Photo credit Retrieval Augmented Generation (RAG) may sound complex, but it accurately represents the process of the system. RAG is a method that enhances the capabilities of Large Language Models (LLMs) by integrating them with external knowledge sources. Each term represents a piece of the puzzle: Retrieval - data retrieved from some external source outside the LLM (most often a database, but can include files, webpages, etc) Augmented - "augmenting" (or adding to) an LLM’s training data.

Photo credit As someone who is pretty familiar with relational and graph databases, I wanted to dig a little deeper into vector databases and understand the strengths and quirks they bring to the database table. I put together a conference abstract on vector RAG versus GraphRAG which got picked up, so I went to work building a demo and learning all I could. I pivoted a few times along the way, but ended up with a Spring AI application that connects to both Pinecone (vector database) and Neo4j (graph database).

Photo credit I recently had the opportunity to play with the Cypher DSL (domain-specific language) library, and I found it difficult to figure out syntax for some of the queries I was trying to construct. This blog post will hopefully help you understand how to construct your own programmatic queries with Cypher DSL by showing you syntax for nodes, relationships, and filtering. Then see how this in action with some examples for assembling those components into full queries translated from common clauses, functions, and result formats in Cypher.