GPT-RAG - AI RAG and Search Tool

Overview

GPT-RAG is an open-source, enterprise-grade Retrieval-Augmented Generation (RAG) solution accelerator published by Microsoft/Azure. According to the GitHub repository, it combines Azure Cognitive Search with Azure OpenAI services to power ChatGPT-style conversational experiences and high-quality Q&A over private document collections. The project provides a modular architecture that separates data ingestion, indexing, orchestration, and presentation, so teams can swap components (for example, different orchestrators or front-end templates) to meet enterprise requirements. The accelerator includes orchestrator options—documented support for Semantic Kernel function-based flows or AutoGen-driven agentic workflows—plus customizable front-end interfaces and deployment patterns tailored to secure, enterprise environments. GPT-RAG is designed to help engineering teams accelerate production-ready RAG deployments by providing sample ingestion pipelines, orchestration templates, and reference UI components while leaving cloud security, telemetry, and Azure-native integrations under customer control. According to the repository, the project emphasizes modularity, extensibility, and alignment with Azure platform services for scalable semantic search and response generation.

Installation

Install via docker:

git clone https://github.com/Azure/gpt-rag.git

cd gpt-rag

cp .env.example .env  # update Azure keys and endpoints in .env

docker compose up --build -d

Key Features

• Azure Cognitive Search integration for scalable semantic and vector-backed document search
• Azure OpenAI integration for embeddings and generation using deployed OpenAI models
• Modular data-ingestion pipeline to prepare and index enterprise content for RAG
• Orchestrator options: Semantic Kernel function flows or AutoGen-driven agentic workflows
• Customizable front-end templates (chat and Q&A) for rapid UI deployment in enterprises

Community

GPT-RAG is hosted on GitHub under the Azure organization and is positioned as a community-extendable accelerator. According to the repository, contributors can file issues, submit pull requests, and use provided samples and templates to adapt the accelerator to their environment. The project is intended for teams building enterprise RAG systems on Azure; community engagement channels are primarily GitHub issues, PRs, and the repository README with usage guidance and deployment examples.

Key Information

• Category: RAG and Search
• Type: AI RAG and Search Tool