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

# Decision models

> Classify text, choose a route, and score requests with Laya on Model APIs

Decision models evaluate questions about your input and return typed answers your application can use. Use them to classify requests, choose a handler, or score content against a rubric.

Baseten hosts Laya, a decision model developed by ConvAI Innovations, through the [System One API](/reference/inference-api/system-one). You don't need to deploy a model. Send text in `state` and define the questions to answer in `questions`.

## Supported models

| Model | Slug |
| - | - |
| Laya | `decision-models/laya` |

## Question types

| Type | Use it to | Read the answer from |
| - | - | - |
| `choice` | Choose one option, such as a support team. | `choice` and `probabilities` |
| `noul` | Evaluate a yes/no question. | `noul`, the probability of yes from 0 to 1 |
| `score` | Rate text against ordered levels you define. | `score`, `legend`, and `probabilities` |

You can include several named questions in one request. Each answer appears under its question's name in the response. Define narrow questions and use application logic to combine the results.

## Send a decision request

Create a [Baseten API key](https://app.baseten.co/settings/api_keys) and set the `BASETEN_API_KEY` environment variable. This example routes a support request, checks whether the customer wants a refund, and scores urgency.

Send plain text in `state`.

<Tabs>
  <Tab title="Python">
    **To evaluate the support request**:

    Install the [Baseten Performance Client](/reference/sdk/performance-client/overview):

    ```bash theme={"system"}
    python3 -m pip install baseten-performance-client
    ```

    Save this example as `decide.py` and run `python3 decide.py`.

    ```python decide.py theme={"system"}
    import os

    from baseten_performance_client import PerformanceClient

    client = PerformanceClient(
        base_url="https://inference.baseten.co",
        api_key=os.environ["BASETEN_API_KEY"],
    )

    body = {
        "model": "decision-models/laya",
        "state": "I was charged twice for my subscription. Please refund the duplicate charge.",
        "questions": {
            "route": {
                "type": "choice",
                "instructions": "Which team should handle this request?",
                "criteria": {
                    "billing": "Billing, payments, and refunds",
                    "technical_support": "Technical troubleshooting"
                }
            },
            "refund": {
                "type": "noul",
                "instructions": "Is the customer asking for a refund?"
            },
            "urgency": {
                "type": "score",
                "instructions": "How urgent is the request?",
                "criteria": [
                    "Routine request",
                    "Needs attention today",
                    "Immediate emergency"
                ]
            }
        }
    }

    response = client.batch_system_one(requests=[body])
    result = response.data[0]

    print(result["answers"]["route"]["choice"])
    print(result["answers"]["refund"]["noul"])
    print(result["answers"]["urgency"]["score"])
    ```

    Pass one request in a list and read its result from `response.data[0]`.

    ```text Output theme={"system"}
    billing
    0.7195
    1.5515
    ```
  </Tab>

  <Tab title="cURL">
    **To evaluate the support request**:

    ```bash Request theme={"system"}
    curl --fail-with-body https://inference.baseten.co/v1/systemone \
      -H "Authorization: Bearer $BASETEN_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
      "model": "decision-models/laya",
      "state": "I was charged twice for my subscription. Please refund the duplicate charge.",
      "questions": {
        "route": {
          "type": "choice",
          "instructions": "Which team should handle this request?",
          "criteria": {
            "billing": "Billing, payments, and refunds",
            "technical_support": "Technical troubleshooting"
          }
        },
        "refund": {
          "type": "noul",
          "instructions": "Is the customer asking for a refund?"
        },
        "urgency": {
          "type": "score",
          "instructions": "How urgent is the request?",
          "criteria": [
            "Routine request",
            "Needs attention today",
            "Immediate emergency"
          ]
        }
      }
    }'
    ```
  </Tab>
</Tabs>

The `answers` object includes these typed values (other response fields are omitted):

```json Answers theme={"system"}
{
  "route": {
    "type": "choice",
    "choice": "billing",
    "probabilities": {
      "billing": 0.8044,
      "technical_support": 0.1956
    }
  },
  "refund": {
    "type": "noul",
    "noul": 0.7195
  },
  "urgency": {
    "type": "score",
    "score": 1.5515,
    "legend": {
      "0": "Routine request",
      "1": "Needs attention today",
      "2": "Immediate emergency"
    },
    "probabilities": {
      "0": 0.0488,
      "1": 0.3509,
      "2": 0.6003
    }
  }
}
```

A `score` can fall between rubric levels. A `noul` value is a probability, so your application chooses the threshold for a yes/no decision.

See the [System One reference](/reference/inference-api/system-one) for request fields and response shapes.

## Usage

The response reports input tokens in `usage.input_tokens` and reports `usage.output_tokens` as `0`.

See [Model API usage](/inference/model-apis/pricing-and-limits#usage) to track requests and tokens.