval_seconds\": 60\n  }\n}\n\nFile v1.0.0:package.json\n\n{\n  \"name\": \"semantic-memory-search\",\n  \"version\": \"1.0.0\",\n  \"description\": \"为 OpenClaw Markdown 记忆文件添加向量驱动的语义搜索\",\n  \"main\": \"scripts/search.cjs\",\n  \"author\": \"sunnyhot\",\n  \"license\": \"MIT\",\n  \"keywords\": [\n    \"memory\",\n    \"search\",\n    \"semantic\",\n    \"vector\",\n    \"milvus\",\n    \"memsearch\"\n  ],\n  \"repository\": {\n    \"type\": \"git\",\n    \"url\": \"https://github.com/sunnyhot/semantic-memory-search\"\n  },\n  \"engines\": {\n    \"node\": \">=18.0.0\",\n    \"python\": \">=3.10\"\n  },\n  \"openclaw\": {\n    \"requires\": {\n      \"bins\": [\"python3\"]\n    },\n    \"optionalBins\": [\"memsearch\"],\n    \"configPaths\": [\"config/settings.json\"]\n  }\n}","readmeExcerpt":"Skill: Semantic Memory Search Owner: sunnyhot Summary: 为 OpenClaw Markdown 记忆文件添加向量驱动的语义搜索。使用 memsearch 库，支持混合搜索（稠密向量 + BM25），SHA-256 智能去重，本地 embedding 无需 API Key。 Tags: latest:1.1.0 Version history: v1.1.0 | 2026-03-30T08:47:51.117Z | user 索引范围扩展(MEMORY.md+根目录文件)+精简SKILL.md+修package.json v1.0.0 | 2026-03-14T08:36:47.921Z | auto Initial release of semantic-memory-search. - Adds vector-driven semantic search for OpenC","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"# 篇引记忆文件（默认）\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch index \\\n  ~/.openclaw/workspace/memory/ \\\n  ~/.openclaw/workspace/MEMORY.md \\\n  ~/.openclaw/workspace/USER.md \\\n  ~/.openclaw/workspace/TOOLS.md \\\n  ~/.openclaw/workspace/AGENTS.md"},{"language":"bash","snippet":"KMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch search \"你的搜索词\""},{"language":"bash","snippet":"pip3 install \"memsearch[local]\""},{"language":"bash","snippet":"cd ~/.openclaw/workspace/skills/semantic-memory-search/scripts\n./index.sh"},{"language":"bash","snippet":"./search.sh \"Discord 频道重组\""},{"language":"toml","snippet":"[milvus]\nuri = \"~/.memsearch/milvus.db\"\n\n[embedding]\nprovider = \"local\"\nmodel = \"all-MiniLM-L6-v2\"\n\n[search]\ntop_k = 5"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: semantic-memory-search\nslug: semantic-memory-search\nversion: 2.0.0\ndescription: 为 OpenClaw 记忆文件添加向量驱动的语义搜索。使用 memsearch 库，支持混合搜索（稠密向量 + BM25)，SHA-256 智能去重，本地 embedding 无需 API Key。\n本地运行，完全离线。\n每天自动索引。\nauthor: sunnyhot\nlicense: MIT\nhomepage: https://github.com/sunnyhot/semantic-memory-search\nkeywords:\n  - memory\n  - search\n  - semantic\n  - vector\n  - memsearch\nmetadata:\n  openclaw:\n    requires:\n      bins: [\"python3\"]\n    optionalBins: [\"memsearch\"]\n---\n\n# Semantic Memory Search\n\n为 OpenClaw 记忆文件提供本地语义搜索，无需 API Key。\n\n 使用 `memsearch` 命令行工具。\n\n 支持索引 memory/ 目录和根目录下的 MEMORY.md、USER.md 综文件。\n\n 混合搜索（向量 + BM25 + RRF 重排序) SHA-256 智能去重。\n\n 增量更新。\n\n## 命令令```bash\n# 篇引记忆文件（默认）\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch index \\\n  ~/.openclaw/workspace/memory/ \\\n  ~/.openclaw/workspace/MEMORY.md \\\n  ~/.openclaw/workspace/USER.md \\\n  ~/.openclaw/workspace/TOOLS.md \\\n  ~/.openclaw/workspace/AGENTS.md\n```\n\n### 语义搜索\n\n```bash\nKMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch search \"你的搜索词\"\n```\n\n### Cron 自动索引\n\n每天 2:30 自动索引，结果推送到 Discord。\n\n 手动索引:`KMP_DUPLICATE_LIB_OK=TRUE ~/Library/Python/3.14/bin/memsearch index` 即可。\n\n 如果没有变更，输出 `已索引 N chunks， 无需操作`。Cron ID: `2ad149da-b593-458f-8eee-5976326b0238`"},{"path":"README.md","content":"# 🔍 Semantic Memory Search\n\n**为 OpenClaw 记忆文件添加语义搜索能力**\n\n[![ClawHub](https://img.shields.io/badge/ClawHub-semantic--memory--search-blue)](https://clawhub.com/skills/semantic-memory-search)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n---\n\n## 痛点\n\nOpenClaw 的记忆以纯 Markdown 文件存储。这对可移植性和人类可读性很好，但：\n- ❌ 没有搜索功能\n- ❌ 只能 grep（仅关键词，会漏掉语义匹配）\n- ❌ 将整个文件加载到上下文（浪费 token）\n\n你需要一种方式来问\"我关于 X 做了什么决定？\"并获得精确的相关片段。\n\n---\n\n## ✨ 功能\n\n| 功能 | 说明 |\n|------|------|\n| 语义搜索 | 通过语义而非关键词找到相关记忆 |\n| 混合搜索 | 稠密向量 + BM25 全文检索 + RRF 重排序 |\n| 智能去重 | SHA-256 哈希，未更改文件不重新嵌入 |\n| 本地运行 | 无需 API Key，完全离线 |\n| 自动同步 | 文件变更时自动重新索引 |\n\n---\n\n## 🚀 快速开始\n\n### 1. 安装依赖\n\n```bash\npip3 install \"memsearch[local]\"\n```\n\n### 2. 索引记忆文件\n\n```bash\ncd ~/.openclaw/workspace/skills/semantic-memory-search/scripts\n./index.sh\n```\n\n### 3. 搜索记忆\n\n```bash\n./search.sh \"Discord 频道重组\"\n```\n\n---\n\n## 📊 搜索示例\n\n| 查询 | 说明 |\n|------|------|\n| \"我们选了什么缓存方案？\" | 即使没有\"缓存\"关键词也能找到 |\n| \"Discord 频道重组\" | 找到所有相关决策和过程 |\n| \"播客制作流程\" | 找到播客相关的所有记忆 |\n| \"财报跟踪配置\" | 找到财报系统的配置历史 |\n\n---\n\n## 🔧 配置\n\n配置文件：`~/.memsearch/config.toml`\n\n```toml\n[milvus]\nuri = \"~/.memsearch/milvus.db\"\n\n[embedding]\nprovider = \"local\"\nmodel = \"all-MiniLM-L6-v2\"\n\n[search]\ntop_k = 5\n```\n\n---\n\n## 📁 文件结构\n\n```\nsemantic-memory-search/\n├── SKILL.md              # 技能说明\n├── README.md             # 使用文档\n├── package.json          # 包信息\n├── LICENSE               # MIT 许可证\n├── .gitignore            # Git 忽略文件\n├── scripts/\n│   ├── index.sh          # 索引脚本\n│   └── search.sh         # 搜索脚本\n└── config/\n    └── settings.json     # 配置文件\n```\n\n---\n\n## 🔗 相关链接\n\n- [memsearch GitHub](https://github.com/zilliztech/memsearch)\n- [memsearch 文档](https://zilliztech.github.io/memsearch/)\n- [Milvus](https://milvus.io/)\n- [用例来源](https://github.com/AlexAnys/awesome-openclaw-usecases-zh/blob/main/usecases/semantic-memory-search.md)\n\n---\n\n## 📝 更新日志\n\n### v1.0.0 (2026-03-14)\n- ✅ 初始版本\n- ✅ 本地 embedding 支持\n- ✅ 语义搜索功能\n\n---\n\n## 📄 许可证\n\nMIT License"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn79e7737znkq4877n6wc93wn58292v9\",\n  \"slug\": \"semantic-memory-search\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1774860471117\n}"},{"path":"skill-card.md","content":"## Description:\n\nAdds vector-driven semantic search for OpenClaw Markdown memory files using memsearch with hybrid dense-vector and BM25 retrieval, SHA-256 deduplication, and local embeddings without an API key.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[sunnyhot](https://clawhub.ai/user/sunnyhot)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and OpenClaw users use this skill to index local Markdown memory files and retrieve semantically related memory snippets with memsearch.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill can index private OpenClaw memory content, and documented Discord reporting conflicts with the offline privacy claim.\n\nMitigation: Review configuration before enabling integrations, confirm exactly what is sent externally, and disable or audit Discord reporting before use.\n\nRisk: Installing or running memsearch without version controls can change retrieval behavior or dependency exposure over time.\n\nMitigation: Install in an isolated environment with pinned versions and review the generated index location before indexing sensitive files.\n\n## Reference(s):\n\n- [Semantic Memory Search on ClawHub](https://clawhub.ai/sunnyhot/skills/semantic-memory-search)\n- [memsearch GitHub](https://github.com/zilliztech/memsearch)\n- [memsearch Documentation](https://zilliztech.github.io/memsearch/)\n- [Milvus](https://milvus.io/)\n- [Semantic Memory Search Use Case](https://github.com/AlexAnys/awesome-openclaw-usecases-zh/blob/main/usecases/semantic-memory-search.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown guidance with shell commands and terminal text output]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Search results depend on local memory files, the memsearch installation, and configured top_k.]\n\n## Skill 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