GraphRAG Explained AI Retrieval with Knowledge Graphs & Cypher
AI Summary
In this video, viewers learn how to populate and query a knowledge graph using a Language Model (LLM) via Graph Retrieval Augmented Generation (GraphRAG). Unlike vector search methods, GraphRAG employs graph databases to store relationships (edges) and data points (vertices), enhancing the depth of information retrieval. The video guides through creating a knowledge graph, transforming unstructured text data into structured data with an LLM, and querying this knowledge graph using Cypher, its query language. Steps include setting up Neo4j, using Python libraries, and crafting prompts for the LLM to generate and interpret queries. The tutorial demonstrates how LLMs can efficiently manage complex relationships in data, highlighting the advantages of GraphRAG over traditional vector-based methods and presenting hybrid systems that integrate both graph and vector databases.