SQLlyIntelligence
Tools that help SQLly explain your database in plain language without pretending it knows more than it does.
A schema can tell you what a column is called; it cannot always tell you what the column means. SQLly's intelligence work is about closing that gap carefully. The live value-aware completion features are under IntelliSense › Data awareness; this section covers the layers built on a language model.
Everything is grounded in your catalog
No feature here asks a model to guess at your database. SQLly keeps a searchable catalog of your schema objects and, for each question, assembles a bounded slice of it: the objects ranked as relevant, trimmed to a token budget, with the list of what was included carried back alongside the answer. So a response comes with its own citation - you can see which tables and columns it was built from.
Where a deterministic check can answer instead, it goes first and the model gets its findings. SQL review, for instance, runs the parser-backed validator, then asks the model to explain and address each real finding - which is why the narrative talks about your query rather than about SQL in general.
Which model runs it
| Where it runs | Options |
|---|---|
| On device | Ollama on any platform, or the MLX runtime on Apple Silicon. Nothing leaves the machine. |
| Your own endpoint | Any OpenAI-compatible or Anthropic-compatible base URL - a self-hosted gateway, a model server on your LAN. SQLly labels a profile pointing at localhost as local. |
| Cloud, if you ask | OpenRouter, OpenAI, Anthropic, Gemini, or DeepSeek. Each is a named provider profile with its own model, context window, reasoning level, and optional proxy - and its key stored as a secret reference, not in the config file. |
local or cloud.Trying it before trusting it
The AI playground lets you point each provider profile at a prompt and compare what comes back - same question, same catalog context, different model - with the token limit under your control. It is the honest way to find out whether a small local model is good enough for the job before you wire it into your daily work.
In this section
- On-device query summarization — Get a plain-language preview of a query from a model you control, before you have to parse every clause yourself.
- Auto-magic string documentation — Trace an unexplained value through DDL and leave behind a useful note for the next person, possibly future you.
- Native grep-ai search — Search across your schema and data by intent, not only by the exact SQL words you happen to remember.
- Decision model — An optional fast cloud model that answers tiny pick-one questions so several features can offer a smart first guess.