CountTokens.swift

// Copyright 2024 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

import GoogleGenerativeAI
import XCTest

// Set up your API Key
// ====================
// To use the Gemini API, you'll need an API key. To learn more, see the "Set up your API Key"
// section in the Gemini API quickstart:
// https://ai.google.dev/gemini-api/docs/quickstart?lang=swift#set-up-api-key

@available(iOS 15.0, macOS 11.0, macCatalyst 15.0, *)
final class CountTokensSnippets: XCTestCase {
  override func setUpWithError() throws {
    try XCTSkipIf(
      APIKey.default.isEmpty,
      "`\(APIKey.apiKeyEnvVar)` environment variable not set."
    )
  }

  func testCountTokensTextOnly() async throws {
    // [START tokens_text_only]
    let generativeModel =
      GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        name: "gemini-1.5-flash",
        // Access your API key from your on-demand resource .plist file (see "Set up your API key"
        // above)
        apiKey: APIKey.default
      )

    let prompt = "Write a story about a magic backpack."

    let response = try await generativeModel.countTokens(prompt)

    print("Total Tokens: \(response.totalTokens)")
    // [END tokens_text_only]
  }

  func testCountTokensChat() async throws {
    // [START tokens_chat]
    let generativeModel =
      GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        name: "gemini-1.5-flash",
        // Access your API key from your on-demand resource .plist file (see "Set up your API key"
        // above)
        apiKey: APIKey.default
      )

    // Optionally specify existing chat history
    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 with optional chat history
    let chat = generativeModel.startChat(history: history)

    let response = try await generativeModel.countTokens(chat.history + [
      ModelContent(role: "user", parts: "This is the message I intend to send"),
    ])
    print("Total Tokens: \(response.totalTokens)")
    // [END tokens_chat]
  }

  #if canImport(UIKit)
    func testCountTokensMultimodalInline() async throws {
      // [START tokens_multimodal_image_inline]
      let generativeModel =
        GenerativeModel(
          // Specify a Gemini model appropriate for your use case
          name: "gemini-1.5-flash",
          // Access your API key from your on-demand resource .plist file (see "Set up your API key"
          // above)
          apiKey: APIKey.default
        )

      guard let image1 = UIImage(systemName: "cloud.sun") else { fatalError() }
      guard let image2 = UIImage(systemName: "cloud.heavyrain") else { fatalError() }

      let prompt = "What's the difference between these pictures?"

      let response = try await generativeModel.countTokens(image1, image2, prompt)
      print("Total Tokens: \(response.totalTokens)")
      // [END tokens_multimodal_image_inline]
    }
  #endif // canImport(UIKit)

  func testCountTokensSystemInstruction() async throws {
    // [START tokens_system_instruction]
    let generativeModel =
      GenerativeModel(
        // Specify a model that supports system instructions, like a Gemini 1.5 model
        name: "gemini-1.5-flash",
        // Access your API key from your on-demand resource .plist file (see "Set up your API key"
        // above)
        apiKey: APIKey.default,
        systemInstruction: ModelContent(role: "system", parts: "You are a cat. Your name is Neko.")
      )

    let prompt = "What is your name?"

    let response = try await generativeModel.countTokens(prompt)
    print("Total Tokens: \(response.totalTokens)")
    // [END tokens_system_instruction]
  }

  func testCountTokensTools() async throws {
    // [START tokens_tools]
    let generativeModel =
      GenerativeModel(
        // Specify a model that supports system instructions, like a Gemini 1.5 model
        name: "gemini-1.5-flash",
        // Access your API key from your on-demand resource .plist file (see "Set up your API key"
        // above)
        apiKey: APIKey.default,
        tools: [Tool(functionDeclarations: [
          FunctionDeclaration(
            name: "controlLight",
            description: "Set the brightness and color temperature of a room light.",
            parameters: [
              "brightness": Schema(
                type: .number,
                format: "double",
                description: "Light level from 0 to 100. Zero is off and 100 is full brightness."
              ),
              "colorTemperature": Schema(
                type: .string,
                format: "enum",
                description: "Color temperature of the light fixture.",
                enumValues: ["daylight", "cool", "warm"]
              ),
            ],
            requiredParameters: ["brightness", "colorTemperature"]
          ),
        ])]
      )

    let prompt = "Dim the lights so the room feels cozy and warm."

    let response = try await generativeModel.countTokens(prompt)
    print("Total Tokens: \(response.totalTokens)")
    // [END tokens_tools]
  }
}