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AI2026-05-17

GraphRAG

Also known as: GraphRAG / Graph Retrieval-Augmented Generation / グラフRAG

An extension of RAG that combines knowledge graphs with vector search to capture entity relationships, enabling more contextually rich answers than pure vector similarity can provide.

Overview

GraphRAG, introduced by Microsoft in 2024, extracts entities and relationships from documents to build a knowledge graph, which is then queried alongside vector search. Where standard RAG is limited to local similarity retrieval, GraphRAG enables global summaries and cross-document reasoning through graph traversal.

Difference from standard RAG

GraphRAG excels when cross-document relationships matter — legal documents, medical literature, and corporate policy libraries. Implementation cost is higher than standard RAG, but answer quality on complex multi-hop questions improves substantially.

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Software Development

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Building a RAG-Enabled Internal Knowledge Base AI with Qwen3.5-9B and OpenClaw

Learn how to build a RAG-enabled internal knowledge base AI using Qwen3.5-9B and OpenClaw. This guide covers document ingestion from PDFs, Word files, and internal wikis, vector database integration, and best practices for achieving accurate information retrieval with natural dialogue. We provide AI agent implementation support for companies in Shinagawa-ku, Minato-ku, Shibuya-ku, Setagaya-ku, and Ota-ku to enhance operational efficiency.

Amazon S3 Vectors Complete Guide — Reduce AI/RAG Costs by 90% with Native Vector Search Storage [2026]

Complete guide to Amazon S3 Vectors (GA since December 2025). Covers up to 90% cost reduction vs dedicated vector DBs, 2-billion vectors per index, RAG with Bedrock Knowledge Bases, and Python code examples.

Related Terms

RAG (Retrieval-Augmented Generation)Embedding Vector Database Semantic Search Grounding

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