Help understanding the FunctionDeclaration object

Google AI Studio
1

I am building applications leveraging the Gemini API and need assistance understanding the FunctionDeclaration object. Specifically, I am curious about the design choice to only accept a single Schema object for the parameters attribute instead of a list[Schema]. This seems limiting and counterintuitive given that many real-world functions often require multiple parameters.

Below is the definition of the FunctionDeclaration object from the API:

class FunctionDeclaration(proto.Message):
    r"""Structured representation of a function declaration as defined by
    the `OpenAPI 3.03 specification <https://spec.openapis.org/oas/v3.0.3>`__.
    This representation includes the function's name and parameters and serves
    as a ``Tool`` that the model can utilize and the client can execute.

    Attributes:
        name (str): The function's name, constrained to alphanumeric characters,
            underscores, and dashes, with a maximum length of 63.
        description (str): A brief description of the function.
        parameters (google.ai.generativelanguage_v1beta.types.Schema):
            Optional. Defines the parameters for this function as an OpenAPI
            3.03 Parameter Object. This field is case-sensitive and reflects
            a single Schema object defining the parameter's type.
    """
    name: str = proto.Field(proto.STRING, number=1)
    description: str = proto.Field(proto.STRING, number=2)
    parameters: "Schema" = proto.Field(
        proto.MESSAGE, number=3, optional=True, message="Schema"
    )

I am currently creating an adapter method to map my custom Function object to Google’s FunctionDeclaration. My custom Function object supports multiple parameters as a list[Parameter]. Relevant imports are shown below:

from google.ai.generativelanguage_v1beta.types.content import (
    FunctionDeclaration as GenAIFunctionDeclaration,
)
from google.ai.generativelanguage_v1beta.types.content import Schema, Type

Additionally, I am curious about Google’s design decision to include similar objects (e.g., FunctionDeclaration, Part, Content) across different modules, such as:

  • vertexai.generative_models
  • google.ai
  • google.generativeai

These objects appear to serve the same purpose but are not interchangeable between modules. This fragmentation is causing significant overhead, as I must rewrite portions of my code to accommodate different API calls depending on whether I am working with Vertex AI or AI Studio.

Could you provide insights into these design decisions, or any guidance on better handling these inconsistencies when developing across multiple Google AI APIs?

2

Welcome to the forum. At first glance, when looking at Caching  |  Gemini API  |  Google AI for Developers , it would appear that only one parameter is ever used. But the name of the field, parameters, indicates there is more to the story.

The type of parameters is Schema, and that is documented just after FunctionDeclaration. Schema is flexible and can contain the entire parameter list (arbitrary number of parameters).

It all makes sense if you look at the json from the tutorial examples - Intro to function calling with the Gemini API  |  Google AI for Developers

Hope that helps.