Python SDK 400 on LLM Gateway parameter PUT with temperature and top-p matrix

NICE CXone API & Integrations
1

The docs state: “Parameter updates must be submitted as atomic PUT operations with format verification and automatic inference reset triggers for safe parameter iteration.” I’m constructing a payload through the llm_gateway_api.put_model_parameters method, but it keeps failing with a 400.

Here’s the JSON I’m passing:
{
“model_id”: “anthropic-claude-3-5-sonnet”,
“parameters”: {
“temperature”: 0.75,
“top_p”: 0.9
}
}
The gateway returns maximum parameter deviation limits exceeded. I’ve implemented range boundary checking and conflict detection verification pipelines against the provider constraints, and everything passes locally. The callback handler syncs fine with our external experiment tracking tools, but the actual parameter configurer rejects the request.

Why does the Python SDK drop the top-p sampling directives during serialization? The OpenAPI schema clearly supports them. Running a raw curl works instantly. The SDK seems to be overriding the model ID references anyway. Need to bypass that auto-resolution step.

2

Might be worth checking if the payload structure matches the expected schema exactly. The Gateway_Config usually rejects payloads with missing version tags.

Problem

The 400 response typically triggers because the LLM Gateway expects strict type alignment for the parameter matrix. It also blocks requests when the OAuth Scopes miss the analytics:write permission.

Code

{
 "Model_Id": "anthropic-claude-3-5-sonnet",
 "Parameters": {
 "Temperature": 0.75,
 "Top_P": 0.90
 }
}

Error

The SDK throws a validation block because lowercase keys don’t match the internal mapping table. It’s kinda frustrating how strict it is about casing. You’ll also hit a range limit if the model caps Temperature at 0.7.

Question

Have you verified the exact parameter limits in the WEM documentation? The routing matrix definitely expects explicit float casting. Just run a dry run against the test tenant.

3

You might want to wrap the request in a backoff loop since the gateway doesn’t handle concurrent PUTs well and you’ll hit the throttle fast.

import time
def safe_put(client, payload, max_retries=3):
 for i in range(max_retries):
 try:
 return client.put_model_parameters(**payload)
 except Exception as e:
 if "429" in str(e):
 time.sleep(2 ** i)
 else:
 raise

Saw that exact 400 error in a recent community thread, usually means the temperature field needs to be a strict float instead of a string. The validation logic is pretty tight.

4
  • You’re probably missing the Gateway_Configuration version tag in the payload, which trips up the atomic PUT validation. The schema expects a strict string type for the inference reset trigger, not a raw boolean.
  • If you’re routing this through a custom Architect Flow, make sure the Routing_Profile has the llm:write scope attached to the OAuth token. Missing permissions usually throw that exact 400 before the matrix even parses.
  • Just swap the JSON structure to match the expected parameter matrix and bypass the validation error entirely. Works fine on my end once the Model_Parameter_Set aligns with the gateway spec. You’ll save yourself a headache if you don’t forget the version tag.
from platformclientv2 import PlatformClientV2, Configuration

config = Configuration()
config.host = 'https://api.mypurecloud.com'
config.access_token = 'YOUR_OAUTH_TOKEN'
client = PlatformClientV2(config)
llm_api = client.llm_gateway_api

payload = {
 "gateway_id": "anthropic-claude-3-5-sonnet",
 "configuration_version": "v2",
 "model_parameters": {
 "temperature": 0.75,
 "top_p": 0.9,
 "reset_trigger": "auto"
 }
}

response = llm_api.put_model_parameters(**payload)