What is Couchbase Vector Search?

Couchbase Vector Search is an AI-powered search capability that enables similarity-based queries on high-dimensional data, making it ideal for AI applications, recommendation engines, and large-scale information retrieval.

How does vector search work in NoSQL databases?

Vector search in NoSQL databases uses mathematical embeddings to compare data points based on similarity rather than exact matches, making it useful for applications like image recognition, natural language processing, and recommendation systems.

What are the benefits of Couchbase Vector Search?

Couchbase Vector Search provides fast and scalable similarity searches, enabling AI-driven applications to process high-dimensional data efficiently. It integrates seamlessly with NoSQL databases and supports multimodal AI workflows.

Can Couchbase Vector Search be integrated with existing applications?

Yes, Couchbase Vector Search is designed to integrate seamlessly with existing applications, offering easy deployment and compatibility with AI and machine learning models.

What are common use cases for vector search?

Vector search is commonly used in AI-driven applications such as recommendation engines, image and video recognition, fraud detection, NLP (Natural Language Processing), and real-time data retrieval for personalized experiences.

Vector Search Database – Scalable, Enterprise-Level Solutions

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.

NEW

Accelerate building safe and scalable agentic apps with Hyperscale Vector Indexes

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BENCHMARK

Vector search tests: Couchbase is 350x faster than MongoDB at billion scale

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PRODUCT

Build mobile GenAI apps that work without internet

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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.

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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.

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Unmatched indexing flexibility

Couchbase uniquely offers three vector indexing options to match your performance, recall, cost, and query needs.

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Billions-scale performance

Couchbase vector search delivers millisecond retrieval at scale with a memory-first architecture and flexible indexing services.

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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.

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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.

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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.

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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.

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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.