Overview of Retrieval Augmented Generation and Its Components

In this video, LlamaIndex CEO Jerry Liu provides an overview of RAG (Retrieval Augmented Generative) and its components.

This video is a part of the GenAI360 Foundational Model Certification by Activeloop, TowardsAI, and Intel, brought to you in collaboration with LlamaIndex.

Jerry discusses the enterprise use cases discovered through generative AI, such as document processing and extraction, asking questions over a knowledge base, and engaging in conversations with context augmented agents. We focus on the retrieval augmentation use case, which involves indexing private data and being able to ask questions or ground model outputs in a relevant context. We also briefly touch on fine-tuning, another paradigm for improving the model's capabilities.

Jerry introduces LlamaIndex, a data framework for LLM (Language Model) applications. It provides data management and query engine functionalities for your LLM application. The components include data ingestion, data structuring, and data querying. Data ingestion connects existing data sources, processes, stores, and indexes the data into a storage system. On the query side, we offer retrieval algorithms to build simple to advanced RAG and agent interfaces over the data.

He then dives deep into the basic stack of RAG and its components. The first component is data ingestion, where an input document is split into text trunks and embeddings are generated for each trunk. The trunks, along with metadata, are stored in Activeloop Deep Lake. The next step is retrieval, where relevant context is fetched from the vector database based on a user query. This context is then used as input prompt for the LLM response.

Timestamps
00:00 Introduction to Generative AI and RAG
00:52 Understanding Retrieval Augmentation and Fine Tuning
01:48 Introduction to LlamaIndex: A Data Framework for LLM Applications
02:15 Data Ingestion and Structuring in Llama Index
02:40 Deep Dive into RAG: Data Ingestion and Querying