Enhancing Knowledge Retrieval with Context-Augmented Agents
Hello everyone! Today, we're diving into the fascinating world of context-augmented knowledge assistance. You've probably heard about retrieval-augmented generation (RAG) and its impressive capabilities. However, RAG by itself is not enough for sophisticated knowledge retrieval. In this blog post, we'll discuss the role of agents, their key components, and how to build them using a framework called Llama Index. We’ll also explore an example of reading a PDF using Llama Parse and leveraging an LLM for enhanced querying. What is Llama Index? Llama Index is a framework designed for building large language model (LLM) enabled applications over your data. It supports both Python and TypeScript and connects to your data wherever it resides. Llama Index helps you parse, index, store, and query your data, enabling you to build sophisticated software for advanced querying and retrieval. The Basics of Retrieval-Augmented Generation (RAG) RAG involves several key steps: Data Ingestion...