In this lesson, you’ll learn how to get started with ScyllaDB Vector Search. You will also understand how to efficiently store, index, and query vectors, enabling you to build high-performance vector search applications.
You can use it to build a wide range of AI-driven and semantic search applications, including:
- Recommendation systems
- Text, image, and audio similarity search
- Retrieval-Augmented Generation (RAG) pipelines
- Semantic search engines
- Semantic caching layers
- And more…
These use cases rely on efficient vector similarity search, where ScyllaDB provides low-latency, high-throughput performance at scale.
The lesson starts with an introduction to Vector Search and its implementation in ScyllaDB, followed by a video demo of how to use it. Finally, there are three hands-on labs that you can run yourself to see it in action.
*Vector Search is currently in Beta, and some features are being finalized. Read more in this post and stay updated in the community forum.