This folder contains examples of how to achieve specific tasks using the LlamaIndex framework. LlamaIndex, formerly known as GPT Index, is a framework designed to integrate with various data sources and create indices optimized for efficient querying. It supports different types of indices, such as keyword and semantic indices, allowing for organized and rapid data retrieval. By leveraging the power of large language models like GPT-3 or GPT-4, LlamaIndex enables complex searches beyond simple keyword matching, making it suitable for extensive datasets.
- Retrieval-Augmented Generation: This example demonstrates how to use the Retrieval-Augmented Generation (RAG) model to generate answers to questions based on a given context. The RAG model combines a retriever and a generator to provide more accurate and relevant answers.