clip-vit-base-patch32 works by encoding images and text with two separate neural networks that are trained to produce comparable vectors. Images are processed by a Vision Transformer that splits each image into fixed-size patches and converts them into a sequence of embeddings. Text is processed by a transformer-based text encoder that converts tokens into embeddings. Despite using different encoders, both outputs are projected into the same vector space.
The key mechanism behind this alignment is contrastive training. During training, the model sees large batches of image–text pairs and learns to increase similarity between matching pairs while decreasing similarity between mismatched pairs. Similarity is typically measured using cosine similarity. Over time, this forces semantically related images and text to cluster together in the embedding space, making cross-modal comparison possible.
For developers, this design means there is no need to manually align images and text after embedding. Both can be embedded independently and compared directly. When these embeddings are stored in a vector database such as Milvus or Zilliz Cloud, the same similarity search logic can be used regardless of modality. This simplifies system design and enables scalable cross-modal retrieval with minimal glue code.
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