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:

   

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

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