with_structured_output

Example: schema=Pydantic class, method="function_calling", include_raw=False:

from typing import Optional

from langchain_groq import ChatGroq
from pydantic import BaseModel, Field

class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    # If we provide default values and/or descriptions for fields, these will be passed
    # to the model. This is an important part of improving a model's ability to
    # correctly return structured outputs.
    justification: str | None = Field(default=None, description="A justification for the answer.")

model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(AnswerWithJustification)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )

Example: schema=Pydantic class, method="function_calling", include_raw=True:

from langchain_groq import ChatGroq
from pydantic import BaseModel

class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str

model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(
    AnswerWithJustification,
    include_raw=True,
)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
#     'parsing_error': None
# }

Example: schema=TypedDict class, method="function_calling", include_raw=False:

from typing_extensions import Annotated, TypedDict

from langchain_groq import ChatGroq

class AnswerWithJustification(TypedDict):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: Annotated[str | None, None, "A justification for the answer."]

model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(AnswerWithJustification)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

Example: schema=OpenAI function schema, method="function_calling", include_raw=False:

from langchain_groq import ChatGroq

oai_schema = {
    'name': 'AnswerWithJustification',
    'description': 'An answer to the user question along with justification for the answer.',
    'parameters': {
        'type': 'object',
        'properties': {
            'answer': {'type': 'string'},
            'justification': {'description': 'A justification for the answer.', 'type': 'string'}
        },
        'required': ['answer']
    }

    model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
    structured_model = model.with_structured_output(oai_schema)

    structured_model.invoke(
        "What weighs more a pound of bricks or a pound of feathers"
    )
    # -> {
    #     'answer': 'They weigh the same',
    #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
    # }

Example: schema=Pydantic class, method="json_schema", include_raw=False:

from typing import Optional

from langchain_groq import ChatGroq
from pydantic import BaseModel, Field

class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    # If we provide default values and/or descriptions for fields, these will be passed
    # to the model. This is an important part of improving a model's ability to
    # correctly return structured outputs.
    justification: str | None = Field(default=None, description="A justification for the answer.")

model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(
    AnswerWithJustification,
    method="json_schema",
)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )

Example: schema=Pydantic class, method="json_mode", include_raw=True:

from langchain_groq import ChatGroq
from pydantic import BaseModel

class AnswerWithJustification(BaseModel):
    answer: str
    justification: str

model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(
    AnswerWithJustification, method="json_mode", include_raw=True
)

structured_model.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
    "What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
#     'raw': AIMessage(content='{\n    "answer": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
#     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
#     'parsing_error': None
# }