azure-search-openai-demo: a reference RAG chat application for querying private documents with citations and multimodal support

What it solves

This project provides a complete reference implementation for building a ChatGPT-like chat interface that can answer questions based on a user's own private documents, rather than relying solely on the general knowledge of a large language model.

How it works

It implements the Retrieval Augmented Generation (RAG) pattern. The system uses Azure AI Search to index and retrieve relevant document snippets from a provided dataset, which are then passed to an Azure OpenAI GPT model to generate a grounded, cited answer.

Who it’s for

Developers and organizations using the Azure ecosystem who want to deploy a production-ready sample of a RAG-based AI assistant for internal policies, benefits, or domain-specific knowledge bases.

Highlights

• Multi-turn Chat: Supports ongoing conversations with context.
• Citations: Renders the specific sources and the thought process used to generate each answer.
• Multimodal Support: Optional integration with vision models to reason over image-heavy documents.
• Flexible Ingestion: Supports various document formats and cloud-based data ingestion.
• Integrated Settings: UI-based controls to tweak prompts and model behavior for experimentation.