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Vanna.AI icon

Vanna.AI

Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality.

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Cost / License

  • Freemium (Subscription)
  • Open Source (MIT)

Platforms

  • Software as a Service (SaaS)
  • Self-Hosted
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  1.  AI-Powered
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Vanna.AI information

  • Developed by

    US flagVanna.AI
  • Licensing

    Open Source (MIT) and Freemium product.
  • Pricing

    Subscription that costs $20 per month + free version with limited functionality.
  • Written in

  • Alternatives

    6 alternatives listed
  • Supported Languages

    • English

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DevelopmentAI Tools & Services

GitHub repository

  •  22,096 Stars
  •  2,116 Forks
  •  257 Open Issues
  •   Updated  
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What is Vanna.AI?

Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality.

Vanna works in two easy steps - train a RAG "model" on your data, and then ask questions which will return SQL queries that can be set up to automatically run on your database.

If you don't know what RAG is, don't worry -- you don't need to know how this works under the hood to use it. You just need to know that you "train" a model, which stores some metadata and then use it to "ask" questions.

  • Open-Source: The Vanna Python package and the various frontend integrations are all open-source. You can run Vanna on your own infrastructure.
  • High accuracy on complex datasets: Vanna’s capabilities are tied to the training data you give it. More training data means better accuracy for large and complex datasets.
  • Designed for security: Your database contents are never sent to the LLM. The metadata storage layer only sees schemas, documentation, and queries.
  • Self learning: As you use Vanna more, your model continuously improves as we augment your training data.
  • Supports many databases: We have out-of-the-box support Snowflake , BigQuery, Postgres. You can easily make a connector for any database.
  • Choose your front end: Start in a Jupyter Notebook. Expose to business users via Slackbot, web app, Streamlit app, any other frontend. Even integrate in your web app for customers.

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