Instructor

Launch Phoenix

Notebook

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

Connect your notebook to Phoenix:

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
)

By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See Persistence or use one of the other deployment options to retain traces.

Command Line

Launch your local Phoenix instance:

python3 -m phoenix.server.main serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Connect your application to your instance using:

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  endpoint="http://localhost:6006",
)

See Quickstart: Deployment for more details

Docker

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Connect your application to your instance using:

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  endpoint="http://localhost:6006",
)

For more info on using Phoenix with Docker, see Docker

app.phoenix.arize.com

If you don't want to host an instance of Phoenix yourself or use a notebook instance, you can use a persistent instance provided on our site. Sign up for an Arize Phoenix account athttps://app.phoenix.arize.com/login

Install packages:

pip install arize-phoenix-otel

Connect your application to your cloud instance:

import os
from phoenix.otel import register

# Add Phoenix API Key for tracing
os.environ["PHOENIX_CLIENT_HEADERS"] = "api_key=...:..."

# configure the Phoenix tracer
register(
  project_name="my-llm-app", # Default is 'default'
  endpoint="https://app.phoenix.arize.com/v1/traces",
)

Your Phoenix API key can be found on the Keys section of your dashboard.

Install

pip install openinference-instrumentation-instructor instructor

Setup

Initialize the InstructorInstrumentor before your application code.

from openinference.instrumentation.instructor import InstructorInstrumentor

InstructorInstrumentor().instrument(tracer_provider=tracer_provider)

Be sure you also instrument the underlying model you're using along with Instructor. For example, if you're using OpenAI calls directly, you would also add:

from openinference.instrumentation.openai import OpenAIInstrumentor

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

Run Instructor

From here you can use instructor as normal.

import instructor
from pydantic import BaseModel
from openai import OpenAI


# Define your desired output structure
class UserInfo(BaseModel):
    name: str
    age: int


# Patch the OpenAI client
client = instructor.from_openai(OpenAI())

# Extract structured data from natural language
user_info = client.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=UserInfo,
    messages=[{"role": "user", "content": "John Doe is 30 years old."}],
)

print(user_info.name)
#> John Doe
print(user_info.age)
#> 30

Observe

Now that you have tracing setup, all invocations of your underlying model (completions, chat completions, embeddings) and instructor triggers will be streamed to your running Phoenix for observability and evaluation.

Resources