Vector databases are transforming how we search, filter, and understand unstructured data like text, images, and audio. Traditional databases rely on exact matches, often missing the nuance of meaning and similarity. In this talk, you’ll discover how embedding models convert content into high-dimensional vectors and how vector databases index and search these vectors to power modern AI applications such as semantic search and recommendation engines.
We’ll start with the fundamentals: what vector embeddings are, how they are created, and why they matter. Then, we’ll explore how vector databases efficiently measure similarity between vectors using distance metrics such as cosine similarity, dot product, Euclidean, and Manhattan distances.
Using clear examples and relatable explanations, I’ll compare these metrics and explain when to use each. Finally, we’ll look at real-world applications and examples of vector databases in production, showing why they have become essential infrastructure for AI systems.
Whether you’re a developer curious about search technology or an AI practitioner working with unstructured data, you’ll leave with a clear understanding of what vector databases are, how they work, and why they are a cornerstone of modern machine learning.