{"repo":"LeDat98/NexusRAG","free":true,"listed":false,"github":"https://github.com/LeDat98/NexusRAG","clone":"git clone https://github.com/LeDat98/NexusRAG.git","description":"Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat, and inline citations. Powered by Gemini or local Ollama models.","language":"Python","stars":424,"topics":["chromadb","citation","docling","document-parsing","fastapi","gemini","knowledge-base","knowledge-graph","lightrag","ollama"],"license":null,"category":"productivity","readme_excerpt":"NexusRAG Hybrid Knowledge Base with Agentic Chat, Citations & Knowledge Graph Upload documents. Ask questions. Get cited answers. NexusRAG combines vector search, knowledge graph, and cross-encoder reranking into one seamless RAG pipeline — powered by Gemini, local Ollama, or fully offline sentence-transformers. Features · Quick Start · Model Recommendations · Tech Stack --- Architecture Showcase https://github.com/user-attachments/assets/fa845fab-dcc3-4a64-86ac-6dda2c073156 --- Beyond Traditional RAG Most RAG systems follow a simple pipeline: split text → embed → retrieve → generate. NexusRAG goes further at every stage: Aspect Traditional RAG NexusRAG --- --- --- Document Parsing Plain text extraction, structure lost Docling or Marker: preserves headings, page boundaries, formulas, layout — switchable via config Images & Tables Ignored entirely Extracted, captioned by vision LLM, embedded as searchable vectors Chunking Fixed-size splits, breaks mid-sentence Hybrid semantic + structural chunking (respects headings, tables) Embeddings Single model for everything Dual-model: BAAI/bge-m3 (1024d, search) + KG embedding (Gemini 3072d / Ollama / sentence-transformers) Retrieval Vector similarity only 3-way parallel: Vector over-fetch + KG entity lookup + Cross-encoder rerank Knowledge No entity awareness LightRAG graph: entity extraction, relationship mapping, multi-hop traversal Context Raw chunks dumped to LLM Structured assembly: KG insights → cited chunks → related images/tabl","default_branch":null,"files":null,"tree":[],"storefront":"/r/LeDat98","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/LeDat98/NexusRAG/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}