Document understanding

Gemini models can process documents in PDF format, using native vision to understand entire document contexts. This goes beyond just text extraction, allowing Gemini to:

  • Analyze and interpret content, including text, images, diagrams, charts, and tables, even in long documents up to 1000 pages.
  • Extract information into structured output formats.
  • Summarize and answer questions based on both the visual and textual elements in a document.
  • Transcribe document content (e.g. to HTML), preserving layouts and formatting, for use in downstream applications.

You can also pass non-PDF documents in the same way but Gemini will see them as normal text which will eliminate context like charts or formatting.

Passing PDF data inline

You can pass PDF data inline in the request. This is best suited for smaller documents or temporary processing where you don't need to reference the file in subsequent requests. We recommend using the Files API for larger documents that you need to refer to in multi-turn interactions to improve request latency and reduce bandwidth usage.

The following example shows you how to pass PDF data inline:

Python

from google import genai
import base64

client = genai.Client()

with open('path/to/document.pdf', 'rb') as f:
    pdf_bytes = f.read()

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input=[
        {
            "type": "document",
            "data": base64.b64encode(pdf_bytes).decode('utf-8'),
            "mime_type": "application/pdf"
        },
        {"type": "text", "text": "Summarize this document"}
    ]
)

print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});

async function main() {
    const pdfData = fs.readFileSync("path/to/document.pdf", {
        encoding: "base64"
    });

    const interaction = await ai.interactions.create({
        model: "gemini-3.7-flash",
        input: [
            { type: "text", text: "Summarize this document" },
            {
                type: "document",
                data: pdfData,
                mime_type: "application/pdf"
            }
        ]
    });
    console.log(interaction.output_text);
}

main();

REST

PDF_PATH="path/to/document.pdf"

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.7-flash",
    "input": [
      {
        "type": "document",
        "data": "'$(base64 $B64FLAGS $PDF_PATH)'",
        "mime_type": "application/pdf"
      },
      {"type": "text", "text": "Summarize this document"}
    ]
  }'

You can also upload a local PDF file for processing:

Python

from google import genai

client = genai.Client()

uploaded_file = client.files.upload(file="file.pdf")

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input=[
        {"type": "document", "uri": uploaded_file.uri, "mime_type": uploaded_file.mime_type},
        {"type": "text", "text": "Summarize this document"}
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
    const uploadedFile = await ai.files.upload({
        file: "file.pdf",
        config: { mime_type: "application/pdf" }
    });

    const interaction = await ai.interactions.create({
        model: "gemini-3.7-flash",
        input: [
            { type: "text", text: "Summarize this document" },
            {
                type: "document",
                uri: uploadedFile.uri,
                mime_type: uploadedFile.mime_type
            }
        ]
    });
    console.log(interaction.output_text);
}

main();

REST

PDF_PATH="file.pdf"
NUM_BYTES=$(wc -c < "${PDF_PATH}")
DISPLAY_NAME="file.pdf"
tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: application/pdf" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${PDF_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-3.7-flash",
      "input": [
        {"type": "document", "uri": "'$file_uri'", "mime_type": "application/pdf"},
        {"type": "text", "text": "Summarize this document"}
      ]
    }' 2> /dev/null > response.json

cat response.json
echo

jq -r ".steps[-1].content[0].text" response.json

Uploading PDFs using the Files API

We recommend you use Files API for larger files or when you intend to reuse a document across multiple requests. This improves request latency and reduces bandwidth usage by decoupling the file upload from the model requests.

Large PDFs from URLs

Use the File API to simplify uploading and processing large PDF files from URLs:

Python

from google import genai
import io
import httpx

client = genai.Client()

long_context_pdf_path = "https://arxiv.org/pdf/2312.11805"

doc_io = io.BytesIO(httpx.get(long_context_pdf_path).content)

sample_doc = client.files.upload(
  file=doc_io,
  config=dict(
    mime_type='application/pdf')
)

prompt = "Summarize this document"

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input=[
        {"type": "document", "uri": sample_doc.uri, "mime_type": sample_doc.mime_type},
        {"type": "text", "text": prompt}
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {

    const pdfBuffer = await fetch("https://arxiv.org/pdf/2312.11805")
        .then((response) => response.arrayBuffer());

    const fileBlob = new Blob([pdfBuffer], { type: 'application/pdf' });

    const file = await ai.files.upload({
        file: fileBlob,
        config: {
            displayName: 'A17_FlightPlan.pdf',
        },
    });

    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    const interaction = await ai.interactions.create({
        model: 'gemini-3.7-flash',
        input: [
            { type: "document", uri: file.uri, mime_type: file.mime_type },
            { type: "text", text: "Summarize this document" }
        ],
    });

    console.log(interaction.output_text);

}

main();

REST

PDF_PATH="https://arxiv.org/pdf/2312.11805"
DISPLAY_NAME="Gemini_paper"
PROMPT="Summarize this document"

# Download the PDF from the provided URL
wget -O "${DISPLAY_NAME}.pdf" "${PDF_PATH}"

MIME_TYPE=$(file -b --mime-type "${DISPLAY_NAME}.pdf")
NUM_BYTES=$(wc -c < "${DISPLAY_NAME}.pdf")

echo "MIME_TYPE: ${MIME_TYPE}"
echo "NUM_BYTES: ${NUM_BYTES}"

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${DISPLAY_NAME}.pdf" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo "file_uri: ${file_uri}"

# Create payload JSON file for safety
cat << EOF > payload.json
{
  "model": "gemini-3.7-flash",
  "input": [
    {"type": "text", "text": "${PROMPT}"},
    {"type": "document", "uri": "${file_uri}", "mime_type": "application/pdf"}
  ]
}
EOF

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d @payload.json 2> /dev/null > response.json

cat response.json
echo

jq ".steps[-1].content[0].text" response.json

# Clean up
rm "${DISPLAY_NAME}.pdf"
rm payload.json

Large PDFs stored locally

Python

from google import genai
import pathlib

client = genai.Client()

file_path = pathlib.Path('large_file.pdf')
sample_file = client.files.upload(
    file=file_path,
)

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input=[
        {"type": "document", "uri": sample_file.uri, "mime_type": sample_file.mime_type},
        {"type": "text", "text"