⚡ Bolt: Optimize regex compilation in job_parser

[anchapin]

⚡ Bolt: Optimize regex compilation in job_parser

💡 What: Moved regex patterns for _extract_salary_from_text, _extract_job_type, and _extract_experience_level to pre-compiled module-level constants.
🎯 Why: To prevent recompiling identical regular expressions on every parsing iteration and reduce allocations on a performance hotpath for ATS applications.
📊 Impact: Reduces parser execution overhead by caching compiled states, enabling faster processing.
🔬 Measurement: python -m pytest tests/test_job_parser_integration.py successfully validates that all logic rules and cases function as expected with the new constant-based patterns.

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PR created automatically by Jules for task 2187414884248275475 started by @anchapin

Summary by Sourcery

Precompile common regex patterns in the job parser to avoid repeated compilation on each parse.

Enhancements:

• Introduce module-level precompiled regex lists for salary, job type, and experience level extraction to reduce runtime overhead.
• Allow the text extraction helper to accept both string and precompiled regex patterns for more efficient matching.

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[sourcery-ai]

Reviewer's Guide

This PR optimizes the job parser by hoisting several frequently used regex patterns to pre-compiled module-level constants and updating the extraction helpers to use these compiled patterns while preserving existing parsing behavior and test coverage.

Class diagram for updated job_parser regex structure

classDiagram
    class JobParserModule {
        <<module>>
        re.Pattern[] _SALARY_PATTERNS
        re.Pattern[] _JOB_TYPE_PATTERNS
        re.Pattern[] _EXPERIENCE_LEVEL_PATTERNS
    }

    class JobParser {
        +_extract_text_by_pattern(text: str, pattern: Union[str, re.Pattern]) Optional[str]
        +_extract_salary_from_text(text: str) Optional[str]
        +_extract_job_type(html: str) Optional[str]
        +_extract_experience_level(html: str) Optional[str]
    }

    JobParserModule <.. JobParser : uses

    JobParser ..> re.Pattern : pattern_search

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Flow diagram for optimized salary extraction with precompiled regex

flowchart TD
    A[Start _extract_salary_from_text] --> B[Input text]
    B --> C[Set iterator over _SALARY_PATTERNS]
    C --> D{More patterns?}
    D -- Yes --> E[Get next precompiled pattern]
    E --> F[pattern.search on text]
    F --> G{Match found?}
    G -- Yes --> H[Determine salary from group 0 or 1]
    H --> I[Clean salary string]
    I --> J[Return cleaned salary]
    G -- No --> D
    D -- No --> K[Return None]
    J --> L[End]
    K --> L[End]

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File-Level Changes

Change	Details	Files
Pre-compile common salary, job type, and experience-level regex patterns at module scope and use them in the corresponding extractor methods to avoid repeated compilation on each call.	Introduce _SALARY_PATTERNS, _JOB_TYPE_PATTERNS, and _EXPERIENCE_LEVEL_PATTERNS lists of compiled regex objects configured with re.IGNORECASE. Refactor _extract_salary_from_text to iterate over _SALARY_PATTERNS and use pattern.search instead of re.search with a raw pattern string and flags. Refactor _extract_job_type and _extract_experience_level to iterate over their respective compiled pattern lists and use pattern.search on the HTML input.	cli/integrations/job_parser.py
Generalize the regex extraction helper to accept both raw pattern strings and pre-compiled regex Pattern objects, enabling a gradual migration to compiled patterns without breaking existing callers.	Update _extract_text_by_pattern signature to accept Union[str, re.Pattern]. Add isinstance(pattern, re.Pattern) branching to call pattern.search when pre-compiled, otherwise fall back to re.search with re.IGNORECASE. Keep the existing group(1).strip() behavior to maintain output shape for existing call sites.	cli/integrations/job_parser.py

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[sourcery-ai]

Hey - I've left some high level feedback:

• Since _SALARY_PATTERNS, _JOB_TYPE_PATTERNS, and _EXPERIENCE_LEVEL_PATTERNS are intended as constants, consider making them tuples instead of lists to better communicate immutability and avoid accidental modification.
• In both _extract_job_type and _extract_experience_level, match.group(1).lower().replace("-", "-") is a no-op; if the intent is to normalize hyphens (e.g., to a space or empty string), update the replacement accordingly or remove it for clarity.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Since `_SALARY_PATTERNS`, `_JOB_TYPE_PATTERNS`, and `_EXPERIENCE_LEVEL_PATTERNS` are intended as constants, consider making them tuples instead of lists to better communicate immutability and avoid accidental modification.
- In both `_extract_job_type` and `_extract_experience_level`, `match.group(1).lower().replace("-", "-")` is a no-op; if the intent is to normalize hyphens (e.g., to a space or empty string), update the replacement accordingly or remove it for clarity.

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