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Generate text using the Gemini API
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You can ask a Gemini model to generate text from a text-only prompt or a multimodal prompt. When you use Vertex AI in Firebase, you can make this request directly from your app.

Multimodal prompts can include multiple types of input (like text along with images, PDFs, plain-text files, audio, and video).

This guide shows how to generate text from a text-only prompt and from a basic multimodal prompt that includes a file.

Jump to code samples for text-only input Jump to code samples for multimodal input

See other guides for additional options for working with text
Generate structured output Multi-turn chat Bidirectional streaming Generate images from text

Before you begin

If you haven't already, complete the getting started guide, which describes how to set up your Firebase project, connect your app to Firebase, add the SDK, initialize the Vertex AI service, and create a GenerativeModel instance.

For testing and iterating on your prompts and even getting a generated code snippet, we recommend using Vertex AI Studio.

Send text & receive text

Make sure that you've completed the Before you begin section of this guide before trying this sample.

You can ask a Gemini model to generate text by prompting with text-only input.

Swift

You can call generateContent() to generate text from text-only input.

import FirebaseVertexAI  // Initialize the Vertex AI service let vertex = VertexAI.vertexAI()  // Create a `GenerativeModel` instance with a model that supports your use case let model = vertex.generativeModel(modelName: "gemini-2.0-flash")  // Provide a prompt that contains text let prompt = "Write a story about a magic backpack."  // To generate text output, call generateContent with the text input let response = try await model.generateContent(prompt) print(response.text ?? "No text in response.")

Kotlin

You can call generateContent() to generate text from text-only input.

^{For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope.}

// Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case val generativeModel = Firebase.vertexAI.generativeModel("gemini-2.0-flash")  // Provide a prompt that contains text val prompt = "Write a story about a magic backpack."  // To generate text output, call generateContent with the text input val response = generativeModel.generateContent(prompt) print(response.text)

Java

You can call generateContent() to generate text from text-only input.

^{For Java, the methods in this SDK return a ListenableFuture.}

// Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case GenerativeModel gm = FirebaseVertexAI.getInstance()         .generativeModel("gemini-2.0-flash"); GenerativeModelFutures model = GenerativeModelFutures.from(gm);  // Provide a prompt that contains text Content prompt = new Content.Builder()     .addText("Write a story about a magic backpack.")     .build();  // To generate text output, call generateContent with the text input ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt); Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {     @Override     public void onSuccess(GenerateContentResponse result) {         String resultText = result.getText();         System.out.println(resultText);     }      @Override     public void onFailure(Throwable t) {         t.printStackTrace();     } }, executor);

Web

You can call generateContent() to generate text from text-only input.

import { initializeApp } from "firebase/app"; import { getVertexAI, getGenerativeModel } from "firebase/vertexai";  // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {   // ... };  // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig);  // Initialize the Vertex AI service const vertexAI = getVertexAI(firebaseApp);  // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(vertexAI, { model: "gemini-2.0-flash" });  // Wrap in an async function so you can use await async function run() {   // Provide a prompt that contains text   const prompt = "Write a story about a magic backpack."    // To generate text output, call generateContent with the text input   const result = await model.generateContent(prompt);    const response = result.response;   const text = response.text();   console.log(text); }  run();

Dart

You can call generateContent() to generate text from text-only input.

import 'package:firebase_vertexai/firebase_vertexai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart';  await Firebase.initializeApp(   options: DefaultFirebaseOptions.currentPlatform, );  // Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case final model =       FirebaseVertexAI.instance.generativeModel(model: 'gemini-2.0-flash');  // Provide a prompt that contains text final prompt = [Content.text('Write a story about a magic backpack.')];  // To generate text output, call generateContent with the text input final response = await model.generateContent(prompt); print(response.text);

Send text and a file (multimodal) & receive text

Make sure that you've completed the Before you begin section of this guide before trying this sample.

You can ask a Gemini model to generate text by prompting with text and a file—providing each input file's mimeType and the file itself. Find requirements and recommendations for input files later on this page.

The following example shows the basics of how to generate text from a file input by analyzing a single video file provided as inline data (base64-encoded file).

Need a sample video file?

You can use this publicly available file with a MIME type of video/mp4 (view or download file). https://storage.googleapis.com/cloud-samples-data/video/animals.mp4

Swift

You can call generateContent() to generate text from multimodal input of text and video files.

import FirebaseVertexAI  // Initialize the Vertex AI service let vertex = VertexAI.vertexAI()  // Create a `GenerativeModel` instance with a model that supports your use case let model = vertex.generativeModel(modelName: "gemini-2.0-flash")  // Provide the video as `Data` with the appropriate MIME type. let video = InlineDataPart(data: try Data(contentsOf: videoURL), mimeType: "video/mp4")  // Provide a text prompt to include with the video let prompt = "What is in the video?"  // To generate text output, call generateContent with the text and video let response = try await model.generateContent(video, prompt) print(response.text ?? "No text in response.")

Kotlin

You can call generateContent() to generate text from multimodal input of text and video files.

^{For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope.}

// Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case val generativeModel = Firebase.vertexAI.generativeModel("gemini-2.0-flash")  val contentResolver = applicationContext.contentResolver contentResolver.openInputStream(videoUri).use { stream ->   stream?.let {     val bytes = stream.readBytes()      // Provide a prompt that includes the video specified above and text     val prompt = content {         inlineData(bytes, "video/mp4")         text("What is in the video?")     }      // To generate text output, call generateContent with the prompt     val response = generativeModel.generateContent(prompt)     Log.d(TAG, response.text ?: "")   } }

Java

You can call generateContent() to generate text from multimodal input of text and video files.

^{For Java, the methods in this SDK return a ListenableFuture.}

// Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case GenerativeModel gm = FirebaseVertexAI.getInstance()         .generativeModel("gemini-2.0-flash"); GenerativeModelFutures model = GenerativeModelFutures.from(gm);  ContentResolver resolver = getApplicationContext().getContentResolver(); try (InputStream stream = resolver.openInputStream(videoUri)) {     File videoFile = new File(new URI(videoUri.toString()));     int videoSize = (int) videoFile.length();     byte[] videoBytes = new byte[videoSize];     if (stream != null) {         stream.read(videoBytes, 0, videoBytes.length);         stream.close();          // Provide a prompt that includes the video specified above and text         Content prompt = new Content.Builder()                 .addInlineData(videoBytes, "video/mp4")                 .addText("What is in the video?")                 .build();          // To generate text output, call generateContent with the prompt         ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);         Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {             @Override             public void onSuccess(GenerateContentResponse result) {                 String resultText = result.getText();                 System.out.println(resultText);             }              @Override             public void onFailure(Throwable t) {                 t.printStackTrace();             }         }, executor);     } } catch (IOException e) {     e.printStackTrace(); } catch (URISyntaxException e) {     e.printStackTrace(); }

Web

You can call generateContent() to generate text from multimodal input of text and video files.

import { initializeApp } from "firebase/app"; import { getVertexAI, getGenerativeModel } from "firebase/vertexai";  // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {   // ... };  // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig);  // Initialize the Vertex AI service const vertexAI = getVertexAI(firebaseApp);  // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(vertexAI, { model: "gemini-2.0-flash" });  // Converts a File object to a Part object. async function fileToGenerativePart(file) {   const base64EncodedDataPromise = new Promise((resolve) => {     const reader = new FileReader();     reader.onloadend = () => resolve(reader.result.split(',')[1]);     reader.readAsDataURL(file);   });   return {     inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },   }; }  async function run() {   // Provide a text prompt to include with the video   const prompt = "What do you see?";    const fileInputEl = document.querySelector("input[type=file]");   const videoPart = await fileToGenerativePart(fileInputEl.files[0]);    // To generate text output, call generateContent with the text and video   const result = await model.generateContent([prompt, videoPart]);    const response = result.response;   const text = response.text();   console.log(text); }  run();

Dart

You can call generateContent() to generate text from multimodal input of text and video files.

import 'package:firebase_vertexai/firebase_vertexai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart';  await Firebase.initializeApp(   options: DefaultFirebaseOptions.currentPlatform, );  // Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case final model =       FirebaseVertexAI.instance.generativeModel(model: 'gemini-2.0-flash');  // Provide a text prompt to include with the video final prompt = TextPart("What's in the video?");  // Prepare video for input final video = await File('video0.mp4').readAsBytes();  // Provide the video as `Data` with the appropriate mimetype final videoPart = InlineDataPart('video/mp4', video);  // To generate text output, call generateContent with the text and images final response = await model.generateContent([   Content.multi([prompt, ...videoPart]) ]); print(response.text);

Learn how to choose a model and optionally a location appropriate for your use case and app.

Stream the response

Make sure that you've completed the Before you begin section of this guide before trying this sample.

You can achieve faster interactions by not waiting for the entire result from the model generation, and instead use streaming to handle partial results. To stream the response, call generateContentStream.

View example: Stream generated text from text-only input

Swift

You can call generateContentStream() to stream generated text from text-only input.

import FirebaseVertexAI  // Initialize the Vertex AI service let vertex = VertexAI.vertexAI()  // Create a `GenerativeModel` instance with a model that supports your use case let model = vertex.generativeModel(modelName: "gemini-2.0-flash")  // Provide a prompt that contains text let prompt = "Write a story about a magic backpack."  // To stream generated text output, call generateContentStream with the text input let contentStream = try model.generateContentStream(prompt) for try await chunk in contentStream {   if let text = chunk.text {     print(text)   } }

Kotlin

You can call generateContentStream() to stream generated text from text-only input.

^{For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope.}

// Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case val generativeModel = Firebase.vertexAI.generativeModel("gemini-2.0-flash")  // Provide a prompt that includes only text val prompt = "Write a story about a magic backpack."  // To stream generated text output, call generateContentStream and pass in the prompt var response = "" generativeModel.generateContentStream(prompt).collect { chunk ->     print(chunk.text)     response += chunk.text }

Java

You can call generateContentStream() to stream generated text from text-only input.

^{For Java, the streaming methods in this SDK return a Publisher type from the Reactive Streams library.}

// Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case GenerativeModel gm = FirebaseVertexAI.getInstance()         .generativeModel("gemini-2.0-flash"); GenerativeModelFutures model = GenerativeModelFutures.from(gm);  // Provide a prompt that contains text Content prompt = new Content.Builder()         .addText("Write a story about a magic backpack.")         .build();  // To stream generated text output, call generateContentStream with the text input Publisher<GenerateContentResponse> streamingResponse =     model.generateContentStream(prompt);  // Subscribe to partial results from the response final String[] fullResponse = {""}; streamingResponse.subscribe(new Subscriber<GenerateContentResponse>() {   @Override   public void onNext(GenerateContentResponse generateContentResponse) {     String chunk = generateContentResponse.getText();     fullResponse[0] += chunk;   }    @Override   public void onComplete() {     System.out.println(fullResponse[0]);   }    @Override   public void onError(Throwable t) {     t.printStackTrace();   }    @Override   public void onSubscribe(Subscription s) { } });

Web

You can call generateContentStream() to stream generated text from text-only input.

import { initializeApp } from "firebase/app"; import { getVertexAI, getGenerativeModel } from "firebase/vertexai";  // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {   // ... };  // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig);  // Initialize the Vertex AI service const vertexAI = getVertexAI(firebaseApp);  // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(vertexAI, { model: "gemini-2.0-flash" });  // Wrap in an async function so you can use await async function run() {   // Provide a prompt that contains text   const prompt = "Write a story about a magic backpack."    // To stream generated text output, call generateContentStream with the text input   const result = await model.generateContentStream(prompt);    for await (const chunk of result.stream) {     const chunkText = chunk.text();     console.log(chunkText);   }    console.log('aggregated response: ', await result.response); }  run();

Dart

You can call generateContentStream() to stream generated text from text-only input.

import 'package:firebase_vertexai/firebase_vertexai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart';  await Firebase.initializeApp(   options: DefaultFirebaseOptions.currentPlatform, );  // Initialize the Vertex AI service and create a `GenerativeModel` instance // Specify a model that supports your use case final model =       FirebaseVertexAI.instance.generativeModel(model: 'gemini-2.0-flash');  // Provide a prompt that contains text final prompt = [Content.text('Write a story about a magic backpack.')];  // To stream generated text output, call generateContentStream with the text input final response = model.generateContentStream(prompt); await for (final chunk in response) {   print(chunk.text); }