Technique

Vector Search

Vector search finds similar items by comparing their mathematical representations (embeddings) rather than matching keywords, enabling AI systems to understand meaning and context.

What Is Vector Search?

Vector search (also called similarity search or nearest-neighbor search) finds items that are semantically similar by comparing embedding vectors in high-dimensional space. Unlike keyword search that matches exact terms, vector search understands that "automobile" and "car" mean the same thing.

How Do Businesses Use Vector Search?

Vector search powers product recommendations ("similar items"), customer support (finding relevant knowledge base articles), duplicate detection, and the retrieval component of RAG systems. It's stored and executed in vector databases optimized for this type of query.

vector searchsimilarity searchnearest neighbor

Related Terms

Embeddings

Embeddings are numerical representations of text, images, or other data that capture semantic meaning — enabling AI systems to understand similarity, search, and retrieve relevant information.

Semantic Search

Semantic search understands the meaning and intent behind queries rather than just matching keywords, using AI to deliver more relevant results from documents, databases, and knowledge bases.

Vector Database

A vector database is a specialized database optimized for storing and querying high-dimensional embeddings, enabling fast similarity search that powers RAG, recommendations, and semantic search.

RAG (Retrieval-Augmented Generation)

RAG is a technique that enhances AI responses by retrieving relevant information from external knowledge bases before generating an answer, combining the power of search with generative AI.

Explore Further

Empowerment Partnership →

Want to apply vector search in your business?

Take our free AI assessment and get a personalized roadmap for implementing AI strategies that drive real results.

Free AI AssessmentSchedule a Call