A measure of similarity between embeddings (e.g., text vs. image) often used in multimodal vector search.
Cosine similarity measures the cosine of the angle between two vectors, providing a metric for similarity based on direction rather than magnitude. It's commonly used in vector space models to compare document similarity.
Calculated as the dot product of two vectors divided by the product of their magnitudes. Values range from -1 (completely dissimilar) to 1 (completely similar), with 0 indicating orthogonality.
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