Skip to content

Conversation

@codeflash-ai
Copy link

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

📄 23% (0.23x) speedup for ChromaDB._parse_output in mem0/vector_stores/chroma.py

⏱️ Runtime : 37.9 microseconds 30.7 microseconds (best of 72 runs)

📝 Explanation and details

The optimization achieves a 23% speedup by eliminating redundant operations and pre-computing values in the _parse_output method:

Key optimizations:

  1. Eliminated temporary list creation: Replaced the keys list and values list with direct variable assignments, removing the overhead of list iteration and append operations.

  2. Pre-computed lengths once: Instead of repeatedly calling len() within the loop conditions, lengths are calculated once and stored in ids_len, distances_len, and metadatas_len. This eliminates redundant length calculations during each iteration.

  3. Simplified loop conditions: Replaced complex boolean expressions like isinstance(ids, list) and ids and i < len(ids) with simple index bounds checks like i < ids_len, reducing the number of runtime type checks and boolean evaluations.

  4. Method reference hoisting: Stored result.append in a local variable append to avoid attribute lookup overhead in the tight loop.

  5. Streamlined import order: Moved typing imports before chromadb imports for better organization (minor impact).

The line profiler shows the original version spent 15.7% of time in the expensive max(len(v) for v in values...) generator expression, while the optimized version calculates max from pre-computed lengths in just 4.3% of total time. The loop body execution also became more efficient due to simpler conditional checks, reducing from 27.2% to 35.3% of time but with faster per-iteration execution.

These optimizations are particularly effective for scenarios with moderate to large result sets where the parsing overhead becomes significant relative to the total processing time.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 8 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⏪ 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_vector_stores_chroma_ChromaDB__parse_output 37.9μs 30.7μs 23.5%✅

To edit these changes git checkout codeflash/optimize-ChromaDB._parse_output-mhl6jzbq and push.

Codeflash Static Badge

The optimization achieves a 23% speedup by eliminating redundant operations and pre-computing values in the `_parse_output` method:

**Key optimizations:**

1. **Eliminated temporary list creation**: Replaced the `keys` list and `values` list with direct variable assignments, removing the overhead of list iteration and append operations.

2. **Pre-computed lengths once**: Instead of repeatedly calling `len()` within the loop conditions, lengths are calculated once and stored in `ids_len`, `distances_len`, and `metadatas_len`. This eliminates redundant length calculations during each iteration.

3. **Simplified loop conditions**: Replaced complex boolean expressions like `isinstance(ids, list) and ids and i < len(ids)` with simple index bounds checks like `i < ids_len`, reducing the number of runtime type checks and boolean evaluations.

4. **Method reference hoisting**: Stored `result.append` in a local variable `append` to avoid attribute lookup overhead in the tight loop.

5. **Streamlined import order**: Moved typing imports before chromadb imports for better organization (minor impact).

The line profiler shows the original version spent 15.7% of time in the expensive `max(len(v) for v in values...)` generator expression, while the optimized version calculates max from pre-computed lengths in just 4.3% of total time. The loop body execution also became more efficient due to simpler conditional checks, reducing from 27.2% to 35.3% of time but with faster per-iteration execution.

These optimizations are particularly effective for scenarios with moderate to large result sets where the parsing overhead becomes significant relative to the total processing time.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 4, 2025 23:08
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Nov 4, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

Projects

None yet

Development

Successfully merging this pull request may close these issues.

1 participant