This example demonstrates how to build a travel assistant using LangGraph and LLM-based tool orchestration — where each tool is represented as an independent node in the workflow graph. Instead of hardcoding if/else logic...
Blackboard If the problem is always the same the previous architectures might have worked … but what if the best next step depends on the results of the previous one? A rigid sequence can...
# How to Implement Sampling in MCP (Model Context Protocol) with Java ## What is MCP Sampling? MCP sampling allows you to capture and process streaming messages from AI models in real-time. It’s useful...
When building AI-powered applications, one of the most common challenges is: 👉 How do we give the LLM access to fresh, structured data without stuffing the entire dataset into its prompt? This is where...
In the rapidly evolving landscape of healthcare technology, the Model Context Protocol (MCP) offers a powerful framework for building intelligent medical applications. When dealing with sensitive and critical patient data – like medical records,...
Introduction The Model Context Protocol (MCP) server needs to handle different types of requests. In this blog post, we’ll focus on the simplest approach – how to extract the method from the request body...
When you hear a model like GPT-3 has 175 billion parameters, it sounds huge — and it is. But what does that actually mean? Let’s break it down with a super simple example, then...
Let’s walk through exactly how WQW_QWQ (the query weight matrix) is multiplied with the input embedding, using a small example with embedding size = 5. 🧠 Context: What is WQW_QWQ? In a Transformer, we...
If you’ve ever wondered how models like ChatGPT or GPT-4 “understand” context and predict the next word so accurately — the secret sauce is attention. In this post, we’ll break it down step-by-step, with...
🟦 1. What is a word embedding? Let’s say a model reads this sentence: “The cat sat on the mat.“ Each word like “cat”, “mat”, “sat” is turned into a vector — imagine this...