OneRAG and langgraph-rag-assistant
OneRAG is a general-purpose RAG framework providing broad database and LLM support, while the LangGraph RAG Assistant specifically demonstrates building a multi-step reasoning RAG system for technical documentation using LangGraph, making them complementary in that OneRAG could provide the underlying RAG infrastructure for an application that implements advanced reasoning workflows like those demonstrated by the LangGraph RAG Assistant.
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 langgraph-rag-assistant
deepashreesiva/langgraph-rag-assistant
🚀 Build an enterprise-ready RAG system to enhance technical documentation querying with LangGraph and multi-step reasoning workflows.
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