Create an agentic RAG application for advanced knowledge discovery with LlamaIndex, and Mistral in Amazon Bedrock

Agentic Retrieval Augmented Generation (RAG) applications represent an advanced approach in AI that integrates foundation models (FMs) with external knowledge retrieval and autonomous agent capabilities. These systems dynamically access and process information, break down complex tasks, use external tools, apply reasoning, and adapt to various contexts. They go beyond simple question answering by performing multi-step processes, making decisions, and generating complex outputs.

In this post, we demonstrate an example of building an agentic RAG application using the LlamaIndex framework. LlamaIndex is a framework that connects FMs with external data sources. It helps ingest, structure, and retrieve information from databases, APIs, PDFs, and more, enabling the agent and RAG for AI applications.

This application serves as a research tool, using the Mistral Large 2 FM on Amazon Bedrock generate responses for the agent flow. The example application interacts with well-known websites, such as Arxiv, GitHub, TechCrunch, and DuckDuckGo, and can access ...