413 Payload Too Large on Web Messaging File Upload via Direct API

• Anyone free to help troubleshoot this 413 Payload Too Large error when pushing customer file attachments directly to the Genesys Cloud Web Messaging endpoint from our Django analytics pipeline.
• We bypass the standard widget to ingest logs directly. The code uses requests.post with a multipart form encoded payload.
• The endpoint is /api/v2/conversations/webmessaging/messages.
• The JSON body includes the attachments array with base64 encoded content.
• Standard text messages work fine.
• PDFs under 2MB succeed.
• A 4.5MB PDF triggers the 413 error immediately.
• I have verified the Nginx client_max_body_size on our Django server is set to 20m.
• The error response body is empty.
• I suspect the Genesys Cloud API gateway enforces a stricter limit than the documentation suggests, or the base64 expansion causes the payload to exceed the limit before transmission.
• Is there a specific header or chunking mechanism required for large attachments in the Web Messaging API?
• Here is the relevant snippet:

payload = {
"type": "customerMessage",
"attachments": [{
"name": "report.pdf",
"contentType": "application/pdf",
"content": base64_string
}]
}

• Any insights on the exact byte limit for this endpoint?

This happens because the sdk’s default serialization behavior when handling binary data in the attachments array. the typescript client expects base64 encoded strings, but if you are sending raw buffer objects or improperly formatted multipart chunks, the internal request builder fails to calculate the correct content-length header, triggering the 413 error on the gateway. you need to explicitly convert the file buffer to a base64 string before passing it to the ConversationWebmessagingMessage model. here is the correct pattern using the platform client:

const fs = require('fs');
const platformClient = require('@genesyscloud/platform-client-sdk');

async function uploadFile(filePath: string) {
 const buffer = fs.readFileSync(filePath);
 const base64Data = buffer.toString('base64');
 
 const message = platformClient.ConversationApi.ConversationWebmessagingMessage({
 conversationId: 'your-conversation-id',
 attachments: [{
 contentType: 'application/pdf',
 data: base64Data,
 name: 'report.pdf',
 size: buffer.length
 }]
 });
 
 await platformClient.ConversationApi.postConversationWebmessagingMessage(message);
}

ensure the size property matches the original buffer length, not the base64 string length, to avoid schema validation errors downstream.

This issue stems from the fundamental mismatch between how Genesys Cloud handles file attachments in Web Messaging versus standard REST file uploads. The endpoint /api/v2/conversations/webmessaging/messages does not accept multipart/form-data for attachments directly in the message creation payload. Instead, it expects a specific JSON structure where attachments are referenced by ID or provided as base64-encoded strings within a strict schema, but even then, there is a hard limit on the payload size for the message body itself. If you are trying to push large files, the 413 error is likely triggered by the gateway rejecting the oversized JSON payload before it even reaches the business logic. The suggestion above about base64 encoding is partially correct, but it misses the architectural constraint. In my ServiceNow integration pipelines, I solve this by decoupling the file upload from the message creation. First, you must upload the file to a temporary Genesys Cloud storage endpoint using POST /api/v2/files/upload with a multipart/form-data payload. This returns a fileId. Then, you construct your Web Messaging message payload with a JSON body containing the attachments array, where each object references the fileId and includes metadata like name, size, and contentType. This keeps the message payload small and compliant. Here is the flow:

import requests

# Step 1: Upload file to get fileId
upload_url = f"{base_url}/api/v2/files/upload"
with open('document.pdf', 'rb') as f:
 files = {'file': f}
 upload_resp = requests.post(upload_url, headers=auth_headers, files=files)
 file_id = upload_resp.json()['id']

# Step 2: Create message with reference
message_url = f"{base_url}/api/v2/conversations/webmessaging/messages"
payload = {
 "from": {"id": "agentId", "name": "Agent"},
 "to": [{"id": "customerId"}],
 "text": "Please find the document attached.",
 "attachments": [{
 "id": file_id,
 "name": "document.pdf",
 "type": "application/pdf"
 }]
}
requests.post(message_url, headers=auth_headers, json=payload)

This approach avoids the payload size limit entirely because the file data is never embedded in the message JSON.

Pretty sure the 413 error in this context is rarely about the file size itself but rather the malformed Content-Type header when mixing JSON and binary data in a single POST body. The /api/v2/conversations/webmessaging/messages endpoint strictly expects a JSON payload where attachments are base64 encoded, not multipart form data. When you send raw multipart chunks, the gateway parser fails to calculate the correct payload length, triggering the size limit prematurely. You need to serialize the file buffer to base64 within the JSON structure before sending. Here is how the payload should look for the requests library:

import base64
import requests

with open('file.pdf', 'rb') as f:
 b64_data = base64.b64encode(f.read()).decode('utf-8')

payload = {
 "type": "text",
 "text": "See attached",
 "attachments": [{
 "name": "file.pdf",
 "mediaType": "application/pdf",
 "data": b64_data
 }]
}

requests.post(url, json=payload, headers={'Authorization': 'Bearer <token>'})

• Base64 encoding limits
• JSON payload structure for web messaging
• OAuth token expiration handling
• Multipart vs JSON content types

Have you tried converting the binary data to base64 before embedding it in the attachments array? The gateway rejects raw multipart chunks in the JSON body. Use base64.b64encode in Python to encode the file content, then set contentType explicitly. This avoids the 413 error caused by incorrect content-length calculation on mixed payloads.