- It will not include following things
- New updates, fixes, security alerts, data fixes, and critical patch updates
- New tax, legal, and regulatory updates
- New upgrade scripts
- Certification with new third-party products/versions
- Certification with new Oracle products
For more specifics on Premier Support, Extended Support, and Sustaining Support, please refer to Oracle’s “Technical Support Policies.”
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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