Couchbase Vector Search

Couchbase provides native vector search integrated with its distributed document database, enabling combined operational and AI workloads. Couchbase Capella (cloud) offers developer tier free; Enterprise pricing based on nodes and capacity. Self-hosted Community edition available. Vector search uses scoped indexes enabling per-collection vector configurations. Supports similarity search with metadata filtering in single queries. Key advantage: operational data and vector embeddings in same database with ACID transactions. Features include automatic index management, multi-modal search (text + vector), and global distribution through Couchbase’s mobile/edge replication. For AI applications: store documents, embeddings, and operational data together with consistent queries. N1QL (SQL-like query language) supports vector operations alongside traditional queries. Page should cover: vector index creation and configuration, N1QL vector syntax, Capella vs self-hosted comparison, integration with existing Couchbase data, comparison with MongoDB Atlas Vector Search, and use cases for combined operational/AI workloads.