create_ernie_fn_chain#

langchain_community.chains.ernie_functions.base.create_ernie_fn_chain(functions: Sequence[Dict[str, Any] | Type[BaseModel] | Callable], llm: BaseLanguageModel, prompt: BasePromptTemplate, *, output_key: str = 'function', output_parser: BaseLLMOutputParser | None = None, **kwargs: Any) → LLMChain[source]#

[遗留] 创建一个使用Ernie函数的LLM链。

Parameters:

  • functions (Sequence[Dict[str, Any] | Type[BaseModel] | Callable]) – 一个由字典、pydantic.BaseModels 类或 Python 函数组成的序列。如果传入的是字典，则假定它们已经是有效的 Ernie 函数。如果只传入一个函数，则会强制模型使用该函数。pydantic.BaseModels 和 Python 函数应具有描述函数功能的文档字符串。为了获得最佳效果，pydantic.BaseModels 应包含参数的描述，而 Python 函数应在文档字符串中包含 Google Python 风格的参数描述。此外，Python 函数的参数应仅使用基本类型（str、int、float、bool）或 pydantic.BaseModels。

  • llm (BaseLanguageModel) – 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_key (str) – 在LLMChain.__call__中返回输出时使用的键。

  • output_parser (BaseLLMOutputParser | None) – 用于解析模型输出的BaseLLMOutputParser。默认情况下，将从函数类型推断。如果传入的是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。如果传入多个函数且它们不是pydantic.BaseModels，则链输出将包括返回的函数名称以及传递给函数的参数。

  • kwargs (Any)

Returns:

一个LLMChain，在运行时将给定的函数传递给模型。

Return type:

LLMChain

示例

from typing import Optional

from langchain.chains.ernie_functions import create_ernie_fn_chain
from langchain_community.chat_models import ErnieBotChat
from langchain_core.prompts import ChatPromptTemplate

from pydantic import BaseModel, Field


class RecordPerson(BaseModel):
    """Record some identifying information about a person."""

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


class RecordDog(BaseModel):
    """Record some 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", "Make calls to the relevant function to record the entities in the following input: {input}"),
        ("assistant", "OK!"),
        ("user", "Tip: Make sure to answer in the correct format"),
    ]
)
chain = create_ernie_fn_chain([RecordPerson, RecordDog], llm, prompt)
chain.run("Harry was a chubby brown beagle who loved chicken")
# -> RecordDog(name="Harry", color="brown", fav_food="chicken")