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aad7814 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | """Result models for a generated report."""
from __future__ import annotations
from pydantic import BaseModel, Field
class ReferenceSource(BaseModel):
"""Provenance for content mapped from an uploaded past report (REFERENCE tier)."""
report_filename: str
section_id: str = ""
section_title: str = ""
paragraph_index: int = 0
tier: str = Field(default="reference", description="Always reference for user-facing attribution.")
class GeneratedSection(BaseModel):
"""One section of mapped output."""
section_id: str
title: str
text: str = Field(default="")
rating_value: str | None = Field(default=None)
status: str = Field(
default="OK",
description="OK | empty | NO_RAG_MATCH | GROUNDING_REVIEW | UNASSIGNED",
)
notes: str = Field(default="", description="Diagnostic note for this section.")
rag_sources: list[str] = Field(
default_factory=list,
description="Human-readable REFERENCE provenance strings.",
)
reference_sources: list[ReferenceSource] = Field(
default_factory=list,
description="Structured provenance from uploaded past reports only.",
)
grounding_passed: bool = Field(default=True)
unmatched_observations: list[str] = Field(default_factory=list)
shorthand_expanded: str | None = Field(
default=None,
description="Optional expanded shorthand for this section's notes.",
)
class ReportResult(BaseModel):
"""Full generation result before DOCX assembly."""
tenant_id: str
schema_version: int
property_type: str = Field(default="")
tenure: str = Field(default="")
sections: list[GeneratedSection] = Field(default_factory=list)
unassigned_text: str = Field(default="")
active_section_count: int = Field(
default=0,
description="Leaf subsections with notes and/or AI-selected photos.",
)
processed_section_count: int = Field(
default=0,
description="Leaf subsections actually processed in this run.",
)
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