Document understanding

You can add documents (PDF and TXT files) to Gemini requests to perform tasks that involve understanding the contents of the included documents. This page shows you how to add PDFs to your requests to Gemini in Vertex AI by using the Google Cloud console and the Vertex AI API.

Supported models

The following table lists the models that support document understanding:

Model

Media details

MIME types

Gemini 2.5 Flash (Preview)

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file for the API or Cloud Storage imports: 50 MB
Maximum file size per file for direct uploads through the console: 7 MB

application/pdf
text/plain

Gemini 2.5 Flash-Lite (Preview)

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file: 50 MB

application/pdf
text/plain

Gemini 2.5 Flash Image

Maximum number of files per prompt: 3
Maximum number of pages per file: 3
Maximum file size per file: 50 MB (API and Cloud Storage imports) or 7 MB (direct upload through Google Cloud console)

application/pdf
text/plain

Gemini 2.5 Flash-Lite

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file: 50 MB

application/pdf
text/plain

Gemini 2.0 Flash with image generation (Preview)

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file: 50 MB

application/pdf
text/plain

Gemini 2.5 Pro

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file for the API or Cloud Storage imports: 50 MB
Maximum file size per file for direct uploads through the console: 7 MB

application/pdf
text/plain

Gemini 2.5 Flash

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file for the API or Cloud Storage imports: 50 MB
Maximum file size per file for direct uploads through the console: 7 MB

application/pdf
text/plain

Gemini 2.0 Flash

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file for the API or Cloud Storage imports: 50 MB
Maximum file size per file for direct uploads through the console: 7 MB
Maximum tokens per minute (TPM) per project1:
- US/Asia: 3.4 M
- EU: 3.4 M

application/pdf
text/plain

Gemini 2.0 Flash-Lite

Maximum number of files per prompt: 3,000
Maximum number of pages per file: 1,000
Maximum file size per file for the API or Cloud Storage imports: 50 MB
Maximum file size per file for direct uploads through the console: 7 MB
Maximum tokens per minute (TPM) per project1:
- US/Asia: 3.4 M
- EU: 3.4 M

¹ This is the maximum TPM from document inputs across all requests of a project. Also use the maximum TPM for other modalities.

The quota metric is generate_content_document_input_per_base_model_id_and_resolution .

For a list of languages supported by Gemini models, see model information Google models . To learn more about how to design multimodal prompts, see Design multimodal prompts . If you're looking for a way to use Gemini directly from your mobile and web apps, see the Firebase AI Logic client SDKs for Swift, Android, Web, Flutter, and Unity apps.

Add documents to a request

The following code sample shows you how to include a PDF in a prompt request. This PDF sample works with all Gemini multimodal models.

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 HttpOptions 
 , 
 Part 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 model_id 
 = 
 "gemini-2.5-flash" 
 prompt 
 = 
 """ 
 You are a highly skilled document summarization specialist. 
 Your task is to provide a concise executive summary of no more than 300 words. 
 Please summarize the given document for a general audience. 
 """ 
 pdf_file 
 = 
 Part 
 . 
 from_uri 
 ( 
 file_uri 
 = 
 "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf" 
 , 
 mime_type 
 = 
 "application/pdf" 
 , 
 ) 
 response 
 = 
 client 
 . 
 models 
 . 
 generate_content 
 ( 
 model 
 = 
 model_id 
 , 
 contents 
 = 
 [ 
 pdf_file 
 , 
 prompt 
 ], 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 # Here is a summary of the document in 300 words. 
 # 
 # The paper introduces the Transformer, a novel neural network architecture for 
 # sequence transduction tasks like machine translation. Unlike existing models that rely on recurrent or 
 # convolutional layers, the Transformer is based entirely on attention mechanisms. 
 # ...

Go

Learn how to install or update the Go .

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  import 
  
 ( 
 "context" 
 "fmt" 
 "io" 
 "google.golang.org/genai" 
 ) 
 // 
 generateTextWithPDF 
 shows 
 how 
 to 
 generate 
 text 
 using 
 a 
 PDF 
 file 
 input 
 . 
 func 
 generateTextWithPDF 
 ( 
 w 
 io 
 . 
 Writer 
 ) 
 error 
 { 
 ctx 
 := 
 context 
 . 
 Background 
 () 
 client 
 , 
 err 
 := 
 genai 
 . 
 NewClient 
 ( 
 ctx 
 , 
& genai 
 . 
 ClientConfig 
 { 
 HTTPOptions 
 : 
 genai 
 . 
 HTTPOptions 
 { 
 APIVersion 
 : 
 "v1" 
 }, 
 }) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to create genai client: %w" 
 , 
 err 
 ) 
 } 
 modelName 
 := 
 "gemini-2.5-flash" 
 contents 
 := 
 [] 
 * 
 genai 
 . 
 Content 
 { 
 { 
 Parts 
 : 
 [] 
 * 
 genai 
 . 
 Part 
 { 
 { 
 Text 
 : 
 ` 
 You 
 are 
 a 
 highly 
 skilled 
 document 
 summarization 
 specialist 
 . 
 Your 
 task 
 is 
 to 
 provide 
 a 
 concise 
 executive 
 summary 
 of 
 no 
 more 
 than 
 300 
 words 
 . 
 Please 
 summarize 
 the 
 given 
 document 
 for 
 a 
 general 
 audience 
 . 
 ` 
 }, 
 { 
 FileData 
 : 
& genai 
 . 
 FileData 
 { 
 FileURI 
 : 
 "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf" 
 , 
 MIMEType 
 : 
 "application/pdf" 
 , 
 }}, 
 }, 
 Role 
 : 
 "user" 
 }, 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 GenerateContent 
 ( 
 ctx 
 , 
 modelName 
 , 
 contents 
 , 
 nil 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
 err 
 ) 
 } 
 respText 
 := 
 resp 
 . 
 Text 
 () 
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
 respText 
 ) 
 // 
 Example 
 response 
 : 
 // 
 "Attention Is All You Need" 
 introduces 
 the 
 Transformer 
 , 
 // 
 a 
 groundbreaking 
 neural 
 network 
 architecture 
 designed 
 for 
 ... 
 // 
 ... 
 return 
 nil 
 }

Java

Learn how to install or update the Java .

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.types.Content 
 ; 
 import 
  
 com.google.genai.types.GenerateContentResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 import 
  
 com.google.genai.types.Part 
 ; 
 public 
 class 
  
 TextGenerationWithPdf 
 { 
 public 
 static 
 void 
 main 
 ( 
 String 
 [] 
 args 
 ) 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Replace 
 these 
 variables 
 before 
 running 
 the 
 sample 
 . 
 String 
 modelId 
 = 
 "gemini-2.5-flash" 
 ; 
 generateContent 
 ( 
 modelId 
 ); 
 } 
 // 
 Generates 
 text 
 with 
 PDF 
 file 
 input 
 public 
 static 
 String 
 generateContent 
 ( 
 String 
 modelId 
 ) 
 { 
 // 
 Initialize 
 client 
 that 
 will 
 be 
 used 
 to 
 send 
 requests 
 . 
 This 
 client 
 only 
 needs 
 to 
 be 
 created 
 // 
 once 
 , 
 and 
 can 
 be 
 reused 
 for 
 multiple 
 requests 
 . 
 try 
 ( 
 Client 
 client 
 = 
 Client 
 . 
 builder 
 () 
 . 
 location 
 ( 
 "global" 
 ) 
 . 
 vertexAI 
 ( 
 true 
 ) 
 . 
 httpOptions 
 ( 
 HttpOptions 
 . 
 builder 
 () 
 . 
 apiVersion 
 ( 
 "v1" 
 ) 
 . 
 build 
 ()) 
 . 
 build 
 ()) 
 { 
 String 
 prompt 
 = 
 "You are a highly skilled document summarization specialist. 
 \n 
 " 
 + 
 " Your task is to provide a concise executive summary of no more than 300 words. 
 \n 
 " 
 + 
 " Please summarize the given document for a general audience" 
 ; 
 GenerateContentResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 generateContent 
 ( 
 modelId 
 , 
 Content 
 . 
 fromParts 
 ( 
 Part 
 . 
 fromUri 
 ( 
 "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf" 
 , 
 "application/pdf" 
 ), 
 Part 
 . 
 fromText 
 ( 
 prompt 
 )), 
 null 
 ); 
 System 
 . 
 out 
 . 
 print 
 ( 
 response 
 . 
 text 
 ()); 
 // 
 Example 
 response 
 : 
 // 
 The 
 document 
 introduces 
 the 
 Transformer 
 , 
 a 
 novel 
 neural 
 network 
 architecture 
 designed 
 for 
 // 
 sequence 
 transduction 
 tasks 
 , 
 such 
 as 
 machine 
 translation 
 . 
 Unlike 
 previous 
 dominant 
 models 
 // 
 that 
 rely 
 on 
 complex 
 recurrent 
 or 
 convolutional 
 neural 
 networks 
 , 
 the 
 Transformer 
 proposes 
 a 
 // 
 simpler 
 , 
 more 
 parallelizable 
 design 
 based 
 * 
 solely 
 * 
 on 
 attention 
 mechanisms 
 , 
 entirely 
 // 
 dispensing 
 with 
 recurrence 
 and 
 convolutions 
 ... 
 return 
 response 
 . 
 text 
 (); 
 } 
 } 
 }

Node.js

Install

npm install @google/genai

To learn more, see the SDK reference documentation .

Set environment variables to use the Gen AI SDK with Vertex AI:

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  const 
  
 { 
 GoogleGenAI 
 } 
  
 = 
  
 require 
 ( 
 '@google/genai' 
 ); 
 const 
  
 GOOGLE_CLOUD_PROJECT 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_PROJECT 
 ; 
 const 
  
 GOOGLE_CLOUD_LOCATION 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_LOCATION 
  
 || 
  
 'global' 
 ; 
 async 
  
 function 
  
 generateText 
 ( 
  
 projectId 
  
 = 
  
 GOOGLE_CLOUD_PROJECT 
 , 
  
 location 
  
 = 
  
 GOOGLE_CLOUD_LOCATION 
 ) 
  
 { 
  
 const 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 prompt 
  
 = 
  
 ` 
 You 
  
 are 
  
 a 
  
 highly 
  
 skilled 
  
 document 
  
 summarization 
  
 specialist 
 . 
  
 Your 
  
 task 
  
 is 
  
 to 
  
 provide 
  
 a 
  
 concise 
  
 executive 
  
 summary 
  
 of 
  
 no 
  
 more 
  
 than 
  
 300 
  
 words 
 . 
  
 Please 
  
 summarize 
  
 the 
  
 given 
  
 document 
  
 for 
  
 a 
  
 general 
  
 audience 
 . 
 ` 
 ; 
  
 const 
  
 pdfFile 
  
 = 
  
 { 
  
 fileData 
 : 
  
 { 
  
 fileUri 
 : 
  
 'gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf' 
 , 
  
 mimeType 
 : 
  
 'application/pdf' 
 , 
  
 }, 
  
 }; 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateContent 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash' 
 , 
  
 contents 
 : 
  
 [ 
 pdfFile 
 , 
  
 prompt 
 ], 
  
 }); 
  
 console 
 . 
 log 
 ( 
 response 
 . 
 text 
 ); 
  
 // 
  
 Example 
  
 response 
 : 
  
 // 
  
 Here 
  
 is 
  
 a 
  
 summary 
  
 of 
  
 the 
  
 document 
  
 in 
  
 300 
  
 words 
 . 
  
 // 
  
 The 
  
 paper 
  
 introduces 
  
 the 
  
 Transformer 
 , 
  
 a 
  
 novel 
  
 neural 
  
 network 
  
 architecture 
  
 for 
  
 // 
  
 sequence 
  
 transduction 
  
 tasks 
  
 like 
  
 machine 
  
 translation 
 . 
  
 Unlike 
  
 existing 
  
 models 
  
 that 
  
 rely 
  
 on 
  
 recurrent 
  
 or 
  
 // 
  
 convolutional 
  
 layers 
 , 
  
 the 
  
 Transformer 
  
 is 
  
 based 
  
 entirely 
  
 on 
  
 attention 
  
 mechanisms 
 . 
  
 // 
  
 ... 
  
 return 
  
 response 
 . 
 text 
 ; 
 }

REST

After you set up your environment , you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

PROJECT_ID : Your project ID .
FILE_URI : The URI or URL of the file to include in the prompt. Acceptable values include the following:
- Cloud Storage bucket URI: The object must either be publicly readable or reside in the same Google Cloud project that's sending the request. For gemini-2.0-flash and gemini-2.0-flash-lite , the size limit is 2 GB.
- HTTP URL: The file URL must be publicly readable. You can specify one video file, one audio file, and up to 10 image files per request. Audio files, video files, and documents can't exceed 15 MB.
- YouTube video URL: The YouTube video must be either owned by the account that you used to sign in to the Google Cloud console or is public. Only one YouTube video URL is supported per request.
When specifying a fileURI , you must also specify the media type ( mimeType ) of the file. If VPC Service Controls is enabled, specifying a media file URL for fileURI is not supported.

If you don't have a PDF file in Cloud Storage, then you can use the following publicly available file: gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf with a mime type of application/pdf . To view this PDF, open the sample PDF file.
MIME_TYPE : The media type of the file specified in the data or fileUri fields. Acceptable values include the following:
Click to expand MIME types
- application/pdf
- audio/mpeg
- audio/mp3
- audio/wav
- image/png
- image/jpeg
- image/webp
- text/plain
- video/mov
- video/mpeg
- video/mp4
- video/mpg
- video/avi
- video/wmv
- video/mpegps
- video/flv
TEXT : The text instructions to include in the prompt. For example, You are a very professional document summarization specialist. Please summarize the given document.

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json . Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "contents": {
    "role": "USER",
    "parts": [
      {
        "fileData": {
          "fileUri": " FILE_URI 
",
          "mimeType": " MIME_TYPE 
"
        }
      },
      {
        "text": " TEXT 
"
      }
    ]
  }
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/global/publishers/google/models/gemini-2.0-flash:generateContent"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json . Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "contents": {
    "role": "USER",
    "parts": [
      {
        "fileData": {
          "fileUri": " FILE_URI 
",
          "mimeType": " MIME_TYPE 
"
        }
      },
      {
        "text": " TEXT 
"
      }
    ]
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/global/publishers/google/models/gemini-2.0-flash:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "This report presents Gemini 2.0 Pro.\n"
          }
        ]
      },
      "finishReason": "STOP",
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.13273923,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.08819004
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.1046602,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.0996453
        },
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.15987214,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.098946586
        },
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE",
          "probabilityScore": 0.056966383,
          "severity": "HARM_SEVERITY_NEGLIGIBLE",
          "severityScore": 0.075721376
        }
      ]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 19882,
    "candidatesTokenCount": 336,
    "totalTokenCount": 20218
  }
}

Note the following in the URL for this sample:

Use the generateContent method to request that the response is returned after it's fully generated. To reduce the perception of latency to a human audience, stream the response as it's being generated by using the streamGenerateContent method.
The multimodal model ID is located at the end of the URL before the method (for example, gemini-2.0-flash ). This sample might support other models as well.

Console

To send a multimodal prompt by using the Google Cloud console, do the following:

In the Vertex AI section of the Google Cloud console, go to the Vertex AI Studiopage.

Go to Vertex AI Studio
Click Create prompt.
Optional: Configure the model and parameters:
- Model: Select a model.

Optional: To configure advanced parameters, click Advancedand configure as follows:

Click to expand advanced configurations

Top-K: Use the slider or textbox to enter a value for top-K.
Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-K of 3 means that the next token is selected from among the three most probable tokens by using temperature.
For each token selection step, the top-K tokens with the highest probabilities are sampled. Then tokens are further filtered based on top-P with the final token selected using temperature sampling.

Specify a lower value for less random responses and a higher value for more random responses.
Top-P: Use the slider or textbox to enter a value for top-P. Tokens are selected from most probable to the least until the sum of their probabilities equals the value of top-P. For the least variable results, set top-P to 0 .
Max responses: Use the slider or textbox to enter a value for the number of responses to generate.
Streaming responses: Enable to print responses as they're generated.
Safety filter threshold: Select the threshold of how likely you are to see responses that could be harmful.
Enable Grounding: Grounding isn't supported for multimodal prompts.
Region: Select the region that you want to use.

Temperature: Use the slider or textbox to enter a value for temperature.

The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible.

If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature. If the model enters infinite generation, increasing the temperature to at least 0.1 may lead to improved results.

1.0 is the recommended starting value for temperature. < /li > <li>**Output token limit**: Use the slider or textbox to enter a value for the max output limit. Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.

Specify a lower value for shorter responses and a higher value for potentially longer responses.

< /li > <li>**Add stop sequence**: Optional. Enter a stop sequence, which is a series of characters that includes spaces. If the model encounters a stop sequence, the response generation stops. The stop sequence isn't included in the response, and you can add up to five stop sequences.</li> < /ul >

Click Insert Media, and select a source for your file.
Upload

Select the file that you want to upload and click Open.

By URL

Enter the URL of the file that you want to use and click Insert.

Cloud Storage

Select the bucket and then the file from the bucket that you want to import and click Select.
Google Drive
1. Choose an account and give consent to Vertex AI Studio to access your account the first time you select this option. You can upload multiple files that have a total size of up to 10 MB. A single file can't exceed 7 MB.
2. Click the file that you want to add.
3. Click Select.
  
  The file thumbnail displays in the Promptpane. The total number of tokens also displays. If your prompt data exceeds the token limit , the tokens are truncated and aren't included in processing your data.
Enter your text prompt in the Promptpane.
Optional: To view the Token ID to textand Token IDs, click the tokens countin the Promptpane.

Note: Media tokens aren't supported.
Click Submit.
Optional: To save your prompt to My prompts, click Save.
Optional: To get the Python code or a curl command for your prompt, click Build with code > Get code.

Set optional model parameters

Each model has a set of optional parameters that you can set. For more information, see Content generation parameters .

Document tokenization

PDF tokenization

PDFs are treated as images, so each page of a PDF is tokenized in the same way as an image.

Also, the cost for PDFs follows Gemini image pricing . For example, if you include a two-page PDF in a Gemini API call, you incur an input fee of processing two images.

PDF best practices

When using PDFs, use the following best practices and information for the best results:

If your prompt contains a single PDF, place the PDF before the text prompt in your request.
If you have a long document, consider splitting it into multiple PDFs to process it.
Use PDFs created with text rendered as text instead of using text in scanned images. This format ensures text is machine-readable so that it's easier for the model to edit, search, and manipulate compared to scanned image PDFs. This practice provides optimal results when working with text-heavy documents like contracts.

Limitations

While Gemini multimodal models are powerful in many multimodal use cases, it's important to understand the limitations of the models:

Spatial reasoning: The models aren't precise at locating text or objects in PDFs. They might only return the approximated counts of objects.
Accuracy: The models might hallucinate when interpreting handwritten text in PDF documents.

What's next

Start building with Gemini multimodal models - new customers get $300 in free Google Cloud credits to explore what they can do with Gemini.
Learn how to send chat prompt requests .
Learn about responsible AI best practices and Vertex AI's safety filters .

Document understanding Stay organized with collections Save and categorize content based on your preferences.

Supported models

Add documents to a request

Python

Install

Go

Java

Node.js

Install

REST

curl

PowerShell

Response

Console

Click to expand advanced configurations

Upload

By URL

Cloud Storage

Google Drive

Set optional model parameters

Document tokenization

PDF best practices

Limitations

What's next

Document understanding