Text-to-SQL

Text-to-SQL (NL2SQL) converts natural language questions directly into executable SQL queries, democratizing database access for non-technical users.

This technology bridges the gap between human language and structured data: it uses Natural Language Processing (NLP) to parse a request—like 'Show last quarter's premium customer revenue'—and automatically generates a valid SQL statement. Modern systems leverage Large Language Models (LLMs) such as GPT and Llama, replacing older rule-based methods for superior accuracy. Key benchmarks like the Yale University-developed Spider dataset (10,181 questions across 200+ databases) drive development, pushing models to handle complex, cross-domain queries. The result is a powerful tool that allows business analysts and managers to get instant, self-service insights without requiring a data engineer to write a single line of code.

https://github.com/eosphoros-ai/Awesome-Text2SQL