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Preview
This product is subject to the "Pre-GA Offerings Terms" in the General Service Terms section
of theService Specific Terms.
Pre-GA products are available "as is" and might have limited support.
For more information, see thelaunch stage descriptions.
This page describes model endpoint management. Model endpoint management lets you experiment with registering an AI model endpoint and invoking predictions.
To use AI models in
production environments, seeInvoke online predictions from Cloud SQL instances.
After the model endpoints are added and registered in model endpoint management, you can
reference them using the model ID to invoke predictions.
Before you begin
Make sure that you complete the following actions:
Use themysql.ml_predict_row()SQL function to call a registered generic model endpoint to invoke
predictions. You can usemysql.ml_predict_row()function with any model type.
MODEL_ID: the model ID you defined when registering the model endpoint
REQUEST_BODY: the parameters to the prediction function, in JSON format
Examples
To generate predictions for a registeredgemini-flashmodel endpoint, run the following statement:
SELECTJSON_EXTRACT(mysql.ml_predict_row('gemini-2.5-flash','{"contents": [{"role": "user","parts": [{"text": "For TPCH database schema as mentioned here https://www.tpc.org/TPC_Documents_Current_Versions/pdf/TPC-H_v3.0.1.pdf , generate a SQL query to find allsupplier names which are located in the India nation."} ]}]}'),'$.candidates[0].content.parts[0].text');
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-03 UTC."],[],[],null,["# Invoke predictions with model endpoint management\n\n| **Preview**\n|\n|\n| This product is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA products are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nMySQL \\| [PostgreSQL](/sql/docs/postgres/model-endpoint-predictions \"View this page for the PostgreSQL database engine\") \\| SQL Server\n\n\u003cbr /\u003e\n\nThis page describes model endpoint management. Model endpoint management lets you experiment with registering an AI model endpoint and invoking predictions.\n\nTo use AI models in\nproduction environments, see\n[Invoke online predictions from Cloud SQL instances](/sql/docs/mysql/invoke-online-predictions).\n\n\nAfter the model endpoints are added and registered in model endpoint management, you can\nreference them using the model ID to invoke predictions.\n\nBefore you begin\n----------------\n\nMake sure that you complete the following actions:\n\n- Register your model endpoint with model endpoint management. For more information, see [Register and call remote AI models using model endpoint management](/sql/docs/mysql/model-endpoint-register-model).\n- Create or update your Cloud SQL instance so that the instance can integrate with Vertex AI. For more information, see [Enable database integration with Vertex AI](/sql/docs/mysql/integrate-cloud-sql-with-vertex-ai#enable-database-integration-with-vertex-ai).\n\nInvoke predictions for generic models\n-------------------------------------\n\nUse the `mysql.ml_predict_row()` SQL function to call a registered generic model endpoint to invoke\npredictions. You can use `mysql.ml_predict_row()` function with any model type. \n\n SELECT\n mysql.ml_predict_row(\n '\u003cvar translate=\"no\"\u003eMODEL_ID\u003c/var\u003e',\n '\u003cvar translate=\"no\"\u003eREQUEST_BODY\u003c/var\u003e');\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003eMODEL_ID\u003c/var\u003e: the model ID you defined when registering the model endpoint\n- \u003cvar translate=\"no\"\u003eREQUEST_BODY\u003c/var\u003e: the parameters to the prediction function, in JSON format\n\nExamples\n--------\n\nTo generate predictions for a registered `gemini-flash` model endpoint, run the following statement: \n\n SELECT JSON_EXTRACT(\n mysql.ml_predict_row(\n 'gemini-2.5-flash',\n '{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"For TPCH database schema as mentioned here https://www.tpc.org/TPC_Documents_Current_Versions/pdf/TPC-H_v3.0.1.pdf , generate a SQL query to find allsupplier names which are located in the India nation.\"\n } ]}]\n }'\n ),\n '$.candidates[0].content.parts[0].text'\n );"]]