rag-postgres-openai-python 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.
rag-postgres-openai-python
RAG over Postgres table rows with hybrid search and SQL filters
chat-with-your-data-solution-accelerator
Azure RAG chat app that answers from your documents with citations
Verdict
rag-postgres-openai-python ranks higher, 51 to 45. Of the 5 score parts we measure for both, rag-postgres-openai-python leads on 2, chat-with-your-data-solution-accelerator on 2 and 1 is even.
- Adoptionchat-with-your-data-solution-accelerator leads by 8 points, 41 to 33.
- FreshnessEven at 100.
- Maintenancechat-with-your-data-solution-accelerator leads by 18 points, 67 to 49: a wide gap.
- Easy to runrag-postgres-openai-python leads by 33 points, 33 to 0: a wide gap.
- Agent-readyrag-postgres-openai-python leads by 30 points, 30 to 0: a wide gap.
Pick rag-postgres-openai-python if you are
Teams whose knowledge base is structured Postgres rows
- 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
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 | rag-postgres-openai-python | chat-with-your-data-solution-accelerator |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 33, known | 41, known |
| Freshness | 100, active | 100, active |
| Maintenance | 49, patchy | 67, fair |
| Easy to run | 33, some setup | 0, hard |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 505 | 1.2k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | Jul 2026 |
| Language | Python | Python |
| Docker | No | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
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
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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