Vanna.AI
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.
Installation
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.
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-...,
})
Usage
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
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>")