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https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-regression-train.html
mysql> SET @variable = 'model_handle'; Replace @variable and model_handle with your own definitions. For example: mysql> SET @model='regression_use_case'; The model handle is set to regression_use_case. mysql> CALL sys.ML_TRAIN('table_name', ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-scoring-a-forecasting-model.html
mysql> CALL sys.ML_MODEL_LOAD(@model, NULL); The following example uses the model handle. mysql> CALL sys.ML_MODEL_LOAD('forecasting_use_case', NULL); Score the model with the ML_SCORE routine and use the neg_sym_mean_abs_percent_error metric.
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-scoring-a-recommendation-model.html
mysql> CALL sys.ML_MODEL_LOAD(@model, NULL); The following example uses the model handle. mysql> CALL sys.ML_MODEL_LOAD('recommendation_use_case', NULL); Score the model with the ML_SCORE routine and use the precision_at_k metric. mysql> CALL ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-topic-modeling-train.html
mysql> SET @variable = 'model_handle'; Replace @variable and model_handle with your own definitions. For example: mysql> SET @model='topic_modeling_use_case'; The model handle is set to topic_modeling_use_case. mysql> CALL sys.ML_TRAIN('table_name', ... After preparing the data for topic modeling, you can train the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-unload-model.html
mysql> CALL sys.ML_MODEL_ACTIVE('all', @model_info); Select the session variable created to view all loaded models. The following example unloads a model by using the model handle: mysql> CALL sys.ML_MODEL_UNLOAD('recommendation_use_case'); Where: ...Before You Begin Review the following Train a Model Load a Model Unload a Model You can verify what models are currently loaded with the ML_MODEL_ACTIVE routine before and after unloading the ...
https://dev.mysql.com/doc/mysql-ai/9.5/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/9.5/en/mys-ai-nl-sql.html
The examples in this topic uses a sample database, airport that you can download from the following locations: https://downloads.mysql.com/docs/airport-db.tar.gz https://downloads.mysql.com/docs/airport-db.zip Generating and Running an SQL Query ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-aiaml-error-messages.html
Allowed options for training are: ['task', 'model_list', 'exclude_model_list', 'optimization_metric', 'exclude_column_list', 'datetime_index', 'endogenous_variables', 'exogenous_variables', 'positive_class', 'users', 'items', 'user_columns', ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-explain-row.html
ML_EXPLAIN_ROW Syntax mysql> SELECT sys.ML_EXPLAIN_ROW(input_data, model_handle, [options]); options: { JSON_OBJECT("key","value"[,"key","value"] ...) "key","value": { ['prediction_explainer', {'permutation_importance'|'shap'}|NULL] } } Required ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-nl-sql.html
Following example specifies the database to consider for the SQL query: mysql> CALL sys.NL_SQL("How many flights are there in total?",@output, JSON_OBJECT('schemas',JSON_ARRAY('airportdb'),'model_id','llama3.2-3b-instruct-v1')); ...The routine also ...