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This topic describes how to view a chat session details. It contains the following sections:
Complete the steps to run GenAI Chat.
To view the chat session details, inspect the
@chat_options
variable:
mysql> SELECT JSON_PRETTY(@chat_options);The output includes the following details about a chat session:
Vector store tables: in the database which were referenced by GenAI Chat.
Text segments: that were retrieved from the vector store and used as context to prepare responses for your queries.
Chat history: which includes both your queries and responses generated by GenAI Chat.
LLM details: which was used by the routine to generate the responses.
The output looks similar to the following:
| {
"tables": [
{
"table_name": "`demo_embeddings`",
"schema_name": "`demo_db`"
}
],
"response": "\nThe output of the follow-up question is:\n| HeatWave AutoML uses a variety of machine learning algorithms, including decision trees, random forests, neural networks, and support vector machines (SVMs). The specific algorithm used depends on the characteristics of the data being analyzed and the goals of the model being created. |",
"documents": [
{
"id": "/export/home/tmp/mysql-files/demo-directory/heatwave-en.pdf",
"title": "heatwave-en.pdf",
"segment": "Repeat this step to ask follow-up questions using the HEATWAVE_CHAT routine:\nCALL sys.HEATWAVE_CHAT(\"What learning algorithms does it use?\");\nThe output looks similar to the following:\n| HeatWave AutoML uses a variety of machine learning algorithms, including decision trees, random forests, neural networks, and support vector machines (SVMs). The specific algorithm used depends on the characteristics of the data being analyzed and the goals of the model being created. |",
"distance": 0.0622
},
{
"id": "/export/home/tmp/mysql-files/demo-directory/heatwave-en.pdf",
"title": "heatwave-en.pdf",
"segment": "HeatWave AutoML makes it easy to use machine learning, whether you are a novice user or an experienced ML practitioner. You provide the data, and HeatWave AutoML analyzes the characteristics of the data and creates an optimized machine learning model that you can use to generate predictions and explanations. An ML model makes predictions by identifying patterns in your data and applying those patterns to unseen data. HeatWave AutoML explanations help you understand how predictions are made,",
"distance": 0.0646
},
{
"id": "/export/home/tmp/mysql-files/demo-directory/heatwave-en.pdf",
"title": "heatwave-en.pdf",
"segment": "HeatWave shapes and scaling, and all HeatWave AutoML makes it easy to use machine learning, whether you are a novice user or an experienced ML practitioner. You provide the data, and HeatWave AutoML analyzes the characteristics of the data and creates an optimized machine learning model that you can use to generate predictions and explanations. An ML model makes predictions by identifying patterns in your data and applying those patterns to unseen data. HeatWave AutoML explanations help you",
"distance": 0.0679
}
],
"chat_history": [
{
"user_message": "What is HeatWave AutoML?",
"chat_query_id": "7aa7824c-5d8a-11f0-a2c5-020017192be1",
"chat_bot_message": "\nHeatWave AutoML is a feature of MySQL HeatWave that makes it easy to use machine learning, allowing users to create optimized machine learning models for predictions and explanations without having to leave the database."
},
{
"user_message": "What learning algorithms does it use?",
"chat_query_id": "93730281-5d8a-11f0-a2c5-020017192be1",
"chat_bot_message": "\nThe output of the follow-up question is:\n| HeatWave AutoML uses a variety of machine learning algorithms, including decision trees, random forests, neural networks, and support vector machines (SVMs). The specific algorithm used depends on the characteristics of the data being analyzed and the goals of the model being created. |"
}
],
"model_options": {
"model_id": "llama3.2-3b-instruct-v1"
},
"request_completed": true
} |