plexus.dashboard.api.models.score_result module

class plexus.dashboard.api.models.score_result.ScoreResult(id: str, value: float, itemId: str, accountId: str, scorecardId: str, confidence: float | None = None, metadata: Dict | None = None, scoringJobId: str | None = None, evaluationId: str | None = None, correct: bool | None = None, client: _BaseAPIClient | None = None)

Bases: BaseModel

Represents a single classification or scoring result in the Plexus dashboard.

ScoreResult is the core data structure for tracking individual scoring operations, used both for real-time scoring and evaluation. It integrates with the GraphQL API to provide:

• Score value tracking with confidence

• Metadata storage for debugging and analysis

• Links to related entities (items, accounts, scorecards)

• Evaluation result tracking

• Batch processing support

Common usage patterns: 1. Creating a single score result:

result = ScoreResult.create(

client=client, value=0.95, itemId=”item-123”, accountId=”acc-456”, scorecardId=”card-789”, metadata={“source”: “phone_call”}

)

2. Batch creation for efficiency:

   results = ScoreResult.batch_create(client, [

   {

   “value”: 0.95, “itemId”: “item-123”, “accountId”: “acc-456”, “scorecardId”: “card-789”

   }, {

   “value”: 0.82, “itemId”: “item-124”, “accountId”: “acc-456”, “scorecardId”: “card-789”

   }

   ])

3. Updating with evaluation results:

   result.update(

   correct=True, evaluationId=”eval-123”, metadata={“human_label”: “Yes”}

   )

ScoreResult is commonly used with: - Evaluation for accuracy testing - ScoringJob for batch processing - LangGraphScore for LLM-based classification

__init__(id: str, value: float, itemId: str, accountId: str, scorecardId: str, confidence: float | None = None, metadata: Dict | None = None, scoringJobId: str | None = None, evaluationId: str | None = None, correct: bool | None = None, client: _BaseAPIClient | None = None)

accountId: str

classmethod batch_create(client: _BaseAPIClient, items: List[Dict]) → List[ScoreResult]

Create multiple score results in a single API request.

confidence: float | None = None

correct: bool | None = None

classmethod create(client: _BaseAPIClient, value: float, itemId: str, accountId: str, scorecardId: str, scoringJobId: str | None = None, evaluationId: str | None = None, **kwargs) → ScoreResult

Create a new score result.

Args:

client: The API client instance value: Score value (required) itemId: ID of scored item (required) accountId: Account context (required) scorecardId: ID of scorecard used (required) scoringJobId: ID of scoring job (optional) evaluationId: ID of evaluation (optional) **kwargs: Optional fields:

• confidence: float

• metadata: dict (will be JSON serialized)

• correct: bool

Note:

Either scoringJobId or evaluationId should be provided, but not required

evaluationId: str | None = None

classmethod fields() → str

Return the GraphQL fields to query for this model

classmethod from_dict(data: Dict[str, Any], client: _BaseAPIClient) → ScoreResult

Create a ScoreResult instance from API response data.

itemId: str

metadata: Dict | None = None

scorecardId: str

scoringJobId: str | None = None

update(**kwargs) → ScoreResult

Update this score result with new values.

value: float