BytePlus

BytePlus is ByteDance’s AI-native enterprise platform offering ModelArk, a comprehensive Platform-as-a-Service (PaaS) solution for deploying and utilizing powerful large language models. It provides access to SkyLark models, DeepSeek V3.1, Kimi-K2, and other cutting-edge AI models with enterprise-grade security and scalability.

This guide explains how to integrate Opik with BytePlus using the OpenAI SDK. BytePlus provides OpenAI-compatible API endpoints that allow you to use the standard OpenAI client with BytePlus models.

Getting started

First, ensure you have both opik and openai packages installed:

$
pip install opik openai

You’ll also need a BytePlus API key. Find a guide on creating your BytePlus API keys for model services here.

Tracking BytePlus API calls

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from opik.integrations.openai import track_openai
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from openai import OpenAI
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# Initialize the OpenAI client with BytePlus base URL
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client = OpenAI(
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    base_url="https://ark.ap-southeast.bytepluses.com/api/v3",
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    api_key="YOUR_BYTEPLUS_API_KEY"
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)
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client = track_openai(client)
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response = client.chat.completions.create(
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    model="kimi-k2-250711",  # You can use any model available on BytePlus
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    messages=[
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        {"role": "user", "content": "Hello, world!"}
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    ],
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    temperature=0.7,
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    max_tokens=100
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)
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print(response.choices[0].message.content)

Advanced Usage

Using with @track decorator

You can combine the tracked client with Opik’s @track decorator for comprehensive tracing:

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from opik import track
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from opik.integrations.openai import track_openai
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from openai import OpenAI
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client = OpenAI(
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    base_url="https://ark.ap-southeast.bytepluses.com/api/v3",
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    api_key="YOUR_BYTEPLUS_API_KEY"
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)
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client = track_openai(client)
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@track
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def analyze_data_with_ai(query: str):
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    """Analyze data using BytePlus AI models."""
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    response = client.chat.completions.create(
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        model="kimi-k2-250711",
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        messages=[
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            {"role": "user", "content": query}
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        ]
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    )
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    return response.choices[0].message.content
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# Call the tracked function
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result = analyze_data_with_ai("Analyze this business data...")

Troubleshooting

Common Issues

1. Authentication Errors: Ensure your API key is correct and has the necessary permissions
2. Model Not Found: Verify the model name is available on BytePlus
3. Rate Limiting: BytePlus may have rate limits; implement appropriate retry logic
4. Base URL Issues: Ensure the base URL is correct for your BytePlus deployment

Getting Help

• Check the BytePlus API documentation for detailed error codes
• Review the BytePlus status page for service issues
• Contact BytePlus support for API-specific problems
• Check Opik documentation for tracing and evaluation features

Next Steps

Once you have BytePlus integrated with Opik, you can:

• Evaluate your LLM applications using Opik’s evaluation framework
• Create datasets to test and improve your models
• Set up feedback collection to gather human evaluations
• Monitor performance across different models and configurations

For more information about using Opik with OpenAI-compatible APIs, see the OpenAI integration guide.