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+---
+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.
+```python
+vn.ask(question=...)
+```
+
+## Further reading
+
+- [Getting started with Vanna.AI](https://vanna.ai/docs/app/)
+- [Vanna.AI documentation](https://vanna.ai/docs/)
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