Vector Search
Store and search billions of vectors right next to your operational data. Three vector index types let teams tune for scale, filtered queries, or hybrid search, and Couchbase Lite runs vector search on device. As part of the Couchbase AI Data Plane™, vector search gives RAG apps and AI agents accurate, current context.
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 commonly powers AI agents and natural language assistants, hybrid search that combines range, text, and vector predicates, and data analysis that spots similarity and anomalies. Couchbase offers Hyperscale, Composite, and Search vector indexes to improve RAG and agent accuracy at scale without hurting performance or operating cost, and Couchbase Lite brings vector search to mobile and edge devices.
Complexity
A separate vector database adds sync jobs, administration, cost, and latency to the overall app. Couchbase keeps vectors, operational data, and agent memory on one platform.
Latency
Returning results as fast as possible is critical to users. Extra hops and poor indexing kill user experience. On-device vector search removes the network round trip entirely.
Segurança
Build AI apps without feeding corporate data to public models and deliver users accurate and up-to-date results. With Couchbase Lite, sensitive queries can stay on the device.
Escalabilidade
Couchbase is proven to handle billions of vectors with millisecond response times, so your application can scale globally without limits.
Vector search key capabilities
Accurate AI apps and agents depend on fast retrieval from current data. Couchbase builds vector search into the same platform that runs your operational workloads, from billion-vector clusters in the cloud to apps running on a single device.
AI Data Plane for agentic applications
Vector search is one layer of the Couchbase AI Data Plane, alongside Agent Memory, the MCP Server, and Model Service, so agents get memory, context, and governed data access on one platform.
Flexible vector indexing
Choose Hyperscale for billion-vector datasets, Composite for filtered queries, or Search for hybrid keyword and semantic search, and match each workload to its performance, recall, and cost needs.
Billions-scale performance
Hyperscale vector indexes keep cluster centroids in memory and vectors on disk, so you can search billions of vectors at high recall without holding them all in RAM.
Cloud-to-edge support
Couchbase Lite runs vector and hybrid search on device in iOS, Android, desktop, and embedded apps and syncs with Couchbase Capella, so edge AI apps answer locally and keep running regardless of connectivity.
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 need relevant context, durable memory, and governed access to live data. On the Couchbase AI Data Plane, vector search works alongside Agent Memory and the MCP Server to deliver all three. 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.
Edge and mobile AI apps
Running vector search on mobile and embedded devices brings the benefits of edge computing: millisecond response times, lower bandwidth and cloud egress costs, and personalized results without sensitive data leaving the device. Apps keep serving users through network outages, and Couchbase Lite 4.1 adds peer-to-peer sync over Bluetooth for teams working beyond network coverage.
O que os clientes estão dizendo
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“Couchbase’s new vector search capabilities transform how we deliver context-aware video discovery for enterprises.”
Ian Merrington, CTO, Seenit92%
implementação mais rápida de novos recursos
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“Couchbase real-time communications data and high concurrency query, greatly improve the performance and stability for the AI Assistant application.”
Andy Qiu, CEO, Jinmu70M
documentos
Diverse colleagues brainstorming and working together on the creation of a new application using laptop during a meeting in a modern workplace. -
"O Couchbase Search nos permite fornecer resultados de pesquisa de clientes a partir de conjuntos de dados extremamente grandes com muita eficiência."
Brant Burnett, Systems Architect, CenterEdge Software360°
customer insights for their clients
Businesspeople working with computers, documents and talking. -
“What Couchbase has done with SQL++ has been one of the most innovative things done in the database space in decades.”
Projeto de lei da Câmara, Vice-presidente de engenharia, SWARM Engineering400%
faster planning time
Vector search FAQ
Quick answers about Couchbase vector search, including how it compares to standalone vector databases, which index types to use, and how it supports edge and RAG workloads.
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.
Yes. Couchbase Lite runs vector and hybrid search on iOS, Android, desktop, and embedded devices, so AI features answer locally, keep sensitive queries private, and work regardless of connectivity.
Couchbase stores embeddings alongside source data, and the AI Data Plane can generate them automatically from structured and unstructured data, so LLMs retrieve accurate, private enterprise context.
Hybrid vector search combines semantic similarity, keyword, geo-spatial, and range filters in one query. Couchbase runs all predicates in a single pass with no application-side result merging.
Keep exploring
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DOCUMENTAÇÃO
Introduction to vector search
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TUTORIAL
Build semantic search with Hyperscale and Composite vector indexes
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BENCHMARK
Vector search tests: Couchbase is 350x faster than MongoDB at billion scale