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.
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.
To run HeatWave Chat, perform the following steps:
-
To delete previous chat output and state, if any, reset the
@chat_options
session variable:set @chat_options=NULL;
To use a language other than English, then set the
language
model option of the@chat_options
session variable:set @chat_options = JSON_OBJECT("model_options", JSON_OBJECT("language", "Language"));
Replace
Language
with the two-letterISO 639-1
code for the language you want to use. Default language isen
, which is English. To view the list of supported languages, see Languages.For example, to use French set
language
tofr
:set @chat_options = JSON_OBJECT("model_options", JSON_OBJECT("language", "fr"));
This resets the
@chat_options
session variables and specifies the language for the chat. -
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. |