You can ask a Gemini model to analyze document files
(like PDFs and plain-text files) that you provide
either inline (base64-encoded) or via URL. 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).

With this capability, you can do things like:

- Analyze diagrams, charts, and tables inside documents
- Extract information into structured output formats
- Answer questions about visual and text contents in documents
- Summarize documents
- Transcribe document content (for example, into HTML), preserving layouts and formatting, for use in downstream applications (such as in RAG pipelines)

This guide is about generating *text* from document input (like PDFs),
but you can also
[generate *images* from document input](https://firebase.google.com/docs/ai-logic/generate-images-gemini.md.txt).

[Jump to code samples](https://firebase.google.com/docs/ai-logic/analyze-documents.md.txt#base64)
[Jump to code for streamed responses](https://firebase.google.com/docs/ai-logic/analyze-documents.md.txt#streaming)

<br />

|---|
| **See other guides for additional options for working with documents (like PDFs)** [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) |

## 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).

<br />

Need a sample PDF file?

<br />

> You can use this publicly available file with a MIME type of `application/pdf`
> ([view or download file](https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf)).
> `https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf`

<br />

<br />

#### Models that support this capability

This guide is about generating text from document input (like PDFs), and it's
applicable to the following Gemini models:

- `gemini-4-carbon`
- `gemini-4-argon`
- `gemini-3.8-flash`
- `gemini-3.5-flash-lite` (and the older `gemini-3.1-flash-lite`)

Older general-use Gemini 3.x Flash models support this capability, but
they're no longer recommended for use due to upcoming shutdown dates and/or
automatic routing to newer models.

> [!NOTE]
> **Note:** Firebase AI Logic does *not* yet support configuring the input media resolution, but that feature is coming soon!

## Generate text from PDF files (base64-encoded)


|---|
| *Before trying this sample, complete the [Before you begin](https://firebase.google.com/docs/ai-logic/analyze-documents.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 PDFs---providing each
input file's `mimeType` and the file itself. Find
[requirements and recommendations for input files](https://firebase.google.com/docs/ai-logic/analyze-documents.md.txt#requirements-recommendations-for-input)
later on this page.


> [!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 PDFs.

```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 PDF as \`Data\` with the appropriate MIME type
let pdf = try InlineDataPart(data: Data(contentsOf: pdfURL), mimeType: "application/pdf")
// Provide a text prompt to include with the PDF file
let prompt = "Summarize the important results in this report."
// To generate text output, call \`generateContent\` with the PDF file and text prompt
let response = try await model.generateContent(pdf, prompt)
// Print the generated text, handling the case where it might be nil
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 PDFs.
^*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
// Provide the URI for the PDF file you want to send to the model
val inputStream = contentResolver.openInputStream(pdfUri)
if (inputStream != null) { // Check if the PDF file loaded successfully
inputStream.use { stream -\>
// Provide a prompt that includes the PDF file specified above and text
val prompt = content {
inlineData(
bytes = stream.readBytes(),
mimeType = "application/pdf" // Specify the appropriate PDF file MIME type
)
text("Summarize the important results in this report.")
}
// To generate text output, call \`generateContent\` with the prompt
val response = model.generateContent(prompt)
// Log the generated text, handling the case where it might be null
Log.d(TAG, response.text ?: "")
}
} else {
Log.e(TAG, "Error getting input stream for file.")
// Handle the error appropriately
}
```

### 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 PDFs.
^*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();
// Provide the URI for the PDF file you want to send to the model
try (InputStream stream = resolver.openInputStream(pdfUri)) {
if (stream != null) {
byte\[\] audioBytes = stream.readAllBytes();
stream.close();
// Provide a prompt that includes the PDF file specified above and text
Content prompt = new Content.Builder()
.addInlineData(audioBytes, "application/pdf") // Specify the appropriate PDF file MIME type
.addText("Summarize the important results in this report.")
.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 text = result.getText();
Log.d(TAG, (text == null) ? "" : text);
}
@Override
public void onFailure(Throwable t) {
Log.e(TAG, "Failed to generate a response", t);
}
}, executor);
} else {
Log.e(TAG, "Error getting input stream for file.");
// Handle the error appropriately
}
} catch (IOException e) {
Log.e(TAG, "Failed to read the pdf file", e);
} catch (URISyntaxException e) {
Log.e(TAG, "Invalid pdf file", e);
}
```

### 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 PDFs.

```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(','));
reader.readAsDataURL(file);
});
return {
type: "inlineData",
inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
};
}
async function run() {
// Provide a text prompt to include with the PDF file
const prompt = "Summarize the important results in this report.";
// Prepare PDF file for input
const fileInputEl = document.querySelector("input\[type=file\]");
const pdfPart = await fileToGenerativePart(fileInputEl.files);
// To generate text output, call \`generateContent\` with the text and PDF file
const result = await model.generateContent(\[prompt, pdfPart\]);
// Log the generated text, handling the case where it might be undefined
console.log(result.response.text() ?? "No text in response.");
}
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 PDFs.

```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 PDF file
final prompt = TextPart("Summarize the important results in this report.");
// Prepare the PDF file for input
final doc = await File('document0.pdf').readAsBytes();
// Provide the PDF file as \`Data\` with the appropriate PDF file MIME type
final docPart = InlineDataPart('application/pdf', doc);
// To generate text output, call \`generateContent\` with the text and PDF file
final response = await model.generateContent(\[
Content.multi(\[prompt,docPart\])
\]);
// Print the generated text
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 PDFs.

```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 text prompt to include with the PDF file
var prompt = ModelContent.Text("Summarize the important results in this report.");
// Provide the PDF file as \`data\` with the appropriate PDF file MIME type
var doc = ModelContent.InlineData("application/pdf",
System.IO.File.ReadAllBytes(System.IO.Path.Combine(
UnityEngine.Application.streamingAssetsPath, "document0.pdf")));
// To generate text output, call \`GenerateContentAsync\` with the text and PDF file
var response = await model.GenerateContentAsync(new \[\] { prompt, doc });
// Print the generated text
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/analyze-documents.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 PDF files

<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 PDFs.

```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 PDF as \`Data\` with the appropriate MIME type
let pdf = try InlineDataPart(data: Data(contentsOf: pdfURL), mimeType: "application/pdf")
// Provide a text prompt to include with the PDF file
let prompt = "Summarize the important results in this report."
// To stream generated text output, call \`generateContentStream\` with the PDF file and text prompt
let contentStream = try model.generateContentStream(pdf, prompt)
// Print the generated text, handling the case where it might be nil
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 PDFs.
^*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
// Provide the URI for the PDF you want to send to the model
val inputStream = contentResolver.openInputStream(pdfUri)
if (inputStream != null) { // Check if the PDF file loaded successfully
inputStream.use { stream -\>
// Provide a prompt that includes the PDF file specified above and text
val prompt = content {
inlineData(
bytes = stream.readBytes(),
mimeType = "application/pdf" // Specify the appropriate PDF file MIME type
)
text("Summarize the important results in this report.")
}
// To stream generated text output, call \`generateContentStream\` with the prompt
var fullResponse = ""
model.generateContentStream(prompt).collect { chunk -\>
// Log the generated text, handling the case where it might be null
val chunkText = chunk.text ?: ""
Log.d(TAG, chunkText)
fullResponse += chunkText
}
}
} else {
Log.e(TAG, "Error getting input stream for file.")
// Handle the error appropriately
}
```

### 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 PDFs.
^*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();
// Provide the URI for the PDF file you want to send to the model
try (InputStream stream = resolver.openInputStream(pdfUri)) {
if (stream != null) {
byte\[\] audioBytes = stream.readAllBytes();
stream.close();
// Provide a prompt that includes the PDF file specified above and text
Content prompt = new Content.Builder()
.addInlineData(audioBytes, "application/pdf") // Specify the appropriate PDF file MIME type
.addText("Summarize the important results in this report.")
.build();
// To stream generated text output, call \`generateContentStream\` with the prompt
Publisher\<GenerateContentResponse\> streamingResponse =
model.generateContentStream(prompt);
StringBuilder fullResponse = new StringBuilder();
streamingResponse.subscribe(new Subscriber\<GenerateContentResponse\>() {
@Override
public void onNext(GenerateContentResponse generateContentResponse) {
String chunk = generateContentResponse.getText();
String text = (chunk == null) ? "" : chunk;
Log.d(TAG, text);
fullResponse.append(text);
}
@Override
public void onComplete() {
Log.d(TAG, fullResponse.toString());
}
@Override
public void onError(Throwable t) {
Log.e(TAG, "Failed to generate a response", t);
}
@Override
public void onSubscribe(Subscription s) {
}
});
} else {
Log.e(TAG, "Error getting input stream for file.");
// Handle the error appropriately
}
} catch (IOException e) {
Log.e(TAG, "Failed to read the pdf file", e);
} catch (URISyntaxException e) {
Log.e(TAG, "Invalid pdf file", e);
}
```

### 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 PDFs.

```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(','));
reader.readAsDataURL(file);
});
return {
type: "inlineData",
inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },
};
}
async function run() {
// Provide a text prompt to include with the PDF file
const prompt = "Summarize the important results in this report.";
// Prepare PDF file for input
const fileInputEl = document.querySelector("input\[type=file\]");
const pdfPart = await fileToGenerativePart(fileInputEl.files);
// To stream generated text output, call \`generateContentStream\` with the text and PDF file
const result = await model.generateContentStream(\[prompt, pdfPart\]);
// Log the generated text
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 PDFs.

```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 PDF file
final prompt = TextPart("Summarize the important results in this report.");
// Prepare the PDF file for input
final doc = await File('document0.pdf').readAsBytes();
// Provide the PDF file as \`Data\` with the appropriate PDF file MIME type
final docPart = InlineDataPart('application/pdf', doc);
// To generate text output, call \`generateContentStream\` with the text and PDF file
final response = await model.generateContentStream(\[
Content.multi(\[prompt,docPart\])
\]);
// Print the generated text
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 PDFs.

```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 text prompt to include with the PDF file
var prompt = ModelContent.Text("Summarize the important results in this report.");
// Provide the PDF file as \`data\` with the appropriate PDF file MIME type
var doc = ModelContent.InlineData("application/pdf",
System.IO.File.ReadAllBytes(System.IO.Path.Combine(
UnityEngine.Application.streamingAssetsPath, "document0.pdf")));
// To stream generated text output, call \`GenerateContentStreamAsync\` with the text and PDF file
var responseStream = model.GenerateContentStreamAsync(new \[\] { prompt, doc });
// Print the generated text
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 documents


> [!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" page to learn detailed information
about the following:

- [Different options for providing a file in a request](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt#options-for-input-files) (either inline or using the file's URL or URI)
- [Requirements and best practices for document files](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt#documents)

#### Supported document MIME types


Gemini multimodal models support the following document MIME types:

- PDF - `application/pdf`
- Text - `text/plain`

<br />

#### Limits per request


PDFs are treated as images, so a single page of a PDF is treated as one
image. The number of pages allowed in a prompt is limited to the number of
images the Gemini multimodal models can support.

- Maximum files per request: 3,000 files
- Maximum pages per file: 1,000 pages per file
- Maximum size per file: 50 MB per file

<br />

<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.
- 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).

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#### 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).

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

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