⚡ Bolt: Pre-compile text extraction regex constants

[anchapin]

💡 What:
Optimized text parsing methods in cli/utils/keyword_density.py and cli/integrations/job_parser.py by hoisting inline list-based regular expressions into module-level and class-level pre-compiled regex constants (re.compile).

🎯 Why:
Prior to this change, functions like _extract_salary_from_text defined their patterns locally and called re.search() inside for loops. Even with Python's internal caching, this required allocating a list of strings on each invocation and a dictionary lookup. Pre-compiling the expressions completely avoids this overhead, making keyword extractions significantly faster especially when processing large job descriptions or resume dumps.

📊 Impact:
Faster parsing speed and reduced allocation overhead on hot paths for JobParser and KeywordDensityGenerator.

🔬 Measurement:
Run the test suite pytest tests/test_keyword_density.py tests/test_job_parser_integration.py to ensure matching logic correctness, verifying performance via basic profilers.

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PR created automatically by Jules for task 1641311279977203139 started by @anchapin

Summary by Sourcery

Enhancements:

• Introduce module- and class-level pre-compiled regex constants for salary, job type, experience level, job title, and company extraction, replacing ad hoc inline regex usage within parsing methods.

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[sourcery-ai]

Reviewer's Guide

Pre-compiles frequently used text-extraction regexes into module/class-level constants and updates parsing helpers to reuse these compiled patterns, reducing per-call allocation and regex compilation overhead in JobParser and KeywordDensityGenerator.

Class diagram for precompiled regex usage in JobParser and KeywordDensityGenerator

classDiagram
    class JobParser {
        -_SALARY_PATTERNS: list
        -_JOB_TYPE_PATTERNS: list
        -_EXPERIENCE_LEVEL_PATTERNS: list
        +__init__(cache_dir: OptionalPath)
        -_extract_salary_from_text(text: str) OptionalStr
        -_extract_job_type(html: str) OptionalStr
        -_extract_experience_level(html: str) OptionalStr
    }

    class KeywordDensityGenerator {
        -_TITLE_PATTERNS: list
        -_COMPANY_PATTERNS: list
        -config: Config
        +__init__(config: Config)
        -_extract_job_details(job_description: str) TupleStrStr
    }

    class Config
    class OptionalPath
    class OptionalStr
    class TupleStrStr

    KeywordDensityGenerator --> Config
    JobParser --> OptionalPath
    JobParser --> OptionalStr
    KeywordDensityGenerator --> TupleStrStr

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File-Level Changes

Change	Details	Files
Hoisted salary, job type, and experience level regexes in JobParser to pre-compiled class-level patterns and updated extraction helpers to use them.	Introduced _SALARY_PATTERNS, _JOB_TYPE_PATTERNS, and _EXPERIENCE_LEVEL_PATTERNS as lists of pre-compiled regex objects on JobParser. Refactored _extract_salary_from_text to iterate over self._SALARY_PATTERNS and use pattern.search instead of re.search with inline strings and flags. Refactored _extract_job_type to use self._JOB_TYPE_PATTERNS and pattern.search instead of constructing a local list of pattern strings. Refactored _extract_experience_level to use self._EXPERIENCE_LEVEL_PATTERNS and pattern.search instead of constructing a local list of pattern strings.	cli/integrations/job_parser.py
Hoisted job title and company extraction regexes in keyword_density to pre-compiled module-level patterns and updated _extract_job_details to use them.	Added module-level _TITLE_PATTERNS and _COMPANY_PATTERNS lists of pre-compiled regex objects configured with appropriate flags. Updated _extract_job_details to iterate over _TITLE_PATTERNS and _COMPANY_PATTERNS and call pattern.search instead of using local pattern lists with re.search calls. Preserved existing capture groups and post-processing (strip, lower, replace) to maintain behavior while reducing per-call allocations.	cli/utils/keyword_density.py

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