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@codeflash-ai codeflash-ai bot commented Nov 5, 2025

📄 72% (0.72x) speedup for S3VectorsConfig.validate_extra_fields in mem0/configs/vector_stores/s3_vectors.py

⏱️ Runtime : 9.15 microseconds 5.33 microseconds (best of 50 runs)

📝 Explanation and details

The optimized code achieves a 71% speedup through class-level caching of allowed fields. The key optimization is storing cls.model_fields.keys() in _allowed_fields_cache on first access, eliminating repeated computation of the same field names on every validation call.

Key Changes:

  • Field Caching: Uses hasattr() check to cache allowed fields as a tuple in _allowed_fields_cache, computed only once per class
  • Efficient Set Operations: Maintains fast set difference operation set(values) - set(allowed_fields) for extra field detection
  • Deterministic Output: Sorts field names in error messages for consistent, testable output

Why This Works:
In Pydantic model validation, cls.model_fields.keys() accesses the class's field registry every time. For a BaseModel with fixed fields like S3VectorsConfig (5 fields: vector_bucket_name, collection_name, etc.), this is pure overhead. The tuple cache eliminates dictionary attribute access and key extraction on repeated calls, while set conversion for the difference operation remains fast for small field counts.

Performance Context:
This optimization is particularly valuable for configuration classes that undergo frequent validation, such as during object initialization or parameter validation in data processing pipelines. The 71% improvement (9.15μs → 5.33μs) compounds significantly when validation occurs in loops or high-frequency operations.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 2 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 83.3%
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_testsconfigstest_prompts_py_testsvector_storestest_weaviate_py_testsllmstest_deepseek_py_test__replay_test_0.py::test_mem0_configs_vector_stores_s3_vectors_S3VectorsConfig_validate_extra_fields 9.15μs 5.33μs 71.8%✅

To edit these changes git checkout codeflash/optimize-S3VectorsConfig.validate_extra_fields-mhln1zo8 and push.

Codeflash Static Badge

The optimized code achieves a **71% speedup** through class-level caching of allowed fields. The key optimization is storing `cls.model_fields.keys()` in `_allowed_fields_cache` on first access, eliminating repeated computation of the same field names on every validation call.

**Key Changes:**
- **Field Caching**: Uses `hasattr()` check to cache allowed fields as a tuple in `_allowed_fields_cache`, computed only once per class
- **Efficient Set Operations**: Maintains fast set difference operation `set(values) - set(allowed_fields)` for extra field detection
- **Deterministic Output**: Sorts field names in error messages for consistent, testable output

**Why This Works:**
In Pydantic model validation, `cls.model_fields.keys()` accesses the class's field registry every time. For a BaseModel with fixed fields like S3VectorsConfig (5 fields: vector_bucket_name, collection_name, etc.), this is pure overhead. The tuple cache eliminates dictionary attribute access and key extraction on repeated calls, while set conversion for the difference operation remains fast for small field counts.

**Performance Context:**
This optimization is particularly valuable for configuration classes that undergo frequent validation, such as during object initialization or parameter validation in data processing pipelines. The 71% improvement (9.15μs → 5.33μs) compounds significantly when validation occurs in loops or high-frequency operations.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 5, 2025 06:50
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Nov 5, 2025
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