What is Retrieval-Augmented Generation?

Retrieval Augmented Generation (RAG) represents a cutting-edge approach in the field of AI. It is a hybrid model that combines the strengths of traditional language models with the dynamic capabilities of information retrieval systems. The primary objective of RAG is to enhance the ability of AI to generate responses that are not only contextually relevant but also rich in factual accuracy and depth.

Overview

RAG operates by integrating two core components of AI: a generative model and a retrieval system. This integration allows the AI to access and incorporate external information sources when generating responses. The process typically involves first retrieving relevant documents or data and then using this information to inform the generation of text.

How can you use it to help generate SQL?

You can give the RAG system information about your database and then ask it to generate SQL queries for you.

Some of that information could be:

1. Table metadata including column names and types
2. Sample SQL queries
3. Any text-based documentation you have about your database, business, or industry

How does it work?

When you ask a question, the RAG system will first retrieve relevant documents from your database. It will then use these documents to generate a response.

A question is turned into an "embedding" which is a vector of numbers that represents the question. The embedding is then used to retrieve relevant information about the database. This information is then passed to the LLM, which generates new SQL queries based on what it knows about the database.