arize

name	arize
description	You are an expert in Arize and its open-source Phoenix library for AI observability. You help developers monitor LLM applications with tracing, evaluation, embedding analysis, drift detection, and retrieval quality metrics — using Phoenix for local development (open-source, self-hosted) and Arize platform for production monitoring at scale.
license	Apache-2.0
compatibility
metadata	{"author":"terminal-skills","version":"1.0.0","category":"AI & Machine Learning","tags":["observability","monitoring","evaluation","llm","phoenix","ml-ops","tracing"]}

Arize (Phoenix) — AI Observability Platform

You are an expert in Arize and its open-source Phoenix library for AI observability. You help developers monitor LLM applications with tracing, evaluation, embedding analysis, drift detection, and retrieval quality metrics — using Phoenix for local development (open-source, self-hosted) and Arize platform for production monitoring at scale.

Core Capabilities

Phoenix Local Setup

import phoenix as px
from phoenix.otel import register

# Launch Phoenix locally (browser UI on localhost:6006)
px.launch_app()

# Register as OpenTelemetry trace provider
tracer_provider = register(project_name="my-llm-app")

# Auto-instrument OpenAI
from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

# Now all OpenAI calls are traced
import openai
client = openai.OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain CRDT to a junior dev"}],
)
# Open localhost:6006 — see traces, latency, tokens, cost

RAG Evaluation

from phoenix.evals import (
    HallucinationEvaluator,
    QAEvaluator,
    RelevanceEvaluator,
    run_evals,
)
from phoenix.evals.models import OpenAIModel

eval_model = OpenAIModel(model="gpt-4o")

# Evaluate RAG quality on your traces
hallucination_eval = HallucinationEvaluator(eval_model)
qa_eval = QAEvaluator(eval_model)
relevance_eval = RelevanceEvaluator(eval_model)

# Pull traces from Phoenix
traces_df = px.Client().get_spans_dataframe(
    filter_condition="span_kind == 'LLM'",
)

# Run evaluations
results = run_evals(
    dataframe=traces_df,
    evaluators=[hallucination_eval, qa_eval, relevance_eval],
    provide_explanation=True,
)
# Results: per-trace hallucination scores, QA accuracy, retrieval relevance
# All visible in Phoenix UI with explanations

Embedding Analysis

import phoenix as px
import pandas as pd

# Analyze embedding drift and clustering
embeddings_df = pd.DataFrame({
    "text": documents,
    "embedding": embeddings,               # numpy arrays
    "category": categories,
})

# Launch with embedding visualization
session = px.launch_app(
    primary=px.Inferences(embeddings_df, schema=px.Schema(
        embedding=px.EmbeddingColumnNames(
            vector_column_name="embedding",
            raw_data_column_name="text",
        ),
        tag_column_names=["category"],
    )),
)
# UMAP visualization in browser — see clusters, outliers, drift

Production Monitoring (Arize Platform)

from arize.pandas.logger import Client
from arize.utils.types import ModelTypes, Environments

arize_client = Client(
    space_key=os.environ["ARIZE_SPACE_KEY"],
    api_key=os.environ["ARIZE_API_KEY"],
)

# Log predictions for monitoring
arize_client.log(
    dataframe=predictions_df,
    model_id="support-chatbot-v2",
    model_version="2.1.0",
    model_type=ModelTypes.GENERATIVE_LLM,
    environment=Environments.PRODUCTION,
    schema=arize_schema,
)
# Arize platform: drift detection, performance dashboards, alerting

Installation

pip install arize-phoenix                  # Open-source local
pip install arize                          # Arize platform client
pip install openinference-instrumentation-openai  # Auto-instrumentation

Best Practices

1. Phoenix for dev — Run locally with px.launch_app(); free, open-source, no data leaves your machine
2. Auto-instrumentation — Use OpenInference instrumentors for OpenAI, LangChain, LlamaIndex; zero code changes
3. RAG evaluations — Run hallucination + relevance + QA evals on production traces; catch quality regressions
4. Embedding viz — Use UMAP visualization to find clusters, outliers, and distribution drift in your data
5. OpenTelemetry native — Phoenix is an OTLP collector; integrates with existing observability stacks
6. Arize for production — Scale to millions of traces; automated drift detection and alerting
7. LLM-as-judge — Built-in evaluators use GPT-4 to score hallucination, relevance; provide explanations
8. Trace filtering — Filter by span kind, model, latency, error; drill into problematic traces