Sessions
Adding SessionID and UserID as attributes to Spans for Tracing
A session is a grouping of traces based on a session ID attribute. When building or debugging a chatbot application, being able to see groups of messages or traces belonging to a series of interactions between a human and the AI can be particularly helpful. By adding session.id
and user.id
as attributes to spans, you can:
Find exactly where a conversation "breaks" or goes off the rails. This can help identify if a user becomes progressively more frustrated or if a chatbot is not helpful.
Find groups of traces where your application is not performing well. Adding
session.id
and/oruser.id
from an application enables back-and-forth interactions to be grouped and filtered further.Construct custom metrics based on evals using
session.id
oruser.id
to find best/worst performingsessions
andusers
.

Adding SessionID and UserID
Session and user IDs can be added to a span using auto instrumentation or manual instrumentation of Open Inference. Any LLM call within the context (the with
block in the example below) will contain corresponding session.id
or user.id
as a span attribute. session.id
and user.id
must be a non-empty string.
When defining your instrumentation, you can pass the sessionID attribute as shown below.
using_session
using_session
Context manager to add session ID to the current OpenTelemetry Context. OpenInference auto instrumentators will read this Context and pass the session ID as a span attribute, following the OpenInference semantic conventions. Its input, the session ID, must be a non-empty string.
from openinference.instrumentation import using_session
with using_session(session_id="my-session-id"):
# Calls within this block will generate spans with the attributes:
# "session.id" = "my-session-id"
...
It can also be used as a decorator:
@using_session(session_id="my-session-id")
def call_fn(*args, **kwargs):
# Calls within this function will generate spans with the attributes:
# "session.id" = "my-session-id"
...
using_user
using_user
Context manager to add user ID to the current OpenTelemetry Context. OpenInference auto instrumentators will read this Context and pass the user ID as a span attribute, following the OpenInference semantic conventions. Its input, the user ID, must be a non-empty string.
from openinference.instrumentation import using_user
with using_user("my-user-id"):
# Calls within this block will generate spans with the attributes:
# "user.id" = "my-user-id"
...
It can also be used as a decorator:
@using_user("my-user-id")
def call_fn(*args, **kwargs):
# Calls within this function will generate spans with the attributes:
# "user.id" = "my-user-id"
...
Additional Examples
Once you define your OpenAI client, any call inside our context managers will attach the corresponding attributes to the spans.
import openai
from openinference.instrumentation import using_attributes
client = openai.OpenAI()
# Defining a Session
with using_attributes(session_id="my-session-id"):
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Write a haiku."}],
max_tokens=20,
)
# Defining a User
with using_attributes(user_id="my-user-id"):
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Write a haiku."}],
max_tokens=20,
)
# Defining a Session AND a User
with using_attributes(
session_id="my-session-id",
user_id="my-user-id",
):
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Write a haiku."}],
max_tokens=20,
)
Alternatively, if you wrap your calls inside functions, you can use them as decorators:
from openinference.instrumentation import using_attributes
client = openai.OpenAI()
# Defining a Session
@using_attributes(session_id="my-session-id")
def call_fn(client, *args, **kwargs):
return client.chat.completions.create(*args, **kwargs)
# Defining a User
@using_attributes(user_id="my-user-id")
def call_fn(client, *args, **kwargs):
return client.chat.completions.create(*args, **kwargs)
# Defining a Session AND a User
@using_attributes(
session_id="my-session-id",
user_id="my-user-id",
)
def call_fn(client, *args, **kwargs):
return client.chat.completions.create(*args, **kwargs)
To access an applications sessions in the platform, select "Sessions" from the left nav.

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