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docs: fix openai api ref (#25639)

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docs/api_reference/guide_imports.json
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47
libs/partners/openai/langchain_openai/chat_models/base.py
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@ -1120,7 +1120,6 @@ class BaseChatOpenAI(BaseChatModel):
Args:
schema:
The output schema. Can be passed in as:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class (support added in 0.1.20),
@ -1138,7 +1137,6 @@ class BaseChatOpenAI(BaseChatModel):
method:
The method for steering model generation, one of:
- "function_calling":
Uses OpenAI's tool-calling (formerly called function calling)
API: https://platform.openai.com/docs/guides/function-calling
@ -1156,8 +1154,8 @@ class BaseChatOpenAI(BaseChatModel):
Learn more about the differences between the methods and which models
support which methods here:
- https://platform.openai.com/docs/guides/structured-outputs/structured-outputs-vs-json-mode
- https://platform.openai.com/docs/guides/structured-outputs/function-calling-vs-response-format
- https://platform.openai.com/docs/guides/structured-outputs/structured-outputs-vs-json-mode
- https://platform.openai.com/docs/guides/structured-outputs/function-calling-vs-response-format
.. versionchanged:: 0.1.21
@ -1200,26 +1198,22 @@ class BaseChatOpenAI(BaseChatModel):
Returns:
A Runnable that takes same inputs as a :class:`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).
| 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.
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
| If ``include_raw`` is True, then Runnable outputs a dict with keys:
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": Optional[BaseException]
- "raw": BaseMessage
- "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- "parsing_error": Optional[BaseException]
.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False, strict=True
Example: schema=Pydantic class, method="function_calling", include_raw=False, strict=True:
.. note:: Valid schemas when using ``strict`` = True
Note, OpenAI has a number of restrictions on what types of schemas can be
provided if ``strict`` = True. When using Pydantic, our model cannot
specify any Field metadata (like min/max constraints) and fields cannot
have default values.
OpenAI has a number of restrictions on what types of schemas can be
provided if ``strict`` = True. When using Pydantic, our model cannot
specify any Field metadata (like min/max constraints) and fields cannot
have default values.
See all constraints here: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
See all constraints here: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
.. code-block:: python
@ -1252,7 +1246,8 @@ class BaseChatOpenAI(BaseChatModel):
# 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:
.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=True
.. code-block:: python
from langchain_openai import ChatOpenAI
@ -1280,7 +1275,8 @@ class BaseChatOpenAI(BaseChatModel):
# 'parsing_error': None
# }
Example: schema=TypedDict class, method="function_calling", include_raw=False:
.. dropdown:: Example: schema=TypedDict class, method="function_calling", include_raw=False
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
@ -1310,7 +1306,8 @@ class BaseChatOpenAI(BaseChatModel):
# '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:
.. dropdown:: Example: schema=OpenAI function schema, method="function_calling", include_raw=False
.. code-block:: python
from langchain_openai import ChatOpenAI
@ -1339,7 +1336,8 @@ class BaseChatOpenAI(BaseChatModel):
# '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:
.. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True
.. code-block::
from langchain_openai import ChatOpenAI
@ -1367,7 +1365,8 @@ class BaseChatOpenAI(BaseChatModel):
# 'parsing_error': None
# }
Example: schema=None, method="json_mode", include_raw=True:
.. dropdown:: Example: schema=None, method="json_mode", include_raw=True
.. code-block::
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)