ファイル入力メソッド

from google import genai
from google.genai import types
import pathlib

client = genai.Client()

filepath = pathlib.Path('my_local_file.pdf')

prompt = "Summarize this document"
response = client.models.generate_content(
  model="gemini-3-flash-preview",
  contents=[
      types.Part.from_bytes(
        data=filepath.read_bytes(),
        mime_type='application/pdf',
      ),
      prompt
  ]
)
print(response.text)

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

const ai = new GoogleGenAI({});
const prompt = "Summarize this document";

async function main() {
    const filePath = path.join('content', 'my_local_file.pdf'); // Adjust path as needed

    const contents = [
        { text: prompt },
        {
            inlineData: {
                mimeType: 'application/pdf',
                data: fs.readFileSync(filePath).toString("base64")
            }
        }
    ];

    const response = await ai.models.generateContent({
        model: "gemini-3-flash-preview",
        contents: contents
    });
    console.log(response.text);
}

main();

# Encode the local file to base64
B64_CONTENT=$(base64 -w 0 my_local_file.pdf)

curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "contents": [
      {
        "parts": [
          {"text": "Summarize this document"}
        ]
      },
      {
        "parts": [
          {
            "inlineData": {
              "mimeType": "application/pdf",
              "data": "'"${B64_CONTENT}"'"
            }
          }
        ]
      }
    ]
  }'

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"
doc_data = httpx.get(doc_url).content

prompt = "Summarize this document"

response = client.models.generate_content(
  model="gemini-3-flash-preview",
  contents=[
      types.Part.from_bytes(
        data=doc_data,
        mime_type='application/pdf',
      ),
      prompt
  ]
)
print(response.text)

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

const ai = new GoogleGenAI({});
const docUrl = 'https://discovery.ucl.ac.uk/id/eprint/10089234/1/343019_3_art_0_py4t4l_convrt.pdf';
const prompt = "Summarize this document";

async function main() {
    const pdfResp = await fetch(docUrl);
      .then((response) => response.arrayBuffer());

    const contents = [
        { text: prompt },
        {
            inlineData: {
                mimeType: 'application/pdf',
                data: Buffer.from(pdfResp).toString("base64")
            }
        }
    ];

    const response = await ai.models.generateContent({
        model: "gemini-3-flash-preview",
        contents: contents
    });
    console.log(response.text);
}

main();

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-flash-preview:generateContent" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"inline_data": {"mime_type": "application/pdf", "data": "'"$ENCODED_PDF"'"}},
          {"text": "'$PROMPT'"}
        ]
      }]
    }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

from google import genai
client = genai.Client()

# Upload the file
audio_file = client.files.upload(file="path/to/your/sample.mp3")
prompt = "Describe this audio clip"

# Use the uploaded file in a prompt
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=[prompt, audio_file]
)
print(response.text)

import {
  GoogleGenAI,
  createUserContent,
  createPartFromUri,
} from "@google/genai";

const ai = new GoogleGenAI({});
const prompt = "Describe this audio clip";

async function main() {
  const filePath = "path/to/your/sample.mp3"; // Adjust path as needed

  const myfile = await ai.files.upload({
    file: filePath,
    config: { mimeType: "audio/mpeg" },
  });

  const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: createUserContent([
      prompt,
      createPartFromUri(myfile.uri, myfile.mimeType),
    ]),
  });
  console.log(response.text);

}
await main();

AUDIO_PATH="path/to/sample.mp3"
MIME_TYPE=$(file -b --mime-type "${AUDIO_PATH}")
NUM_BYTES=$(wc -c < "${AUDIO_PATH}")
DISPLAY_NAME=AUDIO

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 "${BASE_URL}/upload/v1beta/files" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -D "${tmp_header_file}" \
  -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: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${AUDIO_PATH}" 2> /dev/null > file_info.json

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

# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Describe this audio clip"},
          {"file_data":{"mime_type": "${MIME_TYPE}", "file_uri": '$file_uri'}}]
        }]
      }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

from google import genai
from google.genai.types import Part

uri = "https://ontheline.trincoll.edu/images/bookdown/sample-local-pdf.pdf"
prompt = "Summarize this file"

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=[
        Part.from_uri(
            file_uri=uri,
            mime_type="application/pdf",
        ),
        prompt
    ],
)
print(response.text)

import { GoogleGenAI, createPartFromUri } from '@google/genai';

const client = new GoogleGenAI({});

const uri = "https://ontheline.trincoll.edu/images/bookdown/sample-local-pdf.pdf";

async function main() {
  const response = await client.models.generateContent({
    model: 'gemini-3-flash-preview',
    contents: [
      // equivalent to Part.from_uri(file_uri=uri, mime_type="...")
      createPartFromUri(uri, "application/pdf"),
      "summarize this file",
    ],
  });

  console.log(response.text);
}

main();

curl "https://autopush-generativelanguage.sandbox.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent \
      -H 'x-goog-api-key: $GEMINI_API_KEY' \
      -H 'Content-Type: application/json' \
      -d '{
          "contents":[
            {
              "parts":[
                {"text": "Summarize this pdf"},
                {
                  "file_data": {
                    "mime_type":"application/pdf",
                    "file_uri": "https://ontheline.trincoll.edu/images/bookdown/sample-local-pdf.pdf"
                  }
                }
              ]
            }
          ]
        }'