kbx 0.1.29

Local knowledge base CLI — hybrid search over markdown files with AI embeddings

kbx

Local knowledge base CLI with hybrid search over markdown files. Indexes meeting transcripts, notes, and entity records into SQLite (FTS5) and LanceDB (vector) for fast retrieval by humans and AI agents.

Install

pip install kbx                      # core CLI + FTS5 search
pip install "kbx[search]"            # + vector search (Qwen3 embeddings)
pip install "kbx[search,mlx]"        # + Apple Silicon acceleration

Requires Python 3.10+.

Quick Start

kbx init                   # create kbx.toml in the current directory
kbx index run              # index markdown files
kbx search "quarterly planning"      # hybrid search (FTS5 + vector)
kbx search "quarterly planning" --fast   # keyword-only (no model needed)

Features

• File-first architecture -- markdown files are the source of truth; the DB (SQLite + LanceDB) is a derived search index rebuilt from those files
• Full-text search -- SQLite FTS5 with BM25 ranking and natural date filters
• Vector search -- Qwen3-Embedding-0.6B via sentence-transformers, fused with FTS5 using reciprocal rank fusion (RRF)
• Entity linking -- auto-links people, projects, and glossary terms to documents via regex matching
• Entity CRUD -- manage people, projects, and glossary terms from the CLI with markdown file sync
• MCP server -- stdio transport for integration with Claude, Cursor, and other AI tools
• Granola sync -- pull meeting transcripts from the Granola API or ingest local exports
• Configurable -- kbx.toml controls source directories, search behaviour, and extras
• Incremental indexing -- content-hash based; only re-indexes changed files

Configuration

kbx looks for configuration in this order:

1. $KBX_CONFIG environment variable
2. ./kbx.toml in the current directory
3. ~/.config/kbx/config.toml

Run kbx init to generate a starter config file.

Optional Extras

Extra	What it adds
search	LanceDB + sentence-transformers + NumPy for vector search
mlx	MLX backend for faster embeddings on Apple Silicon
mcp	MCP server for AI tool integration
all	Everything above plus test and dev dependencies

Install with: pip install "kbx[search,mlx,mcp]"

Development

git clone https://github.com/tenfourty/kbx.git
cd kbx
uv sync --all-extras
uv run pre-commit install
uv run pytest -x -q --cov

See CONTRIBUTING.md for guidelines.

License

Apache-2.0