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docs: more fixes for refs (#33554)

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Mason Daugherty
2025-10-16 22:54:16 -04:00
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parent 9dd494ddcd
commit 1d2273597a
41 changed files with 1305 additions and 1238 deletions
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286
libs/partners/mistralai/langchain_mistralai/chat_models.py
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@@ -807,15 +807,17 @@ class ChatMistralAI(BaseChatModel):
- an OpenAI function/tool schema,
- a JSON Schema,
- a `TypedDict` class (support added in 0.1.12),
- a `TypedDict` class,
- or a Pydantic class.
If `schema` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or `TypedDict` class.
dict and will not be validated.
See `langchain_core.utils.function_calling.convert_to_openai_tool` for
more on how to properly specify types and descriptions of schema fields
when specifying a Pydantic or `TypedDict` class.
method: The method for steering model generation, one of:
@@ -840,8 +842,10 @@ class ChatMistralAI(BaseChatModel):
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'`.
will be caught and returned as well.
The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
`'parsing_error'`.
kwargs: Any additional parameters are passed directly to
`self.bind(**kwargs)`. This is useful for passing in
@@ -850,97 +854,130 @@ class ChatMistralAI(BaseChatModel):
`stop` to control when the model should stop generating output.
Returns:
A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
A `Runnable` that takes same inputs as a
`langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is
`False` and `schema` is a Pydantic class, `Runnable` outputs an instance
of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is
`False` then `Runnable` outputs a `dict`.
If `include_raw` is False and `schema` is a Pydantic class, Runnable outputs
an instance of `schema` (i.e., a Pydantic object).
If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys:
Otherwise, if `include_raw` is False then Runnable outputs a dict.
If `include_raw` is True, then Runnable outputs a dict with keys:
- `'raw'`: BaseMessage
- `'parsed'`: None if there was a parsing error, otherwise the type depends on the `schema` as described above.
- `'parsing_error'`: BaseException | None
- `'raw'`: `BaseMessage`
- `'parsed'`: `None` if there was a parsing error, otherwise the type
depends on the `schema` as described above.
- `'parsing_error'`: `BaseException | None`
Example: schema=Pydantic class, method="function_calling", include_raw=False:
```python
from typing import Optional
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel, Field
```python
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.'''
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"
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."
)
# -> 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.'
# )
```
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:
```python
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
```python
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
answer: str
justification: str
model = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_model = model.with_structured_output(
AnswerWithJustification, include_raw=True
)
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
# }
```
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:
```python
from typing_extensions import Annotated, TypedDict
from langchain_mistralai import ChatMistralAI
```python
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.'''
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."
]
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:
```python
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(AnswerWithJustification)
structured_model = model.with_structured_output(oai_schema)
structured_model.invoke(
"What weighs more a pound of bricks or a pound of feathers"
@@ -949,85 +986,58 @@ class ChatMistralAI(BaseChatModel):
# '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:
```python
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:
```python
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
```python
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
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
)
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
# }
```
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:
```python
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
# }
```
```python
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
# }
```
""" # noqa: E501
_ = kwargs.pop("strict", None)
if kwargs:

14
libs/partners/mistralai/langchain_mistralai/embeddings.py
View File

@@ -50,21 +50,21 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
```
Key init args — completion params:
model: str
Name of MistralAI model to use.
model:
Name of `MistralAI` model to use.
Key init args — client params:
api_key: SecretStr | None
api_key:
The API key for the MistralAI API. If not provided, it will be read from the
environment variable `MISTRAL_API_KEY`.
max_retries: int
max_retries:
The number of times to retry a request if it fails.
timeout: int
timeout:
The number of seconds to wait for a response before timing out.
wait_time: int
wait_time:
The number of seconds to wait before retrying a request in case of 429
error.
max_concurrent_requests: int
max_concurrent_requests:
The maximum number of concurrent requests to make to the Mistral API.
See full list of supported init args and their descriptions in the params section.