Instrument: Python

While the spans created via Phoenix and OpenInference create a solid foundation for tracing your application, sometimes you need to create and customize your LLM spans

Phoenix and OpenInference use the OpenTelemetry Trace API to create spans. Because Phoenix supports OpenTelemetry, this means that you can perform manual instrumentation, no LLM framework required! This guide will help you understand how to create and customize spans using the OpenTelemetry Trace API.

See here for an end-to-end example of a manually instrumented application.

Google Colaboratory

---

First, ensure you have the API and SDK packages:

pip install opentelemetry-api
pip install opentelemetry-sdk

Let's next install the OpenInference Semantic Conventions package so that we can construct spans with LLM semantic conventions:

pip install openinference-semantic-conventions

For full documentation on the OpenInference semantic conventions, please consult the specification

Semantic Conventionsopeninference

Configuring a Tracer

Configuring an OTel tracer involves some boilerplate code that the instrumentors in phoenix.trace take care of for you. If you're manually instrumenting your application, you'll need to implement this boilerplate yourself:

from openinference.semconv.resource import ResourceAttributes
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from phoenix.config import get_env_host, get_env_port

resource = Resource(attributes={
    ResourceAttributes.PROJECT_NAME: '<your-project-name>'
})
tracer_provider = TracerProvider(resource=resource)
trace.set_tracer_provider(tracer_provider)
tracer = trace.get_tracer(__name__)
collector_endpoint = f"http://{get_env_host()}:{get_env_port()}/v1/traces"
span_exporter = OTLPSpanExporter(endpoint=collector_endpoint)
simple_span_processor = SimpleSpanProcessor(span_exporter=span_exporter)
trace.get_tracer_provider().add_span_processor(simple_span_processor)

This snippet contains a few OTel concepts:

• A resource represents an origin (e.g., a particular service, or in this case, a project) from which your spans are emitted.

• Span processors filter, batch, and perform operations on your spans prior to export.

• Your tracer provides a handle for you to create spans and add attributes in your application code.

• The collector (e.g., Phoenix) receives the spans exported by your application.

Creating spans

To create a span, you'll typically want it to be started as the current span.

def do_work():
    with tracer.start_as_current_span("span-name") as span:
        # do some work that 'span' will track
        print("doing some work...")
        # When the 'with' block goes out of scope, 'span' is closed for you

You can also use start_span to create a span without making it the current span. This is usually done to track concurrent or asynchronous operations.

Creating nested spans

If you have a distinct sub-operation you'd like to track as a part of another one, you can create span to represent the relationship:

def do_work():
    with tracer.start_as_current_span("parent") as parent:
        # do some work that 'parent' tracks
        print("doing some work...")
        # Create a nested span to track nested work
        with tracer.start_as_current_span("child") as child:
            # do some work that 'child' tracks
            print("doing some nested work...")
            # the nested span is closed when it's out of scope

        # This span is also closed when it goes out of scope

When you view spans in a trace visualization tool, child will be tracked as a nested span under parent.

Creating spans with decorators

It's common to have a single span track the execution of an entire function. In that scenario, there is a decorator you can use to reduce code:

@tracer.start_as_current_span("do_work")
def do_work():
    print("doing some work...")

Use of the decorator is equivalent to creating the span inside do_work() and ending it when do_work() is finished.

To use the decorator, you must have a tracer instance in scope for your function declaration.

If you need to add attributes or events then it's less convenient to use a decorator.

Get the current span

Sometimes it's helpful to access whatever the current span is at a point in time so that you can enrich it with more information.

from opentelemetry import trace

current_span = trace.get_current_span()
# enrich 'current_span' with some information

Add attributes to a span

Attributes let you attach key/value pairs to a spans so it carries more information about the current operation that it's tracking.

from opentelemetry import trace

current_span = trace.get_current_span()

current_span.set_attribute("operation.value", 1)
current_span.set_attribute("operation.name", "Saying hello!")
current_span.set_attribute("operation.other-stuff", [1, 2, 3])

Notice above that the attributes have a specific prefix operation. When adding custom attributes, it's best practice to vendor your attributes (e.x. mycompany.) so that your attributes do not clash with semantic conventions.

Add Semantic Attributes

Semantic attributes are pre-defined attributes that are well-known naming conventions for common kinds of data. Using semantic attributes lets you normalize this kind of information across your systems. In the case of Phoenix, the OpenInference Semantic Conventions package provides a set of well-known attributes that are used to represent LLM application specific semantic conventions.

To use OpenInference Semantic Attributes in Python, ensure you have the semantic conventions package:

pip install openinference-semantic-conventions

Then you can use it in code:

from openinference.semconv.trace import SpanAttributes

# ...

current_span = trace.get_current_span()
current_span.set_attribute(SpanAttributes.INPUT_VALUE, "Hello world!")
current_span.set_attribute(SpanAttributes.LLM_MODEL_NAME, "gpt-3.5-turbo")

Adding events

Events are human-readable messages that represent "something happening" at a particular moment during the lifetime of a span. You can think of it as a primitive log.

from opentelemetry import trace

current_span = trace.get_current_span()

current_span.add_event("Gonna try it!")

# Do the thing

current_span.add_event("Did it!")

Set span status

The span status allows you to signal the success or failure of the code executed within the span.

from opentelemetry import trace
from opentelemetry.trace import Status, StatusCode

current_span = trace.get_current_span()

try:
    # something that might fail
except:
    current_span.set_status(Status(StatusCode.ERROR))

Record exceptions in spans

It can be a good idea to record exceptions when they happen. It’s recommended to do this in conjunction with setting span status.

from opentelemetry import trace
from opentelemetry.trace import Status, StatusCode

current_span = trace.get_current_span()

try:
    # something that might fail

# Consider catching a more specific exception in your code
except Exception as ex:
    current_span.set_status(Status(StatusCode.ERROR))
    current_span.record_exception(ex)