chat-with-your-data-solution-accelerator

Azure RAG chat app that answers from your documents with citations

Stars: 1.2k
License: MIT
Last commit: Oct 2026
Last release: Jul 2026
Language: Python

Overview

Deploys a React frontend, a FastAPI backend and an Azure Functions ingestion worker to Azure Container Apps with `azd up`. Uploaded files and web pages are parsed, chunked and embedded, then answered with streamed responses and inline citations. Retrieval and chat history use either Azure AI Search with Cosmos DB or PostgreSQL with pgvector, chosen at deploy time.

Who it is for: Azure teams building cited document Q&A on their own subscription

Strengths

Choice of Azure AI Search + Cosmos DB or PostgreSQL + pgvector at deploy time
Managed identity and RBAC for all calls; no Key Vault or app secrets
Admin UI for ingesting documents and editing prompts without code changes
Two selectable orchestrators: Agent Framework or LangGraph

Weaknesses

Azure-only; requires Foundry, Document Intelligence, Storage, and Container Apps
Needs Contributor and RBAC rights on the subscription, plus model quota
README calls it a starting point, not production-ready
No Docker or compose files detected in the repo; local setup is in docs

What it needs

Python, Azure AI Foundry chat and embedding models
Needs Azure AI Foundry, Azure AI Search, Azure Cosmos DB, Azure Database for PostgreSQL, Azure Document Intelligence, Azure Storage, Azure Container Apps, Azure Functions, Azure Content Safety, Azure AI Speech
Docker
env example file

Also in RAG and search

See all 12

Also in RAG and search
Rank	Project	Score
1	azure-search-openai-demoAzure RAG chat reference on AI Search and Azure OpenAI7.8k stars, MIT	56 out of 100
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
#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/chat-with-your-data-solution-accelerator.svg)](https://archestack.github.io/best-of-vibe-coding/projects/chat-with-your-data-solution-accelerator/)

How badges work

Spot a wrong fact?

Reviews are written from each project's README and checked facts. Report it on GitHub ↗ (opens in a new tab)