4.5.1 Running HeatWave GenAI Chat

When you run HeatWave Chat, it automatically loads the mistral-7b-instruct-v1 LLM.

By default, HeatWave Chat searches for an answer to a query across all ingested documents by automatically discovering available vector stores, and returns the answer along with relevant citations. you can limit the scope of search to specific document collections available in certain vector stores or specify documents to include in the search.

If the vector store tables contain information in different languages, then similar to ML_RAG, the HEATWAVE_CHAT routine also filters the retrieved context using the embedding model name and the language used for ingesting files into the vector store table.

If you do not have a vector store set up, then HeatWave Chat uses information available in public data sources to generate a response for your query.

Before You Begin

• Review the Requirements.

• If you want to extend the vector search functionality and ask specific questions about the information available in your proprietary documents that are stored in the vector store, complete the steps to set up a vector store.

Running the Chat

To run HeatWave Chat, perform the following steps:

1. To delete previous chat output and state, if any, reset the @chat_options variable:

   set @chat_options=NULL;

   Note

   Ensure that you use the name @chat_options for the variable. The HEATWAVE_CHAT routine reserves this variable for specifying and saving various chat parameter settings.

   To use a language other than English, set the language model option of the @chat_options variable:

   set @chat_options = JSON_OBJECT("model_options", JSON_OBJECT("language", "Language"));

   Replace Language with 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, to use French set language to fr:

   The language parameter is supported in MySQL 9.0.1-u1 and later versions.

   set @chat_options = JSON_OBJECT("model_options", JSON_OBJECT("language", "fr"));

   This resets the @chat_options variable, and specifies the language for the chat.

2. Then, add your query to HeatWave Chat by using the HEATWAVE_CHAT routine:

   call sys.HEATWAVE_CHAT("YourQuery");

   For example:

   call sys.HEATWAVE_CHAT("What is HeatWave AutoML?");

   The output looks similar to the following:

   |  HeatWave AutoML is a feature of MySQL HeatWave that makes it easy
   to use machine learning, whether you are a novice user or an
   experienced ML practitioner. It analyzes the characteristics of the
   data and creates an optimized machine learning model that can be used
   to generate predictions and explanations. The data and models never
   leave MySQL HeatWave, saving time and effort while keeping the data
   and models secure. HeatWave AutoML is optimized for HeatWave shapes
   and scaling, and all processing is performed on the HeatWave Cluster. |

   Repeat this step to ask follow-up questions using the HEATWAVE_CHAT routine:

   call sys.HEATWAVE_CHAT("What learning algorithms does it use?");

   The output looks similar to the following:

   |  HeatWave AutoML uses a variety of machine learning algorithms. It
   leverages Oracle AutoML technology which includes a range of algorithms
   such as decision trees, random forests, neural networks, and support vector
   machines (SVMs). The specific algorithm used by HeatWave AutoML depends on
   the characteristics of the data being analyzed and the goals of the model
   being created. |