Embedding models are algorithms that transform complex, high-dimensional data—like words, images, or audio—into dense, low-dimensional numerical vectors. These vectors capture the underlying meaning, context, and relationships within the data, allowing machines to understand and process it more efficiently. By representing data as points in a shared mathematical space, embedding models enable tasks such as semantic search, recommendation systems, and image recognition by placing similar items close together.
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