#32 · Primary category： Business Intelligence & Analytics
SQLBot
Text-to-SQL Generation via LLMs using RAG.
Project last updated：08/28/26
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Why we included this project
SQLBot answers plain-English questions about your data by turning them into SQL, so an analyst can pull results from a database without writing every join and filter by hand. It combines a large language model with RAG, keeping generated queries tied to the actual schema and business vocabulary rather than guessing at table names. The chat interface shows both the SQL it produced and a chart built from the results, which makes it a practical way for teams to try a self-serve reporting layer. You can tune it with a terminology glossary, custom prompts, and stored SQL examples, and it gets sharper on your own dataset the more it is used. It also embeds into n8n, Dify, or DataEase and supports MCP calls, so the querying can sit inside tools a team already has.
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