azure-search-openai-demo

Azure RAG chat reference on AI Search and Azure OpenAI

Stars: 7.8k
License: MIT
Last commit: Oct 2026
Last release: Oct 2026
Language: Python

Overview

The canonical Azure RAG sample: a Python (Quart) backend and React frontend answering multi-turn questions over your documents with citations and a visible thought process, using Azure AI Search for retrieval and Azure OpenAI for generation. azd up provisions Container Apps, AI Search, Document Intelligence and Blob storage, with optional Cosmos DB chat history, Entra login with document ACLs, multimodal and speech. For teams already on Azure.

Who it is for: Teams on Azure wanting the vendor-maintained RAG starting point

Strengths

Optional Entra login with per-document access control and Cosmos DB chat history
Evaluation, safety evaluation, monitoring and productionizing guides in docs/
Multimodal, speech and agentic retrieval are switchable features
Commits within the last week; tests included

Weaknesses

Cannot run locally until azd up has provisioned Azure resources
Provisions paid services by default (AI Search, Document Intelligence); run azd down
Azure OpenAI only; no other provider path
README itself says not production-ready without extra security work

What it needs

Python, azure-openai
Needs azure-subscription, azd, azure-ai-search, azure-openai, azure-document-intelligence, azure-blob-storage

Also in RAG and search

See all 12

Also in RAG and search
Rank	Project	Score
2	rag-postgres-openai-pythonRAG over Postgres table rows with hybrid search and SQL filters505 stars, MIT	51 out of 100
3	llm-appPathway RAG pipeline templates that re-index live data sources Live demo ↗ (opens in a new tab)58.8k stars, MIT	48 out of 100
#4	chat-langchainLangChain docs assistant as a Managed Deep Agent with Next.js UI6.5k stars, MIT	45 out of 100
#5	chat-with-your-data-solution-acceleratorAzure RAG chat app that answers from your documents with citations1.2k stars, MIT	44 out of 100
#6	llm-answer-enginePerplexity-style Next.js answer engine over Brave search results5k stars, MIT	41 out of 100

How to choose

Match the vector store to what you already run (pgvector, Azure AI Search, a hosted index).
Look at how ingestion works; a good chat UI with no re-indexing story will stall in production.
Answer engines need search API keys; count those costs before picking one.

For maintainers

Show this rank in your README. It updates twice a day.

[![Archestack](https://archestack.github.io/badge/azure-samples/azure-search-openai-demo.svg)](https://archestack.github.io/best-of-vibe-coding/projects/azure-search-openai-demo/)

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