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⚡ Bolt: [performance improvement] Memoize regex compilation in clinical context #36

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⚡ Bolt: [performance improvement] Memoize regex compilation in clinical context #36

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3 changes: 3 additions & 0 deletions .jules/bolt.md
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## 2023-11-20 - Memoizing regex compilations in ConText
**Learning:** In the openmed clinical text processing pipeline, deterministic regex compilations (via `_compiled_context_lexicon` in `openmed.clinical.context`) create significant performance bottlenecks when repeatedly evaluated across large document streams. Compiling all ConText regexes every time context cues are evaluated is extremely slow because regex parsing adds immense overhead per execution for static definitions.
**Action:** When working on NLP pipelines, always memoize deterministic regex and lexicon compilations, such as using `@functools.lru_cache` to cache instance instantiations that depend merely on a simple string (e.g. language). Ensure that return values are immutable so callers don't accidentally mutate cached instances.
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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