⚡ Bolt: optimize Jaccard similarity in RAG retrieval

[RohanExploit]

This PR implements a high-performance optimization for the Jaccard similarity calculation in the CivicRAG service.

💡 What:

• Modified _prepare_policies to store pre-calculated token set lengths.
• Updated retrieve to use isdisjoint() for fast early exits.
• Replaced the expensive query_tokens.union(policy_tokens) operation with the inclusion-exclusion formula: query_len + policy_len - intersection_len.

🎯 Why:

In hot loops, set.union() is significantly slower than set.intersection() because it must allocate and populate a completely new set. By using the mathematical relationship between union and intersection, we calculate the union size in O(1) arithmetic time once the intersection is known.

📊 Impact:

Expected performance improvement: ~32% reduction in retrieval latency.
Benchmark results (10k iterations):

• Before: 0.0153ms avg
• After: 0.0104ms avg

🔬 Measurement:

Verified using a custom benchmark_rag.py script and confirmed zero regressions with unit tests for exact matching and threshold behavior.

---

PR created automatically by Jules for task 8096378623730797346 started by @RohanExploit

Summary by CodeRabbit

• Documentation

  • Added optimization notes detailing improvements to backend query similarity processing and computation efficiency.

• Refactor

  • Enhanced RAG service performance by implementing token result caching to avoid redundant computations, optimizing memory usage patterns, and accelerating similarity calculations to reduce overall query latency.

[google-labs-jules]

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[github-actions]

🙏 Thank you for your contribution, @RohanExploit!

PR Details:

• Title: ⚡ Bolt: optimize Jaccard similarity in RAG retrieval
• Number: ⚡ Bolt: optimize Jaccard similarity in RAG retrieval #715

Quality Checklist:
Please ensure your PR meets the following criteria:

• Code follows the project's style guidelines
• Self-review of code completed
• Code is commented where necessary
• Documentation updated (if applicable)
• No new warnings generated
• Tests added/updated (if applicable)
• All tests passing locally
• No breaking changes to existing functionality

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4. Once approved, your PR will be merged! 🎉

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ℹ️ Review info

⚙️ Run configuration

Configuration used: defaults

Review profile: CHILL

Plan: Pro

Run ID: 674663fa-1365-47e0-98a0-ac1c659ef833

📥 Commits

Reviewing files that changed from the base of the PR and between 509b3c7 and cc2602f.

⛔ Files ignored due to path filters (1)

• frontend/package-lock.json is excluded by !**/package-lock.json

📒 Files selected for processing (1)

• frontend/package.json

📝 Walkthrough

Walkthrough

This PR optimizes RAG retrieval performance by caching tokenization results for policies, precomputing token lengths, and replacing explicit set-union operations in Jaccard similarity calculations with O(1) arithmetic formulas using inclusion–exclusion principle and early-exit checks via isdisjoint().

Changes

Cohort / File(s)	Summary
Jaccard Similarity & Tokenization Optimization .jules/bolt.md, backend/rag_service.py	Implements and documents optimization for Jaccard similarity computation: caches content_tokens and content_tokens_len during policy preparation; replaces union-set construction with inclusion–exclusion formula; adds isdisjoint() early-exit to skip intersection when sets are disjoint; precomputes query lengths and preserves title-word boosting via intersection length variable.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

Possibly related PRs

• ⚡ Bolt: optimize CivicRAG retrieval with pre-tokenization #698: Directly related optimization to backend/rag_service.py implementing tokenization caching and RAG retrieval hot-path improvements.

Suggested labels

size/s, ECWoC26-ENDED

Poem

🐰 A clever hop through tokens cached,
No unions built—with math we've matched!
Disjoint checks skip wasted ground,
Inclusion–exclusion makes us bound,
Faster retrieval, swift and sound!

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately summarizes the main change: optimizing Jaccard similarity in RAG retrieval using the Bolt optimization, which is the core focus of the changeset.
Description check	✅ Passed	The description covers the main sections from the template (Description, Type of Change, Testing Done) with clear details about what, why, and impact. However, several template sections are missing or incomplete: Related Issue, Screenshots, and most checklist items are not addressed.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch bolt-optimize-rag-similarity-8096378623730797346

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[Copilot]

Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

Optimizes Jaccard similarity scoring in the CivicRAG retrieval loop by avoiding set.union() allocations and reusing precomputed token set lengths.

Changes:

• Precomputes and stores content_tokens and their length during policy preparation.
• Updates retrieval scoring to use isdisjoint() fast-path and inclusion-exclusion for union size.
• Adds internal notes in .jules/bolt.md documenting the optimization approach.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
backend/rag_service.py	Caches token lengths and computes Jaccard union size via arithmetic to reduce per-policy allocations
.jules/bolt.md	Documents the optimization rationale and approach for future reference

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[coderabbitai]

Actionable comments posted: 2

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In @.jules/bolt.md:
- Line 89: The changelog heading "2026-05-17 - Jaccard Similarity Set
Optimization" is future-dated; update that heading to the correct date (e.g.,
the PR creation date) so the running log timeline is accurate by replacing the
"2026-05-17" prefix in the heading string with the appropriate actual date.

In `@backend/rag_service.py`:
- Around line 95-96: The comment in rag_service.py that documents Jaccard
similarity uses Unicode symbols '∩' and '∪' (currently in the two comment lines
about |A ∩ B| / |A ∪ B| and the inclusion–exclusion formula), which triggers
Ruff RUF003; update those comments in the same place (near the Jaccard
Similarity note) to use ASCII-friendly words such as "intersection" and "union"
(e.g., replace "∩" with "intersection" and "∪" with "union") so the meaning
remains clear but lint warnings are removed.

🪄 Autofix (Beta)

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: defaults

Review profile: CHILL

Plan: Pro

Run ID: 3f8cca38-d4c2-4fc1-aff5-f923cfae8988

📥 Commits

Reviewing files that changed from the base of the PR and between 3166316 and 509b3c7.

📒 Files selected for processing (2)

• .jules/bolt.md
• backend/rag_service.py

[coderabbitai]

⚠️ Potential issue | 🟡 Minor | ⚡ Quick win

Fix future-dated changelog heading.

Line 89 uses a date later than the PR creation date, which makes the timeline misleading in this running log.

Suggested fix

-## 2026-05-17 - Jaccard Similarity Set Optimization
+## 2026-04-30 - Jaccard Similarity Set Optimization

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	## 2026-05-17 - Jaccard Similarity Set Optimization
	## 2026-04-30 - Jaccard Similarity Set Optimization

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In @.jules/bolt.md at line 89, The changelog heading "2026-05-17 - Jaccard
Similarity Set Optimization" is future-dated; update that heading to the correct
date (e.g., the PR creation date) so the running log timeline is accurate by
replacing the "2026-05-17" prefix in the heading string with the appropriate
actual date.

[cubic-dev-ai]

No issues found across 2 files

[RohanExploit]

@copilot resolve the merge conflicts in this pull request

[cubic-dev-ai]

1 issue found across 1 file (changes from recent commits).

Prompt for AI agents (unresolved issues)

Check if these issues are valid — if so, understand the root cause of each and fix them. If appropriate, use sub-agents to investigate and fix each issue separately.


<file name="backend/rag_service.py">

<violation number="1" location="backend/rag_service.py:105">
P2: The title-token intersection is now computed unconditionally, including cases already proven disjoint by `isdisjoint(policy_tokens)`, adding avoidable work in the retrieval hot path.</violation>
</file>

_{Reply with feedback, questions, or to request a fix. Tag @cubic-dev-ai to re-run a review.}

[Copilot]

@copilot resolve the merge conflicts in this pull request

Merged origin/main into the branch and resolved all conflicts (commit 15dd6b8). The resolved backend/rag_service.py keeps the PR's improvements — content_tokens_len key name, isdisjoint() early-exit with score = 0.0 fallback so the title boost always applies regardless of content overlap, and the inclusion-exclusion union formula. Also fixed a pre-existing typo in .jules/bolt.md.

[google-labs-jules]

@copilot resolve the merge conflicts in this pull request

Resolved the merge conflicts and applied the Jaccard similarity optimization using set arithmetic and isdisjoint() early exit. Verified the final state with benchmarks and unit tests.

[github-actions]

🔍 Quality Reminder

Thanks for the updates! Please ensure:
- Your changes don't break existing functionality
- All tests still pass
- Code quality standards are maintained

*The maintainers will verify that the overall project flow remains intact.*

[cubic-dev-ai]

2 issues found across 4 files (changes from recent commits).

Prompt for AI agents (unresolved issues)

Check if these issues are valid — if so, understand the root cause of each and fix them. If appropriate, use sub-agents to investigate and fix each issue separately.


<file name=".jules/bolt.md">

<violation number="1">
P3: Fix the typo `round-triPS` to `round-trips` in this learning note.</violation>
</file>

<file name="backend/rag_service.py">

<violation number="1">
P3: The `union_len == 0` check is unreachable here and introduces dead code.</violation>
</file>

_{Reply with feedback, questions, or to request a fix. Tag @cubic-dev-ai to re-run a review.}