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.
Wenjing Zhang, VP of Engineering, and Caleb Johnson, Principal Engineer at LinkedIn, sit down with Ryan to discuss how semantic search and AI have transformed LinkedIn’s job search feature. They explore the engineering efforts behind transitioning from keyword-based search and the impact of AI models on LinkedIn’s job seekers and employers.
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 senior software engineer Kyle Mitofsky are joined by two people who worked on the launch of Overflow AI: director of data science and data platform Michael Foree and senior software developer Alex Warren. They talk about how and why Stack Overflow launched semantic search, how to ensure a knowledge base is trustworthy, and why user prompts can make LLMs vulnerable to exploits.
Tim Tutt, CEO and cofounder of Night Shift Development, tells the home team about his work in deploying large-scale search and discovery analytics, why he’s working to help nontechnical users understand and utilize their business data, and how GenAI is teaching people to ask better questions.
Semantic search allows users to search using natural language instead of a rigid syntax of keyword manipulation.
