Use the Gemini API for function calling

Function calling makes it easier for you to get structured data outputs from generative models. You can then use these outputs to call other APIs and return the relevant response data to the model. In other words, function calling helps you connect generative models to external systems so that the generated content includes the most up-to-date and accurate information.

You can provide Gemini models with descriptions of functions. These are functions that you write in the language of your app (that is, they're not Cloud Functions). The model may ask you to call a function and send back the result to help the model handle your query.

You can learn more about function calling in the Google Cloud documentation.

Before you begin

If you haven't already, work through the getting started guide for the Vertex AI for Firebase SDKs. Make sure that you've done all of the following:

  • Set up a new or existing Firebase project, including using the Blaze pricing plan and enabling the required APIs.

  • Connect your app to Firebase, including registering your app and adding your Firebase config to your app.

  • Add the SDK and initialize the Vertex AI service and the generative model in your app.

After you've connected your app to Firebase, added the SDK, and initialized the Vertex AI service and the generative model, you're ready to call the Gemini API.

Set up a function call

For this tutorial, you'll have the model interact with a hypothetical currency exchange API that supports the following parameters:

Parameter Type Required Description
currencyFrom string yes Currency to convert from
currencyTo string yes Currency to convert to

Example API request

  "currencyFrom": "USD",
  "currencyTo": "SEK"

Example API response

  "base": "USD",
  "rates": {"SEK": 10.99}

Step 1: Create the function that makes the API request

If you haven't already, start by creating the function that makes an API request.

For demonstration purposes in this tutorial, rather than sending an actual API request, you'll be returning hardcoded values in the same format that an actual API would return.

Step 2: Create a function declaration

Create the function declaration that you'll pass to the generative model (next step of this tutorial).

Include as much detail as possible in the function and parameter descriptions. The generative model uses this information to determine which function to select and how to provide values for the parameters in the function call.

Step 3: Specify the function declaration during model initialization

Specify the function declaration when initializing the generative model by setting the model's tools parameter:

Learn how to choose a Gemini model and optionally a location appropriate for your use case and app.

Step 4: Generate a function call

Now you can prompt the model with the defined function.

The recommended way to use function calling is through the chat interface, since function calls fit nicely into chat's multi-turn structure.

What else can you do?

Try out other capabilities of the Gemini API

Learn how to control content generation

You can also experiment with prompts and model configurations using Vertex AI Studio.

Learn more about the Gemini models

Learn about the models available for various use cases and their quotas and pricing.

Give feedback about your experience with Vertex AI for Firebase