No More Text2SQL, It's Now RAG2SQL!
Text2SQL was awfully popular last year. I talked with a vendor looking to buy, and also developed an in-house solution.
The concept holds promise, the need is there, but the execution falls short.
Our minds always race ahead of our hands, don't they?
One of the most significant challenges we encountered was the lack of rich context and metadata necessary for a highly productive Text2SQL system.
🚀Why RAG2SQL?
Today, we're introducing another framework - Vanna AI - an open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation, execution, and visualization.
Vanna operates in two simple steps - training a RAG "model" on your data, and then posing questions that returns SQL queries. These queries can be configured to run automatically on your database.
In essence, any text-to-SQL endeavor must tackle the challenge of integrating domain knowledge about the database. Vanna excels by incorporating contextual data (metadata, DDL, queries etc.), which is "trained" and embedded for use.
Furthermore, it enhances accuracy by incorporating a "classifier" layer - sending the most relevant context, including past queries, to the task, thus significantly improving result accuracy.
Curious to delve deeper into this?
Join Professor Mehdi as he explains the concept of RAG2SQL and leads us through a step-by-step tutorial using a SQLite database in the video below!👇
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🛠️✨ Happy practicing and happy building! 🚀🌟
Thanks for reading our newsletter. You can follow us here: Angelina Linkedin or Twitter and Mehdi Linkedin or Twitter.
Source of images/quotes:
DataDM: https://open.substack.com/pub/mlnotes/p/datadm-your-private-data-visualization?r=164sm1&utm_campaign=post&utm_medium=web
AI SQL Accuracy: Testing different LLMs + context strategies to maximize SQL generation accuracy: https://vanna.ai/blog/ai-sql-accuracy.html
🔨 Implementation: Vanna AI: https://github.com/vanna-ai/vanna
📚 Also if you'd like to learn more about RAG systems, check out our book on the RAG system:
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