Category: AI Engineering

AI for Java Developers.

Deterministic code meets Probabilistic AI. For decades, software engineering was about “If X, then Y.” Today, it’s about context windows, vector embeddings, and probabilistic outcomes. This section is for the Java Engineer who needs to integrate Large Language Models (LLMs) into enterprise applications without rewriting the backend in Python.

We focus on the tools that bridge the JVM with the Modern AI Stack:

  • RAG (Retrieval-Augmented Generation): Building pipelines that allow LLMs to “chat” with your private enterprise data security.
  • Spring AI & LangChain4j: The emerging standard libraries for orchestrating AI agents in Java.
  • Vector Databases: Practical guides on implementing pgvector, Milvus, or Pinecone within a Spring Boot architecture.
Popular Topics
🤖 RAG Patterns
🧠 Spring AI

Building a RAG Pipeline with Spring AI and pgvector (No Python Required)

Posted on By Govind No Comments on Building a RAG Pipeline with Spring AI and pgvector (No Python Required)

AI Engineering Updated Jan 2026 · 15 min read Building a RAG Pipeline with Spring AI and pgvector The “Python Tax” is officially repealed. For too long, the ‘AI Engineering’ world has been gatekept by Python. If you wanted to build a RAG (Retrieval Augmented Generation) pipeline, you had to spin up a FastAPI service,…

Read More “Building a RAG Pipeline with Spring AI and pgvector (No Python Required)” »

AI Engineering