mirror of
https://github.com/hwchase17/langchain.git
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openai[minor]: release 0.3 (#29100)
## Goal Solve the following problems with `langchain-openai`: - Structured output with `o1` [breaks out of the box](https://langchain.slack.com/archives/C050X0VTN56/p1735232400232099). - `with_structured_output` by default does not use OpenAI’s [structured output feature](https://platform.openai.com/docs/guides/structured-outputs). - We override API defaults for temperature and other parameters. ## Breaking changes: - Default method for structured output is changing to OpenAI’s dedicated [structured output feature](https://platform.openai.com/docs/guides/structured-outputs). For schemas specified via TypedDict or JSON schema, strict schema validation is disabled by default but can be enabled by specifying `strict=True`. - To recover previous default, pass `method="function_calling"` into `with_structured_output`. - Models that don’t support `method="json_schema"` (e.g., `gpt-4` and `gpt-3.5-turbo`, currently the default model for ChatOpenAI) will raise an error unless `method` is explicitly specified. - To recover previous default, pass `method="function_calling"` into `with_structured_output`. - Schemas specified via Pydantic `BaseModel` that have fields with non-null defaults or metadata (like min/max constraints) will raise an error. - To recover previous default, pass `method="function_calling"` into `with_structured_output`. - `strict` now defaults to False for `method="json_schema"` when schemas are specified via TypedDict or JSON schema. - To recover previous behavior, use `with_structured_output(schema, strict=True)` - Schemas specified via Pydantic V1 will raise a warning (and use `method="function_calling"`) unless `method` is explicitly specified. - To remove the warning, pass `method="function_calling"` into `with_structured_output`. - Streaming with default structured output method / Pydantic schema no longer generates intermediate streamed chunks. - To recover previous behavior, pass `method="function_calling"` into `with_structured_output`. - We no longer override default temperature (was 0.7 in LangChain, now will follow OpenAI, currently 1.0). - To recover previous behavior, initialize `ChatOpenAI` or `AzureChatOpenAI` with `temperature=0.7`. - Note: conceptually there is a difference between forcing a tool call and forcing a response format. Tool calls may have more concise arguments vs. generating content adhering to a schema. Prompts may need to be adjusted to recover desired behavior. --------- Co-authored-by: Jacob Lee <jacoblee93@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
ccurme
GitHub
@@ -18,13 +18,15 @@ from typing import (
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)
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import openai
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import LangSmithParams
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from langchain_core.messages import BaseMessage
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from langchain_core.outputs import ChatResult
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from langchain_core.runnables import Runnable
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from langchain_core.utils import from_env, secret_from_env
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from langchain_core.utils.pydantic import is_basemodel_subclass
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from pydantic import BaseModel, Field, SecretStr, model_validator
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from typing_extensions import Self
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from typing_extensions import Literal, Self
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from langchain_openai.chat_models.base import BaseChatOpenAI
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@@ -79,7 +81,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
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https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
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timeout: Union[float, Tuple[float, float], Any, None]
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Timeout for requests.
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max_retries: int
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max_retries: Optional[int]
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Max number of retries.
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organization: Optional[str]
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OpenAI organization ID. If not passed in will be read from env
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@@ -586,9 +588,9 @@ class AzureChatOpenAI(BaseChatOpenAI):
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that api key and python package exists in environment."""
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if self.n < 1:
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if self.n is not None and self.n < 1:
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raise ValueError("n must be at least 1.")
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if self.n > 1 and self.streaming:
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elif self.n is not None and self.n > 1 and self.streaming:
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raise ValueError("n must be 1 when streaming.")
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if self.disabled_params is None:
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@@ -641,10 +643,12 @@ class AzureChatOpenAI(BaseChatOpenAI):
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"organization": self.openai_organization,
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"base_url": self.openai_api_base,
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"timeout": self.request_timeout,
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"max_retries": self.max_retries,
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"default_headers": self.default_headers,
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"default_query": self.default_query,
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}
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if self.max_retries is not None:
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client_params["max_retries"] = self.max_retries
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if not self.client:
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sync_specific = {"http_client": self.http_client}
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self.root_client = openai.AzureOpenAI(**client_params, **sync_specific) # type: ignore[arg-type]
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@@ -737,3 +741,323 @@ class AzureChatOpenAI(BaseChatOpenAI):
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)
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return chat_result
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def with_structured_output(
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self,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
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include_raw: bool = False,
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strict: Optional[bool] = None,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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Args:
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schema:
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The output schema. Can be passed in as:
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- a JSON Schema,
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- a TypedDict class,
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- or a Pydantic class,
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- an OpenAI function/tool schema.
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If ``schema`` is a Pydantic class then the model output will be a
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Pydantic instance of that class, and the model-generated fields will be
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validated by the Pydantic class. Otherwise the model output will be a
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dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
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for more on how to properly specify types and descriptions of
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schema fields when specifying a Pydantic or TypedDict class.
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method: The method for steering model generation, one of:
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- "json_schema":
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Uses OpenAI's Structured Output API:
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https://platform.openai.com/docs/guides/structured-outputs
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Supported for "gpt-4o-mini", "gpt-4o-2024-08-06", "o1", and later
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models.
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- "function_calling":
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Uses OpenAI's tool-calling (formerly called function calling)
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API: https://platform.openai.com/docs/guides/function-calling
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- "json_mode":
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Uses OpenAI's JSON mode. Note that if using JSON mode then you
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must include instructions for formatting the output into the
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desired schema into the model call:
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https://platform.openai.com/docs/guides/structured-outputs/json-mode
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Learn more about the differences between the methods and which models
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support which methods here:
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- https://platform.openai.com/docs/guides/structured-outputs/structured-outputs-vs-json-mode
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- https://platform.openai.com/docs/guides/structured-outputs/function-calling-vs-response-format
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include_raw:
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If False then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If True
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then both the raw model response (a BaseMessage) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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strict:
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- True:
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Model output is guaranteed to exactly match the schema.
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The input schema will also be validated according to
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https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
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- False:
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Input schema will not be validated and model output will not be
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validated.
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- None:
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``strict`` argument will not be passed to the model.
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If schema is specified via TypedDict or JSON schema, ``strict`` is not
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enabled by default. Pass ``strict=True`` to enable it.
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Note: ``strict`` can only be non-null if ``method`` is
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``"json_schema"`` or ``"function_calling"``.
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kwargs: Additional keyword args aren't supported.
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Returns:
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A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
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| 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.
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| If ``include_raw`` is True, then Runnable outputs a dict with keys:
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- "raw": BaseMessage
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- "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
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- "parsing_error": Optional[BaseException]
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.. versionchanged:: 0.1.20
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Added support for TypedDict class ``schema``.
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.. versionchanged:: 0.1.21
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Support for ``strict`` argument added.
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Support for ``method="json_schema"`` added.
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.. versionchanged:: 0.3.0
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``method`` default changed from "function_calling" to "json_schema".
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.. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=False, strict=True
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Note, OpenAI has a number of restrictions on what types of schemas can be
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provided if ``strict`` = True. When using Pydantic, our model cannot
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specify any Field metadata (like min/max constraints) and fields cannot
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have default values.
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See all constraints here: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
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.. code-block:: python
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from typing import Optional
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from langchain_openai import AzureChatOpenAI
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from pydantic import BaseModel, Field
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: Optional[str] = Field(
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default=..., description="A justification for the answer."
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)
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llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# 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.'
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# )
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.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False, strict=False
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.. code-block:: python
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from typing import Optional
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from langchain_openai import AzureChatOpenAI
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from pydantic import BaseModel, Field
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: Optional[str] = Field(
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default=..., description="A justification for the answer."
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)
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llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification, method="function_calling"
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)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# 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.'
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# )
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.. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=True
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.. code-block:: python
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from langchain_openai import AzureChatOpenAI
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from pydantic import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification, include_raw=True
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)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> {
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# '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'}]}),
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# '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.'),
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# 'parsing_error': None
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# }
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.. dropdown:: Example: schema=TypedDict class, method="json_schema", include_raw=False, strict=False
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.. code-block:: python
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from typing_extensions import Annotated, TypedDict
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from langchain_openai import AzureChatOpenAI
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class AnswerWithJustification(TypedDict):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: Annotated[
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Optional[str], None, "A justification for the answer."
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]
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llm = AzureChatOpenAI(azure_deployment="...", model="gpt-4o", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> {
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# 'answer': 'They weigh the same',
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# '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.'
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# }
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.. dropdown:: Example: schema=OpenAI function schema, method="json_schema", include_raw=False
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.. code-block:: python
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from langchain_openai import AzureChatOpenAI
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oai_schema = {
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'name': 'AnswerWithJustification',
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'description': 'An answer to the user question along with justification for the answer.',
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'parameters': {
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'type': 'object',
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'properties': {
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'answer': {'type': 'string'},
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'justification': {'description': 'A justification for the answer.', 'type': 'string'}
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},
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'required': ['answer']
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}
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}
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llm = AzureChatOpenAI(
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azure_deployment="...",
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model="gpt-4o",
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temperature=0,
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)
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structured_llm = llm.with_structured_output(oai_schema)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> {
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# 'answer': 'They weigh the same',
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# '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.'
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# }
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.. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True
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.. code-block::
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from langchain_openai import AzureChatOpenAI
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from pydantic import BaseModel
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class AnswerWithJustification(BaseModel):
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answer: str
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justification: str
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llm = AzureChatOpenAI(
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azure_deployment="...",
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model="gpt-4o",
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temperature=0,
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)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification,
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method="json_mode",
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include_raw=True
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)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# '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}'),
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# '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.'),
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# 'parsing_error': None
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# }
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.. dropdown:: Example: schema=None, method="json_mode", include_raw=True
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.. code-block::
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structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# '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}'),
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# 'parsed': {
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# 'answer': 'They are both the same weight.',
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# '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.'
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# },
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# 'parsing_error': None
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# }
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""" # noqa: E501
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return super().with_structured_output(
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schema, method=method, include_raw=include_raw, strict=strict, **kwargs
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)
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Reference in New Issue
Block a user