Phoenix
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  • Arize Phoenix
  • Quickstarts
  • User Guide
  • Environments
  • Phoenix Demo
  • 🔭Tracing
    • Overview: Tracing
    • Quickstart: Tracing
      • Quickstart: Tracing (Python)
      • Quickstart: Tracing (TS)
    • Features: Tracing
      • Projects
      • Annotations
      • Sessions
    • Integrations: Tracing
    • How-to: Tracing
      • Setup Tracing
        • Setup using Phoenix OTEL
        • Setup using base OTEL
        • Using Phoenix Decorators
        • Setup Tracing (TS)
        • Setup Projects
        • Setup Sessions
      • Add Metadata
        • Add Attributes, Metadata, Users
        • Instrument Prompt Templates and Prompt Variables
      • Annotate Traces
        • Annotating in the UI
        • Annotating via the Client
        • Running Evals on Traces
        • Log Evaluation Results
      • Importing & Exporting Traces
        • Import Existing Traces
        • Export Data & Query Spans
        • Exporting Annotated Spans
      • Advanced
        • Mask Span Attributes
        • Suppress Tracing
        • Filter Spans to Export
        • Capture Multimodal Traces
    • Concepts: Tracing
      • How Tracing Works
      • What are Traces
      • Concepts: Annotations
      • FAQs: Tracing
  • 📃Prompt Engineering
    • Overview: Prompts
      • Prompt Management
      • Prompt Playground
      • Span Replay
      • Prompts in Code
    • Quickstart: Prompts
      • Quickstart: Prompts (UI)
      • Quickstart: Prompts (Python)
      • Quickstart: Prompts (TS)
    • How to: Prompts
      • Configure AI Providers
      • Using the Playground
      • Create a prompt
      • Test a prompt
      • Tag a prompt
      • Using a prompt
    • Concepts: Prompts
  • 🗄️Datasets & Experiments
    • Overview: Datasets & Experiments
    • Quickstart: Datasets & Experiments
    • How-to: Datasets
      • Creating Datasets
      • Exporting Datasets
    • Concepts: Datasets
    • How-to: Experiments
      • Run Experiments
      • Using Evaluators
  • 🧠Evaluation
    • Overview: Evals
      • Agent Evaluation
    • Quickstart: Evals
    • How to: Evals
      • Pre-Built Evals
        • Hallucinations
        • Q&A on Retrieved Data
        • Retrieval (RAG) Relevance
        • Summarization
        • Code Generation
        • Toxicity
        • AI vs Human (Groundtruth)
        • Reference (citation) Link
        • User Frustration
        • SQL Generation Eval
        • Agent Function Calling Eval
        • Agent Path Convergence
        • Agent Planning
        • Agent Reflection
        • Audio Emotion Detection
      • Eval Models
      • Build an Eval
      • Build a Multimodal Eval
      • Online Evals
      • Evals API Reference
    • Concepts: Evals
      • LLM as a Judge
      • Eval Data Types
      • Evals With Explanations
      • Evaluators
      • Custom Task Evaluation
  • 🔍Retrieval
    • Overview: Retrieval
    • Quickstart: Retrieval
    • Concepts: Retrieval
      • Retrieval with Embeddings
      • Benchmarking Retrieval
      • Retrieval Evals on Document Chunks
  • 🌌inferences
    • Quickstart: Inferences
    • How-to: Inferences
      • Import Your Data
        • Prompt and Response (LLM)
        • Retrieval (RAG)
        • Corpus Data
      • Export Data
      • Generate Embeddings
      • Manage the App
      • Use Example Inferences
    • Concepts: Inferences
    • API: Inferences
    • Use-Cases: Inferences
      • Embeddings Analysis
  • ⚙️Settings
    • Access Control (RBAC)
    • API Keys
    • Data Retention
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Platform

  • Tracing
  • Prompts
  • Datasets and Experiments
  • Evals

Software

  • Python Client
  • TypeScript Client
  • Phoenix Evals
  • Phoenix Otel

Resources

  • Container Images
  • X
  • Blue Sky
  • Blog

Integrations

  • OpenTelemetry
  • AI Providers

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Environments

PreviousUser GuideNextOverview: Tracing

Last updated 19 days ago

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The Phoenix app can be run in various environments such as Colab and SageMaker notebooks, as well as be served via the terminal or a docker container.

If you are set up, see to start using Phoenix in your preferred environment.

Phoenix Cloud

provides free-to-use Phoenix instances that are preconfigured for you with 10GBs of storage space. Phoenix Cloud instances are a great starting point, however if you need more storage or more control over your instance, self-hosting options could be a better fit.

If you're using Phoenix Cloud, be sure to set the proper environment variables to connect to your instance:

export PHOENIX_CLIENT_HEADERS = "api_key=ENTER YOUR API KEY"
export PHOENIX_COLLECTOR_ENDPOINT = "https://app.phoenix.arize.com"

Container

See .

Notebooks

To start phoenix in a notebook environment, run:

import phoenix as px

session = px.launch_app()

This will start a local Phoenix server. You can initialize the phoenix server with various kinds of data (traces, inferences).

By default, Phoenix does not persist your data when run in a notebook.

Terminal

If you want to start a phoenix server to collect traces, you can also run phoenix directly from the command line:

phoenix serve

This will start the phoenix server on port 6006. If you are running your instrumented notebook or application on the same machine, traces should automatically be exported to http://127.0.0.1:6006 so no additional configuration is needed. However if the server is running remotely, you will have to modify the environment variable PHOENIX_COLLECTOR_ENDPOINT to point to that machine (e.g. http://<my-remote-machine>:<port>)

Quickstarts
Phoenix Cloud
Self-Hosting
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Phoenix Cloud

Connect to a pre-configured, managed Phoenix instance

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In a Notebook

Run Phoenix in the notebook as you run experiments

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From the Terminal

Run Phoenix via the CLI on your local machine

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As a Container

Self-host your own Phoenix