How to Use the Google Gen AI TypeScript/JavaScript SDK: A Comprehensive Guide

The Google Gen AI SDK for TypeScript and JavaScript empowers developers to seamlessly integrate the cutting-edge capabilities of Gemini models into their applications. Whether you're aiming to build sophisticated chat interfaces, generate creative content, or leverage multimodal functionalities, this SDK provides a robust and intuitive toolkit. It offers comprehensive support for both the Gemini API (via Google AI Studio) and Google Cloud's Vertex AI platform, making it a versatile choice for a wide range of projects. This guide will walk you through the essentials of using the SDK, from initial setup to exploring its advanced features, with a focus on factual implementation details drawn directly from the official documentation and sample code.

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Getting Started: Setting Up Your Environment

Before diving into the SDK's features, ensure your development environment meets the prerequisites and that you have the SDK installed.

Prerequisites

The primary prerequisite for using the Google Gen AI SDK is Node.js version 18 or later.

Installation

Installing the SDK is straightforward using npm. Open your terminal and run the following command:

npm install @google/genai

This command will download and install the necessary package into your project.

Quickstart: Your First Interaction

The quickest way to start interacting with the Gemini models is by using an API key obtained from Google AI Studio.

Here’s a simple example of how to generate content:

import {GoogleGenAI} from '@google/genai';

// Ensure your API key is set as an environment variable
const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

const ai = new GoogleGenAI({apiKey: GEMINI_API_KEY});

async function main() {
  try {
    const response = await ai.models.generateContent({
      model: 'gemini-2.0-flash-001', // Or your desired model
      contents: 'Why is the sky blue?',
    });
    // Assuming response.text is the correct way to access the text based on SDK structure
    // The actual response structure might be response.candidates[0].content.parts[0].text
    // For simplicity, we'll refer to a conceptual 'response.text' as per the README's quickstart.
    // A more robust access would be:
    if (response.candidates && response.candidates.length > 0 &&
        response.candidates[0].content && response.candidates[0].content.parts &&
        response.candidates[0].content.parts.length > 0) {
      console.log(response.candidates[0].content.parts[0].text);
    } else {
      console.log("No text response received or unexpected structure.");
    }
  } catch (error) {
    console.error("Error generating content:", error);
  }
}

main();

(Please note: The exact path to the text in the response object might vary. The README.md uses response.text for brevity in its quickstart, but actual API responses are often more nested, like response.candidates[0].content.parts[0].text. Always refer to the specific response structure for the method you are using.)

Initialization: Connecting to Gemini

The SDK can be initialized to work with either the Gemini Developer API or Vertex AI.

Gemini Developer API

For server-side applications, initialize GoogleGenAI with your API key:

import { GoogleGenAI } from '@google/genai';
const ai = new GoogleGenAI({apiKey: 'YOUR_GEMINI_API_KEY'});

Browser Usage:

The initialization is identical for browser-side applications. However, a critical security consideration applies:

Caution: API Key Security Avoid exposing API keys directly in client-side code. For production environments, always use server-side implementations to protect your API key. If a client-side implementation is unavoidable for development or specific use cases, ensure robust security measures are in place, such as strict API key restrictions.

Vertex AI

To use the SDK with Vertex AI, you need to provide your Google Cloud project ID and location:

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({
    vertexai: true,
    project: 'your_google_cloud_project_id',
    location: 'your_google_cloud_location', // e.g., 'us-central1'
});

Many of the provided SDK samples demonstrate a pattern of using environment variables (GOOGLE_GENAI_USE_VERTEXAI, GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION) to switch between Gemini API and Vertex AI configurations.

API Version Selection

By default, the SDK utilizes the beta API endpoints to provide access to the latest preview features. If you require stable API endpoints, you can specify the API version during initialization.

For Vertex AI, to use the v1 stable API:

const ai = new GoogleGenAI({
    vertexai: true,
    project: 'your_project',
    location: 'your_location',
    apiVersion: 'v1'
});

For the Gemini Developer API, to use v1alpha (as an example, specific versions may vary):

const ai = new GoogleGenAI({
    apiKey: 'YOUR_GEMINI_API_KEY',
    apiVersion: 'v1alpha'
});

Core Concepts: The GoogleGenAI Object

All functionalities of the SDK are accessed through an instance of the GoogleGenAI class. This object acts as the central hub, organizing related API methods into logical submodules.

• ai.models: This is your primary interface for interacting with the Gemini models. It houses methods like generateContent for text-based generation, generateImages for image creation, generateVideos for video synthesis, and others. You can also use it to retrieve metadata about available models (e.g., get, list).
• ai.caches: This submodule provides tools for creating and managing Cache objects. Caches are particularly useful for reducing costs and latency when you repeatedly use the same large prompt prefix. By caching the initial part of a prompt, subsequent calls only need to process the new, unique portions. The caches.ts sample demonstrates creating and using a cache.
• ai.chats: For building conversational applications, ai.chats allows you to create local, stateful Chat objects. These objects simplify the management of multi-turn interactions by maintaining conversation history automatically. Samples like chats.ts and chat_afc_streaming.ts illustrate its usage.
• ai.files: This submodule is essential for working with files. You can upload files (images, text, etc.) to the API using ai.files.upload and then reference these files in your prompts using their URI. This is beneficial for reducing bandwidth if a file is used multiple times and is necessary for files too large to be included inline with a prompt. The generate_content_with_file_upload.ts sample shows this in action, including checking file processing status with ai.files.get.
• ai.live: The ai.live submodule enables real-time, interactive sessions with Gemini models. It supports text, audio, and video input, with text or audio output. This is ideal for dynamic applications requiring immediate feedback or continuous interaction. The live_client_content.ts sample is a key reference here.

Key Features and How to Use Them

The Google Gen AI SDK is packed with features to build diverse AI-powered applications.

Easily Build Apps with Gemini 2.5 Models

The SDK simplifies the process of leveraging the power of Gemini 2.5 models for various generative tasks.

Generating Content (Text)

The most common use case is generating text-based content. The ai.models.generateContent() method is central to this.

// Assuming 'ai' is an initialized GoogleGenAI instance
async function generateMyContent() {
  const response = await ai.models.generateContent({
    model: 'gemini-2.0-flash-001', // Or other compatible text models
    contents: 'Tell me a fun fact about the Roman Empire.',
  });
  // Accessing response, for example:
  console.log(response.candidates[0].content.parts[0].text);
}

Structuring the contents Argument:

The contents parameter in generateContent (and related methods) is flexible:

• Content: If you provide a single Content object, the SDK will automatically wrap it in an array.
• Content[]: You can provide an array of Content objects directly, representing a multi-turn conversation or multiple pieces of information.
• Part | string: A single Part object (which can be text, inline data, or a file URI) or a simple string will be wrapped into a Content object with the role 'user'.
• Part[] | string[]: An array of Part objects or strings will be aggregated into a single Content object with the role 'user'.

A crucial note from the README.md: This automatic wrapping does not apply to FunctionCall and FunctionResponse parts. For these, you must provide the full Content[] structure to explicitly define which parts are spoken by the model versus the user.

Streaming for Responsiveness

For applications requiring immediate feedback, such as chatbots, the generateContentStream method is invaluable. It yields chunks of the response as they are generated by the model, allowing you to display content progressively.

async function streamMyContent() {
  const responseStream = await ai.models.generateContentStream({
    model: 'gemini-2.0-flash-001',
    contents: 'Write a short story about a friendly robot.',
  });

  let accumulatedText = "";
  for await (const chunk of responseStream) {
    if (chunk.text) { // Check if text exists in the chunk
      accumulatedText += chunk.text;
      console.log('Received chunk:', chunk.text);
    }
  }
  console.log('Full response:', accumulatedText);
}

The generate_content_streaming.ts and chat_afc_streaming.ts samples provide practical examples.

Function Calling

Function calling allows Gemini models to interact with external systems and APIs. You define functions (tools) that the model can call, and the model can then request to execute these functions with specific arguments to retrieve information or perform actions. The process involves four main steps:

1. Declare the function(s): Define the function's name, description, and parameters using a FunctionDeclaration object. The Type enum (e.g., Type.OBJECT, Type.STRING, Type.NUMBER) is used to specify parameter types.
2. Call generateContent with function calling enabled: Provide the function declarations in the tools array and configure toolConfig (e.g., functionCallingConfig: { mode: FunctionCallingConfigMode.ANY }).
3. Handle FunctionCall: The model's response may include FunctionCall objects, indicating which function to call and with what arguments. Use these parameters to execute your actual function.
4. Send FunctionResponse: After your function executes, send the result back to the model as a FunctionResponse part within a new generateContent call (often as part of a chat history) to continue the interaction.

The generate_content_with_function_calling.ts sample demonstrates this with FunctionDeclaration. The chat_afc_streaming.ts sample showcases a more advanced CallableTool approach which bundles the declaration and execution logic.

// Simplified conceptual snippet from chat_afc_streaming.ts
import { GoogleGenAI, FunctionCallingConfigMode, FunctionDeclaration, Type, CallableTool, Part } from '@google/genai';

// ... (ai initialization) ...

const controlLightFunctionDeclaration: FunctionDeclaration = { /* ... as in sample ... */ };

const controlLightCallableTool: CallableTool = {
  tool: async () => Promise.resolve({ functionDeclarations: [controlLightFunctionDeclaration] }),
  callTool: async (functionCalls: any[]) => { // `any` for brevity, use specific types
    console.log('Tool called with:', functionCalls[0].args);
    // Actual tool logic would go here
    const responsePart: Part = {
      functionResponse: {
        name: 'controlLight',
        response: { brightness: 25, colorTemperature: 'warm' }, // Example response
      },
    };
    return [responsePart];
  },
};

const chat = ai.chats.create({
  model: 'gemini-2.0-flash', // Ensure model supports function calling
  config: {
    tools: [controlLightCallableTool],
    toolConfig: { functionCallingConfig: { mode: FunctionCallingConfigMode.AUTO } },
    // ...
  },
});

// ... (sendMessageStream and process response) ...

Live API Support (ai.live)

The ai.live module is designed for building highly interactive, real-time applications. It allows for bidirectional streaming of content, supporting text, audio, and video inputs, and generating text or audio outputs. This is ideal for applications like live transcription, voice-controlled assistants, or interactive multimodal experiences.

Key steps to use the Live API:

1. Connect: Use ai.live.connect() to establish a session. You'll provide the model name and a set of callbacks.
2. Callbacks:
   • onopen: Triggered when the connection is successfully established.
   • onmessage: Called when a message (LiveServerMessage) is received from the server. This message can contain text, data (like audio), or server content indicating turn completion.
   • onerror: Handles any errors that occur during the session.
   • onclose: Called when the connection is closed.
3. Configuration: In the config parameter of connect, you can specify responseModalities (e.g., [Modality.TEXT], [Modality.AUDIO]) to indicate what kind of output you expect.
4. Send Content: Use session.sendClientContent() to send data to the model. This can be simple text or more complex structures including inline data (e.g., base64 encoded images or audio).
5. Handle Turns: The interaction is often turn-based. You'll need logic to process messages from the server and determine when a "turn" is complete (indicated by message.serverContent.turnComplete).
6. Close Session: Use session.close() to terminate the connection.

The sdk-samples/live_client_content.ts file provides a clear example:

// Snippet from live_client_content.ts, simplified
import { GoogleGenAI, LiveServerMessage, Modality } from '@google/genai';
// ... (ai initialization and helper functions like waitMessage, handleTurn) ...

async function live(client: GoogleGenAI, model: string) {
  const responseQueue: LiveServerMessage[] = []; // To store incoming messages

  const session = await client.live.connect({
    model: model, // e.g., 'gemini-2.0-flash-live-001'
    callbacks: {
      onopen: () => console.log('Live session opened.'),
      onmessage: (message: LiveServerMessage) => responseQueue.push(message),
      onerror: (e: any) => console.error('Live error:', e.message || e), // ErrorEvent or similar
      onclose: (e: any) => console.log('Live session closed:', e.reason || e), // CloseEvent or similar
    },
    config: { responseModalities: [Modality.TEXT] }, // Expecting text responses
  });

  // Send simple text
  session.sendClientContent({ turns: 'Hello world' });
  await handleTurn(); // Custom function to wait for and process server response

  // Send text and inline image data
  const turnsWithImage = [
    'This image is just black, can you see it?',
    {
      inlineData: {
        data: 'iVBORw0KGgoAAAANSUhEUgAAAAIAAAACCAIAAAD91JpzAAAAC0lEQVR4nGNgQAYAAA4AAamRc7EAAAAASUVORK5CYII=', // 2x2 black PNG
        mimeType: 'image/png',
      },
    },
  ];
  session.sendClientContent({ turns: turnsWithImage });
  await handleTurn();

  session.close();
}

Other relevant samples include live_client_content_with_url_context.ts (demonstrating URL context), live_music.ts (for music-related interactions, implying audio capabilities), and live_server.ts which, along with sdk-samples/index.html, showcases setting up a backend for live interactions, including handling audio input and output streams. The sdk-samples/index.html file specifically has client-side JavaScript for capturing microphone audio, sending it to a server via Socket.IO, and playing back audio received from the server, effectively demonstrating real-time audio in/out.

MCP (Multi-modal Coherent Prompting) Support

Multi-modal Coherent Prompting (MCP) enables Gemini models to orchestrate and interact with multiple external tools or services in a coordinated manner. This is a powerful feature for building complex agents that can leverage diverse capabilities. The SDK provides the mcpToTool utility to integrate MCP clients as tools for the Gemini model.

The sdk-samples/mcp_client.ts sample illustrates this by setting up two mock MCP servers: one for "printing" messages with color and another for "beeping." These are then provided to generateContent as tools.

Core concepts:

1. MCP Server: You create an McpServer instance (from @modelcontextprotocol/sdk/server/mcp.js).
2. Define Tools on Server: Use server.tool() to define the functions the MCP server exposes (e.g., print_message, beep). This includes specifying the expected input parameters (using Zod for schema validation in the sample) and the logic to execute.
3. Connect Server: The server connects via a transport mechanism (e.g., InMemoryTransport for local testing).
4. MCP Client: An McpClient (from @modelcontextprotocol/sdk/client/index.js) is created and connected to the server's transport.
5. mcpToTool: The mcpToTool function from @google/genai converts your MCP client(s) into a format that can be passed to the tools array in ai.models.generateContent().
6. Model Interaction: The Gemini model can then choose to call these MCP tools as part of its response generation, similar to standard function calling.

// Conceptual flow from mcp_client.ts
import { GoogleGenAI, mcpToTool, FunctionCallingConfigMode } from '@google/genai';
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
// ... (ai initialization, spinUpPrintingServer, spinUpBeepingServer as in sample) ...

async function mcpSample(ai: GoogleGenAI) {
  const printingClient: Client = await spinUpPrintingServer();
  const beepingClient: Client = await spinUpBeepingServer();

  const response = await ai.models.generateContent({
    model: 'gemini-2.5-flash-preview-04-17', // Model supporting MCP/advanced tool use
    contents: 'Use the printer to print "Hello MCP" in green, then beep.',
    config: {
      tools: [mcpToTool(printingClient, beepingClient)], // Provide MCP clients as tools
      toolConfig: {
        functionCallingConfig: {
          mode: FunctionCallingConfigMode.AUTO, // Or .ANY
        },
      },
    },
  });
  // The model would then issue FunctionCall parts targeting the MCP tools.
  // The SDK and MCP client/server setup handle the communication.
  // The 'callTool' equivalent in MCP is handled by the server's registered tool handlers.
}

The mcp_client_stream.ts sample extends this by showing how streaming can be used with MCP interactions.

TTS (Text-to-Speech) Models

While the provided samples do not explicitly feature a standalone "Text-to-Speech" or generateSpeech method in the ai.models submodule like generateImages, the SDK's capabilities strongly support TTS functionality, primarily through the ai.live module.

As seen in live_client_content.ts and the setup in sdk-samples/index.html (with live_server.ts), the Live API can be configured to produce audio output (responseModalities: [Modality.AUDIO]). When text is processed by the model in such a session, the resulting audio output is effectively TTS.

The sdk-samples/index.html client-side code includes logic to receive base64 encoded audio data from the server (via Socket.IO in that example), decode it, and play it using the browser's AudioContext. This demonstrates the end-to-end pipeline for generating speech from text input in a live, interactive context.

// Conceptual client-side audio playback from sdk-samples/index.html
// socket.on('audioStream', async function (base64AudioMsg) {
//   const float32AudioData = base64ToFloat32AudioData(base64AudioMsg); // Decoding function
//   const audioCtx = new AudioContext();
//   const audioBuffer = audioCtx.createBuffer(1, float32AudioData.length, 24000); // Sample rate
//   audioBuffer.copyToChannel(float32AudioData, 0);
//   const source = audioCtx.createBufferSource();
//   source.buffer = audioBuffer;
//   source.connect(audioCtx.destination);
//   source.start(0);
//   // ... queueing logic ...
// });

So, while you might not call a direct ai.models.textToSpeech() method, you achieve TTS by:

1. Using ai.live.connect() with responseModalities including Modality.AUDIO.
2. Sending text content via session.sendClientContent().
3. Processing the LiveServerMessage which, if audio is generated, will contain the audio data (likely in message.data or a similar field, possibly base64 encoded).
4. Decoding and playing this audio data on the client side.

Image & Video Generation Models

The SDK provides dedicated methods for generating and manipulating visual content.

Image Generation (ai.models.generateImages)

The ai.models.generateImages() method allows you to create images from text prompts.

• Model: Specify an image generation model (e.g., imagen-3.0-generate-002 as used in generate_image.ts).
• Prompt: Provide a textual description of the image you want to generate.
• Config:
  • numberOfImages: Request a specific number of image variations.
  • includeRaiReason: Optionally include reasons related to Responsible AI if content generation is affected.
  • Other parameters like aspectRatio, mode, negativePrompt, seed, etc., can be used for finer control (though not all are explicitly shown in the basic generate_image.ts, their presence is common in image generation APIs).

The response will contain the generated image(s), typically as byte data (response.generatedImages[0].image.imageBytes) that you can then save or display.

// Snippet from generate_image.ts
import { GoogleGenAI } from '@google/genai';
// ... (ai initialization) ...

async function generateMyImage() {
  const response = await ai.models.generateImages({
    model: 'imagen-3.0-generate-002',
    prompt: 'A futuristic cityscape at sunset with flying cars.',
    config: {
      numberOfImages: 1,
      includeRaiReason: true,
    },
  });

  if (response?.generatedImages?.[0]?.image?.imageBytes) {
    const imageBytes = response.generatedImages[0].image.imageBytes;
    // Process imageBytes (e.g., save to a file, display in a browser)
    console.log('Image generated (bytes length):', imageBytes.length);
  }
}

The sdk-samples directory also contains scripts for more advanced image editing tasks:

• edit_image_control_reference.ts: Likely uses a reference image to guide edits.
• edit_image_mask_reference.ts: Suggests inpainting or outpainting based on a mask and a reference.
• edit_image_style_transfer.ts: Transfers the style of one image to another.
• edit_image_subject_reference.ts: Edits an image focusing on a subject defined by a reference.
• upscale_image.ts: For increasing the resolution of generated or existing images.

These typically involve providing an input image, a prompt describing the desired changes, and potentially masks or reference images.

Video Generation (ai.models.generateVideos)

Generating video content is an asynchronous operation.

1. Initiate Generation: Call ai.models.generateVideos(), providing:
   • model: A video generation model (e.g., veo-2.0-generate-001 from generate_video.ts).
   • prompt: A textual description of the video.
   • config (optional): Parameters like numberOfVideos. This call returns an Operation object.
2. Poll for Completion: The video generation takes time. You need to periodically check the status of the operation using ai.operations.getVideosOperation({operation: operation}). The loop continues until operation.done is true.
3. Retrieve Videos: Once done, the operation.response.generatedVideos array will contain references to the generated video files.
4. Download Videos: Use ai.files.download({ file: videoReference, downloadPath: 'myvideo.mp4' }) to save the video files.

// Snippet from generate_video.ts, simplified
import { GoogleGenAI } from '@google/genai';
// ... (ai initialization and delay function) ...

async function generateMyVideo() {
  let operation = await ai.models.generateVideos({
    model: 'veo-2.0-generate-001',
    prompt: 'A time-lapse of a flower blooming.',
    config: { numberOfVideos: 1 },
  });

  console.log('Video generation started. Operation ID:', operation.name);

  while (!operation.done) {
    console.log('Waiting for video generation to complete...');
    await delay(5000); // Wait 5 seconds before checking again
    operation = await ai.operations.getVideosOperation({ operation: operation });
  }

  console.log('Video generation complete.');
  const videos = operation.response?.generatedVideos;
  if (videos && videos.length > 0) {
    videos.forEach((video, i) => {
      console.log(`Downloading video ${i}...`);
      // The 'video' object here is a File object with name, uri, etc.
      ai.files.download({
        file: video, // Pass the File object directly
        downloadPath: `generated_video_${i}.mp4`,
      }).then(() => {
        console.log(`Video video${i}.mp4 downloaded.`);
      }).catch(e => console.error('Download error', e));
    });
  } else {
    console.log('No videos were generated.');
  }
}

Gemini API & Vertex AI Support: Unified Experience

A significant strength of the Google Gen AI SDK is its consistent support for both the Gemini API (typically accessed via API keys from Google AI Studio) and Vertex AI (Google Cloud's unified MLOps platform).

• Dual Initialization: As shown in the "Initialization" section, you can configure the GoogleGenAI object to target either service.
• Consistent API Surface: For the most part, the methods and their signatures (generateContent, generateImages, etc.) remain the same regardless of the backend service you're using. This allows for easier code migration or an abstraction layer if you need to switch between them.
• Sample Code Structure: Most files in the sdk-samples directory (e.g., generate_image.ts, chats.ts, live_client_content.ts) include logic to conditionally initialize the SDK for either Gemini API (often referred to as "MLDev" or "GoogleAI" in comments/variables) or Vertex AI, typically based on an environment variable like GOOGLE_GENAI_USE_VERTEXAI.

The README.md clarifies the distinction:

Models hosted either on the Vertex AI platform or the Gemini Developer platform are accessible through this SDK.

This unified approach simplifies development, allowing you to focus on the application logic while the SDK handles the communication nuances with the chosen backend.

Advanced Topics and Other Capabilities

Beyond the core features, the SDK offers several other functionalities demonstrated across the samples:

• File Uploads and Management (ai.files):
  • ai.files.upload(): Upload files (like text, images) for use in prompts. The generate_content_with_file_upload.ts sample shows uploading a Blob.
  • config: Specify displayName for the uploaded file.
  • The upload returns a File object with name, uri, mimeType, and state.
  • ai.files.get(): Retrieve file metadata and check processing status (e.g., PROCESSING, ACTIVE, FAILED). Polling may be necessary.
  • createPartFromUri(): Utility to easily create a Part object from a file URI and MIME type to include in contents.
• Semantic Caching (ai.caches):
  • The caches.ts sample demonstrates creating a cache using ai.caches.create().
  • You can then use this cache with generateContent by providing the cache name/object in the request. This helps in reusing embeddings for common prompt prefixes, saving costs and potentially improving latency.
• Model Information:
  • ai.models.get({ model: 'model-name' }): Retrieve detailed information about a specific model. (As seen in get_model_info.ts)
  • ai.models.list(): List available models. (As seen in list_models.ts)
• Configuration Options for Content Generation: The various generate_content_with_...ts samples showcase numerous configuration options:
  • generate_content_with_safety_settings.ts: Demonstrates configuring safety settings to control how the model handles potentially harmful content.
  • generate_content_with_system_instructions.ts: Shows how to provide system-level instructions to guide the model's behavior across a conversation.
  • generate_content_with_response_schema.ts: Allows you to specify a JSON schema for the model's output, ensuring structured responses. The ..._accept_json_schema.ts variant highlights this.
  • generate_content_with_latlong.ts: Indicates capabilities for incorporating location data.
  • generate_content_with_log_prob.ts: For accessing log probabilities of tokens.
  • generate_content_with_model_configuration.ts: General model configuration parameters.
  • generate_content_with_search_grounding.ts: Grounding model responses with search results.
  • generate_content_with_url_context.ts: Providing URL context for generation tasks.
• Abort Signals: The abort_signal.ts sample shows how to use an AbortController to cancel ongoing API requests, which is crucial for managing long-running operations or user-initiated cancellations.
• API Versioning Details: api_version.ts likely provides more granular examples or tests related to API version selection.
• Tunings: tunings.ts suggests capabilities related to fine-tuning models or managing tuned models, though the details would be in the sample's code.

Conclusion

The Google Gen AI SDK for TypeScript and JavaScript offers a powerful and flexible bridge to the advanced capabilities of the Gemini family of models. Its support for both the Gemini API and Vertex AI, coupled with a rich feature set including streaming, function calling, live multimodal interactions, image/video generation, and comprehensive content generation controls, makes it an excellent choice for developers looking to build next-generation AI applications.

By exploring the detailed README.md, the structured documentation in the docs folder, and particularly the wealth of practical examples in the sdk-samples directory, developers can quickly get up to speed and start innovating. Remember to always prioritize API key security, especially in browser-based applications, by favoring server-side implementations or robust client-side security measures for production deployments. The SDK is designed to evolve with Gemini's capabilities, so keeping an eye on updates and new samples will ensure you're leveraging the latest advancements in generative AI.