rag-postgres-openai-python vs chat-langchain
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
chat-langchain
LangChain docs assistant as a Managed Deep Agent with Next.js UI
| What we compare | rag-postgres-openai-python | chat-langchain |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 35, known | 67, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 49, patchy | 45, patchy |
| Easy to run | 33, some setup | 0, hard |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 505 | 6.5k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | None published |
| Language | Python | TypeScript |
| 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
chat-langchain
A documentation assistant for LangChain, LangGraph and LangSmith: a Python agent built with LangChain middleware (guardrails, ingress guards, retry) and deployed through Managed Deep Agents, which owns identity, ingress and the checkpointer. Tools search the docs through a managed MCP connector, a Pylon support knowledge base and a URL validator, and a Next.js chat UI sits in frontend/. For teams wanting a reference for a guarded docs assistant.
Who it is for: Teams building a guarded docs assistant on the LangChain stack
Strengths
- Guardrails and link validation are implemented as reusable middleware
- Supabase token plus guest identity handled in identity.py
- Frontend proxies LangSmith feedback so the API key never reaches the browser
- Tests included
Weaknesses
- Tied to Managed Deep Agents (mda CLI) for identity, ingress and state
- Needs a Pylon account and knowledge base ID to run as written
- Docs retrieval depends on a managed MCP connector, not your own index
- Product-specific: you replace the LangChain docs with your own corpus
- TypeScript, anthropic, langchain, langgraph
- Needs anthropic-api-key, pylon-api-key, managed-deep-agents, supabase
- env example file