Couchbase Vector Search

Couchbase Vector Search is the search capability of the Couchbase data platform, supporting vector storage and similarity search alongside full-text, geospatial, and range queries in the same database. Couchbase positions it as a way to avoid running a separate vector database for RAG and agentic applications.

What It Is

Vector search returns nearest-neighbor results without requiring an exact match. Text, images, audio, and video are converted into vector embeddings and used for semantic search or for grounding LLM answers through retrieval-augmented generation (RAG). Couchbase says it can serve as the vector store for RAG pipelines, automating embedding creation and indexing so LLMs can draw on private enterprise context.

Index Types and Hybrid Search

Couchbase 8.0 introduces Hyperscale and Composite vector indexes. The FAQ lists three primary index types:

• Hyperscale, for billion-scale datasets.
• Composite, for high-speed filtered searches.
• Search, for hybrid semantic-keyword queries.

Hybrid search can combine vector similarity with text, geolocation, range, and operational data predicates, so developers can tune performance and relevance for their use case. Couchbase publishes a benchmark claiming it is 350x faster than MongoDB at billion scale; this is a vendor claim.

Where It Is Used

The page describes several use cases: natural-language chatbots, agentic and RAG applications, and fraud and anomaly detection, where user behavior and transactions are converted to vectors and compared. It also covers mobile and embedded vector search through Couchbase Lite, which Couchbase says supports offline-first vector search on iOS, Android, and IoT platforms.

Fit in the Couchbase Platform

Search sits alongside Capella (database-as-a-service), Couchbase Server (self-managed), Mobile, Analytics, and the Couchbase AI Data Plane for production AI agents. The company says it supports deployment across DBaaS, Kubernetes, cloud, on-premises, edge, mobile, and air-gapped environments.