📘 PromptedGenerator

PromptedGenerator is a data generation operator that produces text based on user-provided prompts. It combines a system prompt with input content from a specified column to generate the desired output text.

init function

def __init__(self, 
            llm_serving: LLMServingABC, 
            system_prompt: str = "You are a helpful agent.",
            json_schema: dict = None
            )

init Parameter Descriptions

Parameter	Type	Default Value	Description
llm_serving	LLMServingABC	Required	LLM serving object for executing inference and generation.
system_prompt	str	"You are a helpful agent."	System prompt to define the model's behavior.
json_schema	dict	None	Optional JSON schema to constrain the output format.

Prompt Template Descriptions

Prompt Template Name	Primary Use	Applicable Scenarios	Feature Description

run function

def run(self, storage: DataFlowStorage, input_key: str = "raw_content", output_key: str = "generated_content")

Parameters

Name	Type	Default Value	Description
storage	DataFlowStorage	Required	DataFlow storage instance, responsible for reading and writing data.
input_key	str	"raw_content"	Input column name, corresponding to the content field.
output_key	str	"generated_content"	Output column name, corresponding to the generated content field.

🧠 Example Usage

🧾 Default Output Format (Output Format)

Field	Type	Description
raw_content	str	The input text content.
generated_content	str	The model-generated content.

Example Input:

{
"raw_content": "Translate the following English text to French: 'Hello, world!'"
}

Example Output:

{
"raw_content": "Translate the following English text to French: 'Hello, world!'",
"generated_content": "Bonjour, le monde !"
}