ElebobaandClaude Opus 5.5
924a51c483
CI / ruff · pytest (pull_request) Successful in 23s
Single entry point app/llm/gateway.py: extract_item, extract_checkin, risk_classify, phrase, summarize_for_doctor. Two-level design: LLMProvider (transport, GigaChat) and LLMTasks (PromptedTasks over a provider, or rule-based MockTasks). Shared policy in the gateway: assert_scrubbed backstop, confidence threshold 0.5, risk failure -> NONE, forbidden-output filter with config/texts/phrases.yaml fallbacks. Refs #7 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
"""Pydantic contracts for LLM outputs (SPEC §7). Anything the model returns is validated against these."""
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from typing import Literal
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from pydantic import BaseModel, Field, ValidationInfo, field_validator
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SideEffect = Literal["nausea", "insomnia", "headache", "dizziness", "sexual", "agitation", "other"]
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AlcoholBucket = Literal["none", "1-2", "more"]
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SleepBucket = Literal["<4", "4-6", "6-8", ">8"]
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class ItemScore(BaseModel):
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"""Score of a single scale item extracted from a free-text answer.
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`score` is bounded by 4 (PCL-5); the per-scale maximum (3 for PHQ-9/GAD-7) is enforced via the
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validation context key `max_score`, because the same model serves scales with different ranges.
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"""
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item: str
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score: int = Field(ge=0, le=4)
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confidence: float = Field(ge=0, le=1)
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evidence: str = ""
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@field_validator("score")
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@classmethod
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def _score_within_item_max(cls, v: int, info: ValidationInfo) -> int:
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max_score = (info.context or {}).get("max_score")
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if max_score is not None and v > max_score:
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raise ValueError(f"score {v} exceeds item max {max_score}")
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return v
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class CheckinFacts(BaseModel):
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"""Facts from a free-text daily check-in (ADR-002). `None` means "not mentioned", not "no"."""
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mood: int | None = Field(default=None, ge=0, le=10)
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med_taken: bool | None = None
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side_effects: list[SideEffect] = Field(default_factory=list)
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quit_intent: bool = False
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alcohol: AlcoholBucket | None = None
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sleep_hours_bucket: SleepBucket | None = None
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nightmares: bool | None = None
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med_question: bool = False
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confidence: float = Field(default=0.0, ge=0, le=1)
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@field_validator("side_effects")
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@classmethod
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def _dedupe(cls, v: list[SideEffect]) -> list[SideEffect]:
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return list(dict.fromkeys(v))
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class RiskVerdict(BaseModel):
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"""Raw risk classification as returned by the model; the gateway maps it to `RiskLevel`."""
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level: Literal["none", "possible", "acute"]
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confidence: float = Field(default=0.5, ge=0, le=1)
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