The discussions surrounding the one-stop-shop principle are among the most highly debated and are still unclear as the standing positions are highly varied. The Commission text has a fairly simple and concise ruling in favor of the principle, the Parliament also promotes a lead DPA and adds more involvement from other concerned DPAs, the Council’s view waters down the ability of the lead DPA even further. A more in depth analysis of the one-stop-shop policy debate can be found here.
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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