- Check restrictions/limitations in question above.
- If you are migrating a database, (a) make sure there are no invalid objects in the source database before making the export.
- Take a full norows export to recreate objects that won't be transported with TTS.
- Keep the source database viable until you have determined all objects are in the target database and there are no issues (i.e. the target database has been thoroughly checked out and exercised).
- Do a dry run to work out any unexpected issues and determine timings.
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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