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Production use of this feature is available for specific editions only. Contact our sales team for more information.
The AI Query transformation component uses the Databricks ai_query() function to obtain an answer to a natural-language question. This function uses a Databricks chat model serving endpoint made available by Databricks Foundation Model APIs. The component takes one or more input columns from your source table, combines the inputs with a user prompt, and sends this data to the Databricks chat model for processing. The output is a string containing the chat model’s response to the question.
  • Make sure you have read and understand the Requirements set out by Databricks before using this component.
  • For Databricks Runtime 14.2 and above, this function is supported in notebook environments including Databricks notebooks and workflows.
  • For Databricks Runtime 14.1 and below, this function is not supported in notebook environments, including Databricks notebooks.

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Use case

You can use the AI Query component to ask questions of a source text in plain English. A typical use case might be analyzing a table of sales data with questions such as:
  • What were the total sales last quarter?
  • Show me the top 5 products by revenue in 2024.
  • How many users signed up each month in 2023?
  • Show orders from California over $1000 in the last 30 days.

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Properties

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Name
string
required
A human-readable name for the component.
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Model
drop-down
required
Select the Databricks model serving endpoint that will be used to answer the query. The following models are currently supported:
  • DBRX Instruct
  • Meta-Llama-3-70B-Instruct
  • Meta-Llama-2-70B-Chat
  • Mixtral-8x7B Instruct
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User Prompt
text editor
required
Use the text editor to write a question for the chat model to respond to.To use variables in this field, type the name of the variable prefixed by the dollar symbol and surrounded by { } brackets, as follows: ${variable}. Once you type ${, a drop-down list of autocompleted suggested variables will appear. This list updates as you type; for example, if you type ${date, functions and variables containing date will be listed.
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Columns
column editor
Select the source columns to feed as input to the chat model.
  • Column Name: A column from the input table.
  • Descriptive Name: An alternate descriptive name to better contextualize the column. Recommended if your column names are low-context.
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Include Input Columns
boolean
required
  • Yes: Outputs both your source input columns and the query response. This will also include those input columns not selected in Columns.
  • No: Only includes the query response.