rag-postgres-openai-python vs llm-app
Two of the top rag and search, side by side: score, setup, license, activity and what each review found.
rag-postgres-openai-python
RAG over Postgres table rows with hybrid search and SQL filters
llm-app
Pathway RAG pipeline templates that re-index live data sources
| What we compare | rag-postgres-openai-python | llm-app |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 35, known | 89, widely used |
| Freshness | 100, active | 97, active |
| Maintenance | 49, patchy | 33, patchy |
| Easy to run | 33, some setup | 0, hard |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 505 | 58.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Jul 2026 |
| Last release | None published | None published |
| Language | Python | Jupyter Notebook |
| Docker | No | No |
| 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
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