> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Instructor Tracing

## Install

```bash theme={null}
pip install openinference-instrumentation-instructor instructor
```

Be sure you also install the OpenInference library for the underlying model you're using along with Instructor. For example, if you're using OpenAI calls directly, you would also add: `openinference-instrumentation-openai`

## Setup

Connect to your Phoenix instance using the register function.

```python theme={null}
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
```

## Run Instructor

From here you can use instructor as normal.

```python expandable theme={null}
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.