azure-search-openai-demo vs llm-app
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
llm-app
Pathway RAG pipeline templates that re-index live data sources
| What we compare | azure-search-openai-demo | llm-app |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 73, popular | 89, widely used |
| Freshness | 100, active | 97, active |
| Maintenance | 92, healthy | 33, patchy |
| Easy to run | 0, hard | 0, hard |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 7.8k | 58.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Jul 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | Jupyter Notebook |
| 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
llm-app
Eight Dockerized Python pipelines on the Pathway framework: question-answering RAG, a live document indexer, multimodal RAG with GPT-4o, unstructured-to-SQL, adaptive RAG, a private Mistral plus Ollama variant, slide search and video RAG. Each watches a source (file system, Google Drive, SharePoint, S3, Kafka, Postgres), keeps an in-memory vector and full-text index current and serves an HTTP API. For teams whose documents change constantly.
Who it is for: Teams running RAG over documents that change constantly
Strengths
- No separate vector DB, cache or API framework; indexing is in-process (usearch, Tantivy)
- Connectors for file system, Google Drive, SharePoint, S3, Kafka and Postgres with live sync
- Private variant runs fully local with Mistral and Ollama
- Docker images and tests included
Weaknesses
- Pathway is the real dependency; its Rust engine is opaque to most Python teams
- Pipelines are backends; the UI is an optional Streamlit demo
- Root README has no setup; each template README is required reading
- Index lives in memory; sizing for millions of pages is on you
- Jupyter Notebook, pathway, openai, mistral, ollama
- Needs docker, openai-api-key, data-source-credentials