Calling the inference endpoint in sync mode

This example demonstrates how to call the inference endpoint in sync mode.

Calling the inference endpoint in sync mode

# Copyright 2026 Verda Cloud Oy
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os

from verda import VerdaClient

# Configuration - replace with your deployment name
DEPLOYMENT_NAME = os.environ.get('VERDA_DEPLOYMENT_NAME')

# Get client secret and id from environment variables
CLIENT_ID = os.environ.get('VERDA_CLIENT_ID')
CLIENT_SECRET = os.environ.get('VERDA_CLIENT_SECRET')
INFERENCE_KEY = os.environ.get('VERDA_INFERENCE_KEY')

# Verda client instance
verda = VerdaClient(
    CLIENT_ID,
    CLIENT_SECRET,
    inference_key=INFERENCE_KEY,
)

# Get the deployment
deployment = verda.containers.get_deployment_by_name(DEPLOYMENT_NAME)

# Make a synchronous request to the endpoint.
# This example demonstrates calling a SGLang deployment which serves LLMs using an OpenAI-compatible API format
data = {
    'model': 'deepseek-ai/deepseek-llm-7b-chat',
    'prompt': 'Is consciousness fundamentally computational, or is there something more to subjective experience that cannot be reduced to information processing?',
    'max_tokens': 128,
    'temperature': 0.7,
    'top_p': 0.9,
}
response = deployment.run_sync(data=data, path='v1/completions')  # wait for the response

# Print the response
print(response.output())