arize-phoenix-client 2.13.0

LLM Observability

arize-phoenix-client

Phoenix Client provides an interface for interacting with the Phoenix platform via its REST API, enabling you to manage datasets, run experiments, analyze traces, and collect feedback programmatically.

Features

• REST API Interface - Interact with Phoenix's OpenAPI REST interface
• Prompts - Create, version, and invoke prompt templates
• Datasets - Create and append to datasets from DataFrames, CSV files, or dictionaries
• Experiments - Run evaluations and track experiment results
• Eval CI (pytest) - Run LLM evals as ordinary pytest tests and record them as Phoenix experiments (pip install "arize-phoenix-client[pytest]")
• Spans - Query and analyze traces with powerful filtering
• Annotations - Add human feedback and automated evaluations
• Evaluation Helpers - Extract span data in formats optimized for RAG evaluation workflows

Installation

Install the Phoenix Client using pip:

pip install arize-phoenix-client

Getting Started

Environment Variables

Configure the Phoenix Client using environment variables for seamless use across different environments:

# For local Phoenix server (default)
export PHOENIX_BASE_URL="http://localhost:6006"

# Cloud Instance
export PHOENIX_API_KEY="your-api-key"
export PHOENIX_BASE_URL="https://app.phoenix.arize.com/s/your-space"

# For custom Phoenix instances with API key authentication
export PHOENIX_BASE_URL="https://your-phoenix-instance.com"
export PHOENIX_API_KEY="your-api-key"

# Customize headers
export PHOENIX_CLIENT_HEADERS="Authorization=Bearer your-api-key,custom-header=value"

Credential File Discovery (.env.phoenix)

When a setting is not provided by argument or environment variable, the client looks for a .env.phoenix file in the current working directory — walking up toward the filesystem root and stopping at the first match — and reads PHOENIX_-prefixed keys from it (dotenv format):

# .env.phoenix
PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
PHOENIX_API_KEY=your-api-key

Explicit arguments and environment variables always take precedence — the file never overrides anything already set. Set PHOENIX_DISCOVER_CONFIG=false to disable discovery entirely.

Credentials (PHOENIX_API_KEY and PHOENIX_CLIENT_HEADERS) and server location (PHOENIX_COLLECTOR_ENDPOINT, OTEL_EXPORTER_OTLP_ENDPOINT, PHOENIX_HOST, and PHOENIX_PORT) are each resolved as a group from one source tier. If explicit or process credentials are paired with an endpoint from .env.phoenix, the client warns once and continues without logging credential values.

Discovery results, including a missing file, are cached per working directory for the process lifetime. Long-running processes can call phoenix.client.utils.config.clear_env_file_cache() after creating or changing the file.

Client Initialization

The client automatically reads environment variables, or you can override them:

from phoenix.client import Client, AsyncClient

# Automatic configuration from environment variables
client = Client()

client = Client(base_url="http://localhost:6006")  # Local Phoenix server

# Cloud instance with API key
client = Client(
    base_url="https://app.phoenix.arize.com/s/your-space",
    api_key="your-api-key"
)

# Custom authentication headers
client = Client(
    base_url="https://your-phoenix-instance.com",
    headers={"Authorization": "Bearer your-api-key"}
)

# Asynchronous client (same configuration options)
async_client = AsyncClient()
async_client = AsyncClient(base_url="http://localhost:6006")
async_client = AsyncClient(
    base_url="https://app.phoenix.arize.com/s/your-space",
    api_key="your-api-key"
)

Resources

The Phoenix Client organizes functionality into resources that correspond to key Phoenix platform features. Each resource provides specialized methods for managing different types of data:

Prompts

Manage prompt templates and versions:

from phoenix.client import Client
from phoenix.client.types import PromptVersion

client = Client()

content = """
You're an expert educator in {{ topic }}. Summarize the following article
in a few concise bullet points that are easy for beginners to understand.

{{ article }}
"""

prompt = client.prompts.create(
    name="article-bullet-summarizer",
    version=PromptVersion(
        messages=[{"role": "user", "content": content}],
        model_name="gpt-4o-mini",
    ),
    prompt_description="Summarize an article in a few bullet points"
)

# Retrieve and use prompts
prompt = client.prompts.get(prompt_identifier="article-bullet-summarizer")

# Format the prompt with variables
prompt_vars = {
    "topic": "Sports",
    "article": "Moises Henriques, the Australian all-rounder, has signed to play for Surrey in this summer's NatWest T20 Blast. He will join after the IPL and is expected to strengthen the squad throughout the campaign."
}
formatted_prompt = prompt.format(variables=prompt_vars)

# Make a request with your Prompt using OpenAI
from openai import OpenAI
oai_client = OpenAI()
resp = oai_client.chat.completions.create(**formatted_prompt)
print(resp.choices[0].message.content)

Datasets

Manage evaluation datasets and examples for experiments and evaluation:

from phoenix.client import Client
import pandas as pd

client = Client()

# List all available datasets
datasets = client.datasets.list()
for dataset in datasets:
    print(f"Dataset: {dataset['name']} ({dataset['example_count']} examples)")

# Get a specific dataset with all examples
dataset = client.datasets.get_dataset(dataset="qa-evaluation")
print(f"Dataset {dataset.name} has {len(dataset)} examples")

# Convert dataset to pandas DataFrame for analysis
df = dataset.to_dataframe()
print(df.columns)  # Index(['input', 'output', 'metadata'], dtype='object')

# Create a new dataset from dictionaries
dataset = client.datasets.create_dataset(
    name="customer-support-qa",
    dataset_description="Q&A dataset for customer support evaluation",
    inputs=[
        {"question": "How do I reset my password?"},
        {"question": "What's your return policy?"},
        {"question": "How do I track my order?"}
    ],
    outputs=[
        {"answer": "You can reset your password by clicking the 'Forgot Password' link on the login page."},
        {"answer": "We offer 30-day returns for unused items in original packaging."},
        {"answer": "You can track your order using the tracking number sent to your email."}
    ],
    metadata=[
        {"category": "account", "difficulty": "easy"},
        {"category": "policy", "difficulty": "medium"},
        {"category": "orders", "difficulty": "easy"}
    ]
)

# Create dataset from pandas DataFrame
df = pd.DataFrame({
    "prompt": ["Hello", "Hi there", "Good morning"],
    "response": ["Hi! How can I help?", "Hello! What can I do for you?", "Good morning! How may I assist?"],
    "sentiment": ["neutral", "positive", "positive"],
    "length": [20, 25, 30]
})

dataset = client.datasets.create_dataset(
    name="greeting-responses",
    dataframe=df,
    input_keys=["prompt"],           # Columns to use as input
    output_keys=["response"],        # Columns to use as expected output
    metadata_keys=["sentiment", "length"]  # Additional metadata columns
)

Traces

Retrieve traces for a project with optional filtering and sorting:

from phoenix.client import Client

client = Client()

# Get the latest 100 traces
traces = client.traces.get_traces(project_identifier="my-llm-app")
for trace in traces:
    print(f"Trace {trace.trace_id}: {trace.status} ({trace.latency_ms}ms)")

# Filter by time range
from datetime import datetime, timedelta

traces = client.traces.get_traces(
    project_identifier="my-llm-app",
    start_time=datetime.now() - timedelta(hours=24),
    end_time=datetime.now(),
    sort="latency_ms",
    order="desc",
    limit=50,
)

# Include full span details
traces = client.traces.get_traces(
    project_identifier="my-llm-app",
    include_spans=True,  # caution: can increase response size significantly
    limit=10,
)

# Filter by session
traces = client.traces.get_traces(
    project_identifier="my-llm-app",
    session_id="my-session-id",
)

Async usage:

from phoenix.client import AsyncClient

async_client = AsyncClient()

traces = await async_client.traces.get_traces(
    project_identifier="my-llm-app",
    limit=50,
)

Parameter	Type	Default	Description
project_identifier	str	—	Project name or ID — required
start_time	datetime | None	None	Inclusive lower bound on trace start time
end_time	datetime | None	None	Exclusive upper bound on trace start time
sort	"start_time" | "latency_ms" | None	None	Sort field (server defaults to "start_time")
order	"asc" | "desc" | None	None	Sort direction (server defaults to "desc")
include_spans	bool	False	Include full span details for each trace
session_id	str | Sequence[str] | None	None	Filter by session ID(s) or GlobalID(s)
limit	int	100	Maximum number of traces to return
timeout	int | None	60	Request timeout in seconds

Note: Requires Phoenix server >= 13.15.0.

Spans

Query for spans and annotations from your projects for custom evaluation and annotation workflows:

from phoenix.client import Client
from datetime import datetime, timedelta

client = Client()

# Get spans as pandas DataFrame for analysis
spans_df = client.spans.get_spans_dataframe(
    project_identifier="my-llm-app",
    limit=1000,
    root_spans_only=True,  # Only get top-level spans
    start_time=datetime.now() - timedelta(hours=24)
)

# Get span annotations as DataFrame
annotations_df = client.spans.get_span_annotations_dataframe(
    spans_dataframe=spans_df,  # Use spans from previous query
    project_identifier="my-llm-app",
    include_annotation_names=["relevance", "accuracy"],  # Only specific annotations
    exclude_annotation_names=["note"]  # Exclude UI notes
)

Annotations

Add annotations to spans for evaluation, user feedback, and custom annotation workflows:

from phoenix.client import Client

client = Client()

# Add a single annotation with human feedback
client.spans.add_span_annotation(
    span_id="span-123",
    annotation_name="helpfulness",
    annotator_kind="HUMAN",
    label="helpful",
    score=0.9,
    explanation="Response directly answered the user's question"
)

# Bulk annotation logging for multiple spans
annotations = [
    {
        "name": "sentiment",
        "span_id": "span-123",
        "annotator_kind": "LLM",
        "result": {"label": "positive", "score": 0.8}
    },
    {
        "name": "accuracy",
        "span_id": "span-456",
        "annotator_kind": "HUMAN",
        "result": {"label": "accurate", "score": 0.95}
    },
]
client.spans.log_span_annotations(span_annotations=annotations)

Sessions

Retrieve and annotate conversation sessions:

from phoenix.client import Client

client = Client()

# List sessions for a project
sessions = client.sessions.list(project_name="my-llm-app")
for session in sessions:
    print(f"Session: {session['session_id']}")

# Get conversation turns for a session
turns = client.sessions.get_session_turns(session_id="my-session-id")
for turn in turns:
    print(f"Input: {turn.get('input', {}).get('value')}")
    print(f"Output: {turn.get('output', {}).get('value')}")

# Add a session-level annotation
client.sessions.add_session_annotation(
    session_id="my-session-id",
    annotation_name="user-satisfaction",
    label="satisfied",
    score=0.9,
    annotator_kind="HUMAN",
)

Experiments

Run tasks across datasets and evaluate their outputs:

from phoenix.client import Client

client = Client()

# Get an existing dataset to run the experiment on
dataset = client.datasets.get_dataset(dataset="my-dataset")

# Define a task function
def my_task(example):
    # Your LLM call or business logic here
    return f"Result for: {example['input']['question']}"

# Run an experiment
experiment = client.experiments.run_experiment(
    dataset=dataset,
    task=my_task,
    experiment_name="my-experiment",
)

# Retrieve an existing experiment
ran_experiment = client.experiments.get_experiment(experiment_id="my-experiment-id")
for run in ran_experiment["task_runs"]:
    print(f"Output: {run['output']}, Error: {run['error']}")

Beyond the batch run_experiment loop, you can post runs and evaluations one at a time with log_run and log_evaluation. These are the incremental primitives the pytest plugin builds on, and they are useful for any consumer that produces results progressively rather than all at once:

from datetime import datetime, timezone

# Record a single run against an existing experiment and example
run = client.experiments.log_run(
    experiment_id="my-experiment-id",
    dataset_example_id="my-example-id",
    output="the task output",
    start_time=datetime.now(timezone.utc),
    end_time=datetime.now(timezone.utc),
)

# Attach an evaluation (annotation) to that run
client.experiments.log_evaluation(
    experiment_run_id=run["id"],
    name="exact_match",
    annotator_kind="CODE",
    score=1.0,
    label="correct",
)

Both methods are available on AsyncClient as awaitable coroutines with the same signatures.

Eval CI (pytest)

Write LLM evals as ordinary pytest tests and record each marked test as a run in a Phoenix experiment, so the same suite that gates your pull requests also builds a history of results in Phoenix. Install the pytest extra and mark tests with @pytest.mark.phoenix:

pip install "arize-phoenix-client[pytest]" pytest

import pytest
from phoenix.client.pytest import evaluate, log_evaluation, log_output


@pytest.mark.phoenix(dataset="qa-suite")
@pytest.mark.parametrize("question,expected", [("2+2?", "4")], ids=["arithmetic"])
def test_answers(question, expected):
    result = my_app(question)
    log_output(result)
    log_evaluation(name="exact_match", score=float(result == expected))
    assert result == expected

See the Eval CI with pytest guide for marker options, environment variables, and a CI recipe.

Projects

Manage Phoenix projects that organize your AI application data:

from phoenix.client import Client

client = Client()

# List all projects
projects = client.projects.list()
for project in projects:
    print(f"Project: {project['name']} (ID: {project['id']})")

# Create a new project
new_project = client.projects.create(
    name="Customer Support Bot",
    description="Traces and evaluations for our customer support chatbot"
)
print(f"Created project with ID: {new_project['id']}")

Documentation

• Full Documentation - Complete API reference and guides
• Phoenix Docs - Main Phoenix documentation
• GitHub Repository - Source code and examples

Community

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• 🌍 Join our Slack community.
• 💡 Ask questions and provide feedback in the #phoenix-support channel.
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