Vanna AI
Open-source Python RAG framework for SQL database generation
About Vanna AI
Vanna AI is an open-source Python-based RAG (Retrieval-Augmented Generation) framework engineered to generate high-accuracy SQL queries from plain English questions. Unlike general-purpose chatbots that frequently hallucinate non-existent database columns and tables, Vanna trains specifically on your database schema, table DDL, documentation, and historical query logs. When a business user or developer asks a natural language question, Vanna retrieves relevant schema definitions and validated SQL examples to construct an accurate, executable SQL query for PostgreSQL, Snowflake, BigQuery, MySQL, SQLite, or SQL Server. With over 12,000 GitHub stars, Vanna allows organizations to deploy self-hosted text-to-SQL agents inside Slack, Streamlit dashboards, or internal REST APIs without exposing private database records to third parties.
Vanna operates on a simple two-phase architecture: training and inference. In the training phase, you feed Vanna your database DDL statements, business documentation, and validated SQL query pairs, which Vanna stores in a vector database (such as ChromaDB, Qdrant, or Pinecone). During inference, Vanna uses semantic vector search to find the closest matching schema definitions and verified SQL patterns, injecting them into the LLM context (supporting OpenAI, Anthropic, Mistral, and local Ollama models) to synthesize the target SQL query. Vanna can automatically execute the query against your database connection, generate interactive Plotly charts, and explain the SQL logic in plain English.
Install the open-source library using `pip install vanna`.
Connect Vanna to your LLM provider and vector database storage.
Train Vanna by providing DDL statements, schema documentation, and reference SQL queries.
Ask natural language questions such as 'What were our top 5 revenue products last quarter?'.
Vanna outputs optimized SQL, runs the query on your database, and visualizes the result.
Capabilities & Features
Common Use Cases
text-to-sql
bi-automation
database-chat
sql-generation
Frequently Asked Questions
Does Vanna send my actual database records to LLMs?
No, Vanna only processes metadata (table DDL, column names, documentation, and query patterns). Your actual row-level database records remain secure inside your private database.
Can I run Vanna AI completely offline?
Yes, Vanna can be configured with local vector databases (ChromaDB) and local LLMs (Ollama with Llama 3 or DeepSeek) for 100% air-gapped execution.
What databases are supported by Vanna?
Vanna supports all major SQL databases including PostgreSQL, Snowflake, Google BigQuery, MySQL, Microsoft SQL Server, SQLite, Oracle, and ClickHouse.
Free Plan
Open-source Python package 100% free with unlimited local execution; Hosted free tier offers 250 monthly queries.
Paid Plan
Paid cloud tiers start at $20/month for team collaboration, high-availability endpoints, and custom model fine-tuning.
Pros & Cons
Open-source Python framework with complete self-hosting and on-premise security
High text-to-SQL accuracy achieved via RAG schema and reference query training
Native connectors for PostgreSQL, Snowflake, BigQuery, MySQL, and SQLite
Automated visualization generation with Plotly charts and plain-English explanations
Compatible with local offline models via Ollama to ensure complete data sovereignty
Requires initial schema training and DDL definition for complex enterprise databases
Complex multi-table sub-queries require curated reference query pairs
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