dirsql-plugin-embeddings

A first-party dirsql plugin for semantic search over files.

Installing the plugin loads the sqlite-vec extension (for vec_distance_cosine() and friends) and declares an embed() SQL scalar function that turns TEXT or BLOB values into embedding vectors:

uvx --with dirsql-plugin-embeddings dirsql "
  SELECT path
  FROM (SELECT path, embed(content ->> 'abstract') AS emb
        FROM './arxiv-firehose/data/**/metadata.json')
  ORDER BY vec_distance_cosine(emb, embed('local private models'))
  LIMIT 10"

For the common case — one glob, one question, top-k paths — the package is also its own command, generating and running exactly that SQL:

uvx dirsql-plugin-embeddings '**/*.md' "local private models" -k 10

• Corpus glob: required first positional. The plugin never picks a default corpus; you always say which files are in scope. A bare glob is fine here — the command normalizes it to the ./-relative form the SQL layer requires (**/*.md → ./**/*.md).
• Query text: second positional. Query text, model id, and glob are SQL-escaped into the generated query.
• -k / --limit (both spellings, default 10): the number of results. It is exactly the SQL LIMIT of the generated query — no other cutoff exists.
• --model <id>: templates the model id as embed()'s second argument in the generated SQL (see Model).

Results print one path<TAB>distance line per match, closest first.

Top-k is LIMIT k. sqlite-vec's MATCH ... AND k = N idiom belongs to its vec0 virtual table, which dirsql does not use. For plain expressions, sqlite-vec's own documented pattern is the one above: ORDER BY vec_distance_cosine(...) LIMIT k.

Zero cost when unused

embed() is inert until a query calls it: no worker process is spawned and no model is loaded for queries that never use it. On the first call, dirsql spawns the plugin's worker process (dirsql-plugin-embeddings worker), which serves every call of the invocation over stdin/stdout. Only the values the query actually selects are embedded — the worker receives values, not paths, and never opens files itself.

Model

Embeddings come from model2vec (static embeddings — numpy + tokenizers, no torch), defaulting to minishlab/potion-retrieval-32M. The model downloads to the standard Hugging Face cache on the first ever run (on the order of a hundred megabytes — seconds to a few minutes depending on your connection), with progress on stderr when stderr is a TTY; every later run loads it from disk.

An optional second argument overrides the model per call — the id must be model2vec-loadable (sentence-transformers/torch models are out of scope):

SELECT embed('some text', 'minishlab/potion-base-8M')

The one-liner's --model flag templates the same second argument.

Vector cache

Computed vectors are cached at ~/.cache/dirsql/embeddings/ (or $XDG_CACHE_HOME/dirsql/embeddings/ when XDG_CACHE_HOME is set), keyed by the SHA-256 of the value bytes plus the model identifier — changing either recomputes; switching models never serves stale vectors. There is no eviction: the directory is safe to wipe at any time; the only cost is re-embedding. The cache never lives inside a queried tree — the worker receives values, not paths, and writes nothing anywhere else.

Docs

• Search documents by meaning — the guide to both invocation styles.
• [[dirsql.function]] — the core mechanism embed() is built on.