Arize tracers instrumented on experiment code will automatically trace the experiments into the platform.
Tracing Using Explicit Spans
from opentelemetry import trace
# Outer function will be traced by Arize with a span
def task_add_1(dataset_row):
tracer = trace.get_tracer(__name__)
# Start the span for the function
with tracer.start_as_current_span("test_function") as span:
# Extract the number from the dataset row
num = dataset_row['attributes.my_number']
# Set 'num' as a span attribute
span.set_attribute("dataset.my_number", num)
# Return the incremented number
return num + 1
Tracing Using Auto-Instrumentor
# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.openai import OpenAIInstrumentor
# Automatic instrumentation --- This will trace all tasks below with LLM Calls
OpenAIInstrumentor().instrument()
task_prompt_template = "Answer in a few words: {question}"
openai_client = OpenAI()
def task(dataset_row) -> str:
question = dataset_row["question"]
message_content = task_prompt_template.format(question=question)
response = openai_client.chat.completions.create(
model="gpt-4o", messages=[{"role": "user", "content": message_content}]
)
return response.choices[0].message.content
