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import os
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# Set your Langfuse API keys
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LANGFUSE_SECRET_KEY="sk-lf-..."
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LANGFUSE_PUBLIC_KEY="pk-lf-..."
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# EU region
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LANGFUSE_HOST="https://cloud.langfuse.com"
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# US region
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# LANGFUSE_HOST="https://us.cloud.langfuse.com"
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# Set your OpenRouter API key
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os.environ["OPENAI_API_KEY"] = "${API_KEY_REF}"

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# Import the Langfuse OpenAI SDK wrapper
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from langfuse.openai import openai
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# Create an OpenAI client with OpenRouter's base URL
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client = openai.OpenAI(
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    base_url="https://openrouter.ai/api/v1",
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    default_headers={
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        "HTTP-Referer": "<YOUR_SITE_URL>",  # Optional: Your site URL
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        "X-Title": "<YOUR_SITE_NAME>",      # Optional: Your site name
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    }
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)
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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",
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    messages=[
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        {"role": "system", "content": "You are a helpful assistant."},
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        {"role": "user", "content": "Tell me a fun fact about space."}
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    ],
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    name="fun-fact-request"  # Optional: Name of the generation in Langfuse
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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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from langfuse import observe
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from langfuse.openai import openai
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# Create an OpenAI client with OpenRouter's base URL
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client = openai.OpenAI(
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    base_url="https://openrouter.ai/api/v1",
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)
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@observe()  # This decorator enables tracing of the function
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def analyze_text(text: str):
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    # First LLM call: Summarize the text
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    summary_response = summarize_text(text)
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    summary = summary_response.choices[0].message.content
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    # Second LLM call: Analyze the sentiment of the summary
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    sentiment_response = analyze_sentiment(summary)
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    sentiment = sentiment_response.choices[0].message.content
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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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@observe()  # Nested function to be traced
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def summarize_text(text: str):
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    return client.chat.completions.create(
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        model="openai/gpt-3.5-turbo",
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        messages=[
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            {"role": "system", "content": "You summarize texts in a concise manner."},
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            {"role": "user", "content": f"Summarize the following text:\n{text}"}
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        ],
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        name="summarize-text"
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    )
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@observe()  # Nested function to be traced
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def analyze_sentiment(summary: str):
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    return client.chat.completions.create(
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        model="openai/gpt-3.5-turbo",
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        messages=[
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            {"role": "system", "content": "You analyze the sentiment of texts."},
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            {"role": "user", "content": f"Analyze the sentiment of the following summary:\n{summary}"}
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        ],
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        name="analyze-sentiment"
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    )
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# Example usage
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text_to_analyze = "OpenRouter's unified API has significantly advanced the field of AI development, setting new standards for model accessibility."
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result = analyze_text(text_to_analyze)
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print(result)