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 PDF modality details

Gemini 1.5 Flash

Go to the Gemini 1.5 Flash model card

Maximum pages per PDF: 1000

Maximum PDF file size: 30 MB

Gemini 1.5 Pro

Go to the Gemini 1.5 Pro model card

Maximum pages per PDF: 1000

Maximum PDF file size: 30 MB

Gemini 1.0 Pro Vision

Go to the Gemini 1.0 Pro Vision model card

Maximum pages per prompt: 16

Maximum PDF file size: 30 MB

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 Vertex AI in Firebase SDKs for Android, Swift, web, and Flutter 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

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

Streaming and non-streaming responses

You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

For a streaming response, use the stream parameter in generate_content.

  response = model.generate_content(contents=[...], stream = True)

For a non-streaming response, remove the parameter, or set the parameter to False.

Sample code

import vertexai

from vertexai.generative_models import GenerativeModel, Part

# TODO(developer): Update project_id and location
vertexai.init(project=PROJECT_ID, location="us-central1")

model = GenerativeModel("gemini-1.5-flash-002")

prompt = """
You are a very professional document summarization specialist.
Please summarize the given document.
"""

pdf_file = Part.from_uri(
    uri="gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
    mime_type="application/pdf",
)
contents = [pdf_file, prompt]

response = model.generate_content(contents)
print(response.text)
# Example response:
# Here's a summary of the provided text, which appears to be a research paper on the Gemini 1.5 Pro
# multimodal large language model:
# **Gemini 1.5 Pro: Key Advancements and Capabilities**
# The paper introduces Gemini 1.5 Pro, a highly compute-efficient multimodal model
# significantly advancing long-context capabilities
# ...

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart. For more information, see the Vertex AI Java SDK for Gemini reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

Streaming and non-streaming responses

You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

For a streaming response, use the generateContentStream method.

  public ResponseStream<GenerateContentResponse> generateContentStream(Content content)

For a non-streaming response, use the generateContent method.

  public GenerateContentResponse generateContent(Content content)

Sample code


import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.ContentMaker;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.PartMaker;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;

public class PdfInput {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-google-cloud-project-id";
    String location = "us-central1";
    String modelName = "gemini-1.5-flash-001";

    pdfInput(projectId, location, modelName);
  }

  // Analyzes the given video input.
  public static String pdfInput(String projectId, String location, String modelName)
      throws IOException {
    // 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 (VertexAI vertexAI = new VertexAI(projectId, location)) {
      String pdfUri = "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf";

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              "You are a very professional document summarization specialist.\n"
                  + "Please summarize the given document.",
              PartMaker.fromMimeTypeAndData("application/pdf", pdfUri)
          ));

      String output = ResponseHandler.getText(response);
      System.out.println(output);
      return output;
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Generative AI quickstart using the Node.js SDK. For more information, see the Node.js SDK for Gemini reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

Streaming and non-streaming responses

You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

For a streaming response, use the generateContentStream method.

  const streamingResp = await generativeModel.generateContentStream(request);

For a non-streaming response, use the generateContent method.

  const streamingResp = await generativeModel.generateContent(request);

Sample code

const {VertexAI} = require('@google-cloud/vertexai');

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function analyze_pdf(projectId = 'PROJECT_ID') {
  const vertexAI = new VertexAI({project: projectId, location: 'us-central1'});

  const generativeModel = vertexAI.getGenerativeModel({
    model: 'gemini-1.5-flash-001',
  });

  const filePart = {
    file_data: {
      file_uri: 'gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf',
      mime_type: 'application/pdf',
    },
  };
  const textPart = {
    text: `
    You are a very professional document summarization specialist.
    Please summarize the given document.`,
  };

  const request = {
    contents: [{role: 'user', parts: [filePart, textPart]}],
  };

  const resp = await generativeModel.generateContent(request);
  const contentResponse = await resp.response;
  console.log(JSON.stringify(contentResponse));
}

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart. For more information, see the Vertex AI Go SDK for Gemini reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

Streaming and non-streaming responses

You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

For a streaming response, use the GenerateContentStream method.

  iter := model.GenerateContentStream(ctx, genai.Text("Tell me a story about a lumberjack and his giant ox. Keep it very short."))

For a non-streaming response, use the GenerateContent method.

  resp, err := model.GenerateContent(ctx, genai.Text("What is the average size of a swallow?"))

Sample code

import (
	"context"
	"errors"
	"fmt"
	"io"

	"cloud.google.com/go/vertexai/genai"
)

// generateContentFromPDF generates a response into the provided io.Writer, based upon the PDF
func generateContentFromPDF(w io.Writer, projectID, location, modelName string) error {
	// location := "us-central1"
	// modelName := "gemini-1.5-flash-001"

	ctx := context.Background()

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %w", err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)

	part := genai.FileData{
		MIMEType: "application/pdf",
		FileURI:  "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
	}

	res, err := model.GenerateContent(ctx, part, genai.Text(`
			You are a very professional document summarization specialist.
    		Please summarize the given document.
	`))
	if err != nil {
		return fmt.Errorf("unable to generate contents: %w", err)
	}

	if len(res.Candidates) == 0 ||
		len(res.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	fmt.Fprintf(w, "generated response: %s\n", res.Candidates[0].Content.Parts[0])
	return nil
}

C#

Before trying this sample, follow the C# setup instructions in the Vertex AI quickstart. For more information, see the Vertex AI C# reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

Streaming and non-streaming responses

You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

For a streaming response, use the StreamGenerateContent method.

  public virtual PredictionServiceClient.StreamGenerateContentStream StreamGenerateContent(GenerateContentRequest request)

For a non-streaming response, use the GenerateContentAsync method.

  public virtual Task<GenerateContentResponse> GenerateContentAsync(GenerateContentRequest request)

For more information on how the server can stream responses, see Streaming RPCs.

Sample code


using Google.Cloud.AIPlatform.V1;
using System;
using System.Threading.Tasks;

public class PdfInput
{
    public async Task<string> SummarizePdf(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-flash-001")
    {

        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        string prompt = @"You are a very professional document summarization specialist.
Please summarize the given document.";

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            Contents =
            {
                new Content
                {
                    Role = "USER",
                    Parts =
                    {
                        new Part { Text = prompt },
                        new Part { FileData = new() { MimeType = "application/pdf", FileUri = "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf" }}
                    }
                }
            }
        };

        GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        string responseText = response.Candidates[0].Content.Parts[0].Text;
        Console.WriteLine(responseText);

        return responseText;
    }
}

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:

  • LOCATION: The region to process the request. Enter a supported region. For the full list of supported regions, see Available locations.

    Click to expand a partial list of available regions

    • us-central1
    • us-west4
    • northamerica-northeast1
    • us-east4
    • us-west1
    • asia-northeast3
    • asia-southeast1
    • asia-northeast1
  • 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-1.5-pro and gemini-1.5-flash, the size limit is 2 GB. For gemini-1.0-pro-vision, the size limit is 20 MB.
    • HTTP URL: The file URL must be publicly readable. You can specify one video file and up to 10 image files per request. Audio 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 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

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/gemini-1.5-flash:generateContent"

PowerShell

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/gemini-1.5-flash:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

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-1.5-flash or gemini-1.0-pro-vision). This sample may support other models as well.

Console

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

  1. In the Vertex AI section of the Google Cloud console, go to the Vertex AI Studio page.

    Go to Vertex AI Studio

  2. Click Open freeform.

  3. Optional: Configure the model and parameters:

    • Model: Select a model.
    • 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.

    • 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.

    • 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.

  4. Optional: To configure advanced parameters, click Advanced and configure as follows:

    Click to expand advanced configurations

    • Top-K: Use the slider or textbox to enter a value for top-K. (not supported for Gemini 1.5).

      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.

  5. 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 Prompt pane. 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.

  6. Enter your text prompt in the Prompt pane.

  7. Optional: To view the Token ID to text and Token IDs, click the tokens count in the Prompt pane.

  8. Click Submit.

  9. Optional: To save your prompt to My prompts, click Save.

  10. Optional: To get the Python code or a curl command for your prompt, click 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 requirements

Gemini multimodal models support the following document MIME types:

Document MIME type Gemini 1.5 Flash Gemini 1.5 Pro Gemini 1.0 Pro Vision
PDF - application/pdf
Text - text/plain

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 model can support:

  • Gemini 1.0 Pro Vision: 16 pages
  • Gemini 1.5 Pro and Gemini 1.5 Flash: 1000 pages

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.

Plain text tokenization

Plain text documents are tokenized as text. For example, if you include a 100-word plain text document in a Gemini API call, you incur an input fee of processing 100 words.

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