List

GET

scoring_system

calibrate

import PiClient from 'withpi';

const client = new PiClient({
  apiKey: process.env['WITHPI_API_KEY'], // This is the default and can be omitted
});

async function main() {
  const calibrates = await client.scoringSystem.calibrate.list();

  console.log(calibrates);
}

main();

[
  {
    "calibrated_scoring_spec": {
      "description": "Write a children's story communicating a simple life lesson.",
      "dimensions": [
        {
          "description": "dimension1 description",
          "label": "dimension1",
          "sub_dimensions": [
            {
              "description": "subdimension1 description",
              "label": "subdimension1",
              "scoring_type": "PI_SCORER"
            }
          ]
        }
      ],
      "name": "Sample Scoring Spec"
    },
    "detailed_status": [
      "Downloading model",
      "Tuning prompt"
    ],
    "job_id": "1234abcd",
    "state": "RUNNING"
  }
]

Authorizations

x-api-key

string

header

required

Query Parameters

state

enum<string> | null

Filter jobs by state

Available options:

QUEUED,

RUNNING,

DONE,

ERROR,

CANCELLED

Response

200

application/json

Successful Response

detailed_status

string[]

required

Detailed status of the job

Example:

["Downloading model", "Tuning prompt"]

job_id

string

required

The job id

Example:

"1234abcd"

state

enum<string>

required

Current state of the job

Available options:

QUEUED,

RUNNING,

DONE,

ERROR,

CANCELLED

calibrated_scoring_spec

object | null

The calibrated scoring spec

calibrated_scoring_spec.description

string

required

The application description

Example:

"Write a children's story communicating a simple life lesson."

calibrated_scoring_spec.dimensions

object[]

required

The dimensions of the scoring spec

calibrated_scoring_spec.dimensions.description

string

required

The description of the dimension

Example:

"Relevance of the response"

calibrated_scoring_spec.dimensions.label

string

required

The label of the dimension

Example:

"Relevance"

calibrated_scoring_spec.dimensions.sub_dimensions

object[]

required

The sub dimensions of the dimension

calibrated_scoring_spec.dimensions.sub_dimensions.description

string

required

The description of the dimension

Example:

"Is the response relevant to the prompt?"

calibrated_scoring_spec.dimensions.sub_dimensions.label

string

required

The label of the dimension

Example:

"Relevance to Prompt"

calibrated_scoring_spec.dimensions.sub_dimensions.scoring_type

enum<string>

required

The type of scoring performed for this dimension

Available options:

PI_SCORER,

PYTHON_CODE,

CUSTOM_MODEL_SCORER

calibrated_scoring_spec.dimensions.sub_dimensions.custom_model_id

string | null

The ID of the custom model to use for scoring. Only relevant for scoring_type of CUSTOM_MODEL_SCORER

Example:

"your-model-id"

calibrated_scoring_spec.dimensions.sub_dimensions.parameters

number[] | null

The learned parameters for the scoring method. This represents piecewise linear interpolation between [0, 1].

Example:

[
  0.14285714285714285,
  0.2857142857142857,
  0.42857142857142855,
  0.5714285714285714,
  0.7142857142857143,
  0.8571428571428571
]

calibrated_scoring_spec.dimensions.sub_dimensions.python_code

string | null

The PYTHON code associated the PYTHON_CODE DimensionScoringType.

Example:

"\ndef score(response_text: str, input_text: str, kwargs: dict) -> dict:\n word_count = len(response_text.split())\n if word_count > 10:\n return {\"score\": 0.2, \"explanation\": \"Response has more than 10 words\"}\n elif word_count > 5:\n return{\"score\": 0.6, \"explanation\": \"Response has more than 5 words\"}\n else:\n return {\"score\": 1, \"explanation\": \"Response has 5 or fewer words\"}\n"

calibrated_scoring_spec.dimensions.sub_dimensions.weight

number | null

The weight of the subdimension. The sum of subdimension weights will be normalized to one internally. A higher weight counts for more when aggregating this subdimension into the parent dimension.

Example:

1

Example:

{
  "description": "subdimension1 description",
  "label": "subdimension1",
  "scoring_type": "PI_SCORER"
}

calibrated_scoring_spec.dimensions.parameters

number[] | null

The learned parameters for the scoring method. This represents piecewise linear interpolation between [0, 1].

Example:

[
  0.14285714285714285,
  0.2857142857142857,
  0.42857142857142855,
  0.5714285714285714,
  0.7142857142857143,
  0.8571428571428571
]

calibrated_scoring_spec.dimensions.weight

number | null

The weight of the dimension The sum of dimension weights will be normalized to one internally. A higher weight counts for more when aggregating this dimension is aggregated into the final score.

Example:

1

Example:

[
  {
    "description": "dimension1 description",
    "label": "dimension1",
    "sub_dimensions": [
      {
        "description": "subdimension1 description",
        "label": "subdimension1",
        "scoring_type": "PI_SCORER"
      }
    ]
  }
]

calibrated_scoring_spec.name

string

required

The name of the scoring spec

Example:

"Sample Scoring Spec"

Generate Start Job

import PiClient from 'withpi';

const client = new PiClient({
  apiKey: process.env['WITHPI_API_KEY'], // This is the default and can be omitted
});

async function main() {
  const calibrates = await client.scoringSystem.calibrate.list();

  console.log(calibrates);
}

main();

[
  {
    "calibrated_scoring_spec": {
      "description": "Write a children's story communicating a simple life lesson.",
      "dimensions": [
        {
          "description": "dimension1 description",
          "label": "dimension1",
          "sub_dimensions": [
            {
              "description": "subdimension1 description",
              "label": "subdimension1",
              "scoring_type": "PI_SCORER"
            }
          ]
        }
      ],
      "name": "Sample Scoring Spec"
    },
    "detailed_status": [
      "Downloading model",
      "Tuning prompt"
    ],
    "job_id": "1234abcd",
    "state": "RUNNING"
  }
]

Get Started

Client Libraries

API Reference

Authorizations

Query Parameters

Response