| Name | Type | Description |
|---|---|---|
schema | dict | type | None | Default: NoneThe output schema. Can be passed in as:
If See |
method | Literal['function_calling', 'json_mode', 'json_schema'] | Default: 'function_calling'The method for steering model generation, one of:
Behavior changed in langchain-mistralai 0.2.5Added method="json_schema" |
include_raw | bool | Default: False |
kwargs | Any | Default: {} |
Model wrapper that returns outputs formatted to match the given schema.
Example: schema=Pydantic class, method="function_calling", include_raw=False:
from typing import Optional
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", 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_mistralai import ChatMistralAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
model = ChatMistralAI(model="mistral-large-latest", 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_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", 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_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", 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_mode", include_raw=True:
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
model = ChatMistralAI(model="mistral-large-latest", 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
# }
Example: schema=None, method="json_mode", include_raw=True:
structured_model = model.with_structured_output(
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': {
# '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
# }If False then only the parsed structured output is returned.
If an error occurs during model output parsing it will be raised.
If True then both the raw model response (a BaseMessage) and the
parsed model response will be returned.
If an error occurs during output parsing it will be caught and returned as well.
The final output is always a dict with keys 'raw', 'parsed', and
'parsing_error'.
Any additional parameters are passed directly to
self.bind(**kwargs). This is useful for passing in
parameters such as tool_choice or tools to control
which tool the model should call, or to pass in parameters such as
stop to control when the model should stop generating output.