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…

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…

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.,…

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…

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…

In earlier lessons, we explored several text-splitting techniques in LangChain. In this tutorial, we will focus on a powerful and lesser-known utility: the HTML Header Text Splitter. This splitter helps you break down HTML documents into logical, structured chunks based on the hierarchy of HTML header tags such as <h1>, <h2>, and <h3>. This becomes…