Q: Is Vanna AI truly open-source?

A: Yes, the core Vanna AI library is open-source and its code is freely available on GitHub for inspection and contribution.

Q: What databases can Vanna connect to?

A: Vanna can connect to any database that has a Python connector, including popular ones like PostgreSQL, MySQL, Snowflake, and BigQuery.

Q: How does Vanna ensure data privacy?

A: Since it's self-hostable, you retain full control over your data. If using the cloud service, Vanna adheres to strict data security protocols.

Q: Can Vanna generate complex SQL queries with joins and aggregations?

A: Yes, with proper training on your schema and example queries, Vanna can generate complex SQL queries involving multiple tables and aggregate functions.

Vanna AI

Vanna AI is an open-source Python library that trains AI models to write SQL queries based on natural language questions, tailored to your specific database schema.

Price: Freemium

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Description

Vanna AI empowers data professionals and developers to build custom natural language to SQL interfaces for their specific datasets. It's a Python library that allows users to train a large language model (LLM) on their database schema, DDL statements, documentation, and even previous SQL queries. Once trained, Vanna can generate highly accurate and context-aware SQL queries from plain English questions, eliminating the common pitfalls of generic text-to-SQL models. Vanna is designed for organizations that require precise, secure, and self-hosted solutions for data access, making it ideal for data teams, software engineers, and businesses looking to embed natural language querying into their applications or internal tools. It stands out by offering an open-source, extensible framework that prioritizes data privacy and schema-specific accuracy.

How to Use

1.Install the Vanna Python library using pip (`pip install vanna`) within your development environment.

2.Connect Vanna to your database and provide it with your DDL statements and schema definitions to understand your data structure.

3.Train Vanna by feeding it example SQL queries and their corresponding natural language questions, along with any relevant documentation.

4.Ask Vanna a question in natural language using the `vn.ask("Your question here")` method.

5.Vanna generates the SQL query, which you can then review and execute against your database.

6.Optionally, integrate Vanna's capabilities into your custom applications or dashboards for broader access.

Use Cases

Building Custom NL-to-SQL InterfacesInternal Data PortalsBusiness Intelligence ToolsData DemocratizationSQL Query Generation for DevelopersData Governance

Pros & Cons

Pros

Open-source and highly customizable Python library.
Trains on your specific database schema for high accuracy.
Supports self-hosting, ensuring data privacy and security.
Generates context-aware and optimized SQL queries.
Integrates with various LLMs and databases for flexibility.

Cons

Requires programming knowledge (Python) to set up and use effectively.
Initial training requires effort and good example queries for optimal performance.
Performance depends on the quality of the training data and the chosen LLM.

Pricing

https://vanna.ai/pricing

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