Embeddings are numerical representations of phrases, sentences, photographs, or paperwork in a high-dimensional house. They permit AI fashions to seize semantic relationships between totally different items of information. As an alternative of utilizing plain textual content, AI converts these parts into vectors (arrays of numbers), enabling environment friendly comparability and retrieval.
Conventional keyword-based search strategies depend on precise phrase matches, which have main limitations:
- They fail to grasp synonyms (e.g., “automotive” and “vehicle” are thought of totally different phrases).
- They don’t seize contextual that means (e.g., “financial institution” as a monetary establishment vs. “financial institution” as a riverbank).
- They battle with giant datasets, making searches inefficient.
Embeddings clear up these issues by representing phrases, phrases, and paperwork as vectors in a mathematical house, permitting AI techniques to discover similarities primarily based on that means reasonably than precise wording.
