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The latest Gemini models, like Gemini 3.5 Flash
, are available to use with Firebase AI Logic! Learn more.
Gemini 2.0 Flash and Flash-Lite models were shut down on June 1, 2026
. To avoid service disruption, update to a newer model like gemini-3.1-flash-lite
. Learn more.
All Imagen models will shut down on June 24, 2026
. Learn about migrating your apps to use Nano Banana.
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Analyze documents (like PDFs) using the Gemini API Stay organized with collections
Save and categorize content based on your preferences.
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 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)
arrow_downward
Jump to code samples
arrow_downward
Jump to code for streamed responses
Before you begin
Click your Gemini API
provider to view provider-specific content
and code on this page.
Gemini Developer API
Vertex AI Gemini API
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 backend service for your chosen Gemini API
provider, and
create a GenerativeModel
instance.
All our docs assume that you're using the latest versions
of the Firebase AI Logic
SDKs.
For testing and iterating on your prompts, we recommend using Google AI Studio
.
Need a sample PDF file?
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
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
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 .
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
later on this page.
Swift
You can call generateContent()
to generate text from multimodal input of text and PDFs.
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-3.5-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()
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
.
// 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-3.5-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()
to generate text from multimodal input of text and PDFs.
For Java, the methods in this SDK return a ListenableFuture
.
// 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-3.5-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()
to generate text from multimodal input of text and PDFs.
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-3.5-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
{
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()
to generate text from multimodal input of text and PDFs.
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-3.5-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()
to generate text from multimodal input of text and PDFs.
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-3.5-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
appropriate for your use case and app.
Stream the response
Before trying this sample, complete the 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 .
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 PDF files
Swift
You can call generateContentStream()
to stream generated text from multimodal input of text and PDFs.
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-3.5-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()
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
.
// 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-3.5-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()
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
.
// 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-3.5-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()
to stream generated text from multimodal input of text and PDFs.
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-3.5-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
{
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()
to stream generated text from multimodal input of text and PDFs.
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-3.5-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()
to stream generated text from multimodal input of text and PDFs.
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-3.5-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
appropriate for your use case and app.
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:
Supported document MIME types
Gemini
multimodal models support the following document MIME types:
PDF - application/pdf
Text - text/plain
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
What else can you do?
Learn how to count tokens
before sending long prompts to the model. Set up Cloud Storage for Firebase
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
):
Try out other capabilities
Learn how to control content generation
You can also experiment with prompts and model configurations and even get a
generated code snippet using Google AI Studio
. Learn more about the supported models
Learn about the models available for various use cases
and their quotas
and pricing
.
Give feedback
about your experience with Firebase AI Logic
Send feedback
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License
, and code samples are licensed under the Apache 2.0 License
. For details, see the Google Developers Site Policies
. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-06-12 UTC.
Need to tell us more?
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-06-12 UTC."],[],[]]
