June 2025
Intermediate to advanced
312 pages
6h 42m
English
Now that we have loaded the data into a graph, in this chapter, we will look at how we can use LangChain4j or Spring AI to augment the graph to enhance its capabilities and build a knowledge graph. We will look into integrating the graph with LLMs to generate a summary of customer purchases and create an embedding of that summary to represent the customer purchase history. These embeddings are crucial for enabling machine learning and graph algorithms to understand and process graph data. These embeddings can help us build a knowledge graph to provide more personal recommendations for customers by understanding purchase behaviors. We will also look at how to create embeddings of the detailed ...
Read now
Unlock full access