Part 2: Master Vector Embeddings with Weaviate

Introduction to Vector Search in Vector Embeddings

Vector search is a powerful technique that uses mathematical similarity to identify and retrieve related data efficiently. This Part 2 webinar of the community series with Weaviate offers an introduction to vector search, explaining its core principles, limitations, and how it scales with advanced technologies like vector databases.

Explore the fundamental concepts behind this concept, learn how vector databases enhance performance and address scalability challenges. Understand the role of Approximate Nearest Neighbor (ANN) algorithms, and see how modern vector databases like Weaviate enhance search capabilities followed by a hands-on demo.

What we will cover:

• Understand how similarity is calculated mathematically and its role in data retrieval.
• Explore what basic search lacks when applied at scale.
• Focus on Approximate Nearest Neighbor (ANN) algorithms, particularly HNSW, and how it optimizes performance.
• Gain insights into CRUD operations and how traditional database features are integrated.
• See a practical demo of implementing vector search over the entire Wikipedia dataset using Weaviate.

Want to join Part 3 of the series? Find it here!