Chat interface for databases: why it matters
SQLChat turns database querying into a conversation. You: 'how many users signed up last week?' It: generates `SELECT count(*) FROM users WHERE created_at > now() - interval '7 days'`, executes, shows 342. You: 'break that down by source.' It: generates `SELECT source, count(*) FROM users... GROUP BY source`, shows a table. This conversational flow is the natural way to explore data: ask a question, see the answer, ask a follow-up. Traditional SQL clients require you to write a new query for each follow-up.
Multi-database support and connection setup
SQLChat supports: PostgreSQL, MySQL, SQL Server, TiDB, and ClickHouse. Connection is via connection string stored in your browser's localStorage (no server-side storage). This means your database credentials never leave your machine. The tradeoff: you can not share queries with your team since each person connects their own database client. For team use, deploy SQLChat on a server with a shared read-only database connection.
Accuracy depends heavily on the GPT model
GPT-4: 90% SQL accuracy. GPT-3.5: 75%. GPT-4o-mini: 80%. The accuracy gap is most visible on queries with multiple JOINs or complex WHERE clauses. SQLChat sends your schema (table names and column types, not data) to the OpenAI API. For sensitive schemas, this is a data leak concern. SQLChat supports Azure OpenAI for enterprise compliance.
Saved queries and team features
SQLChat saves query history locally. You can favorite queries, add descriptions, and share query links with teammates (the link contains the SQL, not the results). There is no built-in dashboard or scheduled query feature. For recurring reports, save the generated SQL and schedule it in your own cron job. SQLChat is for ad-hoc exploration, not production dashboards.
SQLChat vs Vanna vs Text2SQL tools
SQLChat: best UI, conversation history, browser-based, 75-90% accuracy. Best for ad-hoc data exploration. Vanna: trains on your schema, 85-92% accuracy, integrates into Python apps. Best for programmatic use. AI2SQL, Text2SQL, etc.: one-shot query generation, no conversation. Best for generating SQL snippets to paste elsewhere.
