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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 GenAI 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 GenAI 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.
Vector search key capabilities
Building powerful vector and GenAI-based applications requires a powerful database platform with a differentiated architecture that is fast, affordable, and versatile.
Single platform for agentic applications
Build modern applications, supporting GenAI, RAG, and agents at scale, while minimizing privacy concerns and latency.
Unmatched indexing flexibility
Couchbase uniquely offers three vector indexing options to match your performance, recall, cost, and query needs.
Billions-scale performance
Couchbase vector search delivers millisecond retrieval at scale with a memory-first architecture and flexible indexing services.
Cloud-to-edge support
With vector search in the cloud and on-device, you gain the cloud scale required for GenAI and the edge processing to make it effective.
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 GenAI 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 transforms how we deliver context-aware video discovery for enterprises.”
92%
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.”
70M
documents
8K
operations per second
“Couchbase Search allows us to deliver customer search results from extremely large data sets very efficiently.”
360°
customer insights for their clients
“We’re happy with Couchbase’s availability, performance, easy-to-replicate data, security, scalability, and full-text search.”
>30K
queries per minute
<5s
application start time
Explore related resources
Introduction to vector search
See vector and hybrid search in action
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.
How does Couchbase vector search compare to other databases?
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.
What vector index types does Couchbase support?
Couchbase supports three primary index types: Hyperscale for billion-scale datasets, Composite for high-speed filtered searches, and Search for hybrid semantic-keyword queries.
Can Couchbase vector search work on mobile and edge devices?
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.
How does Couchbase vector search support RAG pipelines?
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.
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