Observability for Helicone with Opik

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import os
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import getpass
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if "HELICONE_API_KEY" not in os.environ:
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    os.environ["HELICONE_API_KEY"] = getpass.getpass("Enter your Helicone API key: ")

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import os
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from opik.integrations.openai import track_openai
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from openai import OpenAI
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# Create an OpenAI client with Helicone's base URL
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client = OpenAI(
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    api_key=os.environ["HELICONE_API_KEY"],
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    base_url="https://ai-gateway.helicone.ai"
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)
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# Wrap the client with Opik tracking
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client = track_openai(client, project_name="helicone-integration-demo")
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# Make a chat completion request
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response = client.chat.completions.create(
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    model="gpt-3.5-turbo",
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    messages=[
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        {"role": "system", "content": "You are a knowledgeable AI assistant."},
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        {"role": "user", "content": "What is the largest city in France?"}
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    ]
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)
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# Print the assistant's reply
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print(response.choices[0].message.content)

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import os
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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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# Create and wrap the OpenAI client with Helicone's base URL
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client = OpenAI(
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    api_key=os.environ["HELICONE_API_KEY"],
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    base_url="https://ai-gateway.helicone.ai"
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)
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client = track_openai(client)
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@track
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def generate_response(prompt: str):
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    response = client.chat.completions.create(
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        model="gpt-3.5-turbo",
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        messages=[
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            {"role": "system", "content": "You are a knowledgeable AI assistant."},
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            {"role": "user", "content": prompt}
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        ]
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    )
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    return response.choices[0].message.content
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@track
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def refine_response(initial_response: str):
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    response = client.chat.completions.create(
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        model="gpt-4",
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        messages=[
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            {"role": "system", "content": "You enhance and polish text responses."},
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            {"role": "user", "content": f"Please improve this response: {initial_response}"}
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        ]
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    )
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    return response.choices[0].message.content
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@track(project_name="helicone-integration-demo")
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def generate_and_refine(prompt: str):
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    # First LLM call: Generate initial response
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    initial = generate_response(prompt)
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    # Second LLM call: Refine the response
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    refined = refine_response(initial)
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    return refined
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# Example usage
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result = generate_and_refine("Explain quantum computing in simple terms.")