Filtered ANN Search With Composite Vector Indexes

The series on composite vector indexing in Couchbase explores the significance and implementation of composite vector indexes, using a Smart Grocery Recommendation System as a practical example. It explains how composite vector indexes are structured and optimized within the Couchbase Indexing Service, focusing on performance and execution improvements like ORDER BY pushdown. The article illustrates the use of Filtered Approximate Nearest Neighbor (Filtered ANN) to create a recommendation engine that understands user intent and constraints, such as nutritional filters. It describes how traditional indexing methods fall short in semantic searches and highlights the advantages of composite vector indexes, which integrate vector similarity and scalar filtering for efficient querying. The concept of embedding vectors is detailed, emphasizing how they enable semantic similarity searches, enhanced by ANN for quick identification of relevant products that meet user-defined nutritional criteria. The discussion also covers the architectural components and parameters required to implement composite vector indexes, promising further exploration in subsequent series parts.