The notebook has created text and image embeddings of 50,000 Amazon products. These embeddings are then stored in a vector database named Chroma db. I have embedded the vectors using the torch module and open clip embedding functions outside of the vector database and then the embeddings are passed and stored in chroma. I found this method more efficient and time-saving. Semantic Search is done to retrieve a product using text and image query.
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Thamannahafeez/Open-Clip
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create embeddings using open clip
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