OneRAG and enterprise-rag-system
These are competitors, as both aim to provide a production-ready RAG framework or system for enterprise knowledge bases, offering similar core functionalities to build a RAG pipeline.
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.
Supports hybrid search (dense + BM25), GraphRAG for knowledge graph reasoning, and pluggable rerankers (6 options including Jina and Cohere) through a modular pipeline architecture. Includes built-in PII detection/masking, semantic/Redis caching layers, and query routing that classifies requests before retrieval. Designed for gradual complexity—start with basic vector search and layer in advanced features like agents and tool execution without refactoring the codebase.
About enterprise-rag-system
jinno-ai/enterprise-rag-system
Production-grade RAG pipeline for enterprise knowledge bases
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