Import Scoring Spec

POST

scoring_system

import_spec

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 scoringSpec = await client.scoringSystem.importSpec({
    hf_scoring_spec_name: 'withpi/tldr_scoring_system',
  });

  console.log(scoringSpec.description);
}

main();

{
  "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"
}

Authorizations

x-api-key

string

header

required

Body

application/json

hf_scoring_spec_name

string

required

Huggingface dataset e.g. withpi/my_scoring_system containing the Scoring spec. This is only needed for the source=HUGGINGFACE.

Example:

"withpi/tldr_scoring_system"

hf_token

string | null

Huggingface token to use if you want to read to your own HF organization. This is only needed for the source=HUGGINGFACE.

source

enum<string>

Source of where to get the Scoring spec

Available options:

HUGGINGFACE

Response

200

application/json

Successful Response

description

string

required

The application description

Example:

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

dimensions

object[]

required

The dimensions of the scoring spec

dimensions.description

string

required

The description of the dimension

Example:

"Relevance of the response"

dimensions.label

string

required

The label of the dimension

Example:

"Relevance"

dimensions.sub_dimensions

object[]

required

The sub dimensions of the dimension

dimensions.sub_dimensions.description

string

required

The description of the dimension

Example:

"Is the response relevant to the prompt?"

dimensions.sub_dimensions.label

string

required

The label of the dimension

Example:

"Relevance to Prompt"

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

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"

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
]

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"

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"
}

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
]

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"
      }
    ]
  }
]

name

string

required

The name of the scoring spec

Example:

"Sample Scoring Spec"

Stream Messages List

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 scoringSpec = await client.scoringSystem.importSpec({
    hf_scoring_spec_name: 'withpi/tldr_scoring_system',
  });

  console.log(scoringSpec.description);
}

main();

{
  "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"
}

Get Started

Client Libraries

API Reference

Authorizations

Body

Response