⚡ Bolt: [performance improvement] Pre-compile regexes and use O(1) set in keyword density

[anchapin]

💡 What: Pre-compiled job title and company regexes as module constants (_TITLE_PATTERNS, _COMPANY_PATTERNS) and moved tech_keywords from a local list inside a function to a module-level set _TECH_KEYWORDS.
🎯 Why: Avoids re-compiling the regex patterns on every call to _extract_job_details, preventing redundant overhead. Using a set for tech_keywords upgrades lookups from O(N) to O(1) and prevents re-allocating the large list every time _suggest_sections_for_keyword is called.
📊 Impact: Measurably reduces function overhead during keyword density analysis by completely removing unnecessary memory allocations and redundant regex recompilation per loop/call.
🔬 Measurement: Verified correct behavior across 681 tests via python -m pytest, which continue to pass identically.

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PR created automatically by Jules for task 12124420853449804524 started by @anchapin

Summary by Sourcery

Precompile regex patterns and centralize tech keyword definitions to reduce per-call overhead in keyword density and job detail extraction.

Enhancements:

• Move job title and company regex patterns to module-level compiled pattern lists reused by job detail extraction.
• Promote the tech keyword collection to a shared module-level set for faster membership checks in keyword suggestion logic.

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[sourcery-ai]

Reviewer's Guide

This PR optimizes keyword density analysis by pre-compiling regex patterns for job title and company extraction at module import time and by moving the technology keyword collection to a reusable module-level set, eliminating repeated allocations and enabling O(1) membership checks.

Class diagram for keyword_density module optimizations

classDiagram
class keyword_density {
    <<module>>
    list<Pattern> _TITLE_PATTERNS
    list<Pattern> _COMPANY_PATTERNS
    set<string> _TECH_KEYWORDS
    Tuple<string,string> _extract_job_details(job_description)
    list<string> _suggest_sections_for_keyword(keyword, resume, job_description)
}

keyword_density : _TITLE_PATTERNS precompiled at import
keyword_density : _COMPANY_PATTERNS precompiled at import
keyword_density : _TECH_KEYWORDS shared across calls

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Flow diagram for tech keyword suggestion using _TECH_KEYWORDS

flowchart TD
    start(["Start _suggest_sections_for_keyword"]) --> initSuggestions["Initialize suggestions as empty list"]
    initSuggestions --> lowerKeyword["Convert keyword to lowercase"]
    lowerKeyword --> checkTech["Is lowercase keyword in _TECH_KEYWORDS set?"]
    checkTech -->|Yes| addSuggestion["Append 'Skills section' to suggestions"]
    checkTech -->|No| skipSuggestion["Do not add tech skills suggestion"]
    addSuggestion --> continueLogic["Evaluate other section suggestion rules"]
    skipSuggestion --> continueLogic
    continueLogic --> endNode(["Return suggestions list"])

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File-Level Changes

Change	Details	Files
Pre-compile job title and company extraction regexes as module-level constants and use them in _extract_job_details.	Introduce _TITLE_PATTERNS as a list of pre-compiled regex objects for common job title formats with appropriate flags baked into the patterns. Introduce _COMPANY_PATTERNS as a list of pre-compiled regex objects for company/organization name detection with flags set at compile time. Refactor _extract_job_details to iterate over _TITLE_PATTERNS and _COMPANY_PATTERNS using pattern.search instead of re.search with inline flags.	cli/utils/keyword_density.py
Move the technology keyword collection to a module-level set to avoid reallocation and improve lookup performance.	Define _TECH_KEYWORDS as a module-level set containing the tech-related keywords previously defined in a local list. Update _suggest_sections_for_keyword to test membership against _TECH_KEYWORDS instead of recreating a local list on each call.	cli/utils/keyword_density.py

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[sourcery-ai]

Hey - I've left some high level feedback:

• Consider making _TECH_KEYWORDS a frozenset to prevent accidental mutation and better signal that this collection is intended to be constant.
• You may want to add type hints for _TITLE_PATTERNS and _COMPANY_PATTERNS (e.g., List[Pattern[str]]) to make their intended usage clearer to readers and static analyzers.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider making `_TECH_KEYWORDS` a `frozenset` to prevent accidental mutation and better signal that this collection is intended to be constant.
- You may want to add type hints for `_TITLE_PATTERNS` and `_COMPANY_PATTERNS` (e.g., `List[Pattern[str]]`) to make their intended usage clearer to readers and static analyzers.

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