Ever wonder how Netflix knows you'll love that obscure documentary about penguins, or how Google Photos finds every picture of your dog even though you never tagged them? The secret sauce is vector embeddings and similarity search – technologies that convert complex data (text, images, audio) into high-dimensional numerical representations that capture semantic meaning. While traditional databases excel at exact matches ("find user ID 12345"), vector databases revolutionize "find similar" queries by measuring relationships between these numerical vectors using techniques like cosine similarity. This session demystifies the math and technology behind the AI-powered search and recommendation systems you use every day.

You'll discover how companies transform words into vectors where "king" - "man" + "woman" actually equals "queen" in mathematical space, explore the architecture behind popular vector databases like Pinecone and see live demonstrations of building semantic search systems. We'll cover real-world applications from RAG-powered chatbots to fraud detection, show you how to implement document similarity search in 20 lines of Python, and discuss the practical considerations of scaling vector systems to billions of embeddings. Whether you're building recommendation engines, semantic search, or AI-powered applications, this session provides the foundational knowledge to leverage vector databases effectively in your projects.