Vanna AI

Vanna AI is an open-source Framework built around a simple IDEa: let people ask questions about their data in plain language and get back SQL they can ACTually run. Instead of locking users into a single product, it is designed as a layer that connects large language models to a specific Database so the generated queries reflect the tables, columns, and terminology that organization actually uses. It is most commonly associated with Snowflake, though the project's design allows other SQL databases to be wired in.

What it does

At its core, Vanna stores context about a database — schema DeFinitions, Sample queries, and documentation — and retrieves the most relevant pieces when a user submits a question. That context is passed to a language model, which drafts a SQL statement. Because the model is grounded in real schema and example queries, results tend to be more accurate than asking a generic chatbot to guess at table names. The project is distributed as a Python library that developers embed into their own APPlications, Notebooks, or internal tools.

Key capabilities

• Natural-language to SQL: Converts questions into queries against connected databases, with snowflake as a common target.
• training on your own context: Users can add schema Metadata (DDL), documented question-and-SQL pairs, and reference documentation to improve accuracy over time.
• Vector-based retrieval: Relevant examples are fetched semantically so the model sees the most useful guidance for each question.
• Self-learning loop: Corrected or approved queries can be fed back into the system, gradually improving future ouTPUt.
• Python-first integration: Fits into existing data workflows, notebooks, and custom chat interfaces rather than requiring a separate closed platform.

Who it is for

Vanna AI suits data engineers, analytics engineers, and developers who want to add a text-to-SQL capability to internal tooling without building retrieval and Prompting logic from scratch. It is also useful for analysts comfortable with Python who want faster drafting of queries. Teams hoping for a fully managed, CLIck-and-go business intelligence product may find it more technical than expected, since setup Generally involves writing code and configuring a model provider.

How to get started

1. Install the Python package in your environment.
2. Connect a database and supply schema Information.
3. Add example question-and-SQL pairs and any relevant documentation.
4. Configure access to a supported language model provider.
5. Ask questions through a notebook or a small custom interface, then review and refine generated SQL.

Pricing and access caveat

The framework itself has been available as open-source software, and its site has offered hosted or paid options as well. Exact plans, limits, and model-provider costs may change, so confirm current terms before adopting it. While the tool is generally reachable online, availability of specific services and any account requirements may vary by region.