Using the OpenTelemetry Python SDK

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# Dependencies: opentelemetry-exporter-otlp
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import os
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import time
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from opentelemetry import trace
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.resources import Resource
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from opentelemetry.semconv.resource import ResourceAttributes
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# Configure OpenTelemetry
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# For comet.com
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os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://www.comet.com/opik/api/v1/private/otel"
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os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "Authorization=<your-api-key>,Comet-Workspace=<your-workspace-name>,projectName=<your-project-name>"
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# Configure the tracer provider
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resource = Resource.create({
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 ResourceAttributes.SERVICE_NAME: "opentelemetry-example"
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})
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# Create a tracer provider
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tracer_provider = TracerProvider(resource=resource)
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# Set up the OTLP HTTP exporter
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otlp_exporter = OTLPSpanExporter()
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# Add the exporter to the tracer provider
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tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
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# Set the tracer provider
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trace.set_tracer_provider(tracer_provider)
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# Get a tracer
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tracer = trace.get_tracer("example-tracer")
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def main():
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 # Simulate user request
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 user_request = "What's the weather like today?"
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 # Create a parent span representing the entire chatbot conversation
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 with tracer.start_as_current_span("chatbot_conversation") as conversation_span:
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 print(f"User request: {user_request}")
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 # Add user request as an attribute to the parent span
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 conversation_span.set_attribute("input", user_request)
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 conversation_span.set_attribute("conversation.id", "conv_12345")
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 conversation_span.set_attribute("conversation.type", "weather_inquiry")
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 # Add thread ID as an attribute to the parent span to group related spans into
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 # a single conversational thread
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 conversation_span.set_attribute("thread_id", "user_12345")
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 # Process the user request
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 # Simulate initial processing
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 time.sleep(0.2)
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 # Create a child span for LLM generation using GenAI conventions
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 with tracer.start_as_current_span("llm_completion") as llm_span:
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 print("Generating LLM response...")
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 # Create a prompt for the LLM
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 llm_prompt = f"User question: {user_request}\n\nProvide a concise answer about the weather."
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 # Add GenAI semantic convention attributes
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 llm_span.set_attribute("gen_ai.operation.name", "completion")
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 llm_span.set_attribute("gen_ai.system", "gpt")
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 llm_span.set_attribute("gen_ai.request.model", "gpt-4")
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 llm_span.set_attribute("gen_ai.response.model", "gpt-4")
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 llm_span.set_attribute("gen_ai.request.input", llm_prompt) # Add the prompt
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 llm_span.set_attribute("gen_ai.usage.input_tokens", 10) # Example token count
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 llm_span.set_attribute("gen_ai.usage.output_tokens", 25) # Example token count
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 llm_span.set_attribute("gen_ai.usage.total_tokens", 35) # Example token count
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 llm_span.set_attribute("gen_ai.request.temperature", 0.7)
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 llm_span.set_attribute("gen_ai.request.max_tokens", 100)
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 # Simulate LLM thinking time
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 time.sleep(0.5)
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 # Generate chatbot response
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 chatbot_response = "It's sunny with a high of 75°F in your area today!"
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 # Set response in the LLM span
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 llm_span.set_attribute("gen_ai.response.output", chatbot_response)
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 print("LLM generation completed")
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 # Back in parent span context
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 conversation_span.set_attribute("output", chatbot_response)
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 # Response has been generated
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 print(f"Chatbot response: {chatbot_response}")
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if __name__ == "__main__":
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 main()
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 # Ensure all spans are flushed before the program exits
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 tracer_provider.shutdown()
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 print("\nSpans have been sent to OpenTelemetry collector.")
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 print("If you configured Comet.com, you can view the traces in your Comet project.")