> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Arize-ai/openinference/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI Chat Completions

> Instrument OpenAI chat completions with OpenInference

This example demonstrates how to instrument OpenAI's chat completions API with OpenInference tracing.

## Prerequisites

* Python 3.9+
* OpenAI API key
* Phoenix or another OpenTelemetry collector running

## Installation

<Steps>
  <Step title="Install dependencies">
    ```bash theme={null}
    pip install openai \
      openinference-instrumentation-openai \
      opentelemetry-sdk \
      opentelemetry-exporter-otlp
    ```
  </Step>

  <Step title="Set environment variables">
    ```bash theme={null}
    export OPENAI_API_KEY="your-api-key"
    ```
  </Step>
</Steps>

## Complete Example

```python theme={null}
import openai
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

from openinference.instrumentation import using_attributes
from openinference.instrumentation.openai import OpenAIInstrumentor

# Configure OpenTelemetry with OTLP exporter
endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

# Instrument OpenAI
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

if __name__ == "__main__":
    client = openai.OpenAI()
    
    # Use context attributes for session tracking, metadata, and prompt templates
    with using_attributes(
        session_id="my-test-session",
        user_id="my-test-user",
        metadata={
            "test-int": 1,
            "test-str": "string",
            "test-list": [1, 2, 3],
            "test-dict": {
                "key-1": "val-1",
                "key-2": "val-2",
            },
        },
        tags=["tag-1", "tag-2"],
        prompt_template="Who won the soccer match in {city} on {date}",
        prompt_template_version="v1.0",
        prompt_template_variables={
            "city": "Johannesburg",
            "date": "July 11th",
        },
    ):
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "Write a haiku."}],
            max_tokens=20,
        )
        print(response.choices[0].message.content)
```

## Key Features

### Automatic Instrumentation

The `OpenAIInstrumentor` automatically traces all OpenAI API calls without requiring code changes to your OpenAI usage.

### Context Attributes

Use `using_attributes()` context manager to add:

* **Session tracking**: `session_id` and `user_id`
* **Metadata**: Custom key-value pairs for filtering and analysis
* **Tags**: Labels for categorization
* **Prompt templates**: Track template versions and variables

### Multiple Exporters

This example uses both:

* `OTLPSpanExporter`: Send traces to Phoenix or any OTLP-compatible backend
* `ConsoleSpanExporter`: Print traces to console for debugging

## Next Steps

* Explore [streaming responses](/examples/python/openai-streaming)
* Learn about [function calling](/examples/python/openai-functions)
* See [LangChain integration](/examples/python/langchain-rag)