StructuredGeneration¶

Generate structured content for a given instruction using an LLM.

StructuredGeneration is a pre-defined task that defines the instruction and the structured_output as the inputs, and generation as the output. This task is used to generate structured content based on the input instruction and following the schema provided within the structured_output column per each instruction. The model_name also returned as part of the output in order to enhance it.

Attributes¶

use_system_prompt: Whether to use the system prompt in the generation. Defaults to True, which means that if the column system_prompt is defined within the input batch, then the system_prompt will be used, otherwise, it will be ignored.

Input & Output Columns¶

Inputs¶

instruction (str): The instruction to generate structured content from.
structured_output (Dict[str, Any]): The structured_output to generate structured content from. It should be a Python dictionary with the keys format and schema, where format should be one of json or regex, and the schema should be either the JSON schema or the regex pattern, respectively.

Outputs¶

generation (str): The generated text matching the provided schema, if possible.
model_name (str): The name of the model used to generate the text.

Examples¶

Generate structured output from a JSON schema¶

from distilabel.steps.tasks import StructuredGeneration
from distilabel.models import InferenceEndpointsLLM

structured_gen = StructuredGeneration(
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
    ),
)

structured_gen.load()

result = next(
    structured_gen.process(
        [
            {
                "instruction": "Create an RPG character",
                "structured_output": {
                    "format": "json",
                    "schema": {
                        "properties": {
                            "name": {
                                "title": "Name",
                                "type": "string"
                            },
                            "description": {
                                "title": "Description",
                                "type": "string"
                            },
                            "role": {
                                "title": "Role",
                                "type": "string"
                            },
                            "weapon": {
                                "title": "Weapon",
                                "type": "string"
                            }
                        },
                        "required": [
                            "name",
                            "description",
                            "role",
                            "weapon"
                        ],
                        "title": "Character",
                        "type": "object"
                    }
                },
            }
        ]
    )
)

Generate structured output from a regex pattern (only works with LLMs that support regex, the providers using outlines)¶

from distilabel.steps.tasks import StructuredGeneration
from distilabel.models import InferenceEndpointsLLM

structured_gen = StructuredGeneration(
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
    ),
)

structured_gen.load()

result = next(
    structured_gen.process(
        [
            {
                "instruction": "What's the weather like today in Seattle in Celsius degrees?",
                "structured_output": {
                    "format": "regex",
                    "schema": r"(\d{1,2})°C"
                },

            }
        ]
    )
)