langchain_google_vertexai.chains.create_structured_runnable

langchain_google_vertexai.chains.create_structured_runnable(function: Union[Type[BaseModel], Sequence[Type[BaseModel]]], llm: Runnable, *, prompt: Optional[BasePromptTemplate] = None, use_extra_step: bool = False) → Runnable

创建一个使用OpenAI函数的runnable序列。

参数

• function (Union) – 既可以是一个单一的pydantic.BaseModel类，也可以是一个pydantic.BaseModels类的序列。为了最佳结果，pydantic.BaseModels应具有参数描述。

• llm (Runnable) – 使用的语言模型，假定它支持Google Vertex函数调用API。

• prompt – 要传递给模型的BasePromptTemplate。

• use_extra_step – 是否进行额外的步骤将输出解析为函数

返回值

一个runnable序列，当运行时将在模型中传递给给定函数。

返回类型

Runnable

示例

from typing import Optional

from langchain_google_vertexai import ChatVertexAI, create_structured_runnable
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 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 = ChatVertexAI(model_name="gemini-pro")
prompt = ChatPromptTemplate.from_template("""
You are a world class algorithm for recording entities.
Make calls to the relevant function to record the entities in the following input: {input}
Tip: Make sure to answer in the correct format"""
                         )
chain = create_structured_runnable([RecordPerson, RecordDog], llm, prompt=prompt)
chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
# -> RecordDog(name="Harry", color="brown", fav_food="chicken")

使用create_structured_runnable的示例

• ChatVertexAI