Searching images
In previous lessons, you learned how vectors and iFull definition for embedding (opens in a new tab)Information represented as a numerical vector, positioned so that similar information sits close together. can represent data in different formats. You used embeddings and a iFull definition for vector index (opens in a new tab)A structure over a vector property. The database searches it to find the vectors nearest a given one, rather than comparing every vector stored. to find similar text.
In this lesson, you will explore a dataset of movie posters and see how you can use vectors and embeddings to find similar images.
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