azure-search-openai-demo vs chat-with-your-data-solution-accelerator
Two of the top projects in RAG and search, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
azure-search-openai-demo
Azure RAG chat reference on AI Search and Azure OpenAI
chat-with-your-data-solution-accelerator
Azure RAG chat app that answers from your documents with citations
Verdict
azure-search-openai-demo ranks higher, 57 to 45. Of the 5 score parts we measure for both, azure-search-openai-demo leads on 3, chat-with-your-data-solution-accelerator on none and 2 are even.
- Adoptionazure-search-openai-demo leads by 26 points, 67 to 41: a wide gap.
- FreshnessEven at 100.
- Maintenanceazure-search-openai-demo leads by 22 points, 89 to 67: a wide gap.
- Easy to runEven at 0.
- Agent-readyazure-search-openai-demo leads by 30 points, 30 to 0: a wide gap.
Pick azure-search-openai-demo if you are
Teams on Azure wanting the vendor-maintained RAG starting point
- Optional Entra login with per-document access control and Cosmos DB chat history
- Evaluation, safety evaluation, monitoring and productionizing guides in docs/
Pick chat-with-your-data-solution-accelerator if you are
Azure teams building cited document Q&A on their own subscription
- 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
| What we compare | azure-search-openai-demo | chat-with-your-data-solution-accelerator |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 67, popular | 41, known |
| Freshness | 100, active | 100, active |
| Maintenance | 89, healthy | 67, fair |
| Easy to run | 0, hard | 0, hard |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 7.8k | 1.2k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Jul 2026 |
| Language | Python | Python |
| Docker | No | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
azure-search-openai-demo
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
- Python, azure-openai
- Needs azure-subscription, azd, azure-ai-search, azure-openai, azure-document-intelligence, azure-blob-storage
chat-with-your-data-solution-accelerator
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
- 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
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