ONE Upload Becomes FIVE Indexes | Multimodal Search скачать в хорошем качестве

ONE Upload Becomes FIVE Indexes | Multimodal Search

When you upload ONE video file, your system should create FIVE separate indexes—each optimized for different query patterns. This is object decomposition, and it's the architecture behind production multimodal search. In this video, I break down: → Why one index isn't enough for complex queries → How to decompose files into searchable layers → The five index types (embeddings, objects, text, audio, metadata) → Building this manually (200 lines) vs with Mixpeek (10 lines) → Live demo of layered indexing in production This is Module 6 of Mixpeek University—our free 26-part course on building multimodal AI systems. ━━━━━━━━━━━━━━━━━━━━━━━ 🔗 RESOURCES 📚 Full Course Playlist: https://mixpeek.com/university 🔧 Try Mixpeek Free: https://mixpeek.com/start 📖 Docs: https://docs.mixpeek.com 💬 Multimodal Community: http://mixpeek.com/community ━━━━━━━━━━━━━━━━━━━━━━━ 🏷️ TAGS #MultimodalAI #VectorDatabase #MachineLearning #AI #DevTools #SearchEngine #RAG #LLM #Python #TechEducation ━━━━━━━━━━━━━━━━━━━━━━━ 💡 ABOUT MIXPEEK UNIVERSITY Free, comprehensive course teaching multimodal AI from first principles. Learn to build search systems that understand images, video, audio, and text—all in production. No fluff. No sales pitches. Just deep technical education from developers, for developers. New modules every week. Subscribe to follow along. ━━━━━━━━━━━━━━━━━━━━━━━ ⚡ Mixpeek: Multimodal AI infrastructure for developers 🌐 Website: https://mixpeek.com 🐦 Twitter:   / mixpeek   💼 LinkedIn:   / mixpeek