major refactor

core/schema/models/job.py

@@ -1,14 +1,9 @@
-"""
-Job, Timeline, and Checkpoint Schema Definitions
-
-Source of truth for pipeline jobs, timelines, and checkpoints.
-Generates: SQLModel (core/db/models.py), TypeScript via modelgen.
-"""
+"""Job schema — source of truth for pipeline jobs."""
 
 from dataclasses import dataclass, field
 from datetime import datetime
 from enum import Enum
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 from uuid import UUID
 
 
@@ -68,91 +63,3 @@ class Job:
     created_at: Optional[datetime] = None
     started_at: Optional[datetime] = None
     completed_at: Optional[datetime] = None
-
-
-@dataclass
-class Timeline:
-    """
-    The frame sequence from a source video.
-
-    Independent of stages — exists before any stage runs.
-    Frames stored in MinIO as JPEGs, metadata here.
-    One timeline per job.
-    """
-
-    id: UUID
-    source_asset_id: Optional[UUID] = None
-    source_video: str = ""
-    profile_name: str = ""
-    fps: float = 2.0
-
-    frames_prefix: str = ""  # s3: timeline/{id}/frames/
-    frames_manifest: Dict[int, str] = field(default_factory=dict)  # seq → s3 key
-    frames_meta: List[Dict[str, Any]] = field(default_factory=list)
-
-    created_at: Optional[datetime] = None
-
-
-@dataclass
-class Checkpoint:
-    """
-    A snapshot of pipeline state on a timeline.
-
-    Stage outputs stored as JSONB — each stage serializes to JSON,
-    the checkpoint stores it without knowing the shape.
-
-    parent_id forms a tree: multiple children from the same parent
-    = different config tries from the same starting point.
-    """
-
-    id: UUID
-    timeline_id: UUID
-    parent_id: Optional[UUID] = None  # null = root checkpoint
-
-    # Stage outputs — JSONB per stage, opaque to the checkpoint layer
-    stage_outputs: Dict[str, Any] = field(default_factory=dict)
-
-    # Config that produced this checkpoint
-    config_overrides: Dict[str, Any] = field(default_factory=dict)
-
-    # Pipeline state
-    stats: Dict[str, Any] = field(default_factory=dict)
-
-    # Scenario bookmark
-    is_scenario: bool = False
-    scenario_label: str = ""
-
-    created_at: Optional[datetime] = None
-
-
-# --- Brands ---
-
-class BrandSource(str, Enum):
-    OCR = "ocr"
-    VLM = "local_vlm"
-    CLOUD = "cloud_llm"
-    MANUAL = "manual"
-
-
-@dataclass
-class Brand:
-    """
-    A brand discovered or registered in the system.
-
-    Airings track where/when the brand appeared — each airing
-    references a timeline and a frame range.
-    """
-
-    id: UUID
-    canonical_name: str
-    aliases: List[str] = field(default_factory=list)
-    source: BrandSource = BrandSource.OCR  # how first discovered
-    confirmed: bool = False
-
-    # Airings — JSONB array of appearances
-    # [{timeline_id, frame_start, frame_end, confidence, source, timestamp}]
-    airings: List[Dict[str, Any]] = field(default_factory=list)
-    total_airings: int = 0
-
-    created_at: Optional[datetime] = None
-    updated_at: Optional[datetime] = None