You generate embeddings using clip-vit-base-patch32 by loading the pretrained model and running images or text through the appropriate encoder. Most developers use a standard deep learning framework such as PyTorch and rely on official or well-supported libraries that bundle the model and preprocessing logic. Images are resized, normalized, and converted into tensors, while text is tokenized and padded according to the model’s requirements.
In a typical workflow, images and text are embedded offline in batches to reduce runtime cost. For example, a dataset of product images might be processed once to generate image embeddings. These embeddings are stored alongside metadata such as product IDs. Text queries are embedded at runtime using the same model and preprocessing steps to ensure compatibility.
Once generated, embeddings are commonly stored in a vector database such as Milvus or Zilliz Cloud. Developers define a collection with a vector field matching the embedding dimension and insert the vectors. At query time, similarity search retrieves the most relevant items. This separation between embedding generation and retrieval makes the system easier to scale and maintain.
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