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Now that you have Phoenix up and running, and sent traces to your first project, the next step you can take is running evaluations of your Python application. Evaluations let you measure and monitor the quality of your application by scoring traces against metrics like accuracy, relevance, or custom checks.
1
Before running evals, make sure Phoenix is running & you have sent traces in your project. For more step by step instructions, check out this Get Started guide & Get Started with Tracing guide.
Log in, create a space, navigate to the settings page in your space, and create your API keys.Set your environment variables.
export PHOENIX_API_KEY = "ADD YOUR PHOENIX API KEY"
export PHOENIX_COLLECTOR_ENDPOINT = "ADD YOUR PHOENIX COLLECTOR ENDPOINT"
You can find your collector endpoint here:
After launching your space, go to settings.
Your Collector Endpoint is: https://app.phoenix.arize.com/s/ + your space name.
2
You’ll need to install the evals library that’s apart of Phoenix.
pip install -q "arize-phoenix-evals>=2"
pip install -q "arize-phoenix-client"
3
Since, we are running our evaluations on our trace data from our first project, we’ll need to pull that data into our code.
from phoenix.client import Client

px_client = Client()
primary_df = px_client.spans.get_spans_dataframe(project_identifier="my-llm-app")
4
In this example, we will define, create, and run our own evaluator. There’s a number of different evaluators you can run, but this quick start will go through an LLM as a Judge Model.1) Define your LLM Judge ModelWe’ll use OpenAI as our evaluation model for this example, but Phoenix also supports a number of other models.If you haven’t yet defined your OpenAI API Key from the previous step, let’s first add it to our environment.
import os
from getpass import getpass

if not (openai_api_key := os.getenv("OPENAI_API_KEY")):
    openai_api_key = getpass("🔑 Enter your OpenAI API key: ")

os.environ["OPENAI_API_KEY"] = openai_api_key

from phoenix.evals.llm import LLM
llm = LLM(model="gpt-4o", provider="openai")
2) Define your EvaluatorsWe will set up a Q&A correctness Evaluator with the LLM of choice. I want to first define my LLM-as-a-Judge prompt template. Most LLM-as-a-judge evaluations can be framed as a classification task where the output is one of two or more categorical labels.
CORRECTNESS_TEMPLATE = """
You are given a question and an answer. Decide if the answer is fully correct.
Rules: The answer must be factually accurate, complete, and directly address the question.
If it is, respond with "correct". Otherwise respond with "incorrect".
[BEGIN DATA]
    ************
    [Question]: {attributes.llm.input_messages}
    ************
    [Answer]: {attributes.llm.output_messages}
[END DATA]

Your response must be a single word, either "correct" or "incorrect",
and should not contain any text or characters aside from that word.
"correct" means that the question is correctly and fully answered by the answer.
"incorrect" means that the question is not correctly or only partially answered by the
answer.
"""
Now we want to define our Classification Evaluator
from phoenix.evals import create_classifier

correctness_evaluator = create_classifier(
    name="correctness",
    prompt_template=CORRECTNESS_TEMPLATE,
    llm=llm,
    choices={"correct": 1.0, "incorrect": 0.0},
)
5
Now that we have defined our evaluator, we’re ready to evaluate our traces.
from phoenix.evals import evaluate_dataframe

results_df = evaluate_dataframe(
    dataframe=primary_df,
    evaluators=[correctness_evaluator]
)
6
You’ll now be able to log your evaluations in your project view.First, format the evaluation results for logging using the to_annotation_dataframe utility:
from phoenix.evals.utils import to_annotation_dataframe

# Format evaluation results for logging
annotations_df = to_annotation_dataframe(results_df)
Then log the annotations to Phoenix:
px_client.spans.log_span_annotations_dataframe(dataframe=annotations_df)

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Learn More:

Evaluation Concepts

Evals in Phoenix