arize-phoenix-evals 2.10.0

LLM Evaluations

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()

Evaluating Dataframes

import pandas as pd
from phoenix.evals import create_classifier, 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."},
])

# Evaluate the dataframe
results_df = evaluate_dataframe(
    dataframe=df,
    evaluators=[relevance_evaluator, helpfulness_evaluator],
)

print(results_df.head())

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.
• 🗺️ Check out our roadmap to see where we're heading next.