Stay organized with collectionsSave and categorize content based on your preferences.
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 notCloud Functions). The model may ask you to call a function and send back
the result to help the model handle your query.
Optionally experiment with an alternative "Google AI" version of theGemini API Get free-of-charge access
(within limits and where available) usingGoogle AI StudioandGoogle AIclient SDKs.
These SDKs should be used forprototyping onlyin mobile and web apps.
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 theVertex AIservice and the generative model
in your app.
After you've connected your app to Firebase, added the SDK, and initialized theVertex AIservice and the generative model,
you're ready to call theGemini 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'stoolsparameter:
Learn how to choose aGemini modeland optionally alocationappropriate 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.
