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The ai.generate_response function uses generative AI to generate custom text responses that are based on your own instructions, with a single line of code.
Note
- This article covers using ai.generate_response with pandas. To use ai.generate_response with PySpark, see this article.
- See other AI functions in this overview article.
- Learn how to customize the configuration of AI functions.
Overview
The ai.generate_response function can extend the pandas DataFrame class and the pandas Series class.
To generate custom text responses row by row, you can either call this function on a pandas series or an entire pandas DataFrame.
If calling the function on an entire pandas DataFrame, your prompt can be a literal string, and the function considers all columns of the DataFrame while generating responses. Your prompt can also be a format string, where 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.
Tip
Learn how to craft more effective prompts to get higher-quality responses by following OpenAI's prompting tips for gpt-4.1.
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 apply 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. |
response_format Optional |
A dictionary that specifies the expected structure of the model’s response. The type field can be set to "text" for free-form text, "json_object" to ensure the output is a valid JSON object, or a custom JSON Schema to enforce a specific response structure. If this parameter isn't provided, the response is returned as plain text. |
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.
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)
This example code cell provides the following output:
Response format example
The following example shows how to use the response_format parameter to specify different response formats, including plain text, a JSON object, and a custom JSON schema.
# This code uses AI. Always review output for mistakes.
df = pd.DataFrame([
("Alex Rivera is a 24-year-old soccer midfielder from Barcelona who scored 12 goals last season."),
("Jordan Smith, a 29-year-old basketball guard from Chicago, averaged 22 points per game."),
("William O'Connor is a 22-year-old tennis player from Dublin who won 3 ATP titles this year.")
], columns=["bio"])
# response_format : text
df["card_text"] = df.ai.generate_response(
"Create a player card with the player's details and a motivational quote",
response_format={"type": "text"}
)
# response_format : json object
df["card_json_object"] = df.ai.generate_response(
"Create a player card with the player's details and a motivational quote in JSON",
response_format={"type": "json_object"} # Requires "json" in the prompt
)
# response_format : specified json schema
df["card_json_schema"] = df.ai.generate_response(
"Create a player card with the player's details and a motivational quote",
response_format={
"type": "json_schema",
"json_schema": {
"name": "player_card_schema",
"strict": True,
"schema": {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
"sport": {"type": "string"},
"position": {"type": "string"},
"hometown": {"type": "string"},
"stats": {"type": "string", "description": "Key performance metrics or achievements"},
"motivational_quote": {"type": "string"}
},
"required": ["name", "age", "sport", "position", "hometown", "stats", "motivational_quote"],
"additionalProperties": False,
},
},
},
)
display(df)
This example code cell provides the following output:
Related content
Detect sentiment with ai.analyze_sentiment.
Categorize text with ai.classify.
Generate vector embeddings with ai.embed.
Extract entities with ai_extract.
Fix grammar with ai.fix_grammar.
Calculate similarity with ai.similarity.
Summarize text with ai.summarize.
Translate text with ai.translate.
Learn more about the full set of AI functions.
Customize the configuration of AI functions.
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