plexus.dashboard.api.models.aggregated_metrics module

AggregatedMetrics Model - Python representation of the GraphQL AggregatedMetrics type.

This model represents pre-computed aggregation counts for Items, ScoreResults, Tasks, and Evaluations across different time buckets (1, 5, 15, 60 minutes).

Key Features: - Query aggregated metrics by time range - Create or update aggregation records (upsert logic) - Support for multiple record types (items, scoreResults, tasks, evaluations) - Hierarchical time bucketing for efficient queries

class plexus.dashboard.api.models.aggregated_metrics.AggregatedMetrics(id: str, accountId: str, compositeKey: str, recordType: str, timeRangeStart: datetime, timeRangeEnd: datetime, numberOfMinutes: int, count: int, complete: bool, createdAt: datetime, updatedAt: datetime, client: _BaseAPIClient | None = None, scorecardId: str | None = None, scoreId: str | None = None, cost: int | None = None, decisionCount: int | None = None, externalAiApiCount: int | None = None, cachedAiApiCount: int | None = None, errorCount: int | None = None, metadata: Dict | None = None)

Bases: BaseModel

AggregatedMetrics model for interacting with the API.

__init__(id: str, accountId: str, compositeKey: str, recordType: str, timeRangeStart: datetime, timeRangeEnd: datetime, numberOfMinutes: int, count: int, complete: bool, createdAt: datetime, updatedAt: datetime, client: _BaseAPIClient | None = None, scorecardId: str | None = None, scoreId: str | None = None, cost: int | None = None, decisionCount: int | None = None, externalAiApiCount: int | None = None, cachedAiApiCount: int | None = None, errorCount: int | None = None, metadata: Dict | None = None)

accountId: str

cachedAiApiCount: int | None = None

complete: bool

compositeKey: str

cost: int | None = None

count: int

classmethod create_or_update(client: _BaseAPIClient, account_id: str, record_type: str, time_range_start: datetime, time_range_end: datetime, number_of_minutes: int, count: int, complete: bool = True, scorecard_id: str | None = None, score_id: str | None = None, cost: int | None = None, decision_count: int | None = None, external_ai_api_count: int | None = None, cached_ai_api_count: int | None = None, error_count: int | None = None, metadata: Dict | None = None) → AggregatedMetrics

Create or update an aggregated metrics record (upsert logic).

This method first queries for an existing record matching the key fields (accountId, recordType, timeRangeStart, numberOfMinutes), then either updates it or creates a new one.

Args:

client: The API client instance account_id: The account ID record_type: Type of record (items, scoreResults, tasks, evaluations) time_range_start: Start of the time range time_range_end: End of the time range number_of_minutes: Bucket size in minutes count: The aggregated count complete: Whether this bucket is complete scorecard_id: Optional scorecard ID score_id: Optional score ID cost: Optional cost value decision_count: Optional decision count external_ai_api_count: Optional external AI API count cached_ai_api_count: Optional cached AI API count error_count: Optional error count metadata: Optional metadata dictionary

Returns:

The created or updated AggregatedMetrics instance

createdAt: datetime

decisionCount: int | None = None

errorCount: int | None = None

externalAiApiCount: int | None = None

classmethod fields() → str

Fields to request in queries and mutations

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

Create an AggregatedMetrics instance from a dictionary.

static generate_composite_key(record_type: str, time_range_start: datetime, number_of_minutes: int) → str

Generate composite key for AggregatedMetrics.

classmethod list_by_time_range(client: _BaseAPIClient, account_id: str, start_time: datetime, end_time: datetime, record_type: str | None = None) → List[AggregatedMetrics]

Query aggregated metrics by time range using the GSI.

Args:

client: The API client instance account_id: The account ID to filter by start_time: Start of the time range end_time: End of the time range record_type: Optional record type filter (items, scoreResults, tasks, evaluations)

Returns:

List of AggregatedMetrics instances

metadata: Dict | None = None

numberOfMinutes: int

recordType: str

scoreId: str | None = None

scorecardId: str | None = None

timeRangeEnd: datetime

timeRangeStart: datetime

updatedAt: datetime