SQL> SELECT * FROM (
2 SELECT sequence#, archived, applied,
3 TO_CHAR(completion_time, 'RRRR/MM/DD HH24:MI') AS completed
4 FROM sys.v$archived_log
5 ORDER BY sequence# DESC)
6 WHERE ROWNUM <= 10
7 /
SEQUENCE# ARCHIVED APPLIED COMPLETED
---------- -------- ------- ----------------
11211 YES NO 2004/09/16 09:30
11210 YES YES 2004/09/16 09:00
11209 YES YES 2004/09/16 08:30
11208 YES YES 2004/09/16 08:00
11207 YES YES 2004/09/16 07:30
11206 YES YES 2004/09/16 07:00
11205 YES YES 2004/09/16 06:30
11204 YES YES 2004/09/16 06:30
11203 YES YES 2004/09/16 06:30
11202 YES YES 2004/09/16 06:00
10 rows selected.
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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