OneRAG and minimal-rag-backend

OneRAG
53
Established
minimal-rag-backend
22
Experimental
Maintenance 10/25
Adoption 9/25
Maturity 13/25
Community 21/25
Maintenance 10/25
Adoption 1/25
Maturity 11/25
Community 0/25
Stars: 113
Forks: 35
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 1
Forks: —
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
No Package No Dependents
No Package No Dependents

About OneRAG

notadev-iamaura/OneRAG

Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.

This project helps you quickly build and deploy a smart chatbot or question-answering system for your business using your own documents. You feed in unstructured text like PDFs, Word files, or Markdown, and it outputs intelligent, context-aware answers to user questions. This is ideal for product managers, innovation leads, or internal tool builders looking to create customer service bots, knowledge base assistants, or internal Q&A systems.

AI-powered-chatbots knowledge-management customer-support-automation document-intelligence internal-Q&A-systems

About minimal-rag-backend

nkmohit/minimal-rag-backend

Minimal, production-style RAG backend implementing document ingestion, semantic retrieval, and grounded LLM generation using FastAPI, Chroma, and Gemini.

Related comparisons

OneRAG and Advanced-RAG-monorepo OneRAG and raggo OneRAG and selfhosted-llm-rag-app OneRAG and rag-decision-support-system OneRAG and rag-studio OneRAG and DocQuery

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