Similarity Search

Similarity search is the process of finding data points (such as documents, images, or vectors) that are similar to a given query point based on a specific metric, such as cosine similarity or Euclidean distance. It is often used in tasks like image recognition, recommendation systems, and information retrieval, where the goal is to find items that are closest in meaning or characteristics to the input. Similarity search is commonly applied in vector spaces, where items are represented as embeddings.

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Related terms: Euclidean distance; vector embeddings; embedding models

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On the Onehouse website:

• Managing AI Vector Embeddings with Onehouse