312 lines
12 KiB
Python
312 lines
12 KiB
Python
from __future__ import annotations
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import functools
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import re
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from dataclasses import dataclass, replace
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from difflib import SequenceMatcher
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from enum import StrEnum
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from .models import EvidenceDecision, QueryIntent, RetrievalResult, SubjectScope
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from .service import RetrievalService
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from .text import WORD_RE, normalize_name
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__all__ = [
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"WORD_RE",
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"CatalogDrugResolver",
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"DrugResolution",
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"DrugResolutionStatus",
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"QueryRoutingService",
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"normalize_name",
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]
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class DrugResolutionStatus(StrEnum):
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RESOLVED = "resolved"
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NOT_FOUND = "not_found"
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AMBIGUOUS = "ambiguous"
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@dataclass(frozen=True)
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class DrugResolution:
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status: DrugResolutionStatus
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drug_id: str | None = None
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score: float | None = None
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candidate_drug_ids: tuple[str, ...] = ()
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class CatalogDrugResolver:
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def __init__(
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self,
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catalog: dict[str, set[str]],
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fuzzy_threshold: float = 0.84,
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ambiguity_margin: float = 0.04,
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) -> None:
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self._catalog = {
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drug_id: {
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normalized for alias in aliases if (normalized := normalize_name(alias))
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}
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for drug_id, aliases in catalog.items()
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}
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self._aliases = [
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(drug_id, normalized)
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for drug_id, aliases in catalog.items()
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for alias in aliases
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if (normalized := normalize_name(alias))
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]
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self._fuzzy_threshold = fuzzy_threshold
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self._ambiguity_margin = ambiguity_margin
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# Measured live 2026-08-07: a single `resolve()` call over the real
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# ~10,164-alias catalog costs ~0.65-0.7s, `suggest()` ~0.94-0.97s — both
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# O(aliases) regex/SequenceMatcher work, pure functions of their
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# arguments (only `self._aliases` et al, fixed at construction, feed
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# them). `understanding.py`'s `_candidate_ids` calls both PER HISTORY
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# LINE on every single turn — so the SAME already-seen history lines
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# were being re-resolved from scratch every turn a conversation grew,
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# ~1.6-1.7s of pure CPU per repeated line. A real user's ordinary
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# multi-turn conversation was enough to exceed the 20s F-08 budget
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# before the first Bedrock call ever ran, surfacing as a false
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# "Dịch vụ đang gặp sự cố" — not a provider outage at all. Caching by
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# exact input turns all but the newest turn's own text into a dict
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# lookup on every subsequent call.
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@functools.lru_cache(maxsize=4096)
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def resolve(self, query: str) -> DrugResolution:
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normalized_query = normalize_name(query)
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query_tokens = normalized_query.split()
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exact = [
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(drug_id, alias, match.start(1), match.end(1))
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for drug_id, alias in self._aliases
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for match in [
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re.search(rf"(?:^| )({re.escape(alias)})(?:$| )", normalized_query)
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]
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if match
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]
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if exact:
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maximal = [
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row for row in exact
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if not any(
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other[2] <= row[2] and row[3] <= other[3]
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and (other[2], other[3]) != (row[2], row[3])
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for other in exact
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)
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]
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drug_ids = {drug_id for drug_id, _, _, _ in maximal}
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if len(drug_ids) == 1:
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return DrugResolution(
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DrugResolutionStatus.RESOLVED, next(iter(drug_ids)), 1.0,
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)
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return DrugResolution(
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DrugResolutionStatus.AMBIGUOUS,
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candidate_drug_ids=tuple(sorted(drug_ids)),
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)
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scores: dict[str, float] = {}
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for drug_id, alias in self._aliases:
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width = len(alias.split())
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if width > len(query_tokens):
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continue
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spans = (
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" ".join(query_tokens[start:start + width])
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for start in range(len(query_tokens) - width + 1)
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)
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score = max(
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(SequenceMatcher(None, alias, span).ratio() for span in spans),
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default=0.0,
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)
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scores[drug_id] = max(scores.get(drug_id, 0.0), score)
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ranked = sorted(scores.items(), key=lambda item: (-item[1], item[0]))
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if not ranked or ranked[0][1] < self._fuzzy_threshold:
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return DrugResolution(DrugResolutionStatus.NOT_FOUND)
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if len(ranked) > 1 and ranked[0][1] - ranked[1][1] < self._ambiguity_margin:
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return DrugResolution(
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DrugResolutionStatus.AMBIGUOUS,
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candidate_drug_ids=(ranked[0][0], ranked[1][0]),
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)
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return DrugResolution(DrugResolutionStatus.RESOLVED, *ranked[0])
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def aliases_for(self, drug_id: str) -> set[str]:
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return self._catalog.get(drug_id, set())
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def complete(self, prefix: str, k: int = 8) -> list[str]:
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"""Drug ids whose alias contains `prefix`, for as-you-type autocomplete.
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Substring match on the normalized alias, ranked prefix-first then by
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alias length, so "para" surfaces "paracetamol" ahead of a drug that only
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contains "para" mid-word. Distinct drug ids, best first.
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"""
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needle = normalize_name(prefix)
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if not needle:
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return []
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matches: list[tuple[tuple[int, int], str]] = []
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for drug_id, alias in self._aliases:
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position = alias.find(needle)
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if position < 0:
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continue
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matches.append(((0 if position == 0 else 1, len(alias)), drug_id))
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matches.sort()
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ordered: list[str] = []
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seen: set[str] = set()
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for _, drug_id in matches:
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if drug_id in seen:
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continue
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seen.add(drug_id)
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ordered.append(drug_id)
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if len(ordered) >= k:
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break
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return ordered
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# See the comment on `resolve` above — same cost, same fix, same
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# single-caller read-only usage (safe to hand back a cached list).
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@functools.lru_cache(maxsize=4096)
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def suggest(
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self, query: str, k: int = 3, min_score: float = 0.5
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) -> list[tuple[str, float]]:
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"""Closest drug ids by fuzzy score, for a 'did you mean' on a miss.
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Uses the same windowed SequenceMatcher scoring as `resolve`, but returns
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the top-k *below* the resolution threshold too, so a typo that does not
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confidently resolve ("metfomin") can still be offered as a suggestion.
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`min_score` keeps a genuinely non-drug query ("cái này thế nào") from
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surfacing spurious suggestions.
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"""
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query_tokens = normalize_name(query).split()
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if not query_tokens:
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return []
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scores: dict[str, float] = {}
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for drug_id, alias in self._aliases:
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width = len(alias.split())
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if width > len(query_tokens):
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continue
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spans = (
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" ".join(query_tokens[start:start + width])
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for start in range(len(query_tokens) - width + 1)
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)
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score = max(
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(SequenceMatcher(None, alias, span).ratio() for span in spans),
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default=0.0,
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)
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scores[drug_id] = max(scores.get(drug_id, 0.0), score)
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ranked = sorted(scores.items(), key=lambda item: (-item[1], item[0]))
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return [(drug_id, score) for drug_id, score in ranked[:k] if score >= min_score]
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class QueryRoutingService:
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def __init__(
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self,
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retrieval: RetrievalService,
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resolver: CatalogDrugResolver,
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) -> None:
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self._retrieval = retrieval
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self._resolver = resolver
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@staticmethod
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def _scope_gate(
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subject_scope: SubjectScope, intent: QueryIntent
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) -> RetrievalResult | None:
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# Scope comes from the API/policy layer. Unknown is deliberately
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# fail-closed; retrieval must not infer clinical scope from keywords.
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if subject_scope == SubjectScope.NON_HUMAN:
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return RetrievalResult(EvidenceDecision.ABSTAIN, "out_of_scope_non_human")
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if subject_scope == SubjectScope.UNKNOWN:
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return RetrievalResult(EvidenceDecision.ABSTAIN, "subject_scope_unknown")
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if intent == QueryIntent.RECOMMENDATION:
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return RetrievalResult(EvidenceDecision.ABSTAIN, "recommendation_out_of_scope")
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if intent == QueryIntent.UNKNOWN:
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return RetrievalResult(EvidenceDecision.ABSTAIN, "query_intent_unknown")
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return None
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def retrieve_for_drug(
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self,
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query: str,
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drug_id: str,
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subject_scope: SubjectScope = SubjectScope.UNKNOWN,
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intent: QueryIntent = QueryIntent.UNKNOWN,
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) -> RetrievalResult:
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"""Retrieve for an ALREADY-resolved drug, skipping name resolution.
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The conversational layer has already resolved (and possibly inherited)
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the drug; re-resolving from the rewritten turn text is what produced the
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`drug_resolution_ambiguous` empty answers on follow-ups. The query text
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still drives section routing and the intro/overview decision.
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"""
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gate = self._scope_gate(subject_scope, intent)
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if gate is not None:
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return gate
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result = self._retrieval.retrieve(query, drug_id)
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return replace(
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result,
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resolved_drug_id=drug_id,
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drug_resolution_status=DrugResolutionStatus.RESOLVED,
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)
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def retrieve(
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self,
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query: str,
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subject_scope: SubjectScope = SubjectScope.UNKNOWN,
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intent: QueryIntent = QueryIntent.UNKNOWN,
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) -> RetrievalResult:
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gate = self._scope_gate(subject_scope, intent)
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if gate is not None:
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return gate
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resolution = self._resolver.resolve(query)
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if resolution.status == DrugResolutionStatus.NOT_FOUND:
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return RetrievalResult(
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EvidenceDecision.ABSTAIN,
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"drug_not_resolved",
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drug_resolution_status=resolution.status,
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)
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if resolution.status == DrugResolutionStatus.AMBIGUOUS:
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disambiguated = self._disambiguate_with_evidence(
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query, resolution.candidate_drug_ids,
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)
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if disambiguated is not None:
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drug_id, result = disambiguated
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return replace(
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result,
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resolved_drug_id=drug_id,
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drug_resolution_status=DrugResolutionStatus.RESOLVED,
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)
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return RetrievalResult(
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EvidenceDecision.ABSTAIN, "drug_resolution_ambiguous",
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drug_resolution_status=resolution.status,
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)
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if resolution.drug_id is None:
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return RetrievalResult(
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EvidenceDecision.ABSTAIN,
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"drug_resolution_invalid_state",
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drug_resolution_status="invalid_state",
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)
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result = self._retrieval.retrieve(query, resolution.drug_id)
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return replace(
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result,
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resolved_drug_id=resolution.drug_id,
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drug_resolution_status=resolution.status,
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)
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def _disambiguate_with_evidence(
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self,
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query: str,
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candidate_ids: tuple[str, ...],
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) -> tuple[str, RetrievalResult] | None:
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"""Resolve subject-vs-component ambiguity through asymmetric evidence.
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A candidate wins only when its retrieved evidence explicitly contains
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every other mentioned entity, while the reverse direction does not.
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This keeps genuine multi-drug questions ambiguous.
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"""
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if len(candidate_ids) < 2:
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return None
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winners = []
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for candidate_id in candidate_ids:
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result = self._retrieval.retrieve(query, candidate_id)
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if result.decision == EvidenceDecision.ABSTAIN:
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continue
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evidence_text = normalize_name(" ".join(item.text for item in result.evidence))
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others = [item for item in candidate_ids if item != candidate_id]
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if all(any(
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re.search(rf"(?:^| ){re.escape(alias)}(?:$| )", evidence_text)
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for alias in self._resolver.aliases_for(other)
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) for other in others):
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winners.append((candidate_id, result))
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return winners[0] if len(winners) == 1 else None
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