HeatWave Release Notes
This topic describes how to view a chat session details. It contains the following sections:
Complete the steps to run MySQL HeatWave 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 MySQL HeatWave 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 MySQL HeatWave Chat.
LLM and Embedding Model IDs: IDs of the models used by the routine.
The output looks similar to the following:
| {
"tables": [
{
"table_name": "`demo_embeddings`",
"schema_name": "`demo_db`"
}
],
"response": ""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": "https://objectstorage.Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/heatwave-en.a4.pdf",
"title": "heatwave-en.a4.pdf",
"segment": "3.1 HeatWave AutoML Features 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.",
"distance": 0.1845
},
{
"id": "https://objectstorage.Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/heatwave-en.a4.pdf",
"title": "heatwave-en.a4.pdf",
"segment": "The HeatWave AutoML ML_TRAIN routine leverages Oracle AutoML technology
to automate the process of training a machine learning model. Oracle AutoML replaces
the laborious and time consuming tasks of the data analyst whose workflow is as
follows:\n1. Selecting a model from a large number of viable candidate models.\n2.
For each model, tuning hyperparameters.\n3. Selecting only predictive features
to speed up the pipeline and reduce over-fitting.\n99",
"distance": 0.2268
},
{
"id": "https://objectstorage.Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/heatwave-en.a4.pdf",
"title": "heatwave-en.a4.pdf",
"segment": "3.1.1 HeatWave AutoML Supervised Learning\nHeatWave AutoML supports supervised
machine learning. That is, it creates a machine learning model by analyzing a labeled
dataset to learn patterns that enable it to predict labels based on the features of the
dataset. For example, this guide uses the Census Income Data Set in its examples, where
features such as age, education, occupation, country, and so on, are used to predict the
income of an individual (the label).",
"distance": 0.2275
}
],
"chat_history": [
{
"user_message": "What is HeatWave AutoML?",
"chat_query_id": "99471681-387f-11ef-96d7-020017331ed6",
"chat_bot_message": "HeatWave 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": "c59140f5-387f-11ef-96d7-020017331ed6",
"chat_bot_message": "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"
},
"embed_model_id": "multilingual-e5-small",
"request_completed": true
} |