Get Started: Evaluations

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

Launch Phoenix

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.

Phoenix Cloud

Before sending traces, make sure Phoenix is running. For more step by step instructions, check out this Get Started guide.

Phoenix Cloud

Log in, create a space, navigate to the settings page in your space, and create your API keys.

In your code, set your environment variables.

import os
os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX Collector endpoint"

You can find your collector endpoint here:

Launch your space, navigate to settings & copy your hostname for your collector endpoint

Your Collector Endpoint is: https://app.phoenix.arize.com/s/ + your space name.

Local (Self-hosted)

If you installed Phoenix locally, you have a variety of options for deployment methods including: Terminal, Docker, Kubernetes, Railway, & AWS CloudFormation. (Learn more: Self-Hosting)

To host on your local machine, run phoenix serve in your terminal.

Navigate to your localhost in your browser. (example localhost:6006)

2

Install Phoenix Evals

You'll need to install the evals library that's apart of Phoenix. For the most recent version, run a version above 2.0.

pip install -q "arize-phoenix-evals>=2"
pip install -q "arize-phoenix-client"

3

Pull down your Trace Data

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

Set Up Evaluations

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 Model

We'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 Evaluators

We 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

Run Evaluation

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

Log results to Visualize in Phoenix

You'll now be able to log your evaluations in your project view.

client.log_span_annotations(
    dataframe=results_df,
    annotation_name="QA Correctness",
    annotator_kind="LLM"
)

Learn More:

Evaluation Concepts

Evals in Phoenix