langchain.chains.structured_output.base.create_openai_fn_runnable

langchain.chains.structured_output.base.create_openai_fn_runnable(functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], llm: Runnable, prompt: Optional[BasePromptTemplate] = None, *, enforce_single_function_usage: bool = True, output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, **llm_kwargs: Any) → Runnable[source]

[Deprecated] 创建一个可运行的序列，使用OpenAI函数。

参数：

functions: 一个序列，可以是字典、pydantic.BaseModels类或Python函数。如果传入字典，则假定它们已经是有效的OpenAI函数。如果只传入一个函数，则将强制模型使用该函数。pydantic.BaseModels和Python函数应该有描述函数功能的文档字符串。为了获得最佳结果，pydantic.BaseModels应该有参数描述，Python函数应该在文档字符串中使用Google Python风格的参数描述。此外，Python函数应该只使用原始类型（str、int、float、bool）或pydantic.BaseModels作为参数。 llm: 要使用的语言模型，假定支持OpenAI函数调用API。 prompt: 传递给模型的BasePromptTemplate。 enforce_single_function_usage: 仅在传入单个函数时使用。如果为True，则将强制模型使用给定的函数。如果为False，则模型将有选择使用给定函数或不使用的选项。 output_parser: 用于解析模型输出的BaseLLMOutputParser。默认情况下将从函数类型中推断。如果传入pydantic.BaseModels，则OutputParser将尝试使用这些来解析输出。否则，模型输出将简单地解析为JSON。如果传入多个函数且它们不是pydantic.BaseModels，则链式输出将包括返回的函数名称和传递给函数的参数。 **llm_kwargs: 要传递给语言模型的其他命名参数。

返回：

一个可运行的序列，当运行时将传递给模型给定的函数。

示例：
from typing import Optional

from langchain.chains.structured_output import create_openai_fn_runnable
from langchain_openai import ChatOpenAI
from langchain_core.pydantic_v1 import BaseModel, Field


class RecordPerson(BaseModel):
    '''记录有关一个人的一些身份信息。'''

    name: str = Field(..., description="人的姓名")
    age: int = Field(..., description="人的年龄")
    fav_food: Optional[str] = Field(None, description="人喜欢的食物")


class RecordDog(BaseModel):
    '''记录有关一只狗的一些身份信息。'''

    name: str = Field(..., description="狗的名字")
    color: str = Field(..., description="狗的颜色")
    fav_food: Optional[str] = Field(None, description="狗喜欢的食物")


llm = ChatOpenAI(model="gpt-4", temperature=0)
structured_llm = create_openai_fn_runnable([RecordPerson, RecordDog], llm)
structured_llm.invoke("Harry was a chubby brown beagle who loved chicken)
# -> RecordDog(name="Harry", color="brown", fav_food="chicken")

Notes

Deprecated since version 0.1.14: LangChain has introduced a method called with_structured_output that is available on ChatModels capable of tool calling. You can read more about the method here: https://python.langchain.com/docs/modules/model_io/chat/structured_output/ Please follow our extraction use case documentation for more guidelines on how to do information extraction with LLMs. https://python.langchain.com/docs/use_cases/extraction/. If you notice other issues, please provide feedback here: https://github.com/langchain-ai/langchain/discussions/18154 Use from langchain_core.pydantic_v1 import BaseModel, Field from langchain_anthropic import ChatAnthropic

class Joke(BaseModel):

setup: str = Field(description=”The setup of the joke”) punchline: str = Field(description=”The punchline to the joke”)

# Or any other chat model that supports tools. # Please reference to to the documentation of structured_output # to see an up to date list of which models support # with_structured_output. model = ChatAnthropic(model=”claude-3-opus-20240229”, temperature=0) structured_llm = model.with_structured_output(Joke) structured_llm.invoke(“Tell me a joke about cats.

Make sure to call the Joke function.”)

instead.

Parameters
Return type

Runnable