Embedding models convert text, images, or other content into vectors in a high-dimensional space. Similar items tend to produce vectors that are close together, enabling semantic search, recommendations, clustering, retrieval-augmented generation, and other similarity-based AI workflows. It is commonly used in systems that generate or retrieve language, code, images, or other content and often works alongside prompts, tools, embeddings, and external knowledge.
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©2026 Scaylar Technologies. All rights reserved.