Advanced Retrieval Augmented Generation (RAG) Apps with LlamaIndex

Laurie Voss, VP of Developer Relations at LlamaIndex, discussed building advanced Retrieval Augmented Generation (RAG) apps with LlamaIndex in a recent Unstructured Data Meetup. RAG is designed to overcome the limitations of Language Models (LLMs) by assisting them with retrieval capabilities. The main drawback of LLMs is their limited context windows, which can only handle part of an organization's data simultaneously. LlamaIndex is an open-source framework that connects your data to LLMs and simplifies the creation of RAG applications, allowing developers to build functional RAG systems with minimal code. It provides advanced data ingestion and querying features for RAG applications, such as Data Connectors, PDF Parsing, Embedding Models, Vector Stores, Sub-Question Query Engine, Small to Big Retrieval, Metadata Filtering, Hybrid Search, and Agents.