Adding graph in your genAI architecture for better and explicable answers

Learn how GraphRAG builds knowledge graphs, uses vector search and graph traversal, then combines enriched context to generate accurate, explainable LLM responses.

Overview

After explaining what GraphRAG is, where it came from and how it compares to RAG, I will explain the framework that integrates knowledge graphs to enhance the performance of large language models (LLMs).
The implementation will explain the four key steps:
1- Knowledge Graph Construction to Extract entities and relationships from input to build a structured knowledge graph.
2 - Vector Search to generate vector embeddings for textual content and perform similarity searches to identify relevant nodes within the graph.
3 - Graph Traversal to navigate through the graph to gather additional context by exploring related nodes and relationships.
4 - Response Generation to utilize the enriched contextual information to produce precise and contextually relevant responses to user queries.

Tech stack