Build a RAG System
Retrieval-Augmented Generation (RAG) systems are an approach that combines the power of information retrieval with the generative capabilities of language models. This integration allows a language model to pull in relevant external information, thus greatly enhancing its ability to generate accurate and context-rich responses.
Why are RAG systems essential? In the realm of AI, especially with language models, the challenge has always been to provide contextually relevant and up-to-date information. Traditional language models are limited by the data they were trained on and can become quickly outdated or lack specific knowledge. RAG systems overcome this by using external information (like PDF documents, CSV files and/or databases) to augment the language model's knowledge base.
This part of the Bootcamp will not only introduce you to the concept and workings of RAG systems but also guide you through building one, a skill invaluable in the evolving landscape of AI and ML.
RAGs are a combination of two components: a retrieval component and a language model. In between these two components is a mechanism that integrates the retrieved information with the language model. This integration is designed so that the output capitalizes on both the comprehensive, nuanced understanding of the language model and the specific, relevant information provided by the retrieval component.
Here's a high-level overview of how it works:
Retrieval Component: At the heart of a RAG system is the retrieval mechanism. When a query or prompt is given to the system, this component searches through a vast database or a corpus of documents to find relevant information. This database can be anything from a structured knowledge base to a collection of text documents. The retrieval process is usually powered by algorithms that understand the semantics of the query and can find the most relevant documents.
Integration with Language Models: The retrieved information is then fed into a language model, such as ChatGPT. By integrating the retrieved information, the language model is provided with context or specific data that it might not have been trained on. This allows the language model to generate responses that are not only coherent and contextually appropriate but also factually accurate and up-to-date.
Generative Process: In the final step, the language model synthesizes the input from the retrieval component with its pre-trained knowledge to generate a response. The integration is designed so that the output capitalizes on both the comprehensive, nuanced understanding of the language model and the specific, relevant information provided by the retrieval component.
The key advantage of RAG systems is their ability to produce responses that are more informed, accurate, and context-specific than traditional language models. This makes them particularly useful in applications where the accuracy of information and the ability to incorporate up-to-date knowledge are crucial, such as in question-answering systems, content creation, and various AI-assisted research tools.
In essence, RAG systems are a way to enrich your prompt text with context-specific information from a database or corpus of documents. In the end, your LLM takes in the prompt text and doesn't know anything about the rest of the system.
Our data is a list of bodybuilding exercises. Each exercise has a name, a list of muscle groups it targets, and a description of how to perform it: