Vector Search¶

Vector Search in ScyllaDB¶

Vector Search is a powerful method for efficiently searching and retrieving high-dimensional data based on similarity rather than exact matches. It is particularly useful in AI and machine learning applications, where data is often represented as vectors-mathematical representations of objects such as text, images, audio, or video. In these applications, you typically need to retrieve data that is similar to a given query, rather than relying on keyword-based search or exact matches.

ScyllaDB’s Vector Search feature allows you to store, index, and query high-dimensional vector data at scale. Vector Search is built to work within your existing ScyllaDB infrastructure, taking advantage of its high-performance and highly available architecture.

Using Vector Search¶

• Quick Start Guide to Vector Search

  The quickstart guide will walk you through setting up and using Vector Search in ScyllaDB Cloud. You’ll learn how to define a vector-enabled table, insert data, and run similarity searches using the built-in ANN (Approximate Nearest Neighbor) capabilities.
• Vector Search Deployment

  A guide to creating, enabling, resizing, disabling, and monitoring Vector Search clusters in ScyllaDB Cloud.
• Working with Vector Search

  A comprehensive reference on working with Vector Search, covering the vector data type, the vector index, and performing similarity searches using ANN queries.
• Glossary

  A glossary of terms and concepts related to Vector Search, such as vector types, similarity functions, and ANN queries. Use it as a quick reference for understanding key components of vector-based search systems.
• Reference

  Technical reference for Vector Search in ScyllaDB, including a complete list of Vector Search-related API endpoints and relevant CQL syntax.
• Example Applications

  Learn how you can use ScyllaDB Vector Search to build RAG applications, semantic caching layers, and how it works together with popular LLM libraries like LlamaIndex and LangChain.