You can attach evaluation metrics to a trace to quantify the quality of the respective component of your LLM app, i.e., perform online evaluation. This e.g. allows you to filter the dashboard by low scores. The scores for any step of a trace are visualized on the right side of a trace (top image). All scores are aggregated across logs by time in a chart at the top of the dashboard (bottom image). Note, the logs of the evaluation are automatically attached to the trace in Python. You can deactivate this behavior by setting the environment variable TURN_OFF_PAREA_EVAL_LOGGING to True. Trace View Chart View There are two ways to attach evaluations to a trace:
  1. Using evaluation functions from your code base
  2. Using evaluation functions created on the platform

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Using evaluation functions from your code base

You can define evaluations functions locally in your codebase. The evaluation is required to receive a Log object and return a float or boolean value. The evaluation function will be executed non-blocking in a separate thread and the results will be logged. An example implementation is shown below: 
from parea import trace

def usefulness(log: Log) -> float:
    return 1.0 if log.output == log.target else 0.0


@trace(eval_funcs=[usefulness])
def function_to_trace(*args, **kwargs):
    ...
For a full working example checkout Python cookbook.

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Using Pre-built SOTA evaluation functions

Parea provides a set of state-of-the-art evaluation metrics you can plug into your evaluation process. Their motivation & research are discussed in the blog post on reference-free and reference-based evaluation metrics. Here is an overview of them:
You can reuse these evals in Python by importing the respective evaluation function from the parea.evals module and attaching them to the trace decorator.

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Using evaluation functions created on the platform

After creating an evaluation function on the platform, you can use it to automatically track the performance of the components of your LLM app. For that, simply wrap the function you want to track with the trace decorator and the evaluation function will be executed in the backend in a non-blocking way: 
from parea import trace

@trace(eval_func_names=['Harmfullness Detector'])  # Evaluation function name
def function_to_trace(*args, **kwargs):
    ...
For a full example you can view our Python cookbook or Typescript cookbook

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Run Evaluations on sample of logs

You can also limit how many logs you run evaluations on by setting a sampling rate using the apply_eval_frac or applyEvalFrac argument for Python and Typescript, respectively. This is useful if you want to reduce the cost of evaluating. 
from parea import trace

@trace(eval_funcs=[usefulness], apply_eval_frac=0.1)
def function_to_trace(*args, **kwargs):
    ...