Quickstart: Get started with the Gemini API in Swift apps

This quickstart demonstrates how to access the Gemini API directly from your Swift app using the Google AI Swift SDK. You can use this SDK if you don't want to work directly with REST APIs or server-side code (like Python) for accessing Gemini models in your Swift app.

In this quickstart, you'll learn how to do the following:

In addition, this quickstart contains sections about advanced use cases (like counting tokens) as well as options for controlling content generation.

Prerequisites

This quickstart assumes that you're familiar with using Xcode to develop Swift apps.

To complete this quickstart, make sure that your development environment and Swift app meet the following requirements:

  • Xcode 15.0 or higher
  • Your Swift app must target iOS 15 or higher, or macOS 12 or higher.

Set up your project

Before calling the Gemini API, you need to set up your Xcode project, which includes setting up your API key, adding the SDK package to your Xcode project, and initializing the model.

Set up your API key

To use the Gemini API, you'll need an API key. If you don't already have one, create a key in Google AI Studio.

Get an API key

Secure your API key

It's strongly recommended that you do not check an API key into your version control system. One alternative option is to store it in a GenerativeAI-Info.plist file, and then read the API key from the .plist file. Make sure to put this .plist file in the root folder of your app and exclude it from version control.

You can also review the sample app to learn how to store your API key in a .plist file.

All the snippets in this quickstart assume that you're accessing your API key from this on-demand resource .plist file.

Add the SDK package to your project

To use the Gemini API in your own Swift app, add the GoogleGenerativeAI package to your app:

  1. In Xcode, right-click on your project in the project navigator.

  2. Select Add Packages from the context menu.

  3. In the Add Packages dialog, paste the package URL in the search bar:

    https://github.com/google/generative-ai-swift
    
  4. Click Add Package. Xcode will now add the GoogleGenerativeAI package to your project.

Initialize the Generative Model

Before you can make any API calls, you need to initialize the Generative Model.

  1. Import the GoogleAI module:

    import GoogleGenerativeAI
    
  2. Initialize the Generative Model:

    // Access your API key from your on-demand resource .plist file
    // (see "Set up your API key" above)
    let model = GenerativeModel(name: "MODEL_NAME", apiKey: APIKey.default)
    

When specifying a model, note the following:

  • Use a model that's specific to your use case (for example, gemini-pro-vision is for multimodal input). Within this guide, the instructions for each implementation list the recommended model for each use case.

Implement common use cases

Now that your project is set up, you can explore using the Gemini API to implement different use cases:

Generate text from text-only input

When the prompt input includes only text, use the gemini-pro model with the generateContent method to generate text output:

import GoogleGenerativeAI

// For text-only input, use the gemini-pro model
// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(name: "gemini-pro", apiKey: APIKey.default)

let prompt = "Write a story about a magic backpack."
let response = try await model.generateContent(prompt)
if let text = response.text {
  print(text)
}

Generate text from text-and-image input (multimodal)

Gemini provides a multimodal model (gemini-pro-vision), so you can input both text and images. Make sure to review the image requirements for prompts.

When the prompt input includes both text and images, use the gemini-pro-vision model with the generateContent method to generate text output:

import GoogleGenerativeAI

// For text-and-image input (multimodal), use the gemini-pro-vision model
// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(name: "gemini-pro-vision", apiKey: APIKey.default)

let image1 = UIImage(...)
let image2 = UIImage(...)

let prompt = "What's different between these pictures?"

let response = try await model.generateContent(prompt, image1, image2)
if let text = response.text {
  print(text)
}

Build multi-turn conversations (chat)

Using Gemini, you can build freeform conversations across multiple turns. The SDK simplifies the process by managing the state of the conversation, so unlike with generateContent, you don't have to store the conversation history yourself.

To build a multi-turn conversation (like chat), use the gemini-pro model, and initialize the chat by calling startChat(). Then use sendMessage() to send a new user message, which will also append the message and the response to the chat history.

There are two possible options for role associated with the content in a conversation:

  • user: the role which provides the prompts. This value is the default for sendMessage calls.

  • model: the role which provides the responses. This role can be used when calling startChat() with existing history.

import GoogleGenerativeAI

let config = GenerationConfig(
  maxOutputTokens: 100
)

// For text-only input, use the gemini-pro model
// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(
  name: "gemini-pro",
  apiKey: APIKey.default,
  generationConfig: config
)

let history = [
  ModelContent(role: "user", parts: "Hello, I have 2 dogs in my house."),
  ModelContent(role: "model", parts: "Great to meet you. What would you like to know?"),
]

// Initialize the chat
let chat = model.startChat(history: history)
let response = try await chat.sendMessage("How many paws are in my house?")
if let text = response.text {
  print(text)
}

Use streaming for faster interactions

By default, the model returns a response after completing the entire generation process. You can achieve faster interactions by not waiting for the entire result, and instead use streaming to handle partial results.

The following example shows how to implement streaming with the generateContentStream method to generate text from a text-and-image input prompt.

import GoogleGenerativeAI

// For text-and-image input (multimodal), use the gemini-pro-vision model
// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(name: "gemini-pro", apiKey: APIKey.default)

let image1 = UIImage(named: "")!
let image2 = UIImage(named: "")!

let prompt = "What's different between these pictures?"
var fullResponse = ""
let contentStream = model.generateContentStream(prompt, image1, image2)
for try await chunk in contentStream {
  if let text = chunk.text {
    print(text)
    fullResponse += text
  }
}
print(fullResponse)

You can use a similar approach for text-only input and chat use cases.

// Use streaming with text-only input
let contentStream = model.generateContentStream(prompt)
// Use streaming with multi-turn conversations (like chat)
let responseStream = chat.sendMessageStream(message)

Implement advanced use cases

The common use cases described in the previous section of this quickstart help you become comfortable with using the Gemini API. This section describes some use cases that might be considered more advanced.

Count tokens

When using long prompts, it might be useful to count tokens before sending any content to the model. The following examples show how to use countTokens() for various use cases:

// For text-only input
let response = try await model.countTokens("Why is the sky blue?")
print(response.totalTokens)
// For text-and-image input (multi-modal)
let response = try await model.countTokens(prompt, image1, image2)
print(response.totalTokens)
// For multi-turn conversations (like chat)
let chat = model.startChat()
let history = chat.history
let message = ModelContent(role: "user", "Why is the sky blue?")
let contents = history + [message]
let response = try await model.countTokens(contents)
print(response.totalTokens)

Options to control content generation

You can control content generation by configuring model parameters and by using safety settings.

Configure model parameters

Every prompt you send to the model includes parameter values that control how the model generates a response. The model can generate different results for different parameter values. Learn more about Model parameters. The configuration is maintained for the lifetime of your model instance.

let config = GenerationConfig(
  temperature: 0.9,
  topP: 0.1,
  topK: 16,
  maxOutputTokens: 200,
  stopSequences: ["red"]
)

// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(
  name: "MODEL_NAME",
  apiKey: APIKey.default,
  generationConfig: config
)

Use safety settings

You can use safety settings to adjust the likelihood of getting responses that may be considered harmful. By default, safety settings block content with medium and/or high probability of being unsafe content across all dimensions. Learn more about Safety settings.

Here's how to set one safety setting:

// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(
  name: "MODEL_NAME",
  apiKey: APIKey.default,
  safetySettings: [
    SafetySetting(harmCategory: .harassment, threshold: .blockOnlyHigh)
  ]
)

You can also set more than one safety setting:

let harassmentSafety = SafetySetting(harmCategory: .harassment, threshold: .blockOnlyHigh)
let hateSpeechSafety = SafetySetting(harmCategory: .hateSpeech, threshold: .blockMediumAndAbove)

// Access your API key from your on-demand resource .plist file (see "Set up your API key" above)
let model = GenerativeModel(
  name: "MODEL_NAME",
  apiKey: APIKey.default,
    safetySettings: [harassmentSafety, hateSpeechSafety]
)

What's next

  • Prompt design is the process of creating prompts that elicit the desired response from language models. Writing well structured prompts is an essential part of ensuring accurate, high quality responses from a language model. Learn about best practices for prompt writing.

  • Gemini offers several model variations to meet the needs of different use cases, such as input types and complexity, implementations for chat or other dialog language tasks, and size constraints. Learn about the available Gemini models.

  • Gemini offers options for requesting rate limit increases. The rate limit for Gemini Pro models is 60 requests per minute (RPM).