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https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-load-model.html
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 repetitively by AutoML routines until it is unloaded using the ML_MODEL_UNLOAD routine, or ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-onnx-metadata.html
To learn more about model metadata in the model catalog, see Model Metadata. ONNX Inputs Info Use the data_types_map to map the data type of each column to an ONNX model data type. For example, to convert inputs of the type tensor(float) to ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-supervised-learning.html
AutoML supports the following types of machine learning: supervised, unsupervised, and semi-supervised. Supervised Learning Supervised learning creates a machine learning model by analyzing a labeled dataset to learn patterns. This means that the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-genai-generate-content.html
The following sections in this topic describe how to generate new text-based content using the GenAI feature of MySQL AI: Before You Begin Generating Content Running Batch Queries What's Next Before You Begin Review the GenAI requirements and ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-genai-generate-embeddings.html
This section describes how to generate vector embeddings using the ML_EMBED_ROW routine. Vector embeddings are a numerical representation of the text that capture the semantics of the data and relationships to other data. You can pass the text ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-genai-run-chat.html
When you run GenAI Chat, it automatically loads the llama3.2-3b-instruct-v1 LLM. By default, GenAI 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/mysql-ai/9.5/en/mys-ai-genai-vector-store-load.html
This section describes how to generate vector embeddings for files or folders, and load the embeddings into a vector store table. The following sections in this topic describe how to ingest files into a vector store: Before You Begin Ingesting ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-aiaml-error-messages.html
Each error message includes an error number, SQLSTATE value, and message string, as described in Error Message Sources and Elements. Error number: ML001016; SQLSTATE: HY000 Message: Only classification, regression, and forecasting tasks are ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-embed-row.html
The ML_EMBED_ROW routine uses the specified embedding model to encode the specified text or query into a vector embedding. The routine returns a VECTOR that contains a numerical representation of the specified text. This topic contains the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-explain-row.html
The ML_EXPLAIN_ROW routine generates explanations for one or more rows of unlabeled data. A loaded and trained model with the appropriate prediction explainer is required to run ML_EXPLAIN_ROW. ML_EXPLAIN_ROW does not support recommendation, ...