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⚡ Bolt: [performance improvement] Memoize regex pattern and lexicon generation #28

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⚡ Bolt: [performance improvement] Memoize regex pattern and lexicon generation #28

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3 changes: 3 additions & 0 deletions .jules/bolt.md
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## 2025-10-31 - Memoizing Lexicon Regex Compilations
**Learning:** In the openmed NLP pipelines (e.g., `openmed.clinical.context`), deterministic regex compilations (`_compiled_context_lexicon`) and lexicon generation can cause severe performance bottlenecks during repeated evaluations across text spans.
**Action:** Always memoize deterministic lexicon and regex compilations (using `@functools.lru_cache`) when processing spans or lexicons dynamically in text pipelines.
2 changes: 2 additions & 0 deletions openmed/openmed/clinical/context.py
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from __future__ import annotations

import functools
import re
from collections.abc import Iterable, Iterator, Mapping, Sequence
from dataclasses import dataclass, replace
Expand Down Expand Up @@ -154,6 +155,7 @@ class _CompiledContextLexicon:
backward_context_cues: frozenset[str]


@functools.lru_cache(maxsize=16)
def _compiled_context_lexicon(language: str | None = None) -> _CompiledContextLexicon:
lexicon = get_clinical_cue_lexicon(language)
token_boundaries = lexicon.token_boundaries
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2 changes: 2 additions & 0 deletions openmed/openmed/clinical/experiencer.py
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from __future__ import annotations

import functools
import re
from collections.abc import Mapping
from dataclasses import dataclass, replace
Expand Down Expand Up @@ -106,6 +107,7 @@
_CLAUSE_BOUNDARY_RE = re.compile(r"[.!?;]")


@functools.lru_cache(maxsize=512)
def _cue_pattern(cues: tuple[str, ...]) -> re.Pattern[str]:
alternation = "|".join(
r"\s+".join(re.escape(part) for part in cue.split())
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