Integrating LangChain4j and Spring AI with Neo4j
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 description of each article present in the dataset.
In this chapter, we are going to cover the following main topics:
- Setting up LangChain4j and Spring AI
- Building your recommendation engine with LangChain4j
- Building your...
