arize-phoenix-evals

Tooling to evaluate LLM applications including faithfulness, correctness, relevance, and more.

Phoenix Evals provides:

• Built-in evaluators for common tasks (faithfulness, correctness, relevance, conciseness, and more)
• A unified LLM wrapper supporting OpenAI, Anthropic, Google, LiteLLM, and other providers
• Batch evaluation over pandas DataFrames with evaluate_dataframe
• Custom evaluator creation via create_evaluator decorator or create_classifier factory
• Benchmark datasets for testing evaluator accuracy

Installation

pip install "arize-phoenix-evals>=3"

Install the LLM vendor SDK for your chosen provider:

pip install openai  # or anthropic, google-generativeai, litellm, etc.

Quick example

import os
from phoenix.evals import LLM, evaluate_dataframe
from phoenix.evals.metrics import DocumentRelevanceEvaluator

os.environ["OPENAI_API_KEY"] = "<your-openai-key>"

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

# DataFrame must have columns matching the evaluator's input schema
# DocumentRelevanceEvaluator expects: 'input' (query) and 'output' (document)
results_df = evaluate_dataframe(
    dataframe=df,
    evaluators=[relevance_evaluator],
)

Core API

Symbol	Description
LLM(provider=..., model=...)	Unified LLM wrapper for all supported providers
evaluate_dataframe(dataframe, evaluators)	Run evaluators over a DataFrame (sync)
async_evaluate_dataframe(dataframe, evaluators)	Async variant with concurrency control
create_evaluator(name, kind=...)	Decorator to turn a function into an Evaluator
create_classifier(name, prompt_template, llm, choices)	Factory for ClassificationEvaluator instances
bind_evaluator(evaluator, input_mapping)	Bind field mappings to an evaluator

Built-in evaluators

All built-in evaluators live in phoenix.evals.metrics and accept llm: LLM as their first argument:

Evaluator	Input fields	Labels
FaithfulnessEvaluator	input, output, context	faithful / unfaithful
CorrectnessEvaluator	input, output	correct / incorrect
DocumentRelevanceEvaluator	input, output	relevant / unrelated
ConcisenessEvaluator	input, output	concise / verbose / too_concise
RefusalEvaluator	input, output	refusal / no_refusal
ToolSelectionEvaluator	input, output, tool_name, expected_tool	correct / incorrect
ToolInvocationEvaluator	input, tool_name, tool_call_args	correct / incorrect
ToolResponseHandlingEvaluator	input, tool_response, output	good / bad
exact_match	output, expected	(code-based, no LLM)
MatchesRegex(pattern=...)	output	(code-based, no LLM)

To learn more about LLM Evals, see the evals quickstart.

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Reference Documentation

Full API Reference

Complete API documentation for evaluators, metrics, and LLM classification