create_structured_output_runnable#

langchain_community.chains.ernie_functions.base.create_structured_output_runnable(output_schema: Dict[str, Any] | Type[BaseModel], llm: Runnable, prompt: BasePromptTemplate, *, output_parser: BaseOutputParser | BaseGenerationOutputParser | None = None, **kwargs: Any) → Runnable[source]#

创建一个可运行的程序，使用Ernie函数来获取结构化输出。

Parameters:

output_schema (Dict[str, Any] | Type[BaseModel]) – 可以是一个字典或pydantic.BaseModel类。如果传入的是字典，则假定它已经是有效的JsonSchema。为了获得最佳效果，pydantic.BaseModels应该包含描述模式表示内容的文档字符串以及参数的描述。
llm (Runnable) – 使用的语言模型，假设支持Ernie函数调用API。
prompt (BasePromptTemplate) – 传递给模型的BasePromptTemplate。
output_parser (BaseOutputParser | BaseGenerationOutputParser | None) – 用于解析模型输出的BaseLLMOutputParser。默认情况下，将从函数类型推断。如果传递了pydantic.BaseModels，则OutputParser将尝试使用这些模型解析输出。否则，模型输出将简单地解析为JSON。
kwargs (Any)

Returns:

一个可运行的序列，当运行时将把给定的函数传递给模型。

Return type:

Runnable

示例

from typing import Optional

from langchain.chains.ernie_functions import create_structured_output_chain
from langchain_community.chat_models import ErnieBotChat
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field

class Dog(BaseModel):
    """Identifying information about a dog."""

    name: str = Field(..., description="The dog's name")
    color: str = Field(..., description="The dog's color")
    fav_food: Optional[str] = Field(None, description="The dog's favorite food")

llm = ErnieBotChat(model_name="ERNIE-Bot-4")
prompt = ChatPromptTemplate.from_messages(
    [
        ("user", "Use the given format to extract information from the following input: {input}"),
        ("assistant", "OK!"),
        ("user", "Tip: Make sure to answer in the correct format"),
    ]
)
chain = create_structured_output_chain(Dog, llm, prompt)
chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
# -> Dog(name="Harry", color="brown", fav_food="chicken")