arize-phoenix-evals

Phoenix Evals provides lightweight, composable building blocks for writing and running evaluations on LLM applications, including tools to determine relevance, toxicity, hallucination detection, and much more.

Features

• Works with your preferred model SDKs via adapters (OpenAI, LiteLLM, LangChain)
• Powerful input mapping and binding for working with complex data structures
• Several pre-built metrics for common evaluation tasks like hallucination detection
• Evaluators are natively instrumented via OpenTelemetry tracing for observability and dataset curation
• Blazing fast performance - achieve up to 20x speedup with built-in concurrency and batching
• Tons of convenience features to improve the developer experience!

Installation

Install Phoenix Evals 2.0 using pip:

pip install 'arize-phoenix-evals>=2.0.0' openai

Quick Start

from phoenix.evals import create_classifier
from phoenix.evals.llm import LLM

# Create an LLM instance
llm = LLM(provider="openai", model="gpt-4o")

# Create an evaluator
evaluator = create_classifier(
    name="helpfulness",
    prompt_template="Rate the response to the user query as helpful or not:\n\nQuery: {input}\nResponse: {output}",
    llm=llm,
    choices={"helpful": 1.0, "not_helpful": 0.0},
)

# Simple evaluation
scores = evaluator.evaluate({"input": "How do I reset?", "output": "Go to settings > reset."})
scores[0].pretty_print()

# With input mapping for nested data
scores = evaluator.evaluate(
    {"data": {"query": "How do I reset?", "response": "Go to settings > reset."}},
    input_mapping={"input": "data.query", "output": "data.response"},
)
scores[0].pretty_print()

Pre-Built Evaluators

The phoenix.evals.metrics module provides ready-to-use evaluators for common tasks:

from phoenix.evals.llm import LLM
from phoenix.evals.metrics import FaithfulnessEvaluator, exact_match, MatchesRegex

llm = LLM(provider="openai", model="gpt-4o")

# LLM-powered faithfulness evaluator
faithfulness = FaithfulnessEvaluator(llm=llm)
scores = faithfulness.evaluate(
    {
        "input": "What is the capital of France?",
        "context": "Paris is the capital of France.",
        "output": "The capital of France is Berlin.",
    }
)
scores[0].pretty_print()
# Score(name='faithfulness', score=0.0, label='unfaithful', explanation='...')

# Code-based exact match
match_result = exact_match({"output": "Paris", "expected": "Paris"})

# Regex match
regex_result = MatchesRegex(pattern=r"^\d{4}-\d{2}-\d{2}$").evaluate({"output": "2024-03-15"})

LLM Providers

The LLM class supports multiple AI providers:

from phoenix.evals.llm import LLM

# OpenAI
llm = LLM(provider="openai", model="gpt-4o")

# Anthropic
llm = LLM(provider="anthropic", model="claude-3-5-sonnet-20241022")

# Google Gemini
llm = LLM(provider="google", model="gemini-1.5-pro")

# LiteLLM (unified interface for 100+ providers)
llm = LLM(provider="litellm", model="gpt-4o")

Evaluating Dataframes

import pandas as pd
from phoenix.evals import create_classifier, evaluate_dataframe, async_evaluate_dataframe
from phoenix.evals.llm import LLM

# Create an LLM instance
llm = LLM(provider="openai", model="gpt-4o")

# Create multiple evaluators
relevance_evaluator = create_classifier(
    name="relevance",
    prompt_template="Is the response relevant to the query?\n\nQuery: {input}\nResponse: {output}",
    llm=llm,
    choices={"relevant": 1.0, "irrelevant": 0.0},
)

helpfulness_evaluator = create_classifier(
    name="helpfulness",
    prompt_template="Is the response helpful?\n\nQuery: {input}\nResponse: {output}",
    llm=llm,
    choices={"helpful": 1.0, "not_helpful": 0.0},
)

# Prepare your dataframe
df = pd.DataFrame(
    [
        {
            "input": "How do I reset my password?",
            "output": "Go to settings > account > reset password.",
        },
        {"input": "What's the weather like?", "output": "I can help you with password resets."},
    ]
)

# Synchronous evaluation
results_df = evaluate_dataframe(
    dataframe=df,
    evaluators=[relevance_evaluator, helpfulness_evaluator],
)
print(results_df.head())

# Async evaluation (up to 20x faster with large dataframes)
import asyncio

results_df = asyncio.run(
    async_evaluate_dataframe(
        dataframe=df,
        evaluators=[relevance_evaluator, helpfulness_evaluator],
    )
)

Documentation

• Full Documentation - Complete API reference and guides
• Phoenix Docs - Detailed use-cases and examples
• OpenInference - Auto-instrumentation libraries for frameworks

Community

Join our community to connect with thousands of AI builders:

• 🌍 Join our Slack community.
• 📚 Read the Phoenix documentation.
• 💡 Ask questions and provide feedback in the #phoenix-support channel.
• 🌟 Leave a star on our GitHub.
• 🐞 Report bugs with GitHub Issues.
• 𝕏 Follow us on 𝕏.
• 💼 Follow us on LinkedIn.
• 🗺️ Check out our roadmap to see where we're heading next.