Observability for Novita AI with Opik

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import opik
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opik.configure(
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    project_name="your-project-name",
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    workspace="your-workspace-name",
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)

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import openai
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client = openai.OpenAI(
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    api_key="anything",  # can be anything
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    base_url="http://0.0.0.0:4000"
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)
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response = client.chat.completions.create(
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    model="deepseek-r1-turbo",
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    messages=[
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        {"role": "user", "content": "What are the advantages of using cloud-based AI platforms?"}
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    ]
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)
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print(response.choices[0].message.content)

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import os
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from litellm import completion
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# Configure Opik
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import opik
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opik.configure()
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# Configure LiteLLM for Opik
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from litellm.integrations.opik.opik import OpikLogger
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import litellm
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litellm.callbacks = ["opik"]
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os.environ["NOVITA_API_KEY"] = "your-novita-api-key"
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response = completion(
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    model="novita/deepseek/deepseek-r1-turbo",
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    messages=[
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        {"role": "user", "content": "How can cloud AI platforms improve development efficiency?"}
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    ]
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)
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print(response.choices[0].message.content)

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from litellm import completion
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tools = [
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    {
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        "type": "function",
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        "function": {
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            "name": "get_current_weather",
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            "description": "Get the current weather in a given location",
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            "parameters": {
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                "type": "object",
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                "properties": {
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                    "location": {
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                        "type": "string",
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                        "description": "The city and state, e.g. San Francisco, CA",
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                    },
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                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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                },
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                "required": ["location"],
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            },
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        },
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    }
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]
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response = completion(
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    model="novita/deepseek/deepseek-r1-turbo",
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    messages=[{"role": "user", "content": "What's the weather like in Boston today?"}],
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    tools=tools,
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)

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response = completion(
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    model="novita/deepseek/deepseek-r1-turbo",
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    messages=[
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        {"role": "user", "content": "List 5 popular cookie recipes."}
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    ],
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    response_format={"type": "json_object"}
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)

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from opik.evaluation import evaluate
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from opik.evaluation.metrics import Hallucination
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# Define your evaluation task
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def evaluation_task(x):
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    return {
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        "message": x["message"],
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        "output": x["output"],
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        "reference": x["reference"]
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    }
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# Create the Hallucination metric
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hallucination_metric = Hallucination()
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# Run the evaluation
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evaluation_results = evaluate(
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    experiment_name="novita-ai-evaluation",
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    dataset=your_dataset,
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    task=evaluation_task,
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    scoring_metrics=[hallucination_metric],
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    project_name="my-project",
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)