"Even if you love to code, there probably are times when you'd rather ask a question like, "What topics generated the highest reader interest this year?" than write an SQL query with phrases like STRFTIME('%&Y', Date) = STRFTIME('%Y', 'now'). And, if your data set has dozens of columns, it's nice to be able to avoid looking up or remembering the exact name of each one."
"Querychat is a chatbot data-analysis component for the Shiny web framework. It translates a plain-language request into SQL, runs that SQL on your data, then displays both its result and the SQL code that generated it. Showing raw SQL code might not be ideal for the average user, but it's great for programming-literate users who want to verify a model's response."
Querychat provides a Shiny chatbot component that converts plain-language requests into SQL and executes the queries locally against the data. The component displays both the query results and the SQL code that produced them, allowing programming-literate users to inspect and verify the generated queries. The workflow avoids sending raw data to an LLM or to the cloud by providing the model only with a data structure and column definitions, letting the model write SQL which is then run on the user's machine. Querychat is available for both R and Python and simplifies querying complex datasets.
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