Tracely

Tracely is a tool designed for tracing and monitoring AI model interactions, enabling you to gain real-time insights into your models' performance. This repository offers a straightforward interface for integrating tracing into your Python applications.

Getting Started

Prerequisites

• Python 3.x
• An account on Evidently Cloud
• API Key from your Evidently account

Installation

Tracely is available as a PyPI package. To install it using pip package manager, run:

pip install tracely

Usage

Init

To send your traces to Evidently you need to initialize tracely:

from tracely import init_tracing

init_tracing(
    address="https://app.evidently.cloud",           # Trace Collector Address
    api_key="",                                      # API Key from Evidently Cloud
    team_id="a1d08c46-0624-49e3-a9f5-11a13b4a2aa5",  # Team ID from Evidently Cloud
    export_name="tracing-dataset",
)

All parameters can be set using environment varialbes:

• EVIDENTLY_TRACE_COLLECTOR - trace collector address (default to https://app.evidently.cloud)
• EVIDENTLY_TRACE_COLLECTOR_API_KEY - API Key to access Evidently Cloud for creating dataset and uploading traces
• EVIDENTLY_TRACE_COLLECTOR_EXPORT_NAME - Export name in Evidently Cloud
• EVIDENTLY_TRACE_COLLECTOR_TEAM_ID - Team ID from Evidently Cloud to create Export dataset in

Decorator

Once Tracely is initialized, you can decorate your functions with trace_event to start collecting traces for a specific function:

from tracely import init_tracing
from tracely import trace_event


init_tracing()

@trace_event()
def process_request(question: str, session_id: str):
    # do work
    return "work done"

The trace_event decorator accepts several arguments:

• span_name - the name of the span to send in the event (defaults to the function name)
• track_args - a list of function arguments to include in the event (defaults to None, indicating that all arguments should be included)
• ingore_args - a list of function arguments to exclude (defaults to None, meaning no arguments are ignored)
• track_output - indicates whether the event should track the function's return value (defaults to True)
• parse_output - indicates whether the result should be parsed (e.g., dict, list, and tuple types would be split into separate fields; defaults to True)

Context Manager

If you need to create a trace event without using a decorator (e.g., for a specific piece of code), you can do so with the context manager:

import uuid

from tracely import init_tracing
from tracely import create_trace_event


init_tracing()

session_id = str(uuid.uuid4())

with create_trace_event("external_span", session_id=session_id) as event:
    event.set_attribute("my-attribute", "value")
    # do work
    event.set_result({"data": "data"})

The create_trace_event function accepts the following arguments:

• name - the name of the event to label it
• parse_output - indicates whether the result (if set) should be parsed (dict, list and tuple types would be split in separate fields), default to True
• **params - key-value style parameters to set as attributes

The event object has the following methods:

• set_attribute - set a custom attribute for the event
• set_result - set a result for the event (only one result can be set per event)

Extending events with additional attributes

If you want to add a new attribute to active event span, you can use get_current_span() to get access to current span:

import uuid

from tracely import init_tracing
from tracely import create_trace_event
from tracely import get_current_span

init_tracing()

session_id = str(uuid.uuid4())

with create_trace_event("external_span", session_id=session_id):
    span = get_current_span()
    span.set_attribute("my-attribute", "value")
    # do work
    span.set_result({"data": "data"})

Object from tracely.get_current_span() have 2 methods:

• set_attribute - add new attribute to active span
• set_result - set a result field to an active span (have no effect in decorated functions with return values)