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:

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
} |