Observability for Anannas AI with Opik

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import os
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import getpass
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if "ANANNAS_API_KEY" not in os.environ:
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    os.environ["ANANNAS_API_KEY"] = getpass.getpass("Enter your Anannas AI 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 Anannas AI's base URL
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client = OpenAI(
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    api_key=os.environ["ANANNAS_API_KEY"],
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    base_url="https://api.anannas.ai/v1"
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)
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# Wrap the client with Opik tracking
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client = track_openai(client, project_name="anannas-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="anthropic/claude-3-5-sonnet",  # You can use any of the 500+ models
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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 are some interesting facts about tropical fruits?"}
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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 Anannas AI's base URL
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client = OpenAI(
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    api_key=os.environ["ANANNAS_API_KEY"],
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    base_url="https://api.anannas.ai/v1"
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)
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client = track_openai(client)
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@track
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def summarize_text(text: str):
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    response = client.chat.completions.create(
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        model="openai/gpt-4o",
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        messages=[
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            {"role": "system", "content": "You create concise summaries of text content."},
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            {"role": "user", "content": f"Please summarize this text:\n{text}"}
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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 analyze_sentiment(summary: str):
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    response = client.chat.completions.create(
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        model="openai/gpt-4o-mini",
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        messages=[
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            {"role": "system", "content": "You perform sentiment analysis on text."},
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            {"role": "user", "content": f"What is the sentiment of this summary:\n{summary}"}
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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="anannas-integration-demo")
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def analyze_text(text: str):
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    # First LLM call: Summarize the text
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    summary = summarize_text(text)
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    # Second LLM call: Analyze the sentiment of the summary
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    sentiment = analyze_sentiment(summary)
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    return {
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        "summary": summary,
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        "sentiment": sentiment
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    }
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# Example usage
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text_to_analyze = "Anannas AI provides a unified gateway to access hundreds of LLM models with built-in observability and automatic fallback routing."
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result = analyze_text(text_to_analyze)