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NLP & LLMs Easy

What are embeddings, and how do you measure similarity between them for vector search?

For AI / LLM EngineerML EngineerData Scientist
The short answer

Embeddings are dense vectors that map text or other data into a geometric space where semantically similar items are close together. Vector search ranks candidates by similarity, most commonly cosine similarity or dot product and sometimes Euclidean distance, retrieving the nearest vectors to a query embedding.

How to think about it

Embeddings are dense vectors that map text or other data into a geometric space where semantically similar items are close together. Vector search ranks candidates by similarity, most commonly cosine similarity or dot product and sometimes Euclidean distance, retrieving the nearest vectors to a query embedding.

Learn it properly Embeddings

Keep practising

What are embeddings and why are they central to modern deep learning? What is a vector database and how does it enable semantic retrieval? What is hybrid search and why is it often better than pure vector search? Why do dense word embeddings outperform one-hot vectors? Why is cosine similarity preferred over Euclidean distance for comparing text vectors?
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