Can LlamaIndex handle structured data extraction from SQL databases effectively?

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Have you looked into the "Query Pipeline" feature they released? It allows you to chain the SQL generation with a refinement step, which might help catch errors before the query actually hits your database.

   Answered 2025-11-07 by Jason Brown

• Jason, the Query Pipeline is actually what I was looking at! Does it allow for human-in-the-loop verification? I’m worried about a rogue "DELETE" query being generated, even though the service account is read-only. I want to make sure there's a layer where a manager can see the generated SQL before it executes on our production data.

     Commented 2025-11-08 by Brian Davis

It works well for simple SELECT statements, but complex nested joins still struggle. I suggest creating flattened views in SQL first, then letting the AI query those simplified views.

   Answered 2025-11-10 by Lisa Thomas

• That’s a smart workaround, Lisa. Flattening the views reduces the complexity the LLM has to navigate, leading to much more consistent results for the end-users.

     Commented 2025-11-11 by Kevin Anderson

I've been using the SQL query engine in LlamaIndex for a business intelligence project since early 2023. It is surprisingly robust if your schema is well-defined. The trick is to provide the LLM with a "Context Schema" so it understands the relationships between tables. Without that, it can hallucinate joins that don't exist. I would recommend starting with a small subset of your tables and using a strict prompt template to ensure the generated SQL follows your database's dialect, especially if you are using something specialized like Snowflake.

   Answered 2025-11-12 by Margaret Taylor