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Build an AI Assistant for iOS Using Swift

Tutorials
AI
Chat
Swift
9 min read
Martin M.
Martin M.
Published December 6, 2024

In this tutorial, we will demonstrate how easy it is to create an AI assistant for iOS using Stream Chat. In this example, we will use the Anthropic and OpenAI APIs as our example LLM; however, developers are free to use whichever LLM provider they like and still benefit from Stream’s rich UI support for Markdown, tables, code samples, etc. To follow along with this tutorial, we recommend creating a free account and checking out our main iOS chat SDK tutorial as a refresher.

Talk is cheap, so here’s a video of the result:

We will use our new UI components for AI to render messages as they come, with animations similar to those of popular LLMs, such as ChatGPT. Our UI components can render LLM responses that contain markdown, code, tables, and much more.

We also provide UI for thinking indicators that can react to the new AI-related events we have on the server side.

The entire code can also be found here.

1. Project Setup

We must ensure a minimum version of 4.68.0 of the Stream Chat Swift UI SDK and 0.3.0 of the Stream Chat AI SDK to follow along. These SDKs contain UI components that will help facilitate the integration of AI into our chat feature. Our AI UI components support iOS 15 and above.

First, let’s create and set up the iOS project. Go to Xcode → File → New → Project, and name the project StreamChatAIAssistant (or any other name you prefer).

Next, we add the required dependencies from StreamChat and the UI components.

We use the following steps to add the SDK via Swift Package Manager:

  • Select "Add Packages…" in the File menu
  • Paste the following URL: https://github.com/GetStream/stream-chat-swiftui
  • In the option "Dependency Rule" choose "Up to Next Major Version", and in the text input fields next to it, enter "4.68.0" and "5.0.0" accordingly.
  • Choose "Add Package" and wait for the dialog to complete
  • Only select "StreamChatSwiftUI" and select "Add Package" again

Next, we should add the AI components. To do that, perform the same process with the following package URL: https://github.com/GetStream/stream-chat-swift-ai.

In the option "Dependency Rule" choose "Up to Next Major Version", and in the text input fields next to it, enter "0.3.0" and "1.0.0" accordingly.

With that, we have our iOS project ready and can add some code.

2. Setting Up the Channel List

Next, let’s present the Stream Chat channel list component. When a channel is tapped, we will open the chat view with the message list. To do this, we add the following code in our StreamChatAIAssistantApp file:

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import SwiftUI
import StreamChat
import StreamChatSwiftUI

@main
struct StreamChatAIAssistantApp: App {

    @State var streamChat: StreamChat
    @StateObject var channelListViewModel: ChatChannelListViewModel

    var chatClient: ChatClient = {
        var config = ChatClientConfig(apiKey: .init("zcgvnykxsfm8"))
        config.isLocalStorageEnabled = true
        config.applicationGroupIdentifier = "group.io.getstream.iOS.ChatDemoAppSwiftUI"

        let client = ChatClient(config: config)
        return client
    }()

    init() {
        let utils = Utils(
            messageListConfig: .init(messageDisplayOptions: .init(spacerWidth: { _ in return 60 }))
        )
        _streamChat = State(initialValue: StreamChat(chatClient: chatClient, utils: utils))
        _channelListViewModel = StateObject(wrappedValue: ViewModelsFactory.makeChannelListViewModel())

        chatClient.connectUser(
            userInfo: UserInfo(
                id: "anakin_skywalker",
                imageURL: URL(string: "https://vignette.wikia.nocookie.net/starwars/images/6/6f/Anakin_Skywalker_RotS.png")
            ),
            token: try! Token(rawValue: "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoiYW5ha2luX3NreXdhbGtlciJ9.ZwCV1qPrSAsie7-0n61JQrSEDbp6fcMgVh4V2CB0kM8")
        )
    }

    var body: some Scene {
        WindowGroup {
            ChatChannelListView(
                viewModel: channelListViewModel
            )
        }
    }
}

When we run the app at this point, we will see the channel list. When we tap on an item, we will be navigated to a channel view to see all the messages.

The code above creates the chat client and StreamChat object, connecting a hardcoded user. We would want a proper setup for production apps where the user is provided after a login process. You can learn more about the client's setup in our docs.

3. Running the Backend

Before adding AI features to our iOS app, let’s set up our node.js backend. The backend will expose two methods for starting and stopping an AI agent for a particular channel. If the agent is started, it listens to all new messages and sends them to OpenAI. It provides the results by sending a message and updating its text.

We use the Anthropic API and the new Assistants API from OpenAI in this sample. We also have an example of function calling. By default, Anthropic is selected, but we can pass openai as a platform parameter in the start-ai-agent request if we want to use OpenAI.

The sample also supports sending different states of the typing indicator (for example, Thinking, Checking external sources, etc).

To run the server locally, we need to clone it:

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git clone https://github.com/GetStream/ai-assistant-nodejs.git your_local_location

Next, we need to set up our .env file with the following keys:

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ANTHROPIC_API_KEY=insert_your_key
STREAM_API_KEY=insert_your_key
STREAM_API_SECRET=insert_your_secret
OPENAI_API_KEY=insert_your_key
OPENWEATHER_API_KEY=insert_your_key

The STREAM_API_KEY and STREAM_API_SECRET can be found in our app's dashboard. To get an ANTHROPIC_API_KEY, we can create an account at Anthropic. Alternatively, we can get an OPENAI_API_KEY from OpenAI.

The example also uses function calling from OpenAI, which allows us to call a function if a specific query is recognized. In this sample, we can ask, “What’s the weather like?” in a particular location. If you want to support this feature, you can get your API key from Open Weather (or any other service, but we would need to update the request in that case).

Next, we need to install the dependencies using the npm install command.

After the setup is done, we can run the sample from the root with the following command:

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npm start
Building your own app? Get early access to our Livestream or Video Calling API and launch in days!

This will start listening to requests on localhost:3000.

4. Use a Service for Backend Interaction

We’re returning to the iOS app and writing the necessary code to interact with the server we created in the previous chapter.

To do this, create a new file called StreamAIChatService and add the following code:

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import Foundation

class StreamAIChatService {
    static let shared = StreamAIChatService()

    private let baseURL = "http://localhost:3000"
    private let jsonEncoder = JSONEncoder()

    private let urlSession = URLSession.shared

    func setupAgent(channelId: String) async throws {
        try await executePostRequest(
            body: AIAgentRequest(channelId: channelId),
            endpoint: "start-ai-agent"
        )
    }

    func stopAgent(channelId: String) async throws {
        try await executePostRequest(
            body: AIAgentRequest(channelId: channelId),
            endpoint: "stop-ai-agent"
        )
    }

    private func executePostRequest<RequestBody: Encodable>(body: RequestBody, endpoint: String) async throws {
        let url = URL(string: "\(baseURL)/\(endpoint)")!
        var request = URLRequest(url: url)
        request.httpMethod = "POST"
        request.setValue("application/json", forHTTPHeaderField: "Content-Type")
        request.httpBody = try jsonEncoder.encode(body)
        _ = try await urlSession.data(for: request)
    }

}

struct AIAgentRequest: Encodable {
    let channelId: String

    enum CodingKeys: String, CodingKey {
        case channelId = "channel_id"
    }
}

This service exposes methods that start and stop the AI agent for a given channel identifier.

5. Creating a Typing Indicator

We have covered the interaction with the backend and now switch our focus to building the UI. Let’s now add some code to handle the AI typing indicator. To do this, we create a new file TypingIndicatorHandler, and add the following code:

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import Foundation
import StreamChat
import StreamChatSwiftUI

class TypingIndicatorHandler: ObservableObject, EventsControllerDelegate, ChatChannelWatcherListControllerDelegate {

    @Injected(\.chatClient) var chatClient: ChatClient

    private var eventsController: EventsController!

    @Published var state: String = ""

    private let aiBotId = "ai-bot"

    @Published var aiBotPresent = false

    @Published var generatingMessageId: String?

    var channelId: ChannelId? {
        didSet {
            if let channelId = channelId {
                watcherListController = chatClient.watcherListController(query: .init(cid: channelId))
                watcherListController?.delegate = self
                watcherListController?.synchronize { [weak self] _ in
                    guard let self else { return }
                    self.aiBotPresent = self.isAiBotPresent
                }
            }
        }
    }

    @Published var typingIndicatorShown = false

    var isAiBotPresent: Bool {
        let aiAgent = watcherListController?
            .watchers
            .first(where: { $0.id.contains(self.aiBotId) })
        return aiAgent?.isOnline == true
    }

    var watcherListController: ChatChannelWatcherListController?

    init() {
        eventsController = chatClient.eventsController()
        eventsController.delegate = self
    }

    func eventsController(_ controller: EventsController, didReceiveEvent event: any Event) {
        if event is AIIndicatorClearEvent {
            typingIndicatorShown = false
            generatingMessageId = nil
            return
        }

        guard let typingEvent = event as? AIIndicatorUpdateEvent else {
            return
        }

        state = typingEvent.title
        if typingEvent.state == .generating {
            generatingMessageId = typingEvent.messageId
        } else {
            generatingMessageId = nil
        }
        typingIndicatorShown = !typingEvent.title.isEmpty
    }

    func channelWatcherListController(
        _ controller: ChatChannelWatcherListController,
        didChangeWatchers changes: [ListChange<ChatUser>]
    ) {
        self.aiBotPresent = isAiBotPresent
    }
}

extension AIIndicatorUpdateEvent {
    var title: String {
        switch state {
        case .thinking:
            return "Thinking"
        case .checkingExternalSources:
            return "Checking external sources"
        default:
            return ""
        }
    }
}

This handler reacts to the events that the node.js server sends, and based on that, it provides info about whether the typing indicator is shown and if a message is being generated.

6. Add UI for Handling the AI

Now that we’ve created the UI components, let’s extend Stream Chat’s Swift UI SDK to include these AI capabilities. To do this, we create a view factory to customize the chat views. Our docs provide more details about this approach.

We create a new file called AIViewFactory and add the following code:

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import SwiftUI
import StreamChat
import StreamChatAI
import StreamChatSwiftUI

class AIViewFactory: ViewFactory {

    @Injected(\.chatClient) var chatClient: ChatClient

    let typingIndicatorHandler: TypingIndicatorHandler

    init(typingIndicatorHandler: TypingIndicatorHandler) {
        self.typingIndicatorHandler = typingIndicatorHandler
    }

    func makeMessageListContainerModifier() -> some ViewModifier {
        CustomMessageListContainerModifier(typingIndicatorHandler: typingIndicatorHandler)
    }

    func makeEmptyMessagesView(
        for channel: ChatChannel,
        colors: ColorPalette
    ) -> some View {
        AIAgentOverlayView(typingIndicatorHandler: typingIndicatorHandler)
    }

    @ViewBuilder
    func makeCustomAttachmentViewType(
        for message: ChatMessage,
        isFirst: Bool,
        availableWidth: CGFloat,
        scrolledId: Binding<String?>
    ) -> some View {
        StreamingAIView(
            typingIndicatorHandler: typingIndicatorHandler,
            message: message,
            isFirst: isFirst
        )
    }

    func makeTrailingComposerView(
        enabled: Bool,
        cooldownDuration: Int,
        onTap: @escaping () -> Void
    ) -> some View {
        CustomTrailingComposerView(
            typingIndicatorHandler: typingIndicatorHandler,
            onTap: onTap
        )
    }
}

Let’s also add the defined views here.

First, we want to show a button on the top right corner, which will start and stop the AI agent. To do this, we add the following code (can be in the same file):

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struct CustomMessageListContainerModifier: ViewModifier {

    @ObservedObject var typingIndicatorHandler: TypingIndicatorHandler

    func body(content: Content) -> some View {
        content.overlay {
            AIAgentOverlayView(typingIndicatorHandler: typingIndicatorHandler)
        }
    }
}

struct AIAgentOverlayView: View {

    @ObservedObject var typingIndicatorHandler: TypingIndicatorHandler

    var body: some View {
        VStack {
            HStack {
                Spacer()
                if !typingIndicatorHandler.aiBotPresent {
                    Button {
                        Task {
                            if let channelId = typingIndicatorHandler.channelId {
                                try await StreamAIChatService.shared.setupAgent(channelId: channelId.id)
                            }
                        }
                    } label: {
                        AIIndicatorButton(title: "Start AI")
                    }
                } else {
                    Button {
                        Task {
                            if let channelId = typingIndicatorHandler.channelId {
                                try await StreamAIChatService.shared.stopAgent(channelId: channelId.id)
                            }
                        }
                    } label: {
                        AIIndicatorButton(title: "Stop AI")
                    }

                }
            }
            Spacer()
            if typingIndicatorHandler.typingIndicatorShown {
                HStack {
                    AITypingIndicatorView(text: typingIndicatorHandler.state)
                    Spacer()
                }
                .padding()
                .frame(height: 80)
                .background(Color(UIColor.secondarySystemBackground))
            }
        }
    }
}

struct AIIndicatorButton: View {

    let title: String

    var body: some View {
        HStack {
            Text(title)
                .bold()
            Image(systemName: "wand.and.stars.inverse")
        }
        .padding(.all, 8)
        .padding(.horizontal, 4)
        .background(Color(UIColor.secondarySystemBackground))
        .cornerRadius(16)
        .shadow(color: Color.black.opacity(0.1), radius: 10, x: 0, y: 12)
        .shadow(color: Color.black.opacity(0.1), radius: 1, x: 0, y: 1)
        .padding()
    }
}

Next, we add the view that renders the text message.

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struct StreamingAIView: View {

    @ObservedObject var typingIndicatorHandler: TypingIndicatorHandler

    var message: ChatMessage
    var isFirst: Bool

    var body: some View {
        StreamingMessageView(
            content: message.text,
            isGenerating: typingIndicatorHandler.generatingMessageId == message.id
        )
        .padding()
        .messageBubble(for: message, isFirst: isFirst)
    }

}

This view listens to the changes in the typing indicator and uses the StreamingMessageView from our SDK.

Finally, we want to support the possibility of stopping the generation of the message. To do this, we need to customize our composer view with an additional button when the generation is in progress:

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struct CustomTrailingComposerView: View {

    @Injected(\.utils) private var utils

    @EnvironmentObject var viewModel: MessageComposerViewModel

    var onTap: () -> Void

    @ObservedObject var typingIndicatorHandler: TypingIndicatorHandler

    init(
        typingIndicatorHandler: TypingIndicatorHandler,
        onTap: @escaping () -> Void
    ) {
        self.typingIndicatorHandler = typingIndicatorHandler
        self.onTap = onTap
    }

    public var body: some View {
        Group {
            if typingIndicatorHandler.generatingMessageId != nil {
                Button {
                    Task {
                        viewModel.channelController
                            .eventsController()
                            .sendEvent(
                                AIIndicatorStopEvent(cid: viewModel.channelController.channel?.cid)
                            )
                    }
                } label: {
                    Image(systemName: "stop.circle.fill")
                }
            } else {
                SendMessageButton(
                    enabled: viewModel.sendButtonEnabled,
                    onTap: onTap
                )
            }
        }
        .padding(.bottom, 8)
    }
}

7. Connect UI Components with the SDK

Now, let’s connect everything. We go back to the StreamChatAIAssistantApp file and make the following changes.

Define a new @State variable there and call it TypingIndicatorHandler.

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struct StreamChatAIAssistantApp: App {
    // existing code
    @State var typingIndicatorHandler: TypingIndicatorHandler
    // existing code
}

We update the init method so it initializes the typingIndicatorHandler and sets up a custom message resolver:

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init() {
    let utils = Utils(
        messageTypeResolver: CustomMessageResolver(),
        messageListConfig: .init(
            messageDisplayOptions: .init(spacerWidth: { _ in return 60 }),
            skipEditedMessageLabel: { message in
                message.extraData["ai_generated"]?.boolValue == true
            }
        )
    )
    _streamChat = State(initialValue: StreamChat(chatClient: chatClient, utils: utils))
    typingIndicatorHandler = TypingIndicatorHandler()
    _channelListViewModel = StateObject(wrappedValue: ViewModelsFactory.makeChannelListViewModel())

    chatClient.connectUser(
        userInfo: UserInfo(
           id: "anakin_skywalker",
           imageURL: URL(string: "https://vignette.wikia.nocookie.net/starwars/images/6/6f/Anakin_Skywalker_RotS.png")
        ),
        token: try! Token(rawValue: "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoiYW5ha2luX3NreXdhbGtlciJ9.ZwCV1qPrSAsie7-0n61JQrSEDbp6fcMgVh4V2CB0kM8")
    )
}

We also need to update the body to use the newly created view factory and react to channel selection events.

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var body: some Scene {
    WindowGroup {
        ChatChannelListView(
            viewFactory: AIViewFactory(typingIndicatorHandler: typingIndicatorHandler),
            viewModel: channelListViewModel
        )
        .onChange(of: channelListViewModel.selectedChannel) { oldValue, newValue in
            typingIndicatorHandler.channelId = newValue?.channel.cid
            if newValue == nil, let channelId = oldValue?.channel.cid.id {
                Task {
                    try await StreamAIChatService.shared.stopAgent(channelId: channelId)
                }
            }
        }
    }
}

Lastly, we also need the CustomMessageResolver defined above, which will treat messages with the ai_generated flag as custom attachments.

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class CustomMessageResolver: MessageTypeResolving {

    func hasCustomAttachment(message: ChatMessage) -> Bool {
        message.extraData["ai_generated"] == true
    }
}

Finally, we are ready to run the app! If we open a channel and start the AI agent, we can start asking some questions.

Conclusion

In this tutorial, we have built an AI assistant bot that works seamlessly with Stream Chat’s iOS SDK:

  • We have shown you how to use our AI components for message rendering of LLM responses, such as markdown, code, tables, etc.
  • We have shown how to create our server, which will start and stop AI agents that respond to user questions.
  • You have learned how to customize our SwiftUI SDK to integrate these new AI features.

If you want to learn more about our AI capabilities, head to our AI landing page. Additionally, check our iOS docs to learn how you can provide more customizations to your chat apps.

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