dirsql-plugin-embeddings 0.1.9

First-party dirsql plugin: semantic search via an OpenAI-compatible embeddings endpoint.

dirsql-plugin-embeddings

A first-party dirsql plugin that adds semantic search over a directory of documents -- Markdown, plain text, reStructuredText and PDFs. It is the worked implementation behind the Search documents by meaning how-to, swapping that guide's local model2vec model for any OpenAI-compatible /v1/embeddings endpoint.

uvx --with dirsql-plugin-embeddings dirsql

Deliberately minimal (v0.1): one embedding provider shape, one table, no chunking, no config surface beyond three environment variables.

How it works

The plugin ships a dirsql.toml fragment that dirsql discovers when the package is installed alongside it. The fragment declares:

• the sqlite-vec extension, for vec_distance_cosine();
• a documents table whose on-file hook embeds each **/*.{md,markdown,mdx,rst,txt,pdf} file into a TEXT embedding column;
• a pre-query hook that embeds the incoming question and emits the nearest-neighbor SQL.

Both hooks are console scripts that call the same embedder.

Every matched extension except .pdf is read as UTF-8 text; a .pdf is read with pypdf, whose per-page extracted text is joined and embedded like any other document. The extension check is case-insensitive (.PDF is a PDF), though the glob above is not — globset matches case-sensitively, so an uppercase-suffixed file needs its own glob entry to be picked up at all.

The glob is an allowlist rather than **/* on cost, not correctness. Every matched file costs a hook subprocess, and every file the plugin can decode costs a billed embedding call — so pointing **/* at a tree containing node_modules or .git makes for a slow and expensive scan. The list is what is worth embedding; widening it trades money for recall.

A file the plugin cannot read is skipped, not fatal. The hook exits non-zero, dirsql names the file on stderr and carries on indexing the rest, and the run exits 23 — "completed, some files skipped". From the SDK the same information is on scan_failures() / scanFailures(). A scanned, image-only PDF is not a failure at all: pypdf yields no text, and the file is indexed with an empty text, exactly like an empty .md.

Configuration

The embedder reads three environment variables (point them at any hosted or self-managed OpenAI-compatible inference server):

Variable	Meaning
DIRSQL_EMBEDDINGS_BASE_URL	Base URL; /v1/embeddings is appended.
DIRSQL_EMBEDDINGS_MODEL	Model name sent in the request.
DIRSQL_EMBEDDINGS_API_KEY	Bearer token for Authorization.

Console scripts

Script	Hook	Input	Output
dirsql-embeddings-on-file	on-file	a file's absolute path (argv[1]), text or PDF	one-line JSON row array with path, text, embedding
dirsql-embeddings-pre-query	pre-query	a raw request body (argv[1])	nearest-neighbor SQL over documents

pre-query accepts both a verbatim server body ({"q": ...}) and the CLI query subcommand's {"sql": <arg>} wrapper, so dirsql query '{"q": ...}' and a real POST /query both work.

Tests

Three tiers, per the dirsql testing conventions:

• unit (colocated, mocked seams) — src/dirsql_plugin_embeddings/*_test.py
• integration (tests/integration/) — each console script as a real subprocess against a local stub /v1/embeddings server
• e2e (tests/e2e/) — the full loop through the real launcher + dirsql binary + sqlite-vec, nothing mocked but the embedding endpoint