cowork-semantic-search

[](https://github.com/ZhuBit/cowork-semantic-search/stargazers) [](https://www.python.org/downloads/) [](LICENSE) [](https://modelcontextprotocol.io)

> If you find this useful, consider giving it a ⭐ — it helps others discover the project.

Local semantic search for your documents. No API keys. No cloud. Works with any MCP client.

---

AI coding tools are powerful, but they have blind spots when it comes to your local files:

• Frozen knowledge -- training data has a cutoff. Your latest reports, notes, and contracts don't exist in the model's world.
• Context window limits -- you can't paste 500 documents into a prompt.
• No cross-file search -- your AI tool can read one file at a time, but can't search across your entire document library for the relevant pieces.

This plugin bridges that gap. It indexes your local documents into a small, fast vector database. When you ask a question, it retrieves only the relevant pieces -- so your AI tool can answer with your actual data.

Your documents --> chunked --> embedded --> local vector DB
                                                 |
         Your question --> embedded --> similarity search --> relevant chunks --> AI answers

• Fully offline -- one-time model download (~120MB), then no network calls. No data leaves your machine.
• Incremental indexing -- SHA-256 content hashing. Only changed files get reprocessed. Re-indexing 1000 files where 3 changed takes seconds.
• Multilingual -- handles 50+ languages natively. Search in one language, find results in another.
• Hybrid search -- combines semantic simi