Vector Search & Embeddings Explained: Build Smarter Search in Your AI Apps скачать в хорошем качестве

Vector Search & Embeddings Explained: Build Smarter Search in Your AI Apps

Want to learn how vector search works and how embeddings can make your searches more relevant? This video breaks it all down—from converting text into embeddings to running vector searches using Convex. We explore how embeddings act like GPS coordinates for concepts, helping you build smarter, more efficient search experiences in your apps. 🔹 Learn how to generate embeddings using OpenAI 🔹 Discover how to set up a vector index in Convex 🔹 See a live demo of embedding search in action 🔹 Understand how embeddings differ from LLM-powered search By the end of this video, you’ll know how to integrate vector search into your own full-stack apps using Convex! 📌 Resources Mentioned 🔗 Embedding Soup Repo: https://convex.link/ytsouprepo 🔗 Try Convex: https://convex.link/ytsoupsdemo 🔗 Transformers Explained Visually (LLMs Deep Dive):    • Transformers, the tech behind LLMs | Deep ...   ⏳ Timestamps 00:00 Welcome to the lesson on embeddings and vector search 00:39 Understanding how words and concepts are embedded into vector space 01:05 How AI models generate embeddings and how to use them in Convex 01:53 Setting up a vector index in Convex for efficient searches 02:31 Why using a consistent model is critical for accurate search results 03:04 Making embeddings fun with a real-time demo 03:17 How Embedding Soup automatically syncs data for better search 03:57 The difference between vector search and LLM-based search #VectorSearch #Embeddings #AI #FullStackDevelopment #Convex #Database #Backend #MachineLearning #OpenAI #DevTools #AIsearch