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83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
---
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title: Vanna.AI
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---
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# Vanna.AI
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[Vanna](https://vanna.ai/) is a Python package that uses retrieval augmentation to help you generate accurate SQL queries for your database using LLMs.
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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.
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Qdrant is available as a support vector store for ingesting and retrieving your RAG data.
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## Installation
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```console
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pip install 'vanna[qdrant]'
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```
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## Setup
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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/).
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We'll use OpenAI for demonstration.
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```python
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from vanna.openai import OpenAI_Chat
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from vanna.qdrant import Qdrant_VectorStore
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from qdrant_client import QdrantClient
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class MyVanna(Qdrant, OpenAI_Chat):
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def __init__(self, config=None):
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Qdrant_VectorStore.__init__(self, config=config)
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OpenAI_Chat.__init__(self, config=config)
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vn = MyVanna(config={
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'client': QdrantClient(...),
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'api_key': sk-...,
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'model': gpt-4-...,
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})
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```
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## Usage
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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.
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For example, Postgres.
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```python
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vn.connect_to_postgres(host='my-host', dbname='my-dbname', user='my-user', password='my-password', port='my-port')
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```
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You can now train and begin querying your database with SQL.
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```python
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# You can add DDL statements that specify table names, column names, types, and potentially relationships
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vn.train(ddl="""
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CREATE TABLE IF NOT EXISTS my-table (
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id INT PRIMARY KEY,
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name VARCHAR(100),
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age INT
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)
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""")
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# You can add documentation about your business terminology or definitions.
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vn.train(documentation="Our business defines OTIF score as the percentage of orders that are delivered on time and in full")
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# You can also add SQL queries to your training data. This is useful if you have some queries already laying around.
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vn.train(sql="SELECT * FROM my-table WHERE name = 'John Doe'")
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# You can remove training data if there's obsolete/incorrect information.
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vn.remove_training_data(id='1-ddl')
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# 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.
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vn.ask(question="<YOUR_QUESTION>")
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```
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## Further reading
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- [Getting started with Vanna.AI](https://vanna.ai/docs/app/)
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- [Vanna.AI documentation](https://vanna.ai/docs/)
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- [Source Code](https://github.com/vanna-ai/vanna/tree/main/src/vanna/qdrant)
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