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.

Don’t let these vector search challenges slow you down

Vector search key capabilities

Building powerful vector and GenAI-based applications requires a powerful database platform with a differentiated architecture that is fast, affordable, versatile, and as easy as SQL. Couchbase helps developers build apps using vector search and working with LangChain and LlamaIndex to leverage the artificial intelligence ecosystem.

Similarity search, hybrid search

Similarity is a powerful tool for users to find products and information, but many real-world scenarios have users wanting to search across a variety of methods, like text, geolocations, ranges, and include operational data too. Couchbase lets developers build powerful search functionality to delight users.

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.

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