Ryan welcomes Brian O’Grady, Head of Field Research and Solutions Architecture at Qdrant, to discuss the differences between traditional text search engines powered by Lucene and modern vector databases, when vector search’s exact-match needs work for things like logs and security analytics and when semantic search works for user-facing discovery and non-exact results, and how Qdrant is growing into video embeddings and local-agent contexts.
Ryan is joined by Philippe Saade, the AI project lead at Wikimedia Deutschland, to dive into the Wikidata Embedding Project and how their team vectorized 30 million of Wikidata’s 119 million entries for semantic search.
Ryan speaks with Edo Liberty, Founder and CEO of Pinecone, about building vector databases, the power of embeddings, the evolution of RAG, and fine-tuning AI models.
Ben and Ryan chat with Babak Behzad, senior engineering manager at Verkada, about running a pipeline that vectorizes 25,000 images per second into a custom-built vector database. They discuss whether the speed is due to technical brains or brawn, the benefits of processing on device vs. off, and the importance of privacy when using image recognition on frames from a video camera.
What exactly is a vector database? And how does it relate to generative AI?
Semantic search allows users to search using natural language instead of a rigid syntax of keyword manipulation.
