RAG_Techniques and RAG-ARC
These are complements: the first is an educational resource demonstrating RAG techniques and patterns that could inform the architectural decisions implemented in the second's modular framework.
About RAG_Techniques
NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Covers advanced RAG patterns including agentic retrieval loops, hybrid search strategies (dense-sparse retrieval fusion), query optimization techniques, and multi-document reasoning—beyond basic retrieval pipelines. Implementations target popular frameworks like LangChain and LlamaIndex with code-first Jupyter notebooks, focusing on practical enhancements for production-grade systems.
About RAG-ARC
DataArcTech/RAG-ARC
A modular, high-performance Retrieval-Augmented Generation framework with multi-path retrieval, graph extraction, and fusion ranking
Supports multi-format document parsing (PDF, DOCX, PPT, Excel) with OCR and layout-aware strategies, combining sparse (BM25), dense (FAISS-GPU), and full-text search via Reciprocal Rank Fusion. Built on FastAPI with PostgreSQL, Redis, and Neo4j integration, enabling incremental knowledge graph updates and GraphRAG with subgraph PPR for efficient reasoning.
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