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

# Interactions

> Track prompts, completions, and metrics

An interaction is a prompt, completion, and any associated feedback or labels. Adaptive logs interactions automatically when you run inference, creating a record you can browse, filter, and use for training.

<Tabs>
  <Tab title="SDK" icon="code">
    ## Automatic logging

    Inference requests are logged automatically:

    ```python theme={null}
    response = adaptive.chat.create(
        messages=[
            {"role": "user", "content": "Hello!"}
        ],
        labels={"project": "support-bot", "version": "v2"},
    )
    ```

    Use `labels` to organize interactions. Labels are key-value pairs for filtering and grouping.

    ## Get the completion ID

    Extract `completion_id` from the response to log feedback:

    ```python theme={null}
    completion_id = response.choices[0].completion_id
    ```

    Pass this ID to [Metrics](/v0.14/core/metrics) methods to associate ratings with the interaction.

    ## Manual logging

    Log interactions for completions generated outside Adaptive:

    ```python theme={null}
    adaptive.interactions.create(
        model="model_key",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello!"}
        ],
        completion="Hi there! How can I help you today?",
        labels={"source": "external"},
    )
    ```

    Use manual logging to import historical data or log completions from external models.

    For multimodal interactions, pass image content parts in `messages` (see [Vision models](/v0.14/core/models#vision-models)):

    ```python theme={null}
    adaptive.interactions.create(
        model="model_key",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {"type": "image_url", "image_url": "data:image/png;base64,..."},
                ],
            }
        ],
        completion="The image shows a bar chart comparing model accuracy across three benchmarks.",
        labels={"source": "external"},
    )
    ```

    <Accordion title="Bulk upload with async">
      Upload large datasets efficiently with async concurrency:

      ```python theme={null}
      import asyncio
      from adaptive_sdk import AsyncAdaptive

      async def upload_data(data: list[dict], labels: dict, max_concurrency: int = 30):
          adaptive = AsyncAdaptive(
              base_url=os.environ["ADAPTIVE_URL"],
              api_key=os.environ["ADAPTIVE_API_KEY"],
          )
          adaptive.set_default_project("my-project")

          semaphore = asyncio.Semaphore(max_concurrency)

          async def upload_one(item):
              async with semaphore:
                  await adaptive.interactions.create(
                      messages=item["messages"],
                      completion=item["completion"],
                      labels=labels,
                  )

          await asyncio.gather(*[upload_one(item) for item in data])

      # Example data format
      data = [
          {
              "messages": [{"role": "user", "content": "Hello!"}],
              "completion": "Hi there!"
          }
      ]
      asyncio.run(upload_data(data, labels={"dataset": "training-v1"}))
      ```
    </Accordion>

    See [SDK Reference](/v0.14/reference/sdk) for all interaction methods.
  </Tab>

  <Tab title="UI" icon="mouse-pointer">
    ## Browse interactions

    Navigate to your project and open the **Interactions** tab to view logged data.

    Filter by label, time range, or feedback values to find specific interactions. Click any row to see the full prompt and completion.

    Use interactions to:

    * Audit model responses
    * Find examples for training datasets
    * Debug unexpected behavior

    Multimodal interactions display images inline, so you can review exactly what the model saw.
  </Tab>
</Tabs>