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This tutorial explains graph-based retrieval and walks through a LightRAG setup connected to Claude Code. The speaker contrasts basic vector search with a knowledge graph that records entities and relationships, then explains how those connections can help answer questions across documents.
The demonstrated setup uses Docker Desktop, Docker Compose and an OpenAI API key. Claude Code receives a prompt to clone the LightRAG repository and configure its .env file for GPT-5 mini and text-embedding-3-large with default local storage. The speaker also describes an entirely local LLM option through Ollama, but the demonstration uses cloud models with a locally hosted container. It does not demonstrate an offline workflow.
The LightRAG web interface appears on localhost port 9621. The walkthrough covers document uploads, graph inspection and retrieval answers with source references. API-based skills let the coding assistant query, upload, explore and check status without opening the interface; Claude Code can summarize answers or return the raw response.
The speaker suggests considering RAG around 500 to 2,000 text pages, treating that range as a rough judgment rather than a fixed requirement. Graph construction takes time during ingestion. The demonstrated upload workflow focuses on text and PDFs; RAG-Anything is introduced for multimodal material, with its integration left outside this tutorial.
