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https://dev.mysql.com/doc/x-devapi-userguide-shell-python/en/collection-find.html
The find(SearchConditionStr) function is for searching documents in a collection, similar to the SELECT statement for an SQL database. It takes a search condition string (SearchConditionStr) as a parameter to specify the documents that should be ...
https://dev.mysql.com/doc/x-devapi-userguide-shell-python/en/mysql-x-expressions-ebnf-definitions.html
This section provides a visual reference guide to the grammar for the expression language used in X DevAPI.
https://dev.mysql.com/doc/heatwave/en/mys-hw-defining-secondary-engine.html
Defining Secondary Engine For each table that you want to load into MySQL HeatWave Cluster, you must define the MySQL HeatWave query processing engine (RAPID) as the secondary engine for the table. Then, the MySQL HeatWave query processing engine ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai-run-chat.html
When you run MySQL HeatWave Chat, it automatically loads the default LLM. By default, MySQL HeatWave Chat searches for an answer to a query across all ingested documents by automatically discovering available vector stores, and returns the answer ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-lakehouse-error-handling.html
See MySQL 9.5 Error Message Reference for Lakehouse error messages. As of MySQL 9.2.2, a detailed summary of warnings is available when loading data to Lakehouse. This includes warnings related to the schema inference stage of loading data. The ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-option-tracker.html
The Option Tracker component provides usage information about different features and components of MySQL HeatWave. As of MySQL 9.2.1, the boolean flag used has been replaced with an integer flag usedCounter in the option_tracker_usage_get() and ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-predict-table.html
ML_PREDICT_TABLE generates predictions for an entire table of unlabeled data and saves the results to an output table. ML_PREDICT_TABLE Overview ML_PREDICT_TABLE Syntax Required ML_PREDICT_TABLE Parameters ML_PREDICT_TABLE Options Options for ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-score.html
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 dataset. The dataset used with ML_SCORE should have ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-train.html
Run the ML_TRAIN routine on a training dataset to produce a trained machine learning model. MySQL 9.0.0 introduces changes to how MySQL HeatWave AutoML stores models. Before training models, make sure to review the following: Additional MySQL ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-training-an-anomaly-detection-model.html
After preparing the data for an anomaly detection model, you can train the model. Requirements for Anomaly Detection Training Consider the following based on the type of anomaly detection you are running: Set the task parameter to anomaly_detection ...