Prompt Class

`Prompt`¶

In PromptoGen, there is a data class (pg.Prompt) prepared to represent prompts. Using this data class, you can create prompts. This data class inherits from pydantic.BaseModel.

To create a prompt, the following information is required.

Item	Argument Name	Type
Prompt name	`name`	`str`
Prompt description	`description`	`str`
List of input parameters	`input_parameters`	`List[pg.ParameterInfo]`
List of output parameters	`output_parameters`	`List[pg.ParameterInfo]`
Input-output template	`template`	`pg.IOExample`
List of input-output examples	`examples`	`List[pg.IOExample]`

class Prompt(DataClass):
    """A prompt.

    Attributes:
        name: The name of the prompt.
        description: A description of the prompt.
        input_parameters: The parameter information for the prompt's input.
        output_parameters: The parameter information for the prompt's output.
        template: An example of the prompt.
        examples: A list of examples for the prompt.
    """

    name: str
    description: str
    input_parameters: List[ParameterInfo]
    output_parameters: List[ParameterInfo]
    template: IOExample
    examples: List[IOExample]

`ParameterInfo`¶

ParameterInfo is a data class designed to represent the information of input and output parameters of the prompt. It holds the name and description of the parameter.

class ParameterInfo(DataClass):
    """Information about a parameter.

    Attributes:
        description: A description of the parameter.
    """

    name: str
    description: str

`IOExample`¶

IOExample is a data class designed to represent examples of a prompt's input and output.

Value: TypeAlias = Dict[str, Any]

class IOExample(DataClass):
    """A few-shot example of a prompt.

    Attributes:
        input: The input for the prompt.
        output: The output from the prompt.
    """

    input: Value # dict[str, Any]
    output: Value # dict[str, Any]

Using the above information, you can create a prompt.

import promptogen as pg

summarizer = pg.Prompt(
    name="Text Summarizer and Keyword Extractor",
    description="Summarize text and extract keywords.",
    input_parameters=[
        pg.ParameterInfo(name="text", description="Text to summarize"),
    ],
    output_parameters=[
        pg.ParameterInfo(name="summary", description="Summary of text"),
        pg.ParameterInfo(name="keywords", description="Keywords extracted from text"),
    ],
    template=pg.IOExample(
        input={'text': "This is a sample text to summarize."},
        output={
            'summary': "This is a summary of the text.",
            'keywords': ["sample", "text", "summarize"],
        },
    ),
    examples=[
        pg.IOExample(
            input={
                'text': "One sunny afternoon, a group of friends decided to gather at the nearby park to engage in various games and activities. They played soccer, badminton, and basketball, laughing and enjoying each other's company while creating unforgettable memories together."},
            output={
                'summary': "A group of friends enjoyed an afternoon playing sports and making memories at a local park.",
                'keywords': ["friends", "park", "sports", "memories"],
            },
        )
    ],
)

Displaying the Prompt¶

You can display the information of the created prompt using the print function.

print(summarizer)

Text Summarizer and Keyword Extractor: (text: str) -> (summary: str, keywords: list)

Description
-----------
Summarize text and extract keywords.

Input Parameters
----------------
- text (str): Text to summarize

Output Parameters
-----------------
- summary (str): Summary of text
- keywords (list): Keywords extracted from text

Saving the Prompt¶

You can save the created prompt using the Prompt.to_json_file method.

summarizer.to_json_file("summarizer.json")

Loading the Prompt¶

You can load a saved prompt using the Prompt.from_json_file method.

summarizer = pg.Prompt.from_json_file("summarizer.json")

Representing the Prompt in JSON Format¶

Since Prompt inherits from pydantic's BaseModel, you can represent it in JSON format using the Prompt.model_dump_json method.

print(summarizer.model_dump_json(indent=4))

{
    "name": "Text Summarizer and Keyword Extractor",
    "description": "Summarize text and extract keywords.",
    "input_parameters": [
        {
            "name": "text",
            "description": "Text to summarize"
        }
    ],
    "output_parameters": [
        {
            "name": "summary",
            "description": "Summary of text"
        },
        {
            "name": "keywords",
            "description": "Keywords extracted from text"
        }
    ],
    "template": {
        "input": {
            "text": "This is a sample text to summarize."
        },
        "output": {
            "summary": "This is a summary of the text.",
            'keywords': [
                "sample",
                "text",
                "summarize"
            ]
        }
    },
    "examples": [
        {
            "input": {
                "text": "One sunny afternoon, a group of friends decided to gather at the nearby park to engage in various games and activities. They played soccer, badminton, and basketball, laughing and enjoying each other's company while creating unforgettable memories together."
            },
            "output": {
                "summary": "A group of friends enjoyed an afternoon playing sports and making memories at a local park.",
                'keywords': [
                    "friends",
                    "park",
                    "sports",
                    "memories"
                ]
            }
        }
    ]
}