How to build a knowledge graph for AI

The blog post introduces the concept of knowledge graphs and their significance in enhancing AI agents, particularly in multi-agent Retrieval-Augmented Generation (RAG) architectures. It explains that knowledge graphs provide structured, interconnected data that aids AI agents in making accurate decisions and performing tasks by compensating for the large language models' (LLMs) limitations, such as memory fuzziness. The post outlines the steps required to create a knowledge graph from unstructured data, including extraction, transformation, and loading (ETL), while highlighting the benefits such as deterministic accuracy, multi-hop reasoning, and reduced hallucinations. It also compares knowledge graphs to vector stores, suggesting situations where one might be more beneficial than the other, and provides practical examples of how to parse, chunk, and embed data for integration into a knowledge graph.