Develop an RAG Pipeline Using the LLama Index

Understanding RAG (Retrieval Augmented Generation) and the Role of LLMs

Language Model Models (LLMs) stand out as some of the most efficient and potent natural language processing (NLP) models available today. Their capabilities have been showcased in various applications such as translation, essay writing, and general question-answering. However, when it comes to domain-specific question-answering, LLMs encounter challenges, particularly in terms of hallucinations.

In domain-specific question-answering applications, only a handful of documents typically contain relevant context for each query. To address this, there is a need for a unified system that seamlessly integrates document extraction, answer generation, and all the intermediate processes. This comprehensive approach is referred to as Retrieval Augmented Generation (RAG). RAG aims to enhance the efficiency and accuracy of question-answering systems by combining the strengths of document retrieval and answer generation processes.

Why Opt for RAG?

Learning new data with Large Language Models (LLMs) typically involves three approaches:

1. Training: Large neural networks are trained over trillions of tokens with billions of parameters to create expansive models like GPT-4. The training process incurs substantial costs, often reaching hundreds of millions of dollars. However, re-training such colossal models on new data becomes impractical and beyond the capacity of most.
2. Fine-tuning: Fine-tuning involves utilizing a pre-trained model as a starting point for training on new datasets. While powerful, this process is time-consuming and expensive. Fine-tuning is a viable option only when there’s a specific need, making it less practical for general applications.
3. Prompting: Prompting involves fitting new information within the context window of an LLM, making it respond to queries based on the provided prompt. While not as potent as knowledge learned during training or fine-tuning, prompting suffices for many real-life use cases, such as document question-answering.

Prompting for answers from text documents proves effective, but a common challenge arises when…