Observability for Ollama with Opik

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import ollama
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from opik import track, opik_context
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@track(tags=['ollama', 'python-library'])
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def ollama_llm_call(user_message: str):
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    # Create the Ollama model
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    response = ollama.chat(model='llama3.1', messages=[
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        {
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            'role': 'user',
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            'content': user_message,
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        },
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    ])
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    opik_context.update_current_span(
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        metadata={
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            'model': response['model'],
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            'eval_duration': response['eval_duration'],
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            'load_duration': response['load_duration'],
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            'prompt_eval_duration': response['prompt_eval_duration'],
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            'prompt_eval_count': response['prompt_eval_count'],
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            'done': response['done'],
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            'done_reason': response['done_reason'],
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        },
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        usage={
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            'completion_tokens': response['eval_count'],
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            'prompt_tokens': response['prompt_eval_count'],
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            'total_tokens': response['eval_count'] + response['prompt_eval_count']
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        }
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    )
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    return response['message']
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ollama_llm_call("Say this is a test")

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from openai import OpenAI
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from opik.integrations.openai import track_openai
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import os
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os.environ["OPIK_PROJECT_NAME"] = "ollama-integration"
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# Create an OpenAI client
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client = OpenAI(
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    base_url='http://localhost:11434/v1/',
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    # required but ignored
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    api_key='ollama',
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)
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# Log all traces made to with the OpenAI client to Opik
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client = track_openai(client)
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# call the local ollama model using the OpenAI client
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chat_completion = client.chat.completions.create(
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    messages=[
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        {
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            'role': 'user',
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            'content': 'Say this is a test',
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        }
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    ],
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    model='llama3.1',
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)
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print(chat_completion.choices[0].message.content)

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from langchain_ollama import ChatOllama
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from opik.integrations.langchain import OpikTracer
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# Create the Opik tracer
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opik_tracer = OpikTracer(tags=["langchain", "ollama"])
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# Create the Ollama model and configure it to use the Opik tracer
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llm = ChatOllama(
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    model="llama3.1",
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    temperature=0,
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).with_config({"callbacks": [opik_tracer]})
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# Call the Ollama model
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messages = [
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    (
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        "system",
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        "You are a helpful assistant that translates English to French. Translate the user sentence.",
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    ),
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    (
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        "human",
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        "I love programming.",
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    ),
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]
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ai_msg = llm.invoke(messages)
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ai_msg