Vanna 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.


pip install 'vanna[qdrant]'


You can set up a Vanna agent using Qdrant as your vector store and any of the LLMs supported by Vanna.

We’ll use OpenAI for demonstration.

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-...,


Once a Vanna agent is instantiated, you can connect it to any SQL database of your choosing.

For example, Postgres.

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.

# You can add DDL statements that specify table names, column names, types, and potentially relationships
        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. 

# 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.


Further reading