AutoGluon-RAG#
AutoGluon-RAG: Retrieval-Augmented Generation in 3 Lines of Code!
What is RAG?#
Retrieval-Augmented Generation, commonly known as RAG, is a widely used technique to improve the generation capabilities of language models such as ChatGPT, Llama, Claude, etc.
RAG makes use of preprocessed content as a “knowledge-base” to add relevant content to a user prompt to make sure that the language model has sufficient information to produce responses.
For example, say you have documentation for a new product you have created. You have created a website using these documents and would like to create a chatbot with the language capabilities of ChatGPT. ChatGPT knows nothing about your website since your product was not part of its training data. By using RAG with your website as a knowledge-base, when a user asks a question to your chatbot, RAG will obtain the relevant information from the website based on the user prompt and provide it to the language model as additional context.
What is AutoGluon-RAG?#
AutoGluon-RAG is a framework designed to simplify the creation of RAG pipelines, making it easier to adapt large language models (LLMs) for domain-specific tasks. Traditionally, building RAG pipelines involves dealing with multiple complex components like retrievers, generators, and vector databases.
AutoGluon-RAG streamlines this process, allowing users to build and deploy customized RAG pipelines with just a few lines of code. By abstracting the technical complexities, it enables users to focus on their specific needs, making it accessible to both beginners and experienced practitioners alike.
Goal#
In alignment with the AutoGluon team’s mission to meet evolving user needs and broaden its user base, the team aims to introduce a new package that simplifies the creation and deployment of end-to-end Retrieval-Augmented Generation (RAG) pipelines. This package will allow users to build and deploy RAG pipelines with minimal coding effort, leveraging user-provided data or documents. Following the AutoML (Automated Machine Learning) philosophy of delivering solutions with minimal complexity, the goal is to enable users to achieve robust RAG pipelines in just three lines of code.
