⚡ Bolt: O(N) loop optimization in feedback loops

[daggerstuff]

💡 What: Optimized the nested O(N²) loop matching logic in identify_anti_patterns to run in O(N) single-pass using a compiled regex and tracking unique hits per document.
🎯 Why: Iterating over every context string for every dummy keyword was highly inefficient and scaled poorly.
📊 Impact: Prevents unnecessary iteration overhead by checking all keywords in a single pass of each context string.
🔬 Measurement: Run the test_feedback_loops module to confirm matching logic still correctly counts matching contexts (not inflated by multiple keyword appearances).

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PR created automatically by Jules for task 9074363338587023292 started by @daggerstuff

Summary by Sourcery

Optimize anti-pattern identification in feedback loop analysis to reduce matching complexity and avoid overcounting contexts.

Enhancements:

• Replace nested per-keyword context scanning with a single-pass regex-based matcher for anti-pattern detection in feedback loops.
• Clean up and reorder imports in the feedback_loops pipeline module.

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Summary by cubic

Optimizes anti-pattern detection in identify_anti_patterns by replacing the O(N²) keyword/context scan with a single-pass regex (O(N)). Speeds up feedback loop analysis while keeping match counts accurate.

• Refactors
  • Compile one regex for all keywords and scan each context once.
  • Count unique keyword hits per context to prevent inflated matches.

^{Written for commit 6adef7f. Summary will update on new commits.}

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Project	Deployment	Actions	Updated (UTC)
ai	Error		Mar 31, 2026 8:12pm

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ℹ️ Review info

⚙️ Run configuration

Configuration used: defaults

Review profile: CHILL

Plan: Pro

Run ID: 8f449d5e-5086-4700-b58b-001703b88248

📥 Commits

Reviewing files that changed from the base of the PR and between 2e5eb05 and 6adef7f.

📒 Files selected for processing (1)

• data/pipeline/feedback_loops.py

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch perf/feedback_loops-O_N_optimization-7x4A-9074363338587023292

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[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Refactors the anti-pattern identification logic to replace an O(N²) keyword matching loop with a single-pass O(N) regex-based matcher while also cleaning up imports and type hints in the feedback pipeline module.

File-Level Changes

Change	Details	Files
Optimize anti-pattern keyword matching from nested O(N²) loops to a single-pass O(N) regex-based implementation that counts unique keyword hits per context string.	Introduce a compiled regex built from the dummy keyword list using alternation and proper escaping. Track per-keyword match counts in a dictionary keyed by the actual matched keyword strings. Iterate over each failure context once, incrementing counts only once per keyword per context via a set of regex matches. Reuse the resulting per-keyword counts in the existing anti-pattern threshold check without changing its external behavior.	data/pipeline/feedback_loops.py
Tidy module imports and typing usage in the feedback loop pipeline module.	Reorder and consolidate imports, moving logging import up and removing unused modules such as os and time. Group standard library imports (json, logging, collections.deque, datetime) and typing imports (Any, Dict, List, Optional) more cleanly for readability.	data/pipeline/feedback_loops.py

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[sourcery-ai]

Hey - I've left some high level feedback:

• When constructing the compiled regex for all keywords, make sure each keyword is properly escaped and that word-boundary or partial-match behavior matches the previous implementation to avoid subtle changes in what counts as an anti-pattern.
• Since you’re now tracking unique hits per document, consider explicitly documenting or asserting the intended behavior when a single context contains multiple different keywords (e.g., should it count once or multiple times) to prevent future regressions if the matching logic is refactored again.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- When constructing the compiled regex for all keywords, make sure each keyword is properly escaped and that word-boundary or partial-match behavior matches the previous implementation to avoid subtle changes in what counts as an anti-pattern.
- Since you’re now tracking unique hits per document, consider explicitly documenting or asserting the intended behavior when a single context contains multiple different keywords (e.g., should it count once or multiple times) to prevent future regressions if the matching logic is refactored again.

Fix all in Cursor

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[cubic-dev-ai]

No issues found across 1 file