OneRAG and Advanced-RAG-monorepo
Both frameworks offer production-ready RAG solutions with modular components like vector databases and LLM integrations, making them direct competitors in the "rag-quality-assurance" category.
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 Advanced-RAG-monorepo
MERakram/Advanced-RAG-monorepo
🚀 Production-ready modular RAG monorepo: Local LLM inference (vLLM) • Hybrid retrieval with Qdrant • Semantic caching • Docling document parsing • Cross-encoder reranking • DeepEval evaluation • Full observability with Langfuse • Open WebUI chat interface • OpenAI-compatible API • Fully Dockerized
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