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How to filter table rows with an LLM question in SQL with Quail

Keep only the rows where a language model answers yes to a question about the text.

Who it is forA data analyst with a large text table and a CUDA GPU, who needs a yes or no judgment on every row.

The problemAsking a question of every row in a text column by hand or with one-off scripts is slow and hard to repeat.

The outcomeA filtered result table where each row passed an LLM yes or no check written as a SQL AI.IF condition.

What you need first

  • Python 3.12
  • An NVIDIA CUDA GPU from the supported list (for example H100 SXM)
  • uv installed to run the pip install command
  • A table of text rows (for example reviews with an id and a body column)

Steps

  1. Run uv pip install quail-engine to install the package (the import name is Quail).
  2. Build a table with an id column and a text column, then register it with Quail.DocumentProvider.from_table(table, id_col="id").
  3. Create Quail.EngineConfig with a supported model and device, for example model="qwen3-4b-fp8" and device="h100-sxm".
  4. Open a Quail.Session, then run session.sql with a WHERE AI.IF(PROMPT('Your question\n\n{0}', r.body)) clause and dialect="bq".
  5. Call .collect() on the result and print it to check the matching rows.

Prompt for your agent

Use the Python package quail-engine (import quail) in the repo fsdatalab/quail to filter the table <table_name> with a BigQuery-style AI.IF question about column <text_column>. Return the matching id values as a table. Verify that the number of returned rows is less than or equal to the input row count.

Caveat. Quail needs Python 3.12 and a CUDA GPU from its supported model and device list, so it will not run on a CPU-only laptop.

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