Graph

A quick rewind!! In my last post, we built some of the core social features for our app — users can now follow each other, comment on posts, and hit that like button. Along the way, I also shared a bit about working with Cypher (Neo4j’s query language) and how we used Pinecone as a vector store to index our data for similarity search, which is an important piece for our RAG (Retrieval-Augmented Generation) setup. ...

A quick rewind!! In the previous post, we implemented user creation and successfully tested it via API endpoints. In this post, we’re taking things a step further by adding key features that make our application feel more like a real social network. Here’s what we’ll explore: Enabling users to follow each other Writing expressive queries using Cypher Query Language Allowing users to create posts Adding support for likes and comments on posts Setting up a Vector Database for RAG (Retrieval-Augmented Generation) Integrating Spring AI into the backend Let’s jump in and build out these social features step by step! ...

Graph database what and how? Recently at work, I had the opportunity to dive into a new product that deals with highly complex, hierarchical data schemas—deeply interconnected across multiple levels of business domains. To put it simply: imagine an ETL process pulling structured data from physical documents and presenting that data differently for different teams—each with their own use cases and logic. Now, the fun (and frustration) really began when we start thinking about how to handle these transformations. Some were done using custom functions, others using Java reflection (yes..yes, you read it right, we use java reflection🙃). ...