In this video, Trevor LaViale, ML Solutions Engineer at Arize, introduces Agentic RAG and its applications for enhancing AI-powered retrieval systems. Learn how Agentic RAG differs from standard RAG by incorporating AI agents to intelligently manage queries across multiple data sources. Whether you’re a developer working on complex applications or someone curious about improving AI workflows, this tutorial offers a clear walkthrough of the concepts and practical implementation.
In this example, we build an Agentic RAG system using LlamaIndex with a Chroma vector database, and a Postgres database. We use Phoenix for observability, tracing queries, and debugging retrieval results.
Get Started with Phoenix: https://phoenix.arize.com/
