Publish curated ITWorx-ModelForge source from 990401737f96c8d46a9f7b0d84994516cadcb6a0

backend/src/modelforge_api/domain/lab.py

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+from __future__ import annotations
+
+import uuid
+from datetime import datetime
+from typing import Annotated, Literal
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from modelforge_api.domain.serving import (
+    EmbeddingInvokeRequest,
+    OCRInvokeRequest,
+    RerankingInvokeRequest,
+    SpeechTranscriptionInvokeRequest,
+    VisionEmbeddingInvokeRequest,
+)
+
+
+class LabModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+
+class LabTarget(LabModel):
+    deployment_candidate_id: uuid.UUID
+    artifact_set_id: uuid.UUID
+    runtime_profile_id: uuid.UUID
+    compute_node_id: uuid.UUID
+    execution_approval_id: uuid.UUID
+
+
+class LabEmbeddingInput(LabModel):
+    capability: Literal["rag.embedding"]
+    payload: EmbeddingInvokeRequest
+
+
+class LabRerankingInput(LabModel):
+    capability: Literal["rag.reranking"]
+    payload: RerankingInvokeRequest
+
+
+class LabOCRInput(LabModel):
+    capability: Literal["document.ocr"]
+    payload: OCRInvokeRequest
+
+
+class LabVisionInput(LabModel):
+    capability: Literal["vision.embedding"]
+    payload: VisionEmbeddingInvokeRequest
+
+
+class LabSpeechInput(LabModel):
+    capability: Literal["speech.transcription"]
+    payload: SpeechTranscriptionInvokeRequest
+
+
+LabInput = Annotated[
+    LabEmbeddingInput | LabRerankingInput | LabOCRInput | LabVisionInput | LabSpeechInput,
+    Field(discriminator="capability"),
+]
+
+
+class LabLimits(LabModel):
+    timeout_seconds: int = Field(default=60, ge=5, le=300, strict=True)
+    queue_timeout_seconds: int = Field(default=10, ge=1, le=60, strict=True)
+    retention_seconds: int = Field(default=300, ge=60, le=3600, strict=True)
+    max_output_bytes: int = Field(default=65536, ge=1, le=1048576, strict=True)
+    concurrency: Literal[1] = 1
+
+    @model_validator(mode="after")
+    def bounded_queue(self) -> LabLimits:
+        if self.queue_timeout_seconds > self.timeout_seconds:
+            raise ValueError("queue timeout cannot exceed total timeout")
+        return self
+
+
+class LabPreflightRequest(LabModel):
+    target: LabTarget
+    input: LabInput
+    contract_version: Literal[1] = 1
+    limits: LabLimits = Field(default_factory=LabLimits)
+
+
+class LabExecutionStartRequest(LabPreflightRequest):
+    """An explicitly selected embedding experiment; body input is never echoed in status."""
+
+    deployment_id: uuid.UUID
+    experiment_route_id: uuid.UUID
+
+
+class LabInputSummary(LabModel):
+    sha256: str
+    item_count: int
+    encoded_bytes: int
+
+
+class LabBinding(LabTarget):
+    compatibility_assessment_id: uuid.UUID
+    runtime_probe_id: uuid.UUID
+    capability: str
+    contract_version: Literal[1] = 1
+    runtime_profile_fingerprint: str
+    assessment_fingerprint: str
+    approval_evidence_fingerprint: str
+    probe_environment_fingerprint: str
+    accelerator_id: uuid.UUID | None = None
+
+
+class LabPreflightResponse(LabModel):
+    input_summary: LabInputSummary
+    binding: LabBinding | None = None
+    limits: LabLimits
+    blockers: list[str]
+    checks_passed: bool
+    execution_started: Literal[False] = False
+    permission_granted: Literal[False] = False
+
+
+LabExecutionStatus = Literal[
+    "accepted",
+    "preparing",
+    "queued",
+    "running",
+    "cancel_requested",
+    "completed",
+    "failed",
+    "cancelled",
+    "interrupted",
+]
+
+
+class LabExecutionRunResponse(LabModel):
+    id: uuid.UUID
+    capability: Literal["rag.embedding@1"] = "rag.embedding@1"
+    status: LabExecutionStatus
+    target_snapshot: dict[str, object]
+    input_summary: LabInputSummary
+    output_metadata: dict[str, object] | None = None
+    worker_observations: dict[str, object] | None = None
+    failure_code: str | None = None
+    input_payload_expires_at: datetime
+    retention_seconds: int = Field(ge=60, le=3600)
+    created_at: datetime
+    started_at: datetime | None = None
+    cancellation_requested_at: datetime | None = None
+    cancellation_acknowledged_at: datetime | None = None
+    finished_at: datetime | None = None