---
title: Vanna.AI
weight: 3000
---

# Vanna.AI

[Vanna](https://vanna.ai/) is a Python package that uses retrieval augmentation to help you generate accurate SQL queries for your database using LLMs.

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.

Qdrant is available as a support vector store for ingesting and retrieving your RAG data.

## Installation

```console
pip install 'vanna[qdrant]'
```

## Setup

You can set up a Vanna agent using Qdrant as your vector store and any of the [LLMs supported by Vanna](https://vanna.ai/docs/postgres-openai-vanna-vannadb/).

We'll use OpenAI for demonstration.

```python
from vanna.openai import OpenAI_Chat
from vanna.qdrant import Qdrant_VectorStore
from qdrant_client import QdrantClient

class MyVanna(Qdrant, OpenAI_Chat):
    def __init__(self, config=None):
        Qdrant_VectorStore.__init__(self, config=config)
        OpenAI_Chat.__init__(self, config=config)

vn = MyVanna(config={
    'client': QdrantClient(...),
    'api_key': sk-...,
    'model': gpt-4-...,
})
```

## Usage

Once a Vanna agent is instantiated, you can connect it to [any SQL database](https://vanna.ai/docs/FAQ/#can-i-use-this-with-my-sql-database) of your choosing.

For example, Postgres.

```python
vn.connect_to_postgres(host='my-host', dbname='my-dbname', user='my-user', password='my-password', port='my-port')
```

You can now train and begin querying your database with SQL.

```python
# You can add DDL statements that specify table names, column names, types, and potentially relationships
vn.train(ddl="""
    CREATE TABLE IF NOT EXISTS my-table (
        id INT PRIMARY KEY,
        name VARCHAR(100),
        age INT
    )
""")

# You can add documentation about your business terminology or definitions.
vn.train(documentation="Our business defines OTIF score as the percentage of orders that are delivered on time and in full")

# You can also add SQL queries to your training data. This is useful if you have some queries already laying around.
vn.train(sql="SELECT * FROM my-table WHERE name = 'John Doe'")

# You can remove training data if there's obsolete/incorrect information. 
vn.remove_training_data(id='1-ddl')

# Whenever you ask a new question, Vanna will retrieve 10 most relevant pieces of training data and use it as part of the LLM prompt to generate the SQL.

vn.ask(question="<YOUR_QUESTION>")
```

## Further reading

- [Getting started with Vanna.AI](https://vanna.ai/docs/app/)
- [Vanna.AI documentation](https://vanna.ai/docs/)
- [Source Code](https://github.com/vanna-ai/vanna/tree/main/src/vanna/qdrant)
