v1.0.0 - a chat window for your own database

A chat window for your own database

<x-jeeves::widget /> puts a conversation thread in any Blade view: users ask
in plain English, and the answer comes back as a number, a table or a chart.
Follow-ups work — ask "revenue by city", then "just Guwahati", then
"break that down by client", and rewind. That state is resolved in PHP rather
than asked of the model, so it holds even on small local models.

Voice input is browser-side. No audio reaches your server.

There is a programmatic API too — Jeeves::query("top 5 customers by revenue")
— and an HTTP contract for front ends that are not Blade.

The model never sees a row

Only schema structure is sent: table names, column names, types, and the words
your users use for them. The model returns SQL; your server validates it, runs
it locally, and formats the result. Not one row of data goes upstream, and
that is enforced by tests rather than by intention — PrivacyWallTest seeds
sentinel values, runs real queries end to end, and asserts those values appear
nowhere in anything sent to a provider.

Generated SQL runs on a connection that cannot write

sql.database_connection is required, and it must not be your
application's default. Point it at a database user holding SELECT and nothing
else. The check is on the resolved connection at execution time, not at a call
site, and there is no fallback path anywhere in the package.

Above it sit three more layers: an input guard, a schema-only prompt, and a
validator with a function allowlist and a schema-derived table whitelist.
docs/SECURITY.md states what each one does and — just as importantly — what
it does not cover. php artisan jeeves:doctor checks the lot, and on MySQL and
PostgreSQL proves the connection is read-only by trying to create a table on it.

See docs/CONNECTION.md for the GRANT statements and
statement timeouts.

Any model, hosted or your own

Gemini, OpenAI, Claude, DeepSeek, Groq, OpenRouter — or a model you run
yourself on Ollama, vLLM, LM Studio or llama.cpp. One config block, no code
changes. PostgreSQL, MySQL, MariaDB and SQLite.

Measured, including where it is weak

812 tests and CI across five database engines. Accuracy is published with the
failures included, because a text-to-SQL tool that only quotes its good number
is not one you can plan around:

Benchmark	Score
This package's own 14-table set	35/46 (76%)
Spider dev sample	30-31/36 (83-86%)

Three consecutive runs each, uncurated, against Gemini 2.5 Flash. The weak
spot is not join depth as such — three-table questions scored 2/6 in every
run while four-table ones scored 3-4/4. Superlatives and projection are the
other two known classes. README.md names them and shows how to measure your
own schema.