Author: marjavamitjava

Here’s a layman-friendly example of representing a word as a 2D vector (just two numbers) — even though real embeddings are often 384, 768, or 1536 dimensions. 🔤 Let’s take the word: “king” We...

Let’s take the stream pipeline: List<String> result = names.stream() .filter(name -> name.length() > 3) .map(String::toUpperCase) .limit(2) .collect(Collectors.toList()); and write it using a classic for loop — then compare readability, performance, and flexibility. 🔁 Equivalent...

Understand how limit() short-circuits and how a stream pipeline executes lazily and element-by-element. 🔄 Example Code List<String> names = Arrays.asList(“Alice”, “Bob”, “Charlie”, “David”, “Eve”);List<String> result = names.stream() .filter(name -> { System.out.println(“Filtering: ” + name);...

What Is a Fully Qualified Name (FQN)? In Java, a class’s fully qualified name is its package name + class name. For example: package com.example;public class MyService {} FQN = com.example.MyService But What Really...

RAG Pinecone MCP Server This server implements the Model Context Protocol (MCP) for RAG (Retrieval-Augmented Generation) using Pinecone as the vector database. Environment Variables Required environment variables in your .env file: PINECONE_API_KEY= # Your Pinecone API...

MCP (or an MCP-like system) can absolutely integrate tools/APIs from different vendors, acting as a universal orchestrator. This is one of its most powerful features—it dynamically selects and combines services from multiple providers based on context, without...

This is a VS Code extension (written in TypeScript) that provides RAG‑powered ColdFusion code completions. Here’s how it’s structured: 1. Output Channel 2. SharedContext Singleton 3. ColdFusionCompletionProvider Implements VS Code’s CompletionItemProvider to generate AI‑driven snippets: 4....

Introduction In recent years, AI models have become incredibly powerful at understanding human language. But how does an AI system “understand” a sentence? The answer lies in text embeddings, which transform words and sentences...

Introduction Embeddings are a way to represent text (or other data) as dense numerical vectors that capture semantic meaning. These vectors allow us to perform tasks like similarity searches, clustering, and machine learning efficiently....

Why Not Process an Entire File at Once? The True Value of Batch Processing When working with large datasets, the temptation to process an entire file in one go is understandable. After all, wouldn’t...