SQL Generation

Example demonstrating how to use Pydantic AI to generate SQL queries based on user input.

Demonstrates:

• dynamic system prompt
• structured output_type
• output validation
• agent dependencies

Running the Example

The resulting SQL is validated by running it as an EXPLAIN query on PostgreSQL. To run the example, you first need to run PostgreSQL, e.g. via Docker:

docker run --rm -e POSTGRES_PASSWORD=postgres -p 54320:5432 postgres

(we run postgres on port 54320 to avoid conflicts with any other postgres instances you may have running)

With dependencies installed and environment variables set, run:

python/uv-run -m pydantic_ai_examples.sql_gen

or to use a custom prompt:

python/uv-run -m pydantic_ai_examples.sql_gen "find me errors"

This model uses gemini-3-flash-preview by default since Gemini is good at single shot queries of this kind.

Example Code

snippet {path="/examples/pydantic_ai_examples/sql_gen.py"}