Which Python library is used to vectorize text chunks and the user's question in the following example?
import oracledb
connection = oracledb.connect(user=un, password=pw, dsn=ds)
table_name = "Page"
with connection.cursor() as cursor:
create_table_sql = f"""
CREATE TABLE IF NOT EXISTS {table_name} (
id NUMBER PRIMARY KEY,
payload CLOB CHECK (payload IS JSON),
vector VECTOR
)"""
try:
cursor.execute(create_table_sql)
except oracledb.DatabaseError as e:
raise
connection.autocommit = True
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-MiniLM-L12-v2')
What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?
Why would you choose to NOT define a specific size for the VECTOR column during development?
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?
Which function should you use to determine the storage format of a vector?
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?