Vector Search
Couchbase’s breakthrough vector search features support billion-scale vector storage and search capabilities for applications with incredible performance and accuracy. Multiple vector index type options provide teams with the best results aligned to their use case. Deliver safer AI results with RAG and vector search at a huge scale.
What is vector search used for in a database?
Vector search delivers nearest-neighbor results without needing a direct match. Text, images, audio, and video are converted to mathematical representations and used for semantic searching or overcoming AI challenges using the retrieval-augmented generation (RAG) framework. At the enterprise level, vector search is commonly used for powerful, natural language chatbots, sophisticated search that delivers a hybrid search combining range, text, and vector predicates, and data analysis spotting similarity and anomalies. In Couchbase 8.0, we introduce Hyperscale and Composite vector indexes to improve RAG accuracy at scale without hurting performance or cost of operations.
Don’t let these vector search challenges slow you down
Complexity
There is no need to use a separate database for vector search, which adds complexity, administration, cost, and latency of the overall app.
Latency
Returning results as fast as possible is critical to users. Extra hops and poor indexing kill user experience.
Security
Build AI apps without feeding corporate data to public models and deliver users accurate and up-to-date results.
Scalability
Couchbase is proven to handle billions of vectors with millisecond response times, so your application can scale globally without limits.
Similarity search, hybrid search
Similarity is a powerful tool, but real-world scenarios require hybrid search across text, geolocations, ranges, and operational data. With multiple indexing options, developers can precisely tune their hybrid search strategy for optimal performance and relevance.
Agentic and RAG apps
AI agents will add a new level of sophistication and reasoning to how users will interact with an organization and their data. Using RAG, teams can make AI apps safer, more accurate, and up to date.
Fraud and anomaly detection
By converting user behavior and transactions into vectors, those patterns can be compared to other similar vector representations that might indicate fraud. Vector search is effective in handling high-dimensional data and similarity matching.
Mobile vector apps
Running vector search in mobile and embedded devices comes with all the benefits of edge computing including millisecond response times, reliability, availability even without the internet (“offline-first”), bandwidth savings, and most importantly, customized responses without compromising on data privacy.
What customers are saying
-
“Couchbase’s new vector search capabilities transform how we deliver context-aware video discovery for enterprises.”
Ian Merrington, CTO, Seenit92%
faster implementation of new features
-
“Couchbase real-time communications data and high concurrency query, greatly improve the performance and stability for the AI Assistant application.”
Andy Qiu, CEO, Jinmu70M
documents
8K
operations per second
-
“Couchbase Search allows us to deliver customer search results from extremely large data sets very efficiently.”
Brant Burnett, Systems Architect, CenterEdge Software360°
customer insights for their clients
-
“What Couchbase has done with SQL++ has been one of the most innovative things done in the database space in decades.”
Bill House, VP of Engineering, SWARM Engineering400%
faster planning time
$1M+
savings per optimization
Explore related resources
-
DOCUMENTATION
Introduction to vector search
-
VIDEO
See vector and hybrid search in action
-
BENCHMARK
Vector search tests: Couchbase is 350x faster than MongoDB at billion scale
Vector search FAQ
Get quick answers to questions about vector search, databases, and more.
Couchbase is a multimodel platform that combines high-performance vector search with text, geo-spatial, and other search techniques, eliminating the need for a separate, standalone vector database.
Couchbase supports three primary index types: Hyperscale for billion-scale datasets, Composite for high-speed filtered searches, and Search for hybrid semantic-keyword queries.
Native support for vector search on mobile devices is available in Couchbase Lite, which enables offline-first vector search on iOS, Android, and IoT platforms.
Couchbase supports RAG pipelines by serving as a dedicated vector store to automate embedding creation and indexing, ensuring LLMs have access to accurate, private enterprise context.