Build a Multiagent RAG Solution on OCI

Introduction

Retrieval-augmented generation (RAG) can be useful for straightforward queries. But what if queries are complex, requiring multistep reasoning and decision-making? That’s when, for example, a support chatbot that could troubleshoot issues, not just retrieve FAQs, would be more useful.

In this solution, we’ll set up a multiagent RAG pipeline and deploy it on Oracle Cloud Infrastructure (OCI) to intelligently respond to a query. The large language model (LLM) agents plan, research, and reason the AI’s response; this chain-of-thought (CoT) process emulates human problem-solving. A Gradio interface orchestrates data processing—multiple data sources are uploaded, ingested, and stored as vectors using open source tools. Gradio also provides the chat interface to input a natural language query.

With CoT visualization in Gradio, you’ll see the steps and decisions taken by each agent to deliver the final, synthesized response. This solution provides an easy-to-follow example of how agentic AI can enhance the reasoning capabilities of both local and cloud-based models.

Demo

Prerequisites and setup

1. Oracle Cloud account—sign-up page
2. OCI Generative AI—documentation
3. OCI Generative AI Agents—documentation
4. Oracle Database 23ai—documentation
5. Docling—documentation
6. Gradio—documentation
7. Trafilatura—documentation

Getting started

• Detailed steps and sample code on GitHub