Building your recommendation engine with LangChain4j
In this section, we will look at building a graph augmentation application that leverages LangChain4j. In this project, we will be using the GraphRAG approach to generate embeddings for a transaction chain that meets our requirements. We will be using the Neo4j graph retriever to retrieve the transaction chain that meets our requirements, as well as an LLM to generate a summary of those transactions to describe the customer purchase behavior and generate an embedding. The embedding generated will be a vector representation that describes the text summary in a manner that can be leveraged by machine learning or Graph Data Science algorithms. It can also be leveraged for vector search purposes. This article explains embeddings in the context of LLMs well: https://ml-digest.com/architecture-training-of-the-embedding-layer-of-llms/. We will start with the ZIP file downloaded in the last section. We need to unzip the file we have downloaded...
