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在 OpenClaw 中安装
/install py-mnn-kb
功能描述
Local vector knowledge base with GraphRAG retrieval (vector + BM25 + knowledge graph). Use this skill when the user mentions: "查知识库", "加入知识库", "记住这个", "save...
安全使用建议
This skill appears to do what it says: build and query a local MNN-based KB and (optionally) call an LLM for answers. Before installing, consider: 1) Model download: the first run auto-downloads a ~400MB embedding model from ModelScope (modelscope.cn) into the repo's assets directory — run this on a machine and network where you are comfortable downloading large binary files. 2) Secrets: the tool expects an API key in config.json for LLM generation; that file is written to disk (it is gitignored by the project). Do not put highly sensitive keys there unless you control the environment. 3) Data exfiltration: if you enable the LLM generation path (default examples show this), retrieved KB context and queries will be sent to the configured OpenAI-compatible endpoint. Use --no-llm or point to a trusted/local LLM if you need to keep KB content local. 4) Review code if you have high security needs: the model download and llm invocation are visible in scripts/py_mnn_kb.py (no obfuscated endpoints or hidden backdoors were detected). 5) Run in an isolated environment (virtualenv / container) and inspect config.json and the repo before giving it access to private documents.
功能分析
Type: OpenClaw Skill
Name: py-mnn-kb
Version: 1.0.0
The skill implements a sophisticated local GraphRAG knowledge base using MNN embeddings and SQLite, supporting vector search, BM25, and entity relationship mapping. It includes automated model downloading from ModelScope and background indexing for various document formats (PDF, Word, Excel, etc.) via subprocess management in `scripts/py_mnn_kb.py`. While the skill performs high-risk actions like recursive file system access and background process execution, these behaviors are explicitly documented and strictly aligned with the stated purpose of a local knowledge base, with no evidence of malicious intent, data exfiltration, or obfuscation.
能力评估
Purpose & Capability
Name/description (local MNN KB, GraphRAG) match the delivered artifacts: a Python implementation, CLI, and instructions for building/querying a local KB. Required components (MNN embedding backend, text parsers, optional LLM client) are appropriate for the stated functionality.
Instruction Scope
SKILL.md/README instruct the agent to index local files, insert notes, and return retrieved context. The README and code also permit using an LLM for generation (configurable via config.json / --no-llm). This means retrieved private context may be sent to a remote OpenAI-compatible endpoint if you enable LLM answering — a normal feature for RAG but an important privacy consideration. There is a small inconsistency: SKILL.md explicitly states 'no LLM call is made inside this tool' for kb_query, yet other docs and config include an llm_api section and examples that perform LLM generation. Confirm desired behavior (use --no-llm if you want pure local retrieval).
Install Mechanism
No formal install spec in registry (instruction-only), but code auto-downloads an embedding model (~400 MB) from ModelScope (modelscope.cn) on first run via urllib.request. Downloading from ModelScope is expected for an embedding backend; this does write model files to disk. The skill asks you to pip install requirements.txt (standard).
Credentials
The skill does not require platform environment variables, but it expects a local config.json with an llm_api.api_key and base_url if you want LLM generation. Storing the API key in config.json (gitignored by the project) is consistent but means a secret is kept on disk. The presence of openai (or OpenAI-compatible) client is justified by the optional LLM step; however, enabling it will send KB context and user queries to whatever endpoint you configure. If you do not want outbound data leakage, use --no-llm or provide a local LLM endpoint you trust.
Persistence & Privilege
always:false and the skill does not request force-inclusion or system-wide config changes. It writes its own artifacts (assets/, knowledge_bases/, downloaded model) in its directory and updates the model's llm_config.json for embedding tokenization alignment — expected for this use case. It does not modify other skills or system-wide agent settings.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install py-mnn-kb - 安装完成后，直接呼叫该 Skill 的名称或使用
/py-mnn-kb触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
initial publish
元数据
常见问题
MNN Local Knowledge Base 是什么？
Local vector knowledge base with GraphRAG retrieval (vector + BM25 + knowledge graph). Use this skill when the user mentions: "查知识库", "加入知识库", "记住这个", "save... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 145 次。
如何安装 MNN Local Knowledge Base？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install py-mnn-kb」即可一键安装，无需额外配置。
MNN Local Knowledge Base 是免费的吗？
是的，MNN Local Knowledge Base 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
MNN Local Knowledge Base 支持哪些平台？
MNN Local Knowledge Base 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 MNN Local Knowledge Base？
由 er6y（@er6y）开发并维护，当前版本 v1.0.0。
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