Pythonlangsmith_tracing.py
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import os
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from langsmith import traceable
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from openai import OpenAI
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# Configure via env vars:
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# LANGCHAIN_TRACING_V2=true
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# LANGCHAIN_API_KEY=ls__...
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# LANGCHAIN_PROJECT=my-project
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os.environ.setdefault("LANGCHAIN_TRACING_V2", "true")
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os.environ.setdefault("LANGCHAIN_PROJECT", "ai-observability-demo")
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client = OpenAI()
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@traceable(name="chat_completion", tags=["production"])
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def chat(user_message: str, user_id: str = "anonymous") -> str:
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    response = client.chat.completions.create(
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        model="gpt-4o-mini",
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        messages=[
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            {"role": "system", "content": "You are a helpful assistant."},
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            {"role": "user", "content": user_message},
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        ],
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        metadata={"user_id": user_id},
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    )
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    return response.choices[0].message.content or ""
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@traceable(name="multi_step_pipeline")
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def pipeline(query: str) -> dict:
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    # Step 1: classify
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    category = chat(f"Classify this query in one word: {query}", user_id="system")
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    # Step 2: answer
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    answer = chat(query, user_id="user-123")
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    return {"category": category, "answer": answer}
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if __name__ == "__main__":
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    result = pipeline("Explain the difference between RAG and fine-tuning")
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    print(result["answer"])
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