91 lines
2.1 KiB
Python
91 lines
2.1 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from enum import StrEnum
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class EvidenceDecision(StrEnum):
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ANSWERABLE = "answerable"
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VERIFY_PDF = "verify_pdf"
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ABSTAIN = "abstain"
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class SubjectScope(StrEnum):
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HUMAN = "human"
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NON_HUMAN = "non_human"
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UNKNOWN = "unknown"
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class QueryIntent(StrEnum):
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FACT_LOOKUP = "fact_lookup"
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RECOMMENDATION = "recommendation"
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UNKNOWN = "unknown"
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@dataclass(frozen=True)
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class SourceRef:
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physical_page: int
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precision: str
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block_id: str | None = None
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bbox: tuple[float, float, float, float] | None = None
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source_crop: str | None = None
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page_range: tuple[int, int] | None = None
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printed_page: int | None = None
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printed_page_range: tuple[int, int] | None = None
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@dataclass(frozen=True)
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class RetrievalDocument:
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doc_id: str
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drug_id: str
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kind: str
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text: str
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section_key: str
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source_refs: tuple[SourceRef, ...]
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parent_id: str | None = None
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requires_visual_check: bool = False
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drug_name: str | None = None
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part_index: int | None = None
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part_count: int | None = None
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context_labels: tuple[str, ...] = field(default_factory=tuple)
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@dataclass(frozen=True)
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class ParentDocument:
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parent_id: str
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kind: str
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text: str
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source_refs: tuple[SourceRef, ...]
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requires_visual_check: bool = False
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@dataclass(frozen=True)
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class SearchHit:
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document: RetrievalDocument
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score: float
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@dataclass(frozen=True)
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class Evidence:
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evidence_id: str
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matched_doc_id: str
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kind: str
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text: str
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score: float
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source_refs: tuple[SourceRef, ...]
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hydrated_from_parent: bool
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requires_visual_check: bool
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@dataclass(frozen=True)
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class RetrievalResult:
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decision: EvidenceDecision
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reason: str
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evidence: tuple[Evidence, ...] = field(default_factory=tuple)
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resolved_drug_id: str | None = None
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drug_resolution_status: str = "not_attempted"
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# True when the user typed only a drug name (no attribute): the answer layer
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# should introduce the drug (what it is + what it treats), not restate a
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# section verbatim.
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is_drug_overview: bool = False
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