fmql-semantic

Hybrid semantic search backend plugin for fmql.

• Dense retrieval via LiteLLM embeddings + sqlite-vec.
• Sparse retrieval via SQLite FTS5 (BM25).
• Fusion via reciprocal rank fusion (RRF).
• Optional reranking via LiteLLM rerank providers (Cohere, Voyage, etc.).
• Single-file SQLite index. No server.

Install

pip install fmql-semantic

fmql-semantic requires a Python build with sqlite3 loadable-extension support. Most Python installs qualify: Linux distro Python, Windows Python, uv's bundled Python, Homebrew's python, the python.org macOS installer, conda, and official Docker images. If the extension loader is unavailable, the backend fails fast with a clear error.

With pipx

fmql-semantic is a plugin library (no CLI of its own), so pipx install fmql-semantic does not work. Inject it into fmql's pipx env instead:

pipx inject fmql fmql-semantic

On macOS specifically, pin pipx to Homebrew's Python to sidestep the sqlite loadable-extension problem described below:

brew install python@3.12
pipx install --python /opt/homebrew/bin/python3.12 fmql
pipx inject fmql fmql-semantic

Or set PIPX_DEFAULT_PYTHON=/opt/homebrew/bin/python3.12 in ~/.zshrc so all future pipx install calls use Homebrew Python automatically.

macOS + pyenv: extra setup required

Default pyenv install on macOS links Python against Apple's system sqlite (/usr/lib/libsqlite3.dylib), which is compiled without loadable-extension support for sandboxing reasons. Same is true of the macOS system Python at /usr/bin/python3. In both cases connection.enable_load_extension(True) raises NotSupportedError and fmql-semantic fails fast.

To use fmql-semantic on pyenv-installed Python on macOS, point pyenv at Homebrew's sqlite (which has loadable extensions enabled) and reinstall:

brew install sqlite

export LDFLAGS="-L$(brew --prefix sqlite)/lib"
export CPPFLAGS="-I$(brew --prefix sqlite)/include"
export PKG_CONFIG_PATH="$(brew --prefix sqlite)/lib/pkgconfig"

pyenv uninstall <version>
pyenv install <version>

python -c "import sqlite3; sqlite3.connect(':memory:').enable_load_extension(True); print('OK')"

The LDFLAGS/CPPFLAGS exports must be set while pyenv install runs; they tell Python's build to prefer Homebrew's sqlite over Apple's. Once the OK check passes, recreate your venv and reinstall fmql-semantic. This is a one-time setup per pyenv Python version.

Configure

fmql-semantic reads configuration from three channels, in increasing precedence:

1. Process environment.
2. A dotenv file pointed to by --option env=path/to/.env.
3. --option KEY=VALUE flags on the command line.

Environment variables

Standard LiteLLM provider env vars (OPENAI_API_KEY, VOYAGE_API_KEY, OLLAMA_API_BASE, …) are read by LiteLLM directly from the process environment. A dotenv file passed via --option env=path/to/.env also publishes its non-FMQL_* keys into os.environ (without overriding values already exported by the shell), so the same file can carry both FMQL_* settings and provider credentials.

--option keys

Build: model, api_base, api_key, batch_size, concurrency, max_tokens, fields, force, env.

Query: model, api_base, api_key, reranker_model, reranker_top_n, rerank_required, no_rerank, dense_only, sparse_only, fetch_k, env.

Use

export FMQL_EMBEDDING_MODEL=openai/text-embedding-3-small
export OPENAI_API_KEY=...

# Build once:
fmql index ./my-notes --backend semantic

# Query:
fmql search "quarterly planning" --backend semantic --workspace ./my-notes -k 10

# Dense-only / sparse-only / disable rerank for this query:
fmql search q --backend semantic --workspace ./my-notes --option dense_only=true
fmql search q --backend semantic --workspace ./my-notes --option sparse_only=true
fmql search q --backend semantic --workspace ./my-notes --option no_rerank=true

The default index location is <workspace>/.fmql/semantic.db. Override with --out (for fmql index) or --index (for fmql search).

Indexing

For each packet, the backend indexes:

<first present frontmatter field from --option fields=title,summary,name>

<body>

Frontmatter field values are otherwise not indexed — they're already queryable via fmql's structured layer.

Builds are incremental: packets whose content hash hasn't changed since the last build are skipped. Packets removed from the workspace are removed from the index. The index is committed per batch via SQLite WAL, so a crashed build leaves a queryable index that the next run picks up.

Model pinning

An index is pinned to the embedding model that built it. Rebuilding with a different model refuses unless you pass --force (which drops the existing tables). Dimension mismatches are caught the same way.

Provider notes

• OpenAI (openai/text-embedding-3-small, openai/text-embedding-3-large) — batch caps at 2048; default 100 is fine.
• Voyage (voyage/voyage-3) — batch caps at 128. Set --option batch_size=128 (or lower) for large indexes.
• Cohere rerank (cohere/rerank-v3.5) — works as a reranker model out of the box once COHERE_API_KEY is set.
• Ollama (ollama/nomic-embed-text) — set OLLAMA_API_BASE or --option api_base=http://localhost:11434.

Licensing

MIT. See LICENSE.