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 columnsystem_prompt
is defined within the input batch, then thesystem_prompt
will be used, otherwise, it will be ignored.
Input & Output Columns¶
graph TD
subgraph Dataset
subgraph Columns
ICOL0[instruction]
ICOL1[structured_output]
end
subgraph New columns
OCOL0[generation]
OCOL1[model_name]
end
end
subgraph StructuredGeneration
StepInput[Input Columns: instruction, structured_output]
StepOutput[Output Columns: generation, model_name]
end
ICOL0 --> StepInput
ICOL1 --> StepInput
StepOutput --> OCOL0
StepOutput --> OCOL1
StepInput --> StepOutput
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 keysformat
andschema
, whereformat
should be one ofjson
orregex
, and theschema
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.llms 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": {
"type": "json",
"value": {
"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¶
from distilabel.steps.tasks import StructuredGeneration
from distilabel.llms 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": {
"type": "regex",
"value": r"(\d{1,2})°C"
},
}
]
)
)