pages/generate-content/document-processing.txt

route: /gemini-api/docs/generate-content/document-processing
title: Document understanding
description: Learn how to use the Gemini API to process documents like PDFs

Note: This version of the page covers the previous generateContent API. We recommend using the new Interactions API for access to all the latest features and models. You can use the toggle on this page to switch to the Interactions API version of this page.
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 to generateContent. 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 fetch a PDF from a URL and convert it to
bytes for processing:
Python
from google import genai
from google.genai import types
import httpx
client = genai.Client()
doc_url = "https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf"
# Retrieve and encode the PDF byte
doc_data = httpx.get(doc_url).content
prompt = "Summarize this document"
response = client.models.generate_content(
model="gemini-3.5-flash",
contents=[
types.Part.from_bytes(
data=doc_data,
mime_type='application/pdf',
),
prompt
]
)
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });
async function main() {
const pdfResp = await fetch('https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf')
.then((response) => response.arrayBuffer());
const contents = [
{ text: "Summarize this document" },
{
inlineData: {
mimeType: 'application/pdf',
data: Buffer.from(pdfResp).toString("base64")
}
];
const response = await ai.models.generateContent({
model: "gemini-3.5-flash",
contents: contents
});
console.log(response.text);
}
main();
Go
package main
import (
"context"
"fmt"
"io"
"net/http"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, _ := genai.NewClient(ctx, &genai.ClientConfig{
APIKey: os.Getenv("GEMINI_API_KEY"),
Backend: genai.BackendGeminiAPI,
})
pdfResp, _ := http.Get("https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf")
var pdfBytes []byte
if pdfResp != nil && pdfResp.Body != nil {
pdfBytes, _ = io.ReadAll(pdfResp.Body)
pdfResp.Body.Close()
}
parts := []*genai.Part{
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "application/pdf",
Data: pdfBytes,
},
genai.NewPartFromText("Summarize this document"),
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-3.5-flash",
contents,
nil,
)
fmt.Println(result.Text())
}
REST
DOC_URL="https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf"
PROMPT="Summarize this document"
DISPLAY_NAME="base64_pdf"
# Download the PDF
wget -O "${DISPLAY_NAME}.pdf" "${DOC_URL}"
# Check for FreeBSD base64 and set flags accordingly
if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
B64FLAGS="--input"
else
B64FLAGS="-w0"
fi
# Base64 encode the PDF
ENCODED_PDF=$(base64 $B64FLAGS "${DISPLAY_NAME}.pdf")
# Generate content using the base64 encoded PDF
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent?key=$GOOGLE_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[
{"inline_data": {"mime_type": "application/pdf", "data": "'"$ENCODED_PDF"'"}},
{"text": "'$PROMPT'"}
]
}]
}' 2> /dev/null > response.json
cat response.json
echo
jq ".candidates[].content.parts[].text" response.json
# Clean up the downloaded PDF
rm "${DISPLAY_NAME}.pdf"
You can also read a PDF from a local file for processing:
Python
from google import genai
from google.genai import types
import pathlib
client = genai.Client()
# Re
…