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拼好RAG:手搓并融合了GraphRAG、LightRAG、Neo4j-llm-graph-builder进行知识图谱构建以及搜索;整合DeepSearch技术实现私域RAG的推理;自制针对GraphRAG的评估框架| Integrate GraphRAG, LightRAG, and Neo4j-llm-graph-builder for knowledge graph construction and search. Combine DeepSearch for private RAG reasoning. Create a custom evaluation framework for GraphRAG.
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.
We believe that every SOTA result is only valid on its own dataset. RAGView provides a unified evaluation platform to benchmark different RAG methods on your specific data.
A Go library implementation of LightRAG - an advanced Retrieval-Augmented Generation (RAG) system that uniquely combines vector databases with graph database relationships to enhance knowledge retrieval.
🚀 Complete self-hosted AI stack with 40+ services: Transform YouTube videos & PDFs into AI podcasts (Open Notebook), local LLMs (Ollama CPU/GPU), workflow automation (n8n), AI agents (Flowise), vector databases (Qdrant), German TTS voice, and business tools. One-command Docker deployment. perfect for learning, development, and private teams.
A multimodal RAG dashboard and interactive 3D Knowledge Graph. Process documents locally with Ollama or via Cloud APIs, powered by LightRAG, RAG-Anything and Neo4j.
A cloud-native RAG (Retrieval Augmented Generation) service API built on LightRAG framework, offering plug-and-play knowledge-enhanced generation capabilities.
An AI-powered curriculum engine that generates pedagogically grounded, narrative-driven course modules using Bloom's Taxonomy and instructional design principles.
HyperHierarchicalRAG: Combining Hypergraph Memory (HGMem) with Hierarchical Retrieval (LightRAG) - A novel RAG system with MCP Server integration for AI Agents