1
# Dependencies: opentelemetry-exporter-otlp
2
3
import os
4
import time
5
from opentelemetry import trace
6
from opentelemetry.sdk.trace import TracerProvider
7
from opentelemetry.sdk.trace.export import BatchSpanProcessor
8
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
9
from opentelemetry.sdk.resources import Resource
10
from opentelemetry.semconv.resource import ResourceAttributes
11
12
13
# Configure OpenTelemetry
14
15
# For comet.com
16
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://www.comet.com/opik/api/v1/private/otel"
17
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "Authorization=<your-api-key>,Comet-Workspace=<your-workspace-name>,projectName=<your-project-name>"
18
19
# Configure the tracer provider
20
resource = Resource.create({
21
    ResourceAttributes.SERVICE_NAME: "opentelemetry-example"
22
})
23
24
# Create a tracer provider
25
tracer_provider = TracerProvider(resource=resource)
26
27
# Set up the OTLP HTTP exporter
28
otlp_exporter = OTLPSpanExporter()
29
30
# Add the exporter to the tracer provider
31
tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
32
33
# Set the tracer provider
34
trace.set_tracer_provider(tracer_provider)
35
36
# Get a tracer
37
tracer = trace.get_tracer("example-tracer")
38
39
def main():
40
    # Simulate user request
41
    user_request = "What's the weather like today?"
42
43
    # Create a parent span representing the entire chatbot conversation
44
    with tracer.start_as_current_span("chatbot_conversation") as conversation_span:
45
        print(f"User request: {user_request}")
46
47
        # Add user request as an attribute to the parent span
48
        conversation_span.set_attribute("input", user_request)
49
        conversation_span.set_attribute("conversation.id", "conv_12345")
50
        conversation_span.set_attribute("conversation.type", "weather_inquiry")
51
52
        # Add thread ID as an attribute to the parent span to group related spans into
53
        # a single conversational thread
54
        conversation_span.set_attribute("thread_id", "user_12345")
55
56
        # Process the user request
57
58
        # Simulate initial processing
59
        time.sleep(0.2)
60
61
        # Create a child span for LLM generation using GenAI conventions
62
        with tracer.start_as_current_span("llm_completion") as llm_span:
63
            print("Generating LLM response...")
64
65
            # Create a prompt for the LLM
66
            llm_prompt = f"User question: {user_request}\n\nProvide a concise answer about the weather."
67
68
            # Add GenAI semantic convention attributes
69
            llm_span.set_attribute("gen_ai.operation.name", "completion")
70
            llm_span.set_attribute("gen_ai.system", "gpt")
71
            llm_span.set_attribute("gen_ai.request.model", "gpt-4")
72
            llm_span.set_attribute("gen_ai.response.model", "gpt-4")
73
            llm_span.set_attribute("gen_ai.request.input", llm_prompt)  # Add the prompt
74
            llm_span.set_attribute("gen_ai.usage.input_tokens", 10)  # Example token count
75
            llm_span.set_attribute("gen_ai.usage.output_tokens", 25)  # Example token count
76
            llm_span.set_attribute("gen_ai.usage.total_tokens", 35)   # Example token count
77
            llm_span.set_attribute("gen_ai.request.temperature", 0.7)
78
            llm_span.set_attribute("gen_ai.request.max_tokens", 100)
79
80
            # Simulate LLM thinking time
81
            time.sleep(0.5)
82
83
            # Generate chatbot response
84
            chatbot_response = "It's sunny with a high of 75°F in your area today!"
85
86
            # Set response in the LLM span
87
            llm_span.set_attribute("gen_ai.response.output", chatbot_response)
88
89
            print("LLM generation completed")
90
91
        # Back in parent span context
92
        conversation_span.set_attribute("output", chatbot_response)
93
        # Response has been generated
94
95
        print(f"Chatbot response: {chatbot_response}")
96
97
if __name__ == "__main__":
98
    main()
99
100
    # Ensure all spans are flushed before the program exits
101
    tracer_provider.shutdown()
102
103
    print("\nSpans have been sent to OpenTelemetry collector.")
104
    print("If you configured Comet.com, you can view the traces in your Comet project.")

1
from opentelemetry import trace
2
from opentelemetry.sdk.trace import TracerProvider
3
from opentelemetry.sdk.trace.export import BatchSpanProcessor
4
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
5
from opik.integrations.otel import OpikSpanProcessor
6
7
provider = TracerProvider()
8
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
9
10
# Mints opik.span_id and threads opik.trace_id / opik.parent_span_id onto every
11
# descendant of a span that was attached via distributed_trace.attach_to_parent.
12
provider.add_span_processor(OpikSpanProcessor())
13
14
trace.set_tracer_provider(provider)

1
import opik
2
from opentelemetry import trace
3
4
tracer = trace.get_tracer("my-service")
5
6
@opik.track
7
def handler(question: str) -> str:
8
    # Spans started here nest under the `handler` span automatically.
9
    with tracer.start_as_current_span("retrieve"):
10
        ...
11
    return "answer"