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Embeddings
Numerical representations of text that capture meaning.
Explanation
Embeddings convert text into number vectors that capture semantic meaning. Similar concepts are close to each other in vector space, regardless of the exact formulation.
How it works
A specialized model reads a text and outputs a vector (e.g. 1536 numbers). "Dog" and "puppy" have similar vectors, "dog" and "car" do not.
Example
In a RAG pipeline, all documents are saved as embeddings. When a user query is made, the embedding of the question is compared with the document embeddings.
Why it matters
Embeddings enable semantic search - the system finds relevant information even if other words are used.