llm-app 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.
llm-app
Pathway RAG pipeline templates that re-index live data sources
chat-with-your-data-solution-accelerator
Azure RAG chat app that answers from your documents with citations
Verdict
llm-app ranks higher, 47 to 45. Of the 5 score parts we measure for both, llm-app leads on 1, chat-with-your-data-solution-accelerator on 2 and 2 are even.
- Adoptionllm-app leads by 51 points, 92 to 41: a wide gap.
- Freshnesschat-with-your-data-solution-accelerator leads by 4 points, 100 to 96: close to even.
- Maintenancechat-with-your-data-solution-accelerator leads by 34 points, 67 to 33: a wide gap.
- Easy to runEven at 0.
- Agent-readyEven at 0.
Pick llm-app if you are
Teams running RAG over documents that change constantly
- 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
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 | llm-app | chat-with-your-data-solution-accelerator |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 41, known |
| Freshness | 96, active | 100, active |
| Maintenance | 33, patchy | 67, fair |
| Easy to run | 0, hard | 0, hard |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 58.8k | 1.2k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Jul 2026 | Oct 2026 |
| Last release | None published | Jul 2026 |
| Language | Jupyter Notebook | Python |
| Docker | No | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
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
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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- rag-postgres-openai-python vs llm-app
- rag-postgres-openai-python vs chat-with-your-data-solution-accelerator
- The full RAG and search ranking with reviews
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