Vector search ranks results by assessing the similarity between the query vector and the vectors in the dataset. This is typically done using similarity metrics such as cosine similarity or Euclidean distance, which quantify how close or similar the vectors are. The process involves calculating the distance or angle between vectors in the vector space, with smaller distances or angles indicating higher similarity.
Once the similarities are computed, the results are ranked based on these scores. The most similar items to the query vector are presented at the top of the search results. This ranking method allows for more nuanced and semantically similar results compared to traditional keyword search, which relies heavily on exact term matches.