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What is semantic search?

Traditional search matches words — but what if your AI app could match meaning instead? In this video, ‪@RaphaelDeLio‬ explains how semantic search uses vector similarity search to understand what users really want, even when the words don’t match exactly. Learn how semantic search uses vector similarity search — and how a vector database like Redis makes semantic search fast, scalable, and production-ready. ✅ Find the right results without keyword matching ✅ Combine vector search with filtering (hybrid search) ✅ See why Redis delivers the fastest queries and scales to 1B+ vectors 0:00 Traditional Search 0:40 Semantic Search 0:57 Vector Similarity Search 1:08 Embedding Models 1:23 Vector Databases 2:05 Hybrid Search 2:32 Fast & Scalable Vector Databases 2:49 Vector Databases Benchmarks 3:04 AI Resources 🔍 This is the secret behind advanced RAG systems and production-ready AI search. 🔴 Explore Redis AI Resources: https://github.com/redis-developer/re... 🔴 Try Redis 8.0 (now with blazing vector search): https://redis.io/blog/redis-8-0-m03-i... 🔴 Redis: The Fastest Vector DB for 1B+ vectors: https://redis.io/blog/searching-1-bil... 🔴 Try Redis Cloud for free: https://redis.io/cloud/ 📌 Subscribe for weekly tips on building real-time AI apps with Redis, vector search, and LLMs. #SemanticSearch #VectorSearch #Redis #VectorDatabase #AI #MachineLearning #EmbeddingModels #Database #TechnologyExplained