MySQL AI User Guide

5.5.1 Generating New Content

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

To generate text-based content using GenAI, perform the following steps:

  1. To define your natural-language query, set the @query variable: 

    mysql> SET @query="QueryInNaturalLanguage";

    Replace QueryInNaturalLanguage with a natural-language query of your choice. For example: 

    mysql> SET @query="Write an article on Artificial intelligence in 200 words.";

  2. To generate text-based content, pass the query to the LLM using the ML_GENERATE routine with the task parameter set to generation: 

    mysql> SELECT sys.ML_GENERATE(@query,
JSON_OBJECT("task", "generation", "model_id", "LLM", "language", "Language"));

    Replace the following:

    • LLM: LLM to use, which must be the same as the one you loaded in the previous step.

    • Language: the two-letter ISO 639-1 code for the language you want to use. Default language is en, which is English. To view the list of supported languages, see Languages.

    For example: 

    mysql> SELECT sys.ML_GENERATE(@query,
JSON_OBJECT("task", "generation", "model_id", "llama3.2-3b-instruct-v1", "language", "en"));

    Text-based content that is generated by the LLM in response to your query is printed as output. It looks similar to the text output shown below: 

    | {"text": "\n**The Rise of Artificial Intelligence: Revolutionizing the Future**\n\nArtificial
intelligence (AI) has been a topic of interest for decades, and its impact is becoming increasingly
evident in various aspects of our lives. AI refers to the development of computer systems that can
perform tasks that typically require human intelligence, such as learning, problem-solving, and
decision-making.\n\nThe latest advancements in machine learning algorithms and natural language
processing have enabled AI systems to become more sophisticated and efficient. Applications of AI
are expanding rapidly across industries, including healthcare, finance, transportation, and education.
For instance, AI-powered chatbots are being used to provide customer support, while self-driving
cars are being tested on roads worldwide.\n\nThe benefits of AI are numerous. It can automate
repetitive tasks, improve accuracy, and enhance productivity. Moreover, AI has the potential to solve
complex problems that were previously unsolvable by humans. However, there are also concerns about job
displacement and bias in AI decision-making.\n\nAs AI continues to evolve, it is essential to address
these challenges and ensure that its benefits are shared equitably among all stakeholders. With
continued investment in research and development, AI has the potential to transform industries and
improve lives worldwide. The future of work will be shaped by AI, and it's crucial to prepare for
this", "license": "Your use of this llama model is subject to your Oracle agreements and this llama
license agreement: https://downloads.mysql.com/docs/LLAMA_32_3B_INSTRUCT-license.pdf"} |

Running Batch Queries

To run multiple generation queries in parallel, use the ML_GENERATE_TABLE routine. This method is faster than running the ML_GENERATE routine multiple times.

To run the steps in this section, you can create a new database demo_db and table input_table: 

mysql> CREATE DATABASE demo_db;
mysql> USE demo_db;
mysql> CREATE TABLE input_table (id INT AUTO_INCREMENT, Input TEXT, primary key (id));
mysql> INSERT INTO input_table (Input) VALUES('Describe what is MySQL in 50 words.');
mysql> INSERT INTO input_table (Input) VALUES('Describe Artificial Intelligence in 50 words.');
mysql> INSERT INTO input_table (Input) VALUES('Describe Machine Learning in 50 words.');

To run batch queries using ML_GENERATE_TABLE, perform the following steps:

  1. In the ML_GENERATE_TABLE routine, specify the table columns containing the input queries and for storing the generated text-based responses: 

    mysql> CALL sys.ML_GENERATE_TABLE("InputDBName.InputTableName.InputColumn", "OutputDBName.OutputTableName.OutputColumn",
JSON_OBJECT("task", "generation", "model_id", "LLM", "language", "Language"));

    Replace the following:

    • InputDBName: the name of the database that contains the table column where your input queries are stored.

    • InputTableName: the name of the table that contains the column where your input queries are stored.

    • InputColumn: the name of the column that contains input queries.

    • OutputDBName: the name of the database that contains the table where you want to store the generated outputs. This can be the same as the input database.

    • OutputTableName: the name of the table where you want to create a new column to store the generated outputs. This can be the same as the input table. If the specified table doesn't exist, a new table is created.

    • OutputColumn: the name for the new column where you want to store the output generated for the input queries.

    • LLM: LLM to use, which must be the same as the LLM you loaded in the previous step.

    • Language: the two-letter ISO 639-1 code for the language you want to use. Default language is en, which is English. To view the list of supported languages, see Languages.

    For example: 

    mysql> CALL sys.ML_GENERATE_TABLE("demo_db.input_table.Input", "demo_db.output_table.Output",
JSON_OBJECT("task", "generation", "model_id", "llama3.2-3b-instruct-v1", "language", "en"));

  2. View the contents of the output table: 

    mysql> SELECT * FROM output_table\G
*************************** 1. row ***************************
    id: 1
Output: {"text": "\nMySQL is an open-source relational database
management system (RDBMS) that allows users to store, manage,
and retrieve data in a structured format. It supports various
features like SQL queries, indexing, transactions, and security,
making it a popular choice for web applications, enterprise
software, and mobile apps development.", 
"error": null,
"license": "Your use of this llama model is subject to your
Oracle agreements and this llama license agreement:
https://downloads.mysql.com/docs/LLAMA_32_3B_INSTRUCT-license.pdf"}
*************************** 2. row ***************************
    id: 2
Output: {"text": "\nArtificial Intelligence (AI) refers to the
development of computer systems that can perform tasks that
typically require human intelligence, such as learning,
problem-solving, and decision-making. AI uses algorithms and
data to mimic human thought processes, enabling machines to
analyze, reason, and interact with humans in increasingly
sophisticated ways.", 
"error": null}
*************************** 3. row ***************************
    id: 3
Output: {"text": "\nMachine Learning (ML) is a subset of
Artificial Intelligence that enables systems to automatically
improve performance on a task without being explicitly programmed. 
It involves training algorithms on data, allowing them to learn
patterns and make predictions or decisions based on new, unseen
data, without human intervention.", 
"error": null}

    The output table generated using the ML_GENERATE_TABLE routine contains an additional details for error reporting. In case the routine fails to generate output for specific rows, details of the errors encountered and default values used are added for the row in the output column.

If you created a new database for testing the steps in this section, delete the database to free up space: 

mysql> DROP DATABASE demo_db;

To learn more about the available routine options, see ML_GENERATE_TABLE Syntax.

What's Next

Learn how to Summarize Existing Content.

MySQL Documentation Library