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.
113 stars.
Use this if you need a production-ready RAG (Retrieval Augmented Generation) backend that's easy to set up and customize, allowing you to swap out core AI components with a single configuration line.
Not ideal if you're a beginner simply looking for a simple chatbot UI without the need to integrate with complex backend systems or customize underlying AI models.
Stars
113
Forks
35
Language
Python
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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