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Example Tool Instrumentation

The following example illustrates how to manually trace a tool function alongside a chat completion response. You’ll learn how to establish spans for both the tool and LLM to capture their inputs, outputs, and key events. Before diving into the code, ensure your tracer is properly configured: 
# Python Tracer Setup (Illustrative)
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

# Set up a tracer provider
trace.set_tracer_provider(TracerProvider())
tracer_provider = trace.get_tracer_provider()

# Configure an exporter (e.g., ConsoleExporter for demonstration)
exporter = ConsoleSpanExporter()
span_processor = SimpleSpanProcessor(exporter)
tracer_provider.add_span_processor(span_processor)

# Get a tracer
tracer = trace.get_tracer(__name__)

# Assume openai_client is configured elsewhere
import openai
openai_client = openai.OpenAI()
Now we can create tool spans by starting spans and defining our name and other attributes: 
import json
# Ensure 'tracer' is defined from the setup section above.
# Ensure 'openai_client' is defined, e.g., from openai library

# Placeholder definitions for the example
question = "What is the weather like in London?"
def example_tool_function(input_args):
    print(f"Tool received: {input_args}")
    return f"The weather in {input_args.get('city', 'default city')} is sunny."

tool_args_example = {"city": "London"}
model_version_example = "gpt-3.5-turbo"
current_user_message_example = [{"role": "user", "content": question}]
TEMPERATURE_example = 0.7

def run_tool_py(tool_function, tool_args, current_question, openai_client_instance, model, messages, temp):
    # Begin by setting the context for the current span
    with tracer.start_as_current_span(
        name="Tool - specific tool",
        attributes={
            # Set these attributes prior to invoking the tool, in case the tool raises an exception
            "fi.span.kind": "TOOL",
            "input.value": current_question,
            "message.tool_calls.0.tool_call.function.name": tool_function.__name__,
            "message.tool_calls.0.tool_call.function.arguments": json.dumps(
                tool_args
            ),
        },
    ) as tool_span:
        # Run the tool; the output is a formatted prompt for chat completion
        resulting_prompt = tool_function(input_args=tool_args)
        # Optionally, set the resulting prompt as the tool span output
        tool_span.set_attribute(
            "message.tool_calls.0.tool_call.function.output", resulting_prompt
        )

        # This LLM span is nested under the tool span in the trace
        with tracer.start_as_current_span(
            name="Tool - LLM response",
            # Set these attributes before invoking the LLM
            attributes={
                "fi.span.kind": "LLM",
                "input.value": resulting_prompt,
            },
        ) as llm_span:
            # llm_response = openai_client_instance.chat.completions.create(
            #         model=model,
            #         messages=messages,
            #         temperature=temp,
            #     )
            # llm_span.set_attribute("output.value", str(llm_response)) # Convert to string if necessary
            llm_response_example = "LLM response based on tool output."
            llm_span.set_attribute("output.value", llm_response_example)

# Example call (assuming tracer and openai_client are initialized from setup)
# run_tool_py(example_tool_function, tool_args_example, question, openai_client, model_version_example, current_user_message_example, TEMPERATURE_example)