Feb 11
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In this video, we break down one of the most important concepts in modern AI — embeddings — and how they power tools like LangChain, semantic search, and retrieval-augmented generation (RAG). You’ll learn: ✨ What embeddings actually are — numeric vectors that capture the meaning of text by representing it in a multi-dimensional space. Instead of matching exact keywords, similar ideas end up close together in that space. 🧠 Why language models convert text into vectors — so machines can compare and measure semantic similarity rather than just text similarity. 📚 What a vector store is — a database that stores embeddings and lets you quickly find the most relevant content via similarity search. ⚙️ How this combo is used in real apps: search engines that understand meaning, smarter chatbots, document Q&A, recommendations, and more. We keep it simple and practical, so whether you’re building your first LangChain app or just curious about how LLMs “think,” this video will give you the missing puzzle pieces. ✨ Resources & links 📌 Educative.io course on embeddings & vector stores 👉 https://www.educative.io/courses/langchain-llm/embeddings-and-vector-stores-in-langchain#What-are-embeddings