⚡ Bolt: Optimize keyword density extraction performance

[anchapin]

💡 What:

• Extracted dynamic regex lists title_patterns and company_patterns to module-level pre-compiled objects _TITLE_PATTERNS and _COMPANY_PATTERNS.
• Extracted the dynamically allocated list common_keywords to a module-level constant _COMMON_KEYWORDS.
• Extracted the dynamically allocated list tech_keywords to a module-level set _TECH_KEYWORDS.
• Refactored _extract_job_details, _simple_keyword_extraction, and _suggest_sections_for_keyword to use these optimizations.

🎯 Why:
Instantiating large lists or performing inline .compile on regular expressions within instance methods consumes unnecessary execution time per call, particularly during loops or string processing. By moving them to the module-level, they are computed once at import time. The conversion of _TECH_KEYWORDS to a set also upgrades keyword lookup performance from $O(N)$ to $O(1)$.

📊 Impact:
Reduces processing time per density analysis execution by avoiding redundant parsing and memory allocation inside loop calls.

🔬 Measurement:
This can be verified by running the test suite (python -m pytest tests/test_keyword_density.py), which succeeds without regression, and observing improved average processing times across repeated calls to the KeywordDensityGenerator.

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PR created automatically by Jules for task 10506461077069710837 started by @anchapin

Summary by Sourcery

Optimize keyword density analysis by hoisting reusable patterns and keyword collections to module-level constants for faster execution.

Enhancements:

• Pre-compile job title and company extraction regex patterns at module scope and reuse them across calls.
• Centralize common keyword definitions and tech keyword membership into shared module-level collections to reduce per-call allocations and speed up lookups.

Documentation:

• Add a Bolt learning log entry documenting regex and keyword optimization practices for density analysis.

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[sourcery-ai]

Reviewer's Guide

This PR optimizes keyword density analysis by hoisting regex patterns and keyword collections from frequently called methods into module-level precompiled patterns and constants, and by using a set for tech keyword membership checks, reducing per-call overhead without changing behavior.

File-Level Changes

Change	Details	Files
Precompile job title and company extraction regexes at module scope and reuse them in job detail extraction.	Introduce module-level _TITLE_PATTERNS and _COMPANY_PATTERNS lists of precompiled regex objects. Refactor _extract_job_details to iterate over these compiled patterns using pattern.search instead of re.search with inline flags.	cli/utils/keyword_density.py
Hoist common keyword list to a module-level constant and reuse it in simple keyword extraction.	Add _COMMON_KEYWORDS as a module-level list of (keyword, importance) tuples. Refactor _simple_keyword_extraction to iterate over _COMMON_KEYWORDS instead of recreating the list on each call.	cli/utils/keyword_density.py
Replace dynamically created tech keyword list with a module-level set for faster membership checks in keyword section suggestions.	Add _TECH_KEYWORDS as a module-level set of tech-related keywords. Update _suggest_sections_for_keyword to test membership against _TECH_KEYWORDS instead of recreating a list each time.	cli/utils/keyword_density.py
Document the optimization pattern in the Bolt guide for future performance-oriented refactors.	Append a new dated entry describing regex and keyword collection optimizations for density analysis. Capture the learning about pre-compiling regexes and using module-level constants/sets for repeated lookups.	.jules/bolt.md

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[sourcery-ai]

Hey - I've left some high level feedback:

• The _TECH_KEYWORDS set is missing entries that were present in the original tech_keywords list (e.g., "react native", "hibernate"), which changes behavior for _suggest_sections_for_keyword; please verify whether this is intentional or bring the set back in sync.
• To avoid future drift between _COMMON_KEYWORDS and _TECH_KEYWORDS, consider deriving _TECH_KEYWORDS programmatically from _COMMON_KEYWORDS (e.g., by filtering on importance or a separate tag) instead of maintaining two separate hard-coded collections.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- The `_TECH_KEYWORDS` set is missing entries that were present in the original `tech_keywords` list (e.g., `"react native"`, `"hibernate"`), which changes behavior for `_suggest_sections_for_keyword`; please verify whether this is intentional or bring the set back in sync.
- To avoid future drift between `_COMMON_KEYWORDS` and `_TECH_KEYWORDS`, consider deriving `_TECH_KEYWORDS` programmatically from `_COMMON_KEYWORDS` (e.g., by filtering on importance or a separate tag) instead of maintaining two separate hard-coded collections.

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