Vanna AI takes a retrieval-augmented generation approach to text-to-SQL, building a knowledge base from your specific database schema, table documentation, sample queries, and business rules. When users ask questions in natural language, the system retrieves relevant schema context and generates SQL queries that are accurate to your particular database structure rather than relying on generic SQL knowledge.
The framework supports any SQL database and improves with use as more queries and corrections are added to the training data. Teams can deploy Vanna with various LLM backends including OpenAI, Anthropic, and local models, with vector storage options from ChromaDB to custom solutions. The open-source architecture means complete transparency in how queries are generated and full control over data privacy.
With 6,000+ GitHub stars and MIT licensing, Vanna AI provides zero vendor lock-in while delivering enterprise-grade accuracy. The Python package integrates easily into existing data workflows, Jupyter notebooks, and web applications. Teams use it to build internal analytics tools that let non-technical stakeholders query databases safely without writing SQL, reducing the reporting burden on engineering teams.