Vanna AI: ChatGPT for Your SQL Database is Here!

Want to learn how to query your SQL database with the power of ChatGPT? This article explores Vanna AI and how it works for SQL database.

1000+ Pre-built AI Apps for Any Use Case

Vanna AI: ChatGPT for Your SQL Database is Here!

Start for free

Navigating the complexities of SQL databases typically demands a deep understanding of structured query language and database schemas. However, Vanna AI disrupts this norm by introducing an intuitive way to generate SQL queries using natural language. This open-source Python framework capitalizes on the Retrieval-Augmented Generation (RAG) technique to simplify database interactions, making it a potent tool for experts and novices alike.

Understanding Vanna AI

At its core, Vanna AI is a bridge between conversational language and the structured precision of SQL. It employs RAGโ€”a method that combines the retrieval of relevant documents with the generative capabilities of language modelsโ€”to produce accurate SQL queries from user questions. By training a model on your specific database schema and jargon, Vanna tailors its responses to your unique data environment.

How Vanna AI works for SQL database (source: Vanna AI GitHub)

Why Vanna AI?

The practicality of Vanna is twofold. Firstly, it eliminates the steep learning curve associated with SQL, allowing users to interact with their databases using simple questions. Secondly, it's designed with the future in mind; as new and more powerful language models emerge, they can be integrated with minimal effort, ensuring your database querying capabilities remain cutting-edge.

Getting Started with Vanna AI

Vanna AI is a powerful tool for transforming natural language questions into SQL queries, offering a novel approach to database interaction. This guide provides an in-depth look at how to install, use, and extend Vanna AI, following the guidelines outlined in its GitHub documentation.

Step 1: Installing Vanna AI

The first step in using Vanna AI is to install it. This can be done easily through Python's package manager, pip. Open your command line interface and execute the following command:

pip install vanna

This command downloads and installs the Vanna AI package and its dependencies, preparing your environment for Vanna AI integration.

Step 2: Training Your Model

Once Vanna AI is installed, the next step is to train your model. Training involves preparing the model to understand and process your specific database structure and queries. This typically includes:

  • Defining your database schema using Data Definition Language (DDL) statements.
  • Documenting each part of your schema with descriptive strings to aid Vanna AI in understanding your data structure.
  • Incorporating existing SQL queries to provide Vanna AI with examples of the types of queries it will generate.

The training process is crucial as it tailors Vanna AI to your database, ensuring accurate and efficient query generation.

Step 3: Generating SQL with Natural Language

After training, Vanna AI is ready to generate SQL queries from natural language questions. This is where Vanna AI shines, simplifying database queries to mere conversation. Hereโ€™s how to use it:

  • Import Vanna AI in your Python environment.
  • Initialize the Vanna client.
  • Ask a question in natural language.
  • Vanna AI processes this question and returns an SQL query.


import vanna

vanna_client = vanna.Client()
sql_query = vanna_client.ask("How many sales were made last quarter?")

This process transforms the way you interact with databases, making it more intuitive and user-friendly.

Step 4: Integrating Vanna AI with User Interfaces

Vanna AI's versatility allows it to be integrated into various environments:

  • Jupyter Notebooks: Ideal for data scientists and researchers, Vanna AI can be used within Jupyter Notebooks for quick data queries and analysis.
  • Slackbots: For team environments, integrate Vanna AI into a Slackbot, allowing team members to query databases directly from Slack.
  • Flask Web Applications: Embed Vanna AI into Flask web applications, enabling end-users to interact with your databases through a web interface.

This flexibility ensures that Vanna AI can be adapted to suit various use cases and environments.

Step 5: Extending Vanna AI

For more advanced users, Vanna AI offers extension capabilities. You can extend the VannaBase class to integrate proprietary Language Learning Models (LLMs) or vector databases. This allows for customization and enhancement of Vanna AI's capabilities to suit specific needs or to integrate with specialized systems.

Example of extending Vanna AI:

from vanna import VannaBase

class CustomVanna(VannaBase):
    def custom_method(self):
        # Custom implementation for a specific LLM or vector database

In conclusion, Vanna AI is a robust and flexible tool for converting natural language questions into SQL queries, making database interaction more accessible and efficient. By following these steps, you can successfully install, use, and extend Vanna AI, leveraging its capabilities to enhance your data management and analysis processes.

For detailed and step-by-step guidance on using Vanna AI, including setup, training, querying, and customization, please refer to the official Vanna AI GitHub repository at The repository provides comprehensive documentation, code examples, and the necessary resources to help you get started and effectively use Vanna AI for generating SQL queries from natural languages:
GitHub - vanna-ai/vanna: ๐Ÿค– Chat with your SQL database ๐Ÿ“Š. Accurate Text-to-SQL Generation via LLMs using RAG ๐Ÿ”„.
๐Ÿค– Chat with your SQL database ๐Ÿ“Š. Accurate Text-to-SQL Generation via LLMs using RAG ๐Ÿ”„. - GitHub - vanna-ai/vanna: ๐Ÿค– Chat with your SQL database ๐Ÿ“Š. Accurate Text-to-SQL Generation via LLMs using RAG ๐Ÿ”„.

Practical Use Cases of Vanna AI

Vanna's real-world applications are vast and varied. In a financial context, Vanna can enable analysts to retrieve complex financial reports through simple questions, bypassing the need to write detailed SQL queries. In customer relationship management, support staff could use Vanna to quickly pull up customer information, order histories, or interaction logs without ever touching a database client.

The practicality of Vanna goes beyond just its query capabilities. With its secure and private nature, Vanna ensures that the sensitive content of the database remains confidential. SQL execution occurs within the local environment, meaning sensitive data never leaves the organization's secure perimeter.

Pricing for Vanna AI

Vanna AI offers a versatile pricing structure tailored to various needs. Below is a breakdown of its pricing tiers:

Tier Cost Features
Open Source Bring Your Own LLM (BYO) Integrate with any LLM, Local metadata storage using ChromaDB or other vector databases, Use via self-hosted Streamlit, Flask app, Slackbot, Customize as desired
Free Tier $0 / month Rate-limited access to LLM, Free use of hosted metadata storage, GPT 3.5 Foundational Model
Paid Tier $20 / user/month No rate limits, Access to GPT-4 with fallback to other LLMs, Hosted Slack Bot, Email support

The Open Source tier is ideal for those who prefer a customizable, self-hosted solution. The Free Tier offers basic services suitable for smaller-scale or trial use, and the Paid Tier caters to businesses needing more robust features and support.

But wait, if you are looking to build up your own AI Apps, you can also check up Anakin AI!
Aanakin AI gives you to full freedom to build highly customized AI apps workflow for turbocharge your productivity, with No Code. ๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡


Vanna AI stands at the forefront of innovation in data querying, bridging the gap between complex SQL databases and the simplicity of natural language. By combining the power of RAG with a user-centric design, Vanna democratizes access to data, enabling users to leverage their databases' full potential with unprecedented ease. As data continues to underpin the modern business landscape, tools like Vanna AI will play a crucial role in empowering teams to make data-driven decisions without the bottleneck of technical SQL expertise.