azure-search-openai-demo vs rag-postgres-openai-python
Two of the top rag and search, side by side: score, setup, license, activity and what each review found.
azure-search-openai-demo
Azure RAG chat reference on AI Search and Azure OpenAI
rag-postgres-openai-python
RAG over Postgres table rows with hybrid search and SQL filters
| What we compare | azure-search-openai-demo | rag-postgres-openai-python |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 73, popular | 35, known |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 49, patchy |
| Easy to run | 0, hard | 33, some setup |
| Agent-ready | 30, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 7.8k | 505 |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | Python |
| Docker | No | No |
| 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
rag-postgres-openai-python
A FastAPI backend and React frontend that answer chat questions about rows in a PostgreSQL table. Retrieval is hybrid (pgvector similarity plus full-text search fused with RRF), an OpenAI function call turns phrases like cheaper than 30 dollars into WHERE clauses, and it runs against Azure OpenAI, OpenAI.com or Ollama; azd deploys it to Container Apps with managed identity. For teams whose knowledge is structured rows, not documents.
Who it is for: Teams whose knowledge base is structured Postgres rows
Strengths
- Hybrid vector plus full-text search with RRF is implemented in SQL, not a vendor service
- Provider switch by env var: Azure OpenAI, OpenAI.com or Ollama
- Evaluation, safety evaluation and load-testing docs included
- Tests included; dev container and Codespaces configs
Weaknesses
- Deploy path is Azure-only (azd, Container Apps, Flexible Server)
- Local run expects Postgres 14+ with pgvector installed yourself
- Sample schema is one products table; multi-table questions need new code
- No auth in the app itself
- Python, azure-openai, openai, ollama
- Needs postgres-pgvector, azure-openai-or-openai-or-ollama, azd
- GitHub template
- env example file