⚡ Bolt: optimize CivicRAG retrieval with pre-tokenization#698
⚡ Bolt: optimize CivicRAG retrieval with pre-tokenization#698RohanExploit merged 1 commit intomainfrom
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- Pre-compile tokenizer regex in CivicRAG initialization. - Implement _prepare_policies to pre-tokenize and pre-format policy data. - Refactor retrieve method to use pre-processed data, avoiding redundant O(N) operations on the hot-path. - Performance impact: ~5.3x speedup in retrieval latency.
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📝 WalkthroughWalkthroughThe changes optimize RAG pipeline performance by relocating deterministic preprocessing operations (token cleaning, tokenization, output formatting) from the per-query retrieval hot-path to initialization time. Supporting guidance documentation is added to Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Suggested labels
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Pull request overview
Optimizes the CivicRAG retrieval hot path by moving deterministic preprocessing work (regex compilation, tokenization, formatting) out of the per-query loop and into initialization.
Changes:
- Pre-compiles the tokenizer regex and uses it in
_tokenize(). - Adds
_prepare_policies()to precompute per-policy token sets and formatted output at startup, and updatesretrieve()to use these prepared objects. - Updates the Bolt performance notes with a new entry describing this 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 |
Pre-tokenizes and pre-formats policies during initialization; retrieve() now uses precomputed tokens/strings. |
.jules/bolt.md |
Adds a performance log entry documenting the RAG preprocessing optimization. |
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| 'formatted': f"**{title}**: {text} (Source: {source})", | ||
| 'original': policy |
| # Use pre-calculated set for union if possible? | ||
| # Union depends on query_tokens, so must be calculated. |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 2
🧹 Nitpick comments (1)
backend/rag_service.py (1)
86-94: Avoid allocating a union set in the hot loop.Since only the union size is needed, compute it from set sizes and the intersection count. This removes one allocation per policy per query.
⚡ Proposed hot-path refactor
# Jaccard Similarity intersection = query_tokens.intersection(policy_tokens) - # Use pre-calculated set for union if possible? - # Union depends on query_tokens, so must be calculated. - union = query_tokens.union(policy_tokens) - - if not union: - continue - - score = len(intersection) / len(union) + union_size = len(query_tokens) + len(policy_tokens) - len(intersection) + + if union_size == 0: + continue + + score = len(intersection) / union_size🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@backend/rag_service.py` around lines 86 - 94, In the hot loop in rag_service.py where you compute similarity (variables query_tokens, policy_tokens, intersection, union, score), avoid allocating the union set; instead compute union_size = len(query_tokens) + len(policy_tokens) - len(intersection) and use score = len(intersection) / union_size (guarding against union_size == 0), and remove the union = query_tokens.union(policy_tokens) allocation.
🤖 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 85: The heading "## 2026-05-16 - Pre-processing for RAG Retrieval" uses a
future date; update that header to the actual entry/review date (e.g., "##
2026-04-23 - Pre-processing for RAG Retrieval") so the chronology is
correct—locate the markdown header line in .jules/bolt.md and replace the date
portion only, leaving the rest of the heading text unchanged.
In `@backend/rag_service.py`:
- Around line 37-55: The current _prepare_policies builds
self._prepared_policies incrementally which can leave a partial cache if an
error occurs; change it to construct a local list (e.g., prepared = []) and
populate that using _tokenize and policy.get(...) for each policy, then assign
self._prepared_policies = prepared only after the loop completes; also wrap the
whole routine so any exception clears self._prepared_policies (set to [] or
None) and re-raises or logs a fatal error so the singleton is never left with a
partially prepared cache.
---
Nitpick comments:
In `@backend/rag_service.py`:
- Around line 86-94: In the hot loop in rag_service.py where you compute
similarity (variables query_tokens, policy_tokens, intersection, union, score),
avoid allocating the union set; instead compute union_size = len(query_tokens) +
len(policy_tokens) - len(intersection) and use score = len(intersection) /
union_size (guarding against union_size == 0), and remove the union =
query_tokens.union(policy_tokens) allocation.
🪄 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
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Run ID: 536bf737-943b-4ca5-a635-932564ce89dc
📒 Files selected for processing (2)
.jules/bolt.mdbackend/rag_service.py
| **Learning:** Caching raw Python objects (like SQLAlchemy models or Pydantic instances) in a high-traffic API still incurs significant overhead because FastAPI/Pydantic must re-serialize the data on every request. | ||
| **Action:** Serialize data to a JSON string using `json.dumps()` BEFORE caching. On cache hits, return a raw `fastapi.Response(content=..., media_type="application/json")`. This bypasses the validation and serialization layer, resulting in significant performance gains (up to 50x in benchmarks). | ||
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| ## 2026-05-16 - Pre-processing for RAG Retrieval |
There was a problem hiding this comment.
Use the actual entry date instead of a future date.
Line 85 is dated 2026-05-16, but this PR was created/reviewed on April 23, 2026. Future-dated learnings make this chronology harder to trust.
🗓️ Proposed fix
-## 2026-05-16 - Pre-processing for RAG Retrieval
+## 2026-04-23 - Pre-processing for RAG Retrieval📝 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.
| ## 2026-05-16 - Pre-processing for RAG Retrieval | |
| ## 2026-04-23 - Pre-processing for RAG Retrieval |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In @.jules/bolt.md at line 85, The heading "## 2026-05-16 - Pre-processing for
RAG Retrieval" uses a future date; update that header to the actual entry/review
date (e.g., "## 2026-04-23 - Pre-processing for RAG Retrieval") so the
chronology is correct—locate the markdown header line in .jules/bolt.md and
replace the date portion only, leaving the rest of the heading text unchanged.
| except Exception as e: | ||
| logger.error(f"Error loading policies: {e}") | ||
|
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||
| def _prepare_policies(self): | ||
| """Pre-tokenize and pre-format policies for faster retrieval.""" | ||
| self._prepared_policies = [] | ||
| for policy in self.policies: | ||
| title = policy.get('title', '') | ||
| text = policy.get('text', '') | ||
| source = policy.get('source', 'Unknown') | ||
|
|
||
| content = f"{title} {text}" | ||
|
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| self._prepared_policies.append({ | ||
| 'title_tokens': self._tokenize(title), | ||
| 'content_tokens': self._tokenize(content), | ||
| 'formatted': f"**{title}**: {text} (Source: {source})", | ||
| 'original': policy | ||
| }) |
There was a problem hiding this comment.
Avoid leaving the singleton with a partially prepared policy cache.
_prepare_policies() clears and appends to self._prepared_policies incrementally, while the broad except Exception keeps the service alive after preparation failures. A malformed policy could leave retrieval running against a partial cache.
🛡️ Proposed fix
- except Exception as e:
- logger.error(f"Error loading policies: {e}")
+ except (OSError, json.JSONDecodeError, TypeError, ValueError) as e:
+ self.policies = []
+ self._prepared_policies = []
+ logger.error("Error loading policies: %s", e)
def _prepare_policies(self):
"""Pre-tokenize and pre-format policies for faster retrieval."""
- self._prepared_policies = []
- for policy in self.policies:
- title = policy.get('title', '')
- text = policy.get('text', '')
- source = policy.get('source', 'Unknown')
+ prepared_policies = []
+ for index, policy in enumerate(self.policies):
+ if not isinstance(policy, dict):
+ raise ValueError(f"Invalid policy entry at index {index}")
+
+ title = str(policy.get('title') or '')
+ text = str(policy.get('text') or '')
+ source = str(policy.get('source') or 'Unknown')
content = f"{title} {text}"
- self._prepared_policies.append({
+ prepared_policies.append({
'title_tokens': self._tokenize(title),
'content_tokens': self._tokenize(content),
'formatted': f"**{title}**: {text} (Source: {source})",
'original': policy
})
+
+ self._prepared_policies = prepared_policies🧰 Tools
🪛 Ruff (0.15.10)
[warning] 37-37: Do not catch blind exception: Exception
(BLE001)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@backend/rag_service.py` around lines 37 - 55, The current _prepare_policies
builds self._prepared_policies incrementally which can leave a partial cache if
an error occurs; change it to construct a local list (e.g., prepared = []) and
populate that using _tokenize and policy.get(...) for each policy, then assign
self._prepared_policies = prepared only after the loop completes; also wrap the
whole routine so any exception clears self._prepared_policies (set to [] or
None) and re-raises or logs a fatal error so the singleton is never left with a
partially prepared cache.
![cubic-dev-ai[bot]](https://avatars.githubusercontent.com/in/1082092?s=60&v=4){kind=small}
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1 issue found across 2 files
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:54">
P3: `'original': policy` is unused dead data in `_prepared_policies` and unnecessarily duplicates policy objects in memory.</violation>
</file>
Reply with feedback, questions, or to request a fix. Tag @cubic-dev-ai to re-run a review.
| 'original': policy | ||
| }) |
There was a problem hiding this comment.
P3: 'original': policy is unused dead data in _prepared_policies and unnecessarily duplicates policy objects in memory.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At backend/rag_service.py, line 54:
<comment>`'original': policy` is unused dead data in `_prepared_policies` and unnecessarily duplicates policy objects in memory.</comment>
<file context>
@@ -22,48 +25,67 @@ def __init__(self, policies_path: str = "backend/data/civic_policies.json"):
+ 'title_tokens': self._tokenize(title),
+ 'content_tokens': self._tokenize(content),
+ 'formatted': f"**{title}**: {text} (Source: {source})",
+ 'original': policy
+ })
+
</file context>
| 'original': policy | |
| }) | |
| }) |
💡 What: Optimized the
CivicRAGservice inbackend/rag_service.pyby pre-compiling the tokenizer regex and pre-processing the policy dataset (tokenization and formatting) during initialization.🎯 Why: Previously, every call to
retrievetriggered redundant regex substitutions and tokenization for every policy in the dataset, leading to unnecessary CPU overhead.📊 Impact: Reduced average retrieval latency from ~0.0865 ms to ~0.0162 ms per call, representing a ~5.3x speedup in the retrieval hot-path.
🔬 Measurement: Verified using
benchmark_rag.py(iterations=1000) and confirmed functional correctness viabackend/tests/test_rag_service.py.PR created automatically by Jules for task 3338056896194688491 started by @RohanExploit
Summary by CodeRabbit
Release Notes
Performance
Documentation
Summary by cubic
Speeds up
CivicRAGretrieval by moving tokenization and formatting to initialization. Average call time drops ~5.3x on the hot path.backend/rag_service.py.retrievenow uses prepared tokens instead of re-tokenizing per call.benchmark_rag.py; tests inbackend/tests/test_rag_service.pypass..jules/bolt.md.Written for commit c3727ad. Summary will update on new commits.