gpt-oss-120b è un modello all'avanguardia progettato per ragionamenti complessi, esecuzione di istruzioni e capacità avanzate degli agenti. Eccelle in casi d'uso versatili, offrendo prestazioni potenti per applicazioni sia generiche che specializzate.
First install the requests library:
pip install requestsNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.
Finally, run the following Python code:
import os
import requests
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
"max_tokens": 512,
"messages": [
{
"content": "Explain gravity to a 6 years old",
"role": "user"
}
],
"model": "gpt-oss-120b",
"temperature": 0,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
# Handle response
response_data = response.json()
# Parse JSON response
choices = response_data["choices"]
for choice in choices:
text = choice["message"]["content"]
# Process text and finish_reason
print(text)
reasoning_text = choice["message"]["reasoning_content"]
# Process reasoning_content
print(reasoning_text)
else:
print("Error:", response.status_code, response.text)The gpt-oss-120b API is compatible with the OpenAI specification.
First install the openai library:
pip install openaiNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>Finally, run the following Python code:
import os
from openai import OpenAI
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
client = OpenAI(
base_url=url,
api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)
def chat_completion(new_message: str) -> str:
history_openai_format = [{"role": "user", "content": new_message}]
choice = client.chat.completions.create(
model="gpt-oss-120b",
messages=history_openai_format,
temperature=0,
max_tokens=1024,
reasoning_effort="low", # Either low, medium, or high
).choices.pop()
print(f"** REASONING ** \n {choice.message.reasoning_content}\n")
print(f"** CONTENT ** \n {choice.message.content}")
if __name__ == '__main__':
chat_completion("Explain gravity for a 6 years old")When using AI Endpoints, the following rate limits apply:
If you exceed this limit, a 429 error code will be returned.
If you require higher usage, please get in touch with us to discuss increasing your rate limits.
Want to explore the full capabilities of the LLM API? Dive into our dedicated Structured Output and Function Calling guides.
For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.
Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.
New to AI Endpoints? This guide walks you through everything you need to get an access token, call AI models, and integrate AI APIs into your apps with ease.
Inizia il tutorialExplore what AI Endpoints can do. This guide breaks down current features, future roadmap items, and the platform's core capabilities so you know exactly what to expect.
Inizia il tutorialRunning into issues? This guide helps you solve common problems on AI Endpoints, from error codes to unexpected responses. Get quick answers, clear fixes, and helpful tips to keep your projects running smoothly.
Inizia il tutorialLearn how to use Structured Output with OVHcloud AI Endpoints.
Inizia il tutorialLearn how to use Function Calling with OVHcloud AI Endpoints.
Inizia il tutorialLearn how to use OVHcloud AI Endpoints Virtual Models.
Inizia il tutorial