What is Vector Search?

Vector Search is a retrieval method that finds items by comparing embedding vectors for similarity. Vector Search is used for semantic search where meaning matters more than exact wording.

Quick definition

Vector Search finds similar content by comparing meaning vectors instead of matching keywords.

How Vector Search works

• Vector Search converts queries and documents into embeddings.
• Vector Search retrieves nearest neighbors using a similarity metric.
• Vector Search can be combined with keyword filters and ranking signals.
• Vector Search is commonly used in retrieval-augmented generation (RAG) systems.

Why Vector Search matters

Vector Search matters because vector search improves retrieval for paraphrases and long-tail queries.

Vector Search can improve answer quality when retrieved sources are more relevant.

Example use cases

• Retrieving documentation passages relevant to a question.
• Finding similar prompts to reduce duplication in a prompt set.
• Powering semantic search across a knowledge base.

Related terms

• Embeddings
• Semantic Search
• Retrieval-Augmented Generation (RAG)
• Contextual Relevance
• AI Search Results