Helicone

You can use the Helicone observability platform to gather statistics on your requests to Nebius Token Factory. Helicone routes your requests to Nebius Token Factory through its Gateway and collects usage data. The statistics and costs are then visualized on the Helicone dashboard. This integration works with all Nebius Token Factory models for inference, including text-to-text, text-to-image and other modalities. To use the integration, do the following:

Create a Nebius API key for authentication.
Save the API key to an environment variable:
```
export NEBIUS_API_KEY=<API_key>
```
Create a Helicone account.
Log in to Helicone.
Go to Settings → API Keys and generate a Helicone API key.
Save the API key into a HELICONE_API_KEY environment variable:
```
export HELICONE_API_KEY="<Helicone_API_key>"
```
Configure the integration:

Python
Node.js
cURL

Create the following helicone-test.py script:

import openai
import os

# Create an OpenAI client
client = openai.Client(
    base_url="https://nebius.helicone.ai/v1",
    api_key=os.environ.get("NEBIUS_API_KEY"),
    default_headers={
        "Helicone-Auth": f"Bearer {os.environ.get("HELICONE_API_KEY")}",
    }
)

# Use the client to create a multi-message request and print the response
response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ]
)

print(response.choices[0].message.content)

In this script, the following settings are specific to the Helicone integration:

base_url contains the address of the Helicone API Gateway for the Nebius Token Factory provider.
default_headers contains a Helicone-Auth header with the Helicone API key.

Run the script:
```
python helicone-test.py
```
The output is the following:
```
Hello. How can I assist you today?
```

You can change this code to fit your needs:

To work with a different model, change model. You can copy the model ID from the model card or look it up in this list.
Modify messages to specify your questions.

To work with models of different modalities, you can use the same OpenAI client and call other methods, for example, client.images.generate for image generation. See also examples.

Create the following helicone-test.js script:

const OpenAI = require('openai');

// Create an OpenAI client
const client = new OpenAI({
  baseURL: "https://nebius.helicone.ai/v1/",
  apiKey: process.env.NEBIUS_API_KEY,
  defaultHeaders: {
        "Helicone-Auth": "Bearer " + (process.env.HELICONE_API_KEY),
    }
});

// Use the client to create a multi-message request and print the response
client.chat.completions.create({
   "model": "meta-llama/Llama-3.3-70B-Instruct",
   "messages": [
      {
         "role": "system",
         "content": "You are a helpful assistant."
      },
      {
         "role": "user",
         "content": "Hello!"
      }
   ]
})
.then((response) => console.log(response.choices[0].message.content));

In this script, the following settings are specific to the Helicone integration:

baseUrl contains the address of the Helicone API Gateway for the Nebius Token Factory provider.
defaultHeaders contains a Helicone-Auth header with the Helicone API key.

Run the script:
```
node helicone-test.js
```
The output is the following:
```
Hello. How can I assist you today?
```

You can change this code to fit your needs:

To work with a different model, change model. You can copy the model ID from the model card or look it up in this list.
Modify messages to specify your questions.

To work with models of different modalities, you can use the same OpenAI client and call other methods, for example, client.images.generate for image generation. See also examples.

Run the following command:

curl -X 'POST' \
  'https://nebius.helicone.ai/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Accept: */*' \
  -H "Authorization: Bearer $NEBIUS_API_KEY" \
  -H "Helicone-Auth: Bearer $HELICONE_API_KEY" \
  --data-binary '{
    "model": "meta-llama/Llama-3.3-70B-Instruct",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

In this command, the following settings are specific to the Helicone integration:

Request URL contains the address of the Helicone API Gateway for the Nebius Token Factory provider.
Additional Helicone-Auth header contains the Helicone API key.

The output is the following (below is a pretty-printed version, the curl command returns output in one line):

Response

{
  "id": "chatcmpl-5876fcb564cf4917a15d5b79c736b400",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Hello. How can I assist you today?",
        "refusal": null,
        "role": "assistant",
        "audio": null,
        "function_call": null,
        "tool_calls": [],
        "reasoning_content": null
      },
      "stop_reason": null
    }
  ],
  "created": 1744804838,
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": null,
  "usage": {
    "completion_tokens": 10,
    "prompt_tokens": 43,
    "total_tokens": 53,
    "completion_tokens_details": null,
    "prompt_tokens_details": null
  },
  "prompt_logprobs": null
}

You can change this command to fit your needs:

To work with a different model, change model. You can copy the model ID from the model card or look it up in this list.
Modify messages to specify your questions.

To work with models of different modalities, you can call other endpoints, for example, v1/images/generations for image generation. See also examples.

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