⚡ Bolt: Optimize find_similar_entities N+1 Query

[Theory903]

💡 What: Replaced the N+1 iterative database query in find_similar_entities with a single SQL query utilizing Common Table Expressions (CTEs) to perform Jaccard similarity calculations entirely database-side.
🎯 Why: The previous approach iteratively fetched relationships for every entity in the database to calculate similarities, causing massive slowdowns as the dataset grew. The CTE approach calculates the intersection strictly for neighbors that share at least one edge, limiting memory overhead and drastically reducing database roundtrips.
📊 Measured Improvement: Execution time for finding 100 similar entities among 10,000 unrelated entities improved from 9.8855 seconds to 0.0084 seconds.
🔬 Measurement: Used an in-memory SQLite database (aiosqlite) populated with 10k entities and measured time over 5 iterations of identical queries.

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PR created automatically by Jules for task 10783423077384220369 started by @Theory903

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ℹ️ Review info

⚙️ Run configuration

Configuration used: defaults

Review profile: CHILL

Plan: Pro

Run ID: a4c84a8e-a2f4-46b9-8a08-8f0fcccc1a68

📥 Commits

Reviewing files that changed from the base of the PR and between 29c13ba and 86918fd.

📒 Files selected for processing (6)

• .jules/bolt.md
• benchmark_ippoc.py
• data/action_log.jsonl
• data/economy.json
• infra/src/mnemosyne/graph/manager.py
• src/ippoc/mnemosyne/graph/manager.py

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch perf/optimize-find-similar-entities-10783423077384220369

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