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- Implemented `_build_evaluate_empty_slot_gap_specs` function to generate gap offer specifications for unfilled roadmap slots in evaluate-only mode. - Enhanced `ProgressionFindingsPanel` to display AI offers for empty slots and gaps, improving user interaction and clarity. - Updated `ProgressionGraphEditor` and `ProgressionSlotCard` components to support new functionalities for managing slots and offers. - Refactored utility functions in `progressionGraphDraft.js` to streamline slot management and offer handling. - Incremented application version to reflect these updates.
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
"""Validierung und Normalisierung des Planungs-Artefakts am Progressionsgraph."""
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from __future__ import annotations
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import json
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Field, field_validator
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ARTIFACT_SCHEMA_VERSION = 1
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_MAX_JSON_BYTES = 64_000
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class SlotExerciseContent(BaseModel):
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kind: str = Field(default="empty", pattern=r"^(empty|library|proposal)$")
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exercise_id: Optional[int] = Field(default=None, ge=1)
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variant_id: Optional[int] = Field(default=None, ge=1)
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title: Optional[str] = Field(default=None, max_length=500)
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variant_name: Optional[str] = Field(default=None, max_length=200)
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proposal_key: Optional[str] = Field(default=None, max_length=120)
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ai_suggestion: Optional[Dict[str, Any]] = None
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class SlotContentEntry(BaseModel):
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major_step_index: int = Field(ge=0, le=20)
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primary: SlotExerciseContent = Field(default_factory=SlotExerciseContent)
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siblings: List[SlotExerciseContent] = Field(default_factory=list)
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class GraphPlanningRoadmapArtifact(BaseModel):
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schema_version: int = Field(default=ARTIFACT_SCHEMA_VERSION, ge=1, le=1)
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goal_query: str = Field(default="", max_length=2000)
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start_situation: Optional[str] = Field(default=None, max_length=2000)
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target_state: Optional[str] = Field(default=None, max_length=2000)
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roadmap_notes: Optional[str] = Field(default=None, max_length=2000)
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max_steps: int = Field(default=5, ge=2, le=10)
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progression_roadmap: Optional[Dict[str, Any]] = None
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path_skill_expectations: Optional[Dict[str, Any]] = None
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slot_contents: Optional[List[SlotContentEntry]] = None
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last_findings: Optional[Dict[str, Any]] = None
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@field_validator("progression_roadmap", "path_skill_expectations", "last_findings", mode="before")
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@classmethod
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def _empty_dict_to_none(cls, v):
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if v == {}:
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return None
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return v
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@field_validator("slot_contents", mode="before")
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@classmethod
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def _empty_slot_list_to_none(cls, v):
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if v == []:
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return None
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return v
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def normalize_planning_roadmap_payload(raw: Any) -> Optional[Dict[str, Any]]:
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"""None erlaubt (löschen); sonst validiertes Dict."""
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if raw is None:
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return None
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if not isinstance(raw, dict):
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raise ValueError("planning_roadmap muss ein JSON-Objekt sein")
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artifact = GraphPlanningRoadmapArtifact.model_validate(raw)
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out = artifact.model_dump(exclude_none=True)
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blob = json.dumps(out, ensure_ascii=False)
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if len(blob.encode("utf-8")) > _MAX_JSON_BYTES:
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raise ValueError("planning_roadmap ist zu groß (max. 64 KB)")
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return out
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__all__ = [
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"ARTIFACT_SCHEMA_VERSION",
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"GraphPlanningRoadmapArtifact",
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"SlotContentEntry",
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"SlotExerciseContent",
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"normalize_planning_roadmap_payload",
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]
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