Learnitweb

Author: Editorial Team

  • Introduction to Linear Regression and Intuition

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    Linear regression is one of the most fundamental and widely used algorithms in machine learning. If you are beginning your ML journey, this is the perfect starting point because it teaches you how models learn relationships, make predictions, and optimize accuracy. In this tutorial, we will break down the concepts behind linear regression, understand where…

  • Building Your First LangChain + LangSmith Chat App with OpenAI

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    1. Prerequisites and Setup In the previous video, we: Example .env (already created earlier): 1.1 Required Python packages Make sure your requirements.txt has at least: Then install: ipykernel is needed so Jupyter can run this environment as a kernel. 2. Loading Environment Variables in Python First, we load variables from .env and ensure they’re available…

  • Understanding JVM Just-In-Time (JIT) Compilation

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    1. The JVM Starts as an Interpreter When you start your Java application, the JVM initially behaves like a classic interpreter: This approach offers an important benefit: Write Once, Run Anywhere Your Java bytecode runs on any platform with a JVM implementation — Windows, macOS, Linux, and more. However, there’s a drawback: Interpreted execution is…

  • How the JVM Runs Your Code

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    What exactly does the JVM do when it runs our code? Most developers know that Java is a compiled language—yet not compiled in the same sense as C or C++.Instead, Java follows a unique two-step model: Understanding this lifecycle is essential because every optimization, performance decision, and runtime behavior in Java flows from this model.…

  • JDK Vendors and JVM Implementations

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    Java is one of the world’s most widely used technologies, and with its growth has come a rich ecosystem of multiple JDK vendors and multiple JVM implementations.Understanding the differences among them is essential, especially when you’re tuning performance, deploying to production, or choosing a runtime for long-term stability. Part 1: JDK Vendors — Oracle vs…

  • Working with ChromaDB Using LangChain + Hugging Face Embeddings

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    Vector databases play a crucial role in modern LLM-powered applications. Whenever we want to store, search, or retrieve information semantically (using meaning instead of keywords), we rely on vector stores. In this tutorial, we will focus on ChromaDB, one of the most popular and developer-friendly open-source vector databases. This guide is written in simple language…

  • Building a Vector Store Using FAISS and HuggingFace Embeddings

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    In modern RAG (Retrieval-Augmented Generation) systems, embeddings and vector stores are core components.This tutorial walks you step-by-step through: We will use open-source embeddings and run everything locally with no external API requirements. 1. Understanding Vector Stores A vector store is a special database designed to store high-dimensional vectors (embeddings).It enables fast similarity search and is…

  • Using Hugging Face Embeddings with LangChain

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    In this tutorial, you’ll learn how to: This is one of the key building blocks in a RAG (Retrieval-Augmented Generation) or search system: converting text into vectors (embeddings) that capture semantic meaning. 1. What Are Embeddings (Quick Recap)? Embeddings are numeric representations of text (usually large vectors of floats). Texts that are semantically similar (e.g.,…

  • Converting Text Chunks into Vector Embeddings Using HuggingFace

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    In the previous sessions, you learned: We now move to Step 3, where you convert these text chunks into vector embeddings.These embeddings are the foundation of modern retrieval systems, semantic search, and RAG (Retrieval-Augmented Generation) applications. This tutorial focuses entirely on OpenAI embeddings. What Are Embeddings? Embeddings are one of the most important concepts in…

  • Building a Custom JSON Splitter for Large and Nested API Responses

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    When working with real-world APIs, we often receive large JSON responses that contain deeply nested objects, arrays, and long fields. Before we send this data to an LLM or convert it into embeddings for retrieval, we must break it into smaller and meaningful chunks. However, depending on your LangChain version, the built-in RecursiveJsonSplitter may not…