# Sessions (Python)

Track multi-turn conversations by grouping traces with session IDs.

## Setup

```python
from openinference.instrumentation import using_session

with using_session(session_id="user_123_conv_456"):
    response = llm.invoke(prompt)
```

## Best Practices

**Bad: Only parent span gets session ID**

```python
from openinference.semconv.trace import SpanAttributes
from opentelemetry import trace

span = trace.get_current_span()
span.set_attribute(SpanAttributes.SESSION_ID, session_id)
response = client.chat.completions.create(...)
```

**Good: All child spans inherit session ID**

```python
with using_session(session_id):
    response = client.chat.completions.create(...)
    result = my_custom_function()
```

**Why:** `using_session()` propagates session ID to all nested spans automatically.

## Session ID Patterns

```python
import uuid

session_id = str(uuid.uuid4())
session_id = f"user_{user_id}_conv_{conversation_id}"
session_id = f"debug_{timestamp}"
```

Good: `str(uuid.uuid4())`, `"user_123_conv_456"`
Bad: `"session_1"`, `"test"`, empty string

## Multi-Turn Chatbot Example

```python
import uuid
from openinference.instrumentation import using_session

session_id = str(uuid.uuid4())
messages = []

def send_message(user_input: str) -> str:
    messages.append({"role": "user", "content": user_input})

    with using_session(session_id):
        response = client.chat.completions.create(
            model="gpt-4",
            messages=messages
        )

    assistant_message = response.choices[0].message.content
    messages.append({"role": "assistant", "content": assistant_message})
    return assistant_message
```

## Additional Attributes

```python
from openinference.instrumentation import using_attributes

with using_attributes(
    user_id="user_123",
    session_id="conv_456",
    metadata={"tier": "premium", "region": "us-west"}
):
    response = llm.invoke(prompt)
```

## LangChain Integration

LangChain threads are automatically recognized as sessions:

```python
from langchain.chat_models import ChatOpenAI

response = llm.invoke(
    [HumanMessage(content="Hi!")],
    config={"metadata": {"thread_id": "user_123_thread"}}
)
```

Phoenix recognizes: `thread_id`, `session_id`, `conversation_id`

## See Also

- **TypeScript sessions:** `sessions-typescript.md`
- **Session docs:** https://docs.arize.com/phoenix/tracing/sessions