Filtered ANN Search With Composite Vector Indexes (Part 3)

Part three of a series on composite vector indexing in Couchbase delves into the implementation and efficiency of using composite vector indexes for complex queries, using a smart grocery recommendation system as an example. The post explains how Couchbase's Indexing Service efficiently manages queries that require semantic similarity and nutritional filtering by utilizing Approximate Nearest Neighbor (ANN) scans and a smart concatenated sort key for ordering. This approach enables Couchbase to handle ORDER BY and LIMIT directly within the index service, enhancing performance by avoiding large intermediate result sets and ensuring that only the top results are processed. The flexibility of Couchbase allows developers to combine scalar fields and vector similarity measures in ORDER BY clauses, offering varied ranking strategies like semantic-first, protein-first, and sugar-first, catering to different use cases. The piece emphasizes Couchbase's capability to integrate ANN similarity, scalar filtering, and custom rankings within a single SQL++ query, facilitating the creation of intelligent search features at scale.