comet-ml--opik/apps/opik-documentation/documentation/templates/integration_template_code.md

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Observability for [INTEGRATION_NAME] with Opik	Start here to integrate Opik into your [INTEGRATION_NAME]-based genai application for end-to-end LLM observability, unit testing, and optimization.

INTEGRATION_NAME is [INTEGRATION_DESCRIPTION].

This guide explains how to integrate Opik with [INTEGRATION_NAME] using the [INTEGRATION_NAME] integration provided by Opik. By using the [INTEGRATION_NAME] integration provided by Opik, you can easily track and evaluate your [INTEGRATION_NAME] API calls within your Opik projects as Opik will automatically log the input prompt, model used, token usage, and response generated.

Account Setup

Comet provides a hosted version of the Opik platform, simply create an account and grab your API Key.

You can also run the Opik platform locally, see the installation guide for more information.

Getting Started

Installation

Install the required packages:

pip install opik [integration_package]

Configuring Opik

Configure the Opik Python SDK for your deployment type. See the Python SDK Configuration guide for detailed instructions on:

• CLI configuration: opik configure
• Code configuration: opik.configure()
• Self-hosted vs Cloud vs Enterprise setup
• Configuration files and environment variables

Configuring [INTEGRATION_NAME]

In order to configure [INTEGRATION_NAME], you will need to have your [INTEGRATION_NAME] API Key. You can find or create your [INTEGRATION_NAME] API Key in this page.

You can set it as an environment variable:

export [INTEGRATION_API_KEY_NAME]="YOUR_API_KEY"

Or set it programmatically:

import os
import getpass

if "[INTEGRATION_API_KEY_NAME]" not in os.environ:
    os.environ["[INTEGRATION_API_KEY_NAME]"] = getpass.getpass("Enter your [INTEGRATION_NAME] API key: ")

# Set project name for organization
os.environ["OPIK_PROJECT_NAME"] = "[integration_name]-integration-demo"

Usage

Basic Usage

Set up [INTEGRATION_NAME] with Opik tracking:

from opik.integrations.[integration_module] import track_[integration_name]
from [package] import [ClientClass]

# Initialize the [INTEGRATION_NAME] client
client = [ClientClass]()
tracked_client = track_[integration_name](client)

# Set project name for organization
os.environ["OPIK_PROJECT_NAME"] = "[integration_name]-integration-demo"

# Make API calls
response = tracked_client.some_method()

Using with @track decorator

Use the @track decorator to create comprehensive traces:

from opik import track
from opik.integrations.[integration_module] import track_[integration_name]
from [package] import [ClientClass]

client = [ClientClass]()
tracked_client = track_[integration_name](client)

@track
def my_function(input_data):
    """Process data using [INTEGRATION_NAME]."""

    response = tracked_client.some_method(input_data)
    return response

# Call the tracked function
result = my_function("example input")

[INTEGRATION_NAME]-Specific Features

[DESCRIBE_SPECIFIC_FEATURES_OF_THE_INTEGRATION]

Results viewing

Once your [INTEGRATION_NAME] calls are logged with Opik, you can view them in the Opik UI. Each API call will create a trace with detailed information including:

• Input messages and parameters
• Model used and configuration
• Response content
• Token usage and cost information
• Timing and performance metrics

Feedback Scores and Evaluation

Once your [INTEGRATION_NAME] calls are logged with Opik, you can evaluate your LLM application using Opik's evaluation framework:

from opik.evaluation import evaluate
from opik.evaluation.metrics import Hallucination

# Define your evaluation task
def evaluation_task(x):
    return {
        "message": x["message"],
        "output": x["output"],
        "reference": x["reference"]
    }

# Create the Hallucination metric
hallucination_metric = Hallucination()

# Run the evaluation
evaluation_results = evaluate(
    experiment_name="[integration_name]-evaluation",
    dataset=your_dataset,
    task=evaluation_task,
    scoring_metrics=[hallucination_metric],
)

Environment Variables

Make sure to set the following environment variables:

# [INTEGRATION_NAME] Configuration
export [INTEGRATION_API_KEY_NAME]="your-[integration-name]-api-key"

# Opik Configuration
export OPIK_PROJECT_NAME="your-project-name"
export OPIK_WORKSPACE="your-workspace-name"

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 [INTEGRATION_NAME]
3. Rate Limiting: [INTEGRATION_NAME] may have rate limits; implement appropriate retry logic
4. Base URL Issues: Ensure the base URL is correct for your [INTEGRATION_NAME] deployment

Getting Help

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

Next Steps

Once you have [INTEGRATION_NAME] 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

Required Placeholders

Replace these placeholders in templates:

Code Integrations:

• [INTEGRATION_NAME] → Actual integration name (e.g., "OpenAI")
• [integration_name] → Lowercase version (e.g., "openai")
• [integration_module] → Python module name (e.g., "openai")
• [integration_package] → Package to install (e.g., "openai")
• [ClientClass] → Main client class (e.g., "OpenAI")
• [INTEGRATION_API_KEY_NAME] → Environment variable name (e.g., "OPENAI_API_KEY")
• [INTEGRATION_API_KEY_URL] → URL where users can create/manage API keys
• [INTEGRATION_WEBSITE_URL] → Main website URL for the integration
• [INTEGRATION_DESCRIPTION] → Brief description of what the integration does