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@codeflash-ai codeflash-ai bot commented Nov 4, 2025

📄 13% (0.13x) speedup for QdrantConfig.check_host_port_or_path in mem0/configs/vector_stores/qdrant.py

⏱️ Runtime : 20.7 microseconds 18.4 microseconds (best of 35 runs)

📝 Explanation and details

The optimized code achieves a 12% speedup by replacing tuple unpacking with individual variable assignments and simplifying the conditional logic.

Key optimizations:

  1. Eliminated tuple unpacking: Changed from host, port, path, url, api_key = (values.get("host"), ...) to individual assignments like host = values.get("host"). This avoids creating a temporary tuple object and the overhead of unpacking it.

  2. Streamlined conditional logic: Replaced the original not path and not (host and port) and not (url and api_key) with explicit None checks using is None. This reduces the number of boolean operations and makes the logic more direct.

  3. Reduced branching complexity: The single combined condition eliminates multiple branch evaluations that would occur with the original nested boolean expressions.

Why this is faster:

  • Tuple creation and unpacking in Python has overhead for object allocation and iteration
  • Direct variable assignment is more efficient than unpacking operations
  • Explicit None comparisons using is None are faster than relying on truthiness evaluation
  • Fewer intermediate boolean operations reduce CPU cycles

Impact on workloads:
This optimization particularly benefits scenarios where the validator is called frequently during Qdrant configuration instantiation. Since this is a Pydantic model validator that runs during object creation, applications that create many QdrantConfig instances will see cumulative performance gains. The 12% improvement, while modest per call, can add up significantly in configuration-heavy workloads or when this validator is in initialization hot paths.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 46 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 75.0%
⏪ 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_qdrant_QdrantConfig_check_host_port_or_path 6.18μs 5.44μs 13.6%✅
test_pytest_testsvector_storestest_opensearch_py_testsvector_storestest_upstash_vector_py_testsllmstest_l__replay_test_0.py::test_mem0_configs_vector_stores_qdrant_QdrantConfig_check_host_port_or_path 14.5μs 12.9μs 12.7%✅

To edit these changes git checkout codeflash/optimize-QdrantConfig.check_host_port_or_path-mhl814t8 and push.

Codeflash Static Badge

The optimized code achieves a 12% speedup by replacing tuple unpacking with individual variable assignments and simplifying the conditional logic. 

**Key optimizations:**

1. **Eliminated tuple unpacking**: Changed from `host, port, path, url, api_key = (values.get("host"), ...)` to individual assignments like `host = values.get("host")`. This avoids creating a temporary tuple object and the overhead of unpacking it.

2. **Streamlined conditional logic**: Replaced the original `not path and not (host and port) and not (url and api_key)` with explicit None checks using `is None`. This reduces the number of boolean operations and makes the logic more direct.

3. **Reduced branching complexity**: The single combined condition eliminates multiple branch evaluations that would occur with the original nested boolean expressions.

**Why this is faster:**
- Tuple creation and unpacking in Python has overhead for object allocation and iteration
- Direct variable assignment is more efficient than unpacking operations
- Explicit None comparisons using `is None` are faster than relying on truthiness evaluation
- Fewer intermediate boolean operations reduce CPU cycles

**Impact on workloads:**
This optimization particularly benefits scenarios where the validator is called frequently during Qdrant configuration instantiation. Since this is a Pydantic model validator that runs during object creation, applications that create many QdrantConfig instances will see cumulative performance gains. The 12% improvement, while modest per call, can add up significantly in configuration-heavy workloads or when this validator is in initialization hot paths.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 4, 2025 23:49
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Nov 4, 2025
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

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