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This article provides a practical guide to building a GraphRAG system using LangExtract, Neo4j, Qdrant, and Ollama, combining entity extraction, knowledge graphs, and vector search for context-aware retrieval.
A modular, high-performance Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) with support for WebGPU acceleration and three deployment architectures: server-only, WASM-only (client-side), and hybrid.
IONS is an open-source approach to AI memory and reasoning that uses a graph of evidence-backed claims called Cognitive Building Blocks (CBBs) to store knowledge outside model weights, making reasoning inspectable.
A detailed technical query about building a local document RAG system covering storage, ingestion, query, and highlighting, seeking advice on vector databases, GraphRAG feasibility, and document highlighting implementations.
Hyper-Extract is an open-source framework that converts messy documents into typed knowledge structures, supporting multiple graph architectures like GraphRAG, LightRAG, and KG-Gen, with 10+ extraction engines and 80+ YAML templates for various domains.
An open-source full-stack pipeline that constructs a Knowledge Graph from raw text, uses hybrid search (dense + sparse + graph traversal) to solve multi-hop reasoning problems in LLMs, and re-ranks results with Reciprocal Rank Fusion and a Cross-Encoder.
A detailed guide on optimizing knowledge graph ingestion for AI agents, presenting a five-step pipeline (extraction, resolution, embedding, deduplication, routing) to prevent graph corruption and improve retrieval quality.
The article provides an overview of 20 advanced RAG (Retrieval-Augmented Generation) types expected to be relevant in 2026, covering long-document memory, adaptive retrieval, multimodal grounding, multilingual QA, graph reasoning, and security-focused RAG approaches.
This article compares the applicable scenarios and selection suggestions of three knowledge base retrieval schemes: Regular RAG, Knowledge Graph RAG, and LLM Wiki, emphasizing choosing the right scheme based on data morphology and avoiding blind use of complex tools.
This paper benchmarks GraphRAG for EHR schema retrieval using local LLMs on consumer hardware, evaluating models like Llama 3.1, Mistral, Qwen 2.5, and Phi-4-mini.
A developer shares an architecture using Neo4j knowledge graphs with typed entities and deduplication to solve the problem of AI agents forgetting entity identity across sessions, moving beyond flat files and vector stores.
This paper proposes SoVA, a framework using GraphRAG to align LLM-based agents with human social values by converting psychological theories into prescriptive instructions. Experiments on the DAILYDILEMMAS benchmark show significant improvements over prompt-based baselines.
JustHireMe V0.1.31 is a major update to the local-first AI job search tool, adding a command center dashboard, custom job analysis, outreach generator, rebuilt knowledge graph, job pipeline CRM, live activity stream, built-in AI assistant, and modular agent configuration. It remains open source, free, and privacy-first.
PersonalAI 2.0 introduces a framework that enhances LLM-based systems by integrating external knowledge graphs with dynamic multistage query processing and adaptive planning mechanisms, achieving reductions in hallucination rates and improved precision across multiple benchmarks.
The author argues that GraphRAG is fundamentally a data modeling problem rather than just a retrieval algorithm, proposing a five-component architecture using ontologies, knowledge graphs, and an MCP server for unified agent memory.
Researchers fine-tuned BioMistral-7B with QLoRA and GraphRAG to create a TB-care LLM for South Africa, showing improved contextual alignment over the base model.