Phoenix Client provides a 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
Spans - Query and analyze traces with powerful filtering
Annotations - Add human feedback and automated evaluations
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"
Client Initialization
The client automatically reads environment variables, or you can override them:
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
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:
import pandas as pd
# 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
)
Spans
Query for spans and annotations from your projects for custom evaluation and annotation workflows:
from datetime import datetime, timedelta
# 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:
# Add a single annotation with human feedback
client.annotations.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.annotations.log_span_annotations(span_annotations=annotations)
Projects
Manage Phoenix projects that organize your AI application data:
# 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']}")
