sql-eval evaluates the accuracy of SQL queries generated by large language models (LLMs). It takes LLM-generated queries and compares them to ground truth queries executed on a database. The evaluation process includes comparing dataframes generated from both queries using both exact and subset matching, and logging the results. The goal is to provide a rigorous assessment of the SQL generation capabilities of different LLMs.
sql-eval offers a comprehensive framework for evaluating SQL generation. It includes a curated dataset based on the Spider benchmark, supports multiple database backends (Postgres, Snowflake, BigQuery, MySQL, SQLite, SQL Server), and facilitates easy integration with custom datasets. The design allows customization for private datasets and focuses on practical metrics like exact and subset matching.
- Query Execution: Executes both generated and ground truth SQL queries on a database.
- Dataframe Comparison: Compares results using exact and subset matching of dataframes.
- Multi-Database Support: Supports diverse database systems including Postgres, Snowflake, and MySQL.
- Extensible Data Sources: Allows integration of private datasets with flexible data import procedures.
- Metric Logging: Logs key metrics such as tokens used and latency for detailed analysis.
- Configuration Flexibility: Provides options to customize evaluation parameters and database settings.
- Developer Friendly: Offers clear setup instructions and well-structured code for extending functionality.
sql-eval is an active project with ongoing maintenance and development. The project has a demonstrated usage history evidenced by the number of stars and forks. Recent commits indicate active development, and the documentation is reasonably complete, guiding users through setup, data import, and usage. The project's reliance on established libraries and clear architecture suggest a relatively stable and reliable codebase.
This project benefits data scientists, machine learning engineers, and anyone working with LLMs for SQL generation. It provides a standardized way to measure the accuracy of LLM-generated SQL, enabling more reliable deployment of AI-driven data solutions. sql-eval offers a practical alternative to manual SQL validation, leading to faster iteration and improved SQL generation performance.