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https://dev.mysql.com/doc/c-api/9.7/en/query-results.html
mysql_insert_id() returns the ID generated by the last query that inserted a row into a table with an AUTO_INCREMENT index. In addition to the result set returned by a query, you can also get the following information: mysql_affected_rows() returns ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-aml-automl-privileges.html
Database Privileges You need the following privileges to access the database that stores the input tables (training datasets). mysql> GRANT SELECT, ALTER ON database_name.* TO 'user_name'@'%'; You need the following privileges to access the database ... To use AutoML, ask the admin user to grant you the following ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-aml-classification-train.html
Training the Model Train the model with the ML_TRAIN routine and use the training_data table previously created. mysql> CALL sys.ML_TRAIN('table_name', 'target_column_name', JSON_OBJECT('task', 'task_name'), model_handle); Replace table_name, ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-aml-load-model.html
Query the model handle, model owner, and the trained table name from the model catalog table. You must load a machine learning model from the model catalog before running AutoML routines other than ML_TRAIN. A model remains loaded and can be called ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-aml-model-quality.html
After training and loading the model, prepare a table of labeled data to score that has a different set of data from the trained model. mysql> CALL sys.ML_SCORE(table_name, target_column_name, model_handle, metric, score, [options]); The following ... ML_SCORE scores a model by generating predictions using the feature columns in a labeled dataset as input and comparing the predictions to ground truth values in the target column of the labeled ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-aml-regression-train.html
Training the Model Train the model with the ML_TRAIN routine and use the house_price_training table previously created. mysql> CALL sys.ML_TRAIN('table_name', 'target_column_name', JSON_OBJECT('task', 'task_name'), @variable); Replace table_name, ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-aml-topic-modeling-train.html
Therefore, you cannot use the following options for topic modeling: model_list optimization_metric exclude_model_list exclude_column_list include_column_list Unsupported Routines You cannot run the following routines for topic modeling: ML_EXPLAIN ... After preparing the data for topic modeling, you can train the ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-genai-chat-details.html
Viewing Details To view the chat session details, inspect the @chat_options variable: mysql> SELECT JSON_PRETTY(@chat_options); The output includes the following details about a chat session: Vector store tables: in the database which were ... This ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-genai-overview.html
Accelerated Vector-Based Query Processing GenAI lets you run queries on tables that contain vector embeddings at an accelerated pace by offloading them to the MySQL AI Engine (AI engine). For more information, see About Accelerated Processing of ...
https://dev.mysql.com/doc/mysql-ai/9.7/en/mys-ai-genai-privileges.html
mysql> GRANT SELECT, INSERT, CREATE, DROP, ALTER, UPDATE ON output_schema.* TO 'user_name'@'%'; For more information, see Privileges Provided by MySQL and Default MySQL Privileges.