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https://dev.mysql.com/doc/mysql-shell/9.4/en/mysql-shell-utilities-dump-binlogs.html
Where binary-log-file is the name of the binary log and binary-log-file-position is an unsigned integer specifying a position in that file. This utility dumps binary logs generated since a specific point in time to the given local or remote ...
https://dev.mysql.com/doc/mysql-shell/9.4/en/mysql-shell-utilities-json.html
The default table is created as follows: CREATE TABLE `dbname`.`tablename` ( target_column JSON, id INTEGER AUTO_INCREMENT PRIMARY KEY ) CHARSET utf8mb4 ENGINE=InnoDB; The default collection name or table name is the name of the supplied import file ... MySQL Shell's JSON import utility util.importJSON() enables you to import JSON documents from a file (or FIFO special file) or standard input to a MySQL Server collection or relational ...
https://dev.mysql.com/doc/mysql-shell/9.4/en/mysql-shell-utilities-parallel-table.html
For information on the mysqlsh command-line integration, see Section 5.8, “API Command Line Integration”. When you use an integer value in place of a column name in the array, that column in the import file or files is captured as a user ...
https://dev.mysql.com/doc/mysql-ai/9.4/en/mys-ai-aml-scoring-an-anomaly-detection-model.html
To generate a score, the target_column_name column must only contain the anomaly scores as an integer: 1 for an anomaly, or 0 for normal. After generating predictions, you can score the model to assess its reliability. For a list of scoring metrics ...
https://dev.mysql.com/doc/mysql-ai/9.4/en/mys-ai-aml-topic-modeling-prediction.html
This option must be an integer greater or equal to one. After training the model, you can run topic modeling on the trained text. To run topic modeling, use the sample data from the movies dataset. When the output table generates, you can review ...
https://dev.mysql.com/doc/mysql-ai/9.4/en/mys-ai-aml-using-an-anomaly-detection-model.html
The ML_PREDICT_TABLE and ML_SCORE routines include the topk option, which is an integer between 1 and the table length. To generate predictions, use the sample data from the two anomaly detection datasets: credit_card_train and training_data. Both ...
https://dev.mysql.com/doc/mysql-ai/9.4/en/mys-ai-genai-summarize-content.html
MySQL also supports ', 'a wide range of data types, including integers, floating-point numbers, dates, and strings. The following sections in this topic describe how to summarize exiting content using the GenAI: Before You Begin Summarizing Content ...
https://dev.mysql.com/doc/mysql-ai/9.4/en/mys-mysqlai-ml-embed-table.html
The ML_EMBED_TABLE routine runs multiple embedding generations in a batch, in parallel. This topic contains the following sections: ML_EMBED_TABLE Syntax Syntax Examples See Also To learn about the privileges you need to run this routine, see ...
https://dev.mysql.com/doc/mysql-ai/9.4/en/mys-mysqlai-ml-predict-row.html
topk: Specify the number of recommendations to provide as a positive integer. ML_PREDICT_ROW generates predictions for one or more rows of unlabeled data specified in JSON format. A call to ML_PREDICT_ROW can include columns that were not present ...