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[aungkyamoe2908-a11y]

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[navagjonaj188-ops]

#44033 The Backup Protocol
Before we touch a single line of code, we must ensure data integrity.
Action: Secure a comprehensive backup of your theme files, specifically functions.php and header.php.
Rationale: We do not perform structural modifications without a safety net; stability is our primary KPI.

#44920 Snippet Injection & ID Accuracy is to maintain a clean, high-performance codebase, avoid hard-coding scripts directly into the header. Instead, use the wp_head and wp_body_open actions within your functions.php file.
You must ensure that the unique Container ID, JREQ196193, is inserted with absolute accuracy into your snippets. Any deviation here results in a "broken" tracking implant, which is unacceptable for a high-performing digital palette.

#44915 Launch, Test, and Validate
Once the code is seated, we move to the verification phase:
Launch Preview: Click the Preview button in your GTM Workspace.
Test and Validate: Navigate your site in the connected window.
The Debug view: Use the ga4 Debugging to ensure all signals are firing.
As you move through the site, check the Tag Assistant overlay. It should feel as cohesive as an office adorned with emerald velvet curtains or sapphire glass partitions. If the tags are firing, the infrastructure is sound.

[orlandodessa255-sys]

repo-sync

[navagjonaj188-ops]

---

https://github.blog/changelog/feed/
[§2195.2194]

#// https://ai.google.dev/api/generate-content

Text generation

https://ai.google.dev/gemini-api/docs/interactions-overview#why-interactions

You can use the toggle on this page to switch to the generateContent API versionofthispage.

The Gemini API can generate text output from text, images, video, and audio
inputs.

Here's a basic example:

Note

Note: This version of the page covers the Interactions API . You can use the toggle on this page to switch to the generateContent API version of this page.

This guide explains the different ways you can include media files such as
images, audio, video, and documents when making requests to the Gemini API.
The new methods are supported in all of the Gemini API endpoints, including
Batch, Interactions and Live API.
Choosing the right method depends on the size of your file, where your data is
stored, and how frequently you plan to use the file.

The simplest way to include a file as your input is to read a local file and
include it in a prompt. The following example shows how to read a local PDF
file. PDFs are limited to 50MB for this method. See the
Input method comparison table for a complete list of file
input types and limits.

Python

from google import genai
import pathlib
import base64

client = genai.Client()

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

prompt = "Summarize this document"
interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input=[
        {"type": "text", "text": prompt},
        {"type": "document", "data": base64.b64encode(filepath.read_bytes()).decode('utf-8'), "mime_type": "application/pdf"}
    ]
)
print(interaction.output_text)

JavaScript

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

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

async function main() {
    const filePath = 'my_local_file.pdf';

    const interaction = await client.interactions.create({
        model: "gemini-3.5-flash",
        input: [
            { type: "text", text: prompt },
            {
                type: "document",
                data: fs.readFileSync(filePath).toString("base64"),
                mime_type: "application/pdf"
            }
        ]
    });
    console.log(interaction.output_text);
}

main();

REST

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

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.5-flash",
    "input": [
      {"type": "text", "text": "Summarize this document"},
      {
        "type": "document",
        "data": "'${B64_CONTENT}'",
        "mime_type": "application/pdf"
      }
    ]
  }'

Input method comparison

The following table compares each input method with file limits and best use
cases. Note that the file size limit may vary depending on the file type and
model or tokenizer used to process the file.

Method	Best for	Max file size	Persistence
Inline data	Quick testing, small files, real-time applications.	100 MB per request or payload (50 MB for PDFs)	None (sent with every request)
File API upload	Large files, files used multiple times.	2 GB per file, up to 20GB per project	48 Hours
File API GCS URI registration	Large files already in Google Cloud Storage, files used multiple times.	2 GB per file, no overall storage limits	None (fetched per request). One time registration can give access for up to 30 days.
External URLs	Public data or data in cloud buckets (AWS, Azure, GCS) without re-uploading.	100 MB per request/payload	None (fetched per request)

Inline data

For smaller files (under 100MB, or 50MB for PDFs), you can pass the data
directly in the request payload. This is the simplest method for quick tests or
applications handling real-time, transient data. You can provide data as
base64 encoded strings or by reading local files directly.

For an example of reading from a local file, see the example at the beginning of
this page.

Fetch from a URL

You can also fetch a file from a URL, convert it to bytes, and include it in the
input.

Python

from google import genai
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"

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input=[
        {"type": "document", "data": base64.b64encode(doc_data).decode('utf-8'), "mime_type": "application/pdf"},
        {"type": "text", "text": prompt}
    ]
)
print(interaction.output_text)

JavaScript

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

const client = 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 interaction = await client.interactions.create({
        model: "gemini-3.5-flash",
        input: [
            { type: "text", text: prompt },
            {
                type: "document",
                data: Buffer.from(pdfResp).toString("base64"),
                mime_type: "application/pdf"
            }
        ]
    });
    console.log(interaction.output_text);
}

main();

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")

# Create JSON payload file
cat <<EOF > payload.json
{
"model": "gemini-3.5-flash",
"input": [
{"type": "document", "data": "${ENCODED_PDF}", "mime_type": "application/pdf"},
{"type": "text", "text": "${PROMPT}"}
]
}
EOF

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

cat response.json
echo

jq ".outputs[] | select(.type == \"text\") | .text" response.json

Gemini File API

The File API is designed for larger files (up to 2GB) or files you intend to
use in multiple requests.

Standard file upload

Upload a local file to the Gemini API. Files uploaded this way are stored
temporarily (48 hours) and processed for efficient retrieval by the model.

Python

from google import genai

client = genai.Client()

doc_file = client.files.upload(file="path/to/your/sample.pdf")
prompt = "Summarize this document"

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input=[
        {"type": "text", "text": prompt},
        {"type": "document", "uri": doc_file.uri, "mime_type": doc_file.mime_type}
    ]
)
print(interaction.output_text)

JavaScript

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

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

async function main() {
  const filePath = "path/to/your/sample.pdf";

  const myfile = await client.files.upload({
    file: filePath,
    config: { mime_type: "application/pdf" },
  });

  const interaction = await client.interactions.create({
    model: "gemini-3.5-flash",
    input: [
        { type: "text", text: prompt },
        { type: "document", uri: myfile.uri, mime_type: myfile.mimeType }
    ]
  });
  console.log(interaction.output_text);
}

await main();

REST

FILE_PATH="path/to/sample.pdf"
MIME_TYPE=$(file -b --mime-type "${FILE_PATH}")
NUM_BYTES=$(wc -c < "${FILE_PATH}")
DISPLAY_NAME=DOCUMENT

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
  -D "${tmp_header_file}" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -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: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${FILE_PATH}" 2> /dev/null > file_info.json

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

# Now use in an interaction
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3.5-flash",
      "input": [
        {"type": "text", "text": "Summarize this document"},
        {"type": "document", "uri": '$file_uri', "mime_type": "'${MIME_TYPE}'"}
      ]
    }'

Register Google Cloud Storage files

If your data is already in Google Cloud Storage, you don't need to
download and re-upload it. You can register it directly with the File API.

1. Grant Service Agent access to each bucket

   1. Enable the Gemini API in your Google Cloud project.

   2. Create the Service Agent:

      gcloud beta services identity create --service=generativelanguage.googleapis.com --project=<your_project>

   3. Grant the Gemini API Service Agent permissions to read your storage
      buckets.

      The user needs to assign the Storage Object Viewer
      IAM role
      to this service agent on the specific storage buckets they intend to use.

   This access doesn't expire by default, but can be changed at any time. You can
   also use the
   Google Cloud Storage IAM SDK
   commands to grant permissions.

2. Authenticate your service

   Prerequisites

   • Enable API
   • Create a service account or agent with appropriate permissions.

   You first need to authenticate as the service that has storage object viewer
   permissions. How this happens depends on the environment in which your file
   management code will be running.

   Outside of Google Cloud

   If your code is running from outside of Google Cloud, such as your desktop,
   download the account credentials from the Google Cloud Console with the
   following steps:

   1. Browse to the Service Account console
   2. Select the relevant service account
   3. Select the Keys tab and choose Add key, Create new key
   4. Choose the JSON key type, and note where the file was downloaded to on your machine.

   For more details, see the official Google Cloud documentation on
   service account key management.

   Then use the following commands to authenticate. These commands assume your
   service account file is in the current directory, named
   service-account.json.

   Python

   from google.oauth2.service_account import Credentials
   
   GCS_READ_SCOPES = [       
     'https://www.googleapis.com/auth/devstorage.read_only',
     'https://www.googleapis.com/auth/cloud-platform'
   ]
   
   SERVICE_ACCOUNT_FILE = 'service-account.json'
   
   credentials = Credentials.from_service_account_file(
       SERVICE_ACCOUNT_FILE,
       scopes=GCS_READ_SCOPES
   )
   

   Javascript

   const { GoogleAuth } = require('google-auth-library');
   
   const GCS_READ_SCOPES = [
     'https://www.googleapis.com/auth/devstorage.read_only',
     'https://www.googleapis.com/auth/cloud-platform'
   ];
   
   const SERVICE_ACCOUNT_FILE = 'service-account.json';
   
   const auth = new GoogleAuth({
     keyFile: SERVICE_ACCOUNT_FILE,
     scopes: GCS_READ_SCOPES
   });
   

   CLI

   gcloud auth application-default login \
     --client-id-file=service-account.json \
     --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/devstorage.read_only'
   

   On Google Cloud

   If you are running directly in Google Cloud, for example by using Cloud
   Run functions or a
   Compute Engine instance, you will
   have implicit credentials but will need to re-authenticate to grant the
   appropriate scopes.

   Python

   This code expects that the service is running in an environment where
   Application Default Credentials
   can be obtained automatically, such as Cloud Run or Compute Engine.

   import google.auth
   
   GCS_READ_SCOPES = [       
     'https://www.googleapis.com/auth/devstorage.read_only',
     'https://www.googleapis.com/auth/cloud-platform'
   ]
   
   credentials, project = google.auth.default(scopes=GCS_READ_SCOPES)
   

   JavaScript

   This code expects that the service is running in an environment where
   Application Default Credentials
   can be obtained automatically, such as Cloud Run or Compute Engine.

   const { GoogleAuth } = require('google-auth-library');
   
   const auth = new GoogleAuth({
     scopes: [
       'https://www.googleapis.com/auth/devstorage.read_only',
       'https://www.googleapis.com/auth/cloud-platform'
     ]
   });
   

   CLI

   This is an interactive command. For services like Compute Engine you can attach scopes to
   the running service at the config level. See the user-managed service
   docs
   for an example.

   gcloud auth application-default login \
   --scopes="https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/devstorage.read_only"
   

3. File registration (Files API)

   Use the Files API to register files and produce a Files API path that can
   directly be used in the Gemini API.

   Python

   from google import genai
   
   client = genai.Client(credentials=credentials)
   
   registered_gcs_files = client.files.register_files(
       uris=["gs://my_bucket/some_object.pdf", "gs://bucket2/object2.txt"]
   )
   prompt = "Summarize this file."
   
   for f in registered_gcs_files.files:
     print(f.name)
     interaction = client.interactions.create(
       model="gemini-3.5-flash",
       input=[
         {"type": "text", "text": prompt},
         {"type": "document", "uri": f.uri, "mime_type": f.mime_type}
       ],
     )
     print(interaction.output_text)
   

   JavaScript

   import { GoogleGenAI } from "@google/genai";
   
   const ai = new GoogleGenAI({ auth: auth });
   
   async function main() {
       const registeredGcsFiles = await ai.files.registerFiles({
           uris: ["gs://my_bucket/some_object.pdf", "gs://bucket2/object2.txt"]
       });
   
       const prompt = "Summarize this file.";
   
       for (const file of registeredGcsFiles.files) {
           console.log(file.name);
           const interaction = await ai.interactions.create({
               model: "gemini-3.5-flash",
               input: [
                   { type: "text", text: prompt },
                   { type: "document", uri: file.uri, mime_type: file.mimeType }
               ]
           });
   
           console.log(interaction.output_text);
       }
   }
   
   main();
   

   CLI

   access_token=$(gcloud auth application-default print-access-token)
   project_id=$(gcloud config get-value project)
   curl -X POST https://generativelanguage.googleapis.com/v1beta/files:register \
       -H 'Content-Type: application/json' \
       -H "Authorization: Bearer ${access_token}" \
       -H "x-goog-user-project: ${project_id}" \
       -d '{"uris": ["gs://bucket/object1", "gs://bucket/object2"]}'
   

External HTTP / Signed URLs

You can pass publicly accessible HTTPS URLs or pre-signed URLs directly in your
request. The Gemini API will fetch the content securely during processing.
This is ideal for files up to 100MB that you don't want to re-upload.

Note

Note: Gemini 2.0 family of models are not supported

Python

from google import genai

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

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input=[
        {"type": "document", "uri": uri, "mime_type": "application/pdf"},
        {"type": "text", "text": prompt}
    ]
)
print(interaction.output_text)

Javascript

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

const client = new GoogleGenAI({});

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

async function main() {
  const interaction = await client.interactions.create({
    model: 'gemini-3.5-flash',
    input: [
      { type: "document", uri: uri, mime_type: "application/pdf" },
      { type: "text", text: "summarize this file" }
    ]
  });

  console.log(interaction.output_text);
}

main();

REST

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-3.5-flash",
          "input": [
            {"type": "text", "text": "Summarize this pdf"},
            {
              "type": "document",
              "uri": "https://ontheline.trincoll.edu/images/bookdown/sample-local-pdf.pdf",
              "mime_type": "application/pdf"
            }
          ]
        }'

Accessibility

Verify that the URLs you provide don't lead to pages that require a login or
are behind a paywall. For private databases, ensure you create a signed URL
with the correct access permissions and expiry.

Safety checks

The system performs a content moderation check on the URL to conf