create_structured_runnable#
- langchain_google_vertexai.chains.create_structured_runnable(function: Type[BaseModel] | Sequence[Type[BaseModel]], llm: Runnable, *, prompt: BasePromptTemplate | None = None, use_extra_step: bool = False) Runnable [source]#
Create a runnable sequence that uses OpenAI functions.
- Parameters:
function (Type[BaseModel] | Sequence[Type[BaseModel]]) – Either a single pydantic.BaseModel class or a sequence of pydantic.BaseModels classes. For best results, pydantic.BaseModels should have descriptions of the parameters.
llm (Runnable) – Language model to use, assumed to support the Google Vertex function-calling API.
prompt (BasePromptTemplate | None) – BasePromptTemplate to pass to the model.
use_extra_step (bool) – whether to make an extra step to parse output into a function
- Returns:
A runnable sequence that will pass in the given functions to the model when run.
- Return type:
Example
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")