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https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-genai-summarize-content.html
The following sections in this topic describe how to summarize exiting content using the GenAI: Before You Begin Summarizing Content Running Batch Queries What's Next Before You Begin Review the GenAI requirements and privileges. For Running Batch ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-chat.html
The HEATWAVE_CHAT routine automatically calls the ML_RAG routine which loads an LLM and runs a semantic search on the available vector stores by default. If the routine cannot find a vector store, then it calls the ML_GENERATE routine and uses ...
https://dev.mysql.com/doc/mysql-ai/9.5/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/9.5/en/mys-mysqlai-ml-generate.html
The ML_GENERATE routine uses the specified large language model (LLM) to generate text-based content as a response for the given natural-language query. It can include the following parameters: task: specifies the task expected from the LLM.
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-model-export.html
Use the ML_MODEL_EXPORT routine to export a model from the model catalog to a user defined table. To learn how to use ML_MODEL_EXPORT to share models, see Grant Other Users Access to a Model. ML_MODEL_EXPORT Overview After you run ML_MODEL_EXPORT, ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-predict-row.html
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 during ML_TRAIN. A table can include extra columns, and still use the AutoML ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-rag-table.html
The ML_RAG_TABLE routine runs multiple retrieval-augmented generation (RAG) queries in a batch, in parallel. The output generated for every input query is the same as the output generated by the ML_RAG routine. This topic contains the following ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-rag.html
The ML_RAG routine performs retrieval-augmented generation (RAG) by: Taking a natural-language query. This routine aims to provide detailed, accurate, and contextually relevant answers by augmenting a generative model with information retrieved ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-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/workbench/en/wb-migration-database-concepts.html
The following table shows a comparison between each DBMS product supported by the Migration Wizard and MySQL. Table 10.1 Conceptual equivalents between supported DBMS products and MySQL Concept MS SQL Server Sybase ASE PostgreSQL MySQL Note ...