OneRAG and raggo
These are competitors offering production-ready RAG frameworks in different languages (Python/FastAPI vs Go), each providing their own abstractions for swapping vector databases and LLM providers rather than interoperating together.
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.
About raggo
teilomillet/raggo
A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.
This is a tool for developers who are building applications that need to intelligently answer questions from documents. It allows you to input various documents (like PDFs or web pages) and then ask natural language questions, receiving context-aware responses. It's designed for software engineers and backend developers creating AI-powered features for their users.
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