Introduction to Vector Search

What is vector search?

Vector search is also known as semantic search or similarity search. It uses vector representations to retrieve a dataset's most relevant (or similar) items. For example, given the phrase “delicious Italian cuisine,” a vector search might return closely related phrases like “tasty pasta dishes” or “authentic Mediterranean food” based on the semantic similarity, even if they don’t share exact keywords.

In traditional keyword-based search, textual data is typically indexed using inverted indexes, which map words to the documents or passages containing them. This approach does not consider the semantic relationships between words and documents. Vector search, on the other hand, operates in a high-dimensional vector space, where each item (document, image, or any other data point) is represented as a dense vector embedding. These vector embeddings capture the semantic relationships between items, allowing for more accurate and relevant search results.