Mar 23
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Description Large Language Models don't read text—they read numbers. In this video, we dive into Embedding Models, the fundamental technology that allows machines to understand the meaning and context of our language. We start with a simple analogy: think of an embedding as a "personality test" for a piece of text. Just as a person can be represented by scores on different traits, a word or document can be represented as a fixed-size vector of numbers. What you will learn in this lesson: What are Embeddings? Learn how neural networks transform raw text into high-dimensional numeric vectors. The Personality Test Analogy: A beginner-friendly way to visualize how vectors represent meaning. Measuring Similarity: Why we use Cosine Similarity and Euclidean Distance to find the "distance" between ideas. Embedding Models in LangChain: How to connect to industry leaders like OpenAI, Google Gemini, and open-source models from Hugging Face. LangChain Usage Patterns: Understanding the difference between embedDocuments (for your knowledge base) and embedQuery (for the user's question). Understanding embeddings is the bridge between simple keyword search and the powerful semantic search used in modern RAG systems. #RAG #LangChain #Embeddings #GenerativeAI #SemanticSearch #OpenAI #GoogleGemini #MachineLearning #VectorSearch #AITutorial