Introduction

Motivation

Recent advancements in retrieval-based LLM inference frameworks have brought the GraphRAG (Graph-based Retrieval-Augmented Generation) system into the spotlight for its superior retrieval capabilities. By incorporating enriched metadata and leveraging fine-grained graph structures and relationships, GraphRAG delivers precise and efficient information retrieval, even for large-scale, unstructured datasets.

What This Tutorial Covers

This tutorial provides a comprehensive, step-by-step guide to building and deploying a GraphRAG system on the Shakudo platform. It covers everything from data preprocessing and metadata extraction to data ingestion with Neo4j, deploying an inference microservice, and integrating the inference endpoint into workflows. Following this guide, you’ll build a fully functional GraphRAG system tailored to the Financial10k dataset and learn how GraphRAG combines Generative AI with graph-based systems to create scalable solutions for real-world applications.

Estimated Time: 2-4 hours

Who This is For

This tutorial is for AI developers, business analysts, and product managers who want to leverage the latest Generative AI technologies with graph databases to automate document processing and information retrieval tasks.

What You’ll Learn

• Preprocess text data, including OCR and chunking.
• Extract metadata using LLMs and embed texts into embedding vectors using Ollama models for semantic search.
• Create graphs, including nodes, relationships and indexes in Neo4j for data ingestion.
• Query data in Neo4j with relationships indexing using structure Cypher queries.
• Deploy a GraphRAG inference service as a microservice on Shakudo and integrate it with tools such as Dify and n8n.
• Create a Dify workflow as the GraphRAG inference service.
• Compare GraphRAG’s inference results with that of a naive RAG approach.

Required Shakudo Stack Components

• MinIO or similar s3-based data storage
• Neo4j (graph database)