You can ask a Gemini model to generate text from a text-only prompt or
a multimodal prompt. When you use Firebase AI Logic, you can make this
request directly from your mobile or web app (such as your Apple (Swift),
Android (Kotlin / Java), Web (JavaScript), Flutter (Dart), or Unity 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 for text-only input](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#text-in-text-out)
[Jump to code for multimodal input](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#base64)
[Jump to code for streamed responses](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#streaming)

<br />

|---|
| **See other guides for additional options for working with text** [Generate structured output](https://firebase.google.com/docs/ai-logic/generate-structured-output.md.txt) [Multi-turn chat](https://firebase.google.com/docs/ai-logic/chat.md.txt) [Generate text on-device](https://firebase.google.com/docs/ai-logic/hybrid.md.txt) [Text-to-speech (TTS)](https://firebase.google.com/docs/ai-logic/generate-speech.md.txt) [Generate images from text](https://firebase.google.com/docs/ai-logic/generate-images-gemini.md.txt) |

## Before you begin


|---|
| *Click your Gemini API provider to view provider-specific content and code on this page.* <button value="dev" default="">Gemini Developer API</button> <button value="vertex">Agent Platform Gemini API</button> |

<br />

If you haven't already, complete the
[getting started guide](https://firebase.google.com/docs/ai-logic/get-started.md.txt), which describes how to
set up your Firebase project, connect your app to Firebase, add the SDK,
initialize the backend service for your chosen Gemini API provider, and
create a `GenerativeModel` instance.


> [!NOTE]
> All our docs assume that you're using the [latest versions](https://firebase.google.com/support/releases.md.txt) of the Firebase AI Logic SDKs.

<br />

For testing and iterating on your prompts, we recommend using [Google AI Studio](https://aistudio.google.com).

## Generate text from text-only input


|---|
| *Before trying this sample, complete the [Before you begin](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#before-you-begin) section of this guide to set up your project and app. **In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page**.* |

<br />

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

### Swift

You can call
[`generateContent()`](https://firebase.google.com/docs/reference/swift/firebaseailogic/api/reference/Classes/GenerativeModel.md.txt#generatecontent_:_1)
to generate text from text-only input.

```swift

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(modelName: "gemini-3.8-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()`](https://firebase.google.com/docs/reference/kotlin/com/google/firebase/ai/GenerativeModel.md.txt#generateContent(kotlin.String))
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](https://developer.android.com/kotlin/coroutines).*^

```kotlin

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3.8-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 = model.generateContent(prompt)
print(response.text)
```

### Java

You can call
[`generateContent()`](https://firebase.google.com/docs/reference/android/com/google/firebase/ai/GenerativeModel.md.txt#generateContent(kotlin.String))
to generate text from text-only input.
^*For Java, the methods in this SDK return a
[`ListenableFuture`](https://developer.android.com/guide/background/asynchronous/listenablefuture).*^

```java

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3.8-flash");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);


// 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()`](https://firebase.google.com/docs/reference/js/ai.generativemodel.md.txt#generativemodelgeneratecontent)
to generate text from text-only input.

```javascript

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(ai, { model: "gemini-3.8-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()`](https://pub.dev/documentation/firebase_ai/latest/firebase_ai/GenerativeModel/generateContent.html)
to generate text from text-only input.

```dart

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3.8-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);
```

### Unity

You can call
[`GenerateContentAsync()`](https://firebase.google.com/docs/reference/unity/class/firebase/a-i/generative-model.md.txt#generatecontentasync)
to generate text from text-only input.

```c#

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(modelName: "gemini-3.8-flash");


// Provide a prompt that contains text
var prompt = "Write a story about a magic backpack.";
// To generate text output, call GenerateContentAsync with the text input
var response = await model.GenerateContentAsync(prompt);
UnityEngine.Debug.Log(response.Text ?? "No text in response.");
```


Learn how to choose a [model](https://firebase.google.com/docs/ai-logic/models.md.txt)

appropriate for your use case and app.

## Generate text from text-and-file (multimodal) input


|---|
| *Before trying this sample, complete the [Before you begin](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#before-you-begin) section of this guide to set up your project and app. **In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page**.* |

<br />

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](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#requirements-recommendations-for-input)
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).
Note that this example shows providing the file inline, but the SDKs also support [providing a YouTube URL](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt).

<br />

Need a sample video file?

<br />

> 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)).
> `https://storage.googleapis.com/cloud-samples-data/video/animals.mp4`

<br />

<br />


> [!NOTE]
> **Important** : **The total request size limit is
> 20 MB.** To send large files, review the [options for providing files in multimodal requests](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt).

<br />

### Swift

You can call
[`generateContent()`](https://firebase.google.com/docs/reference/swift/firebaseailogic/api/reference/Classes/GenerativeModel.md.txt#generatecontent_:)
to generate text from multimodal input of text and video files.

```swift

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(modelName: "gemini-3.8-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()`](https://firebase.google.com/docs/reference/kotlin/com/google/firebase/ai/GenerativeModel.md.txt#generateContent(kotlin.Array))
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](https://developer.android.com/kotlin/coroutines).*^

```kotlin

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3.8-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 = model.generateContent(prompt)
Log.d(TAG, response.text ?: "")
}
}
```

### Java

You can call
[`generateContent()`](https://firebase.google.com/docs/reference/android/com/google/firebase/ai/GenerativeModel.md.txt#generateContent(kotlin.Array))
to generate text from multimodal input of text and video files.
^*For Java, the methods in this SDK return a
[`ListenableFuture`](https://developer.android.com/guide/background/asynchronous/listenablefuture).*^

```java

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3.8-flash");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);


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()`](https://firebase.google.com/docs/reference/js/ai.generativemodel.md.txt#generativemodelgeneratecontent)
to generate text from multimodal input of text and video files.

```javascript

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(ai, { model: "gemini-3.8-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 {
type: "inlineData",
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()`](https://pub.dev/documentation/firebase_ai/latest/firebase_ai/GenerativeModel/generateContent.html)
to generate text from multimodal input of text and video files.

```dart

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3.8-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);
```

### Unity

You can call
[`GenerateContentAsync()`](https://firebase.google.com/docs/reference/unity/class/firebase/a-i/generative-model.md.txt#generatecontentasync)
to generate text from multimodal input of text and video files.

```c#

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(modelName: "gemini-3.8-flash");


// Provide the video as \`data\` with the appropriate MIME type.
var video = ModelContent.InlineData("video/mp4",
System.IO.File.ReadAllBytes(System.IO.Path.Combine(
UnityEngine.Application.streamingAssetsPath, "yourVideo.mp4")));
// Provide a text prompt to include with the video
var prompt = ModelContent.Text("What is in the video?");
// To generate text output, call GenerateContentAsync with the text and video
var response = await model.GenerateContentAsync(new \[\] { video, prompt });
UnityEngine.Debug.Log(response.Text ?? "No text in response.");
```


Learn how to choose a [model](https://firebase.google.com/docs/ai-logic/models.md.txt)

appropriate for your use case and app.

## Stream the response


|---|
| *Before trying this sample, complete the [Before you begin](https://firebase.google.com/docs/ai-logic/generate-text.md.txt#before-you-begin) section of this guide to set up your project and app. **In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page**.* |

<br />

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`.

<br />

#### View example: Stream generated text from text-only input

<br />

### Swift

You can call
[`generateContentStream()`](https://firebase.google.com/docs/reference/swift/firebaseailogic/api/reference/Classes/GenerativeModel.md.txt#generatecontentstream_:_1)
to stream generated text from text-only input.

```swift

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(modelName: "gemini-3.8-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()`](https://firebase.google.com/docs/reference/kotlin/com/google/firebase/ai/GenerativeModel.md.txt#generateContentStream(kotlin.String))
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](https://developer.android.com/kotlin/coroutines).*^

```kotlin

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3.8-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 = ""
model.generateContentStream(prompt).collect { chunk -\>
print(chunk.text)
response += chunk.text
}
```

### Java

You can call
[`generateContentStream()`](https://firebase.google.com/docs/reference/android/com/google/firebase/ai/GenerativeModel.md.txt#generateContentStream(kotlin.String))
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](https://www.reactive-streams.org/).*^

```java

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3.8-flash");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);


// 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()`](https://firebase.google.com/docs/reference/js/ai.generativemodel.md.txt#generativemodelgeneratecontent)
to stream generated text from text-only input.

```javascript

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(ai, { model: "gemini-3.8-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()`](https://pub.dev/documentation/firebase_ai/latest/firebase_ai/GenerativeModel/generateContentStream.html)
to stream generated text from text-only input.

```dart

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3.8-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);
}
```

### Unity

You can call
[`GenerateContentStreamAsync()`](https://firebase.google.com/docs/reference/unity/class/firebase/a-i/generative-model.md.txt#generatecontentstreamasync)
to stream generated text from text-only input.

```c#

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(modelName: "gemini-3.8-flash");


// Provide a prompt that contains text
var prompt = "Write a story about a magic backpack.";
// To stream generated text output, call GenerateContentStreamAsync with the text input
var responseStream = model.GenerateContentStreamAsync(prompt);
await foreach (var response in responseStream) {
if (!string.IsNullOrWhiteSpace(response.Text)) {
UnityEngine.Debug.Log(response.Text);
}
}
```


Learn how to choose a [model](https://firebase.google.com/docs/ai-logic/models.md.txt)

appropriate for your use case and app.

<br />

<br />

<br />

#### View example: Stream generated text from multimodal input

<br />

### Swift

You can call
[`generateContentStream()`](https://firebase.google.com/docs/reference/swift/firebaseailogic/api/reference/Classes/GenerativeModel.md.txt#generatecontentstream_:)
to stream generated text from multimodal input of text and a single video.

```swift

import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case.
let model = ai.generativeModel(modelName: "gemini-3.8-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 stream generated text output, call generateContentStream with the text and video
let contentStream = try model.generateContentStream(video, prompt)
for try await chunk in contentStream {
if let text = chunk.text {
print(text)
}
}
```

### Kotlin

You can call
[`generateContentStream()`](https://firebase.google.com/docs/reference/kotlin/com/google/firebase/ai/GenerativeModel.md.txt#generateContentStream(kotlin.Array))
to stream generated text from multimodal input of text and a single video.
^*For Kotlin, the methods in this SDK are suspend functions and need to be called
from a [Coroutine scope](https://developer.android.com/kotlin/coroutines).*^

```kotlin

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3.8-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 stream generated text output, call generateContentStream with the prompt
var fullResponse = ""
model.generateContentStream(prompt).collect { chunk -\>
Log.d(TAG, chunk.text ?: "")
fullResponse += chunk.text
}
}
}
```

### Java

You can call
[`generateContentStream()`](https://firebase.google.com/docs/reference/android/com/google/firebase/ai/GenerativeModel.md.txt#generateContentStream(kotlin.Array))
to stream generated text from multimodal input of text and a single video.
^*For Java, the streaming methods in this SDK return a
`Publisher` type from the [Reactive Streams library](https://www.reactive-streams.org/).*^

```java

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3.8-flash");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);


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 stream generated text output, call generateContentStream with the prompt
Publisher\<GenerateContentResponse\> streamingResponse =
model.generateContentStream(prompt);
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) {
}
});
}
} catch (IOException e) {
e.printStackTrace();
} catch (URISyntaxException e) {
e.printStackTrace();
}
```

### Web

You can call
[`generateContentStream()`](https://firebase.google.com/docs/reference/js/ai.generativemodel.md.txt#generativemodelgeneratecontentstream)
to stream generated text from multimodal input of text and a single video.

```javascript

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// 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 Gemini Developer API backend service.
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case.
const model = getGenerativeModel(ai, { model: "gemini-3.8-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 {
type: "inlineData",
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 stream generated text output, call generateContentStream with the text and video
const result = await model.generateContentStream(\[prompt, videoPart\]);
for await (const chunk of result.stream) {
const chunkText = chunk.text();
console.log(chunkText);
}
}
run();
```

### Dart

You can call
[`generateContentStream()`](https://pub.dev/documentation/firebase_ai/latest/firebase_ai/GenerativeModel/generateContentStream.html)
to stream generated text from multimodal input of text and a single video.

```dart

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service.
// Create a `GenerativeModel` instance with a model that supports your use case.
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3.8-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 stream generated text output, call generateContentStream with the text and image
final response = await model.generateContentStream(\[
Content.multi(\[prompt,videoPart\])
\]);
await for (final chunk in response) {
print(chunk.text);
}
```

### Unity

You can call
[`GenerateContentStreamAsync()`](https://firebase.google.com/docs/reference/unity/class/firebase/a-i/generative-model.md.txt#generatecontentstreamasync)
to stream generated text from multimodal input of text and a single video.

```c#

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service.
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case.
var model = ai.GetGenerativeModel(modelName: "gemini-3.8-flash");


// Provide the video as \`data\` with the appropriate MIME type.
var video = ModelContent.InlineData("video/mp4",
System.IO.File.ReadAllBytes(System.IO.Path.Combine(
UnityEngine.Application.streamingAssetsPath, "yourVideo.mp4")));
// Provide a text prompt to include with the video
var prompt = ModelContent.Text("What is in the video?");
// To stream generated text output, call GenerateContentStreamAsync with the text and video
var responseStream = model.GenerateContentStreamAsync(new \[\] { video, prompt });
await foreach (var response in responseStream) {
if (!string.IsNullOrWhiteSpace(response.Text)) {
UnityEngine.Debug.Log(response.Text);
}
}
```


Learn how to choose a [model](https://firebase.google.com/docs/ai-logic/models.md.txt)

appropriate for your use case and app.

<br />

<br />

<br />

*** ** * ** ***

## Requirements and recommendations for input image files


> [!NOTE]
> **Important** : **The total request size limit is
> 20 MB.** To send large files, review the [options for providing files in multimodal requests](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt).

<br />

Note that a file provided as inline data is encoded to base64 in transit, which
increases the size of the request. You get an HTTP 413 error if a request is
too large.

See
[Supported input files and requirements for the Agent Platform Gemini API (formerly Vertex AI)](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt)
to learn detailed information about the following:

- Different options for providing a file in a request (either inline or using the file's URL or URI)
- Supported file types
- Supported MIME types and how to specify them
- Requirements and best practices for files and multimodal requests

<br />

*** ** * ** ***

## What else can you do?

- Learn how to [count tokens](https://firebase.google.com/docs/ai-logic/count-tokens.md.txt) before sending long prompts to the model.
- [Set up Cloud Storage for Firebase](https://firebase.google.com/docs/ai-logic/solutions/cloud-storage.md.txt) so that you can include large files in your multimodal requests and have a more managed solution for providing files in prompts. Files can include images, PDFs, video, and audio.
- Start thinking about preparing for production (see the [production checklist](https://firebase.google.com/docs/ai-logic/production-checklist.md.txt)):
  - [Enforce Firebase App Check](https://firebase.google.com/docs/ai-logic/app-check.md.txt) *as early as possible* to help protect the Gemini API from abuse by unauthorized clients.
  - Use [Firebase Remote Config](https://firebase.google.com/docs/ai-logic/change-model-name-remotely.md.txt) or [server prompt templates](https://firebase.google.com/docs/ai-logic/server-prompt-templates/get-started.md.txt) so that you can make on-demand changes to configurations for your AI feature (like model name) without releasing a new version of your app.


#### Try out other capabilities

<br />

- Build [multi-turn conversations (chat)](https://firebase.google.com/docs/ai-logic/chat.md.txt).
- Generate text from [text-only prompts](https://firebase.google.com/docs/ai-logic/generate-text.md.txt).
- [Generate structured output (like JSON)](https://firebase.google.com/docs/ai-logic/generate-structured-output.md.txt) from both text and multimodal prompts.
- [Generate and edit images](https://firebase.google.com/docs/ai-logic/generate-images-gemini.md.txt) from both text and multimodal prompts.
- [Generate speech (both single- and
  multiple-speakers)](https://firebase.google.com/docs/ai-logic/generate-speech) using Gemini text-to-speech (TTS) models.
- [Stream input and output](https://firebase.google.com/docs/ai-logic/live-api.md.txt) (including audio) using the Gemini Live API.
- Use tools (like [function calling](https://firebase.google.com/docs/ai-logic/function-calling.md.txt) and Grounding with [Google Search](https://firebase.google.com/docs/ai-logic/grounding-google-search.md.txt) or [Google Maps](https://firebase.google.com/docs/ai-logic/grounding-google-maps.md.txt)) to connect a Gemini model to other parts of your app and external systems and information.


#### Learn how to control content generation

- [Understand prompt design](https://firebase.google.com/docs/ai-logic/prompt-design.md.txt), including best practices, strategies, and example prompts.
- [Configure model parameters](https://firebase.google.com/docs/ai-logic/model-parameters.md.txt) like max output tokens, the probability of repeated output tokens, etc.
- [Use safety settings](https://firebase.google.com/docs/ai-logic/safety-settings.md.txt) to adjust the likelihood of getting responses that may be considered harmful.

You can also experiment with prompts and model configurations and even get a generated code snippet using [Google AI Studio](https://aistudio.google.com).

<br />


#### Learn more about the supported models

Learn about the [models available for various use cases](https://firebase.google.com/docs/ai-logic/models.md.txt) and their [quotas](https://firebase.google.com/docs/ai-logic/quotas.md.txt) and [pricing](https://firebase.google.com/docs/ai-logic/pricing.md.txt).

<br />

<br />

[Give feedback
about your experience with Firebase AI Logic](https://firebase.google.com/docs/ai-logic/feedback)

<br />