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Answer custom user prompts with the
The ai.generate_response function uses Generative AI to generate custom text responses based on your own instructions—all with a single line of code.
AI functions turbocharge data engineering by putting the power of Fabric's built-in large languages models into your hands. To learn more, visit this overview article.
Important
This feature is in preview, for use in the Fabric 1.3 runtime and higher.
- Review the prerequisites in this overview article, including the library installations that are temporarily required to use AI functions.
- By default, AI functions are currently powered by the gpt-3.5-turbo (0125) model. To learn more about billing and consumption rates, visit this article.
- Although the underlying model can handle several languages, most of the AI functions are optimized for use on English-language texts.
- During the initial rollout of AI functions, users are temporarily limited to 1,000 requests per minute with Fabric's built-in AI endpoint.
Use ai.generate_response with pandas
The ai.generate_response function extends the pandas DataFrame class. The ai.generate_response function differs from the other AI functions, because those functions extend the pandas Series class. Call this function on an entire pandas DataFrame to generate custom text responses row by row. Your prompt can be a literal string, in which case the function considers all columns of the DataFrame while generating responses. Or your prompt can be a format string, in which case the function considers only those column values that appear between curly braces in the prompt.
The function returns a pandas Series that contains custom text responses for each row of input. The text responses can be stored in a new DataFrame column.
Syntax
df["response"] = df.ai.generate_response(prompt="Instructions for a custom response based on all column values")
Parameters
| Name | Description |
|---|---|
prompt Required |
A string that contains prompt instructions to be applied to input text values for custom responses. |
is_prompt_template Optional |
A boolean that indicates whether the prompt is a format string or a literal string. If this parameter is set to True, then the function considers only the specific row values from each column name that appears in the format string. In this case, those column names must appear between curly braces, and other columns are ignored. If this parameter is set to its default value of False, then the function considers all column values as context for each input row. |
Returns
The function returns a pandas DataFrame that contains custom text responses to the prompt for each input text row.
Example
# This code uses AI. Always review output for mistakes.
# Read terms: https://azure.microsoft.com/support/legal/preview-supplemental-terms/
df = pd.DataFrame([
("Scarves"),
("Snow pants"),
("Ski goggles")
], columns=["product"])
df["response"] = df.ai.generate_response("Write a short, punchy email subject line for a winter sale.")
display(df)
Use ai.generate_response with PySpark
The ai.generate_response function is also available for Spark DataFrames. You must specify the name of an existing input column as a parameter. You must also specify a string-based prompt, and a boolean that indicates whether that prompt should be treated as a format string.
The function returns a new DataFrame, with custom responses for each input text row stored in an output column.
Syntax
df.ai.generate_response(prompt="Instructions for a custom response based on all column values", output_col="response")
Parameters
| Name | Description |
|---|---|
prompt Required |
A string that contains prompt instructions to be applied to input text values, for custom responses. |
is_prompt_template Optional |
A boolean that indicates whether the prompt is a format string or a literal string. If this parameter is set to True, then the function considers only the specific row values from each column that appears in the format string. In this case, those column names must appear between curly braces, and other columns are ignored. If this parameter is set to its default value of False, then the function considers all column values as context for each input row. |
output_col Optional |
A string that contains the name of a new column to store custom responses for each row of input text. If this parameter isn't set, a default name is generated for the output column. |
error_col Optional |
A string that contains the name of a new column to store any OpenAI errors that result from processing each row of input text. If this parameter isn't set, a default name is generated for the error column. If there are no errors for a row of input, the value in this column is null. |
Returns
A Spark DataFrame with a new column that contains custom text responses to the prompt for each input text row.
Example
# This code uses AI. Always review output for mistakes.
# Read terms: https://azure.microsoft.com/support/legal/preview-supplemental-terms/
df = spark.createDataFrame([
("Scarves",),
("Snow pants",),
("Ski goggles",)
], ["product"])
responses = df.ai.generate_response(prompt="Write a short, punchy email subject line for a winter sale.", output_col="response")
display(responses)
Related content
- Calculate similarity with
ai.similarity. - Categorize text with
ai.classify. - Detect sentiment with
ai.analyze_sentiment. - Extract entities with
ai_extract. - Fix grammar with
ai.fix_grammar. - Summarize text with
ai.summarize. - Translate text with
ai.translate. - Learn more about the full set of AI functions here.
- Learn how to customize the configuration of AI functions here.
- Did we miss a feature you need? Suggest it on the Fabric Ideas forum.