⚡️ Speed up method SearchQueryVector.as_dict by 118%

[codeflash-ai]

📄 118% (1.18x) speedup for SearchQueryVector.as_dict in pinecone/db_data/dataclasses/search_query_vector.py

⏱️ Runtime : 50.7 microseconds → 23.3 microseconds (best of 525 runs)

📝 Explanation and details

The optimization replaces a dictionary comprehension approach with direct conditional assignment, eliminating the overhead of creating an intermediate dictionary and iterating through it.

Key Changes:

• Eliminated intermediate dictionary creation: The original code created a full dictionary with all fields first, then filtered out None values using a dictionary comprehension
• Direct conditional construction: The optimized version builds the result dictionary incrementally, only adding keys when their values are not None
• Single attribute access: Each attribute (self.values, self.sparse_values, self.sparse_indices) is accessed only once and stored in a local variable

Performance Benefits:
The line profiler shows the original dictionary comprehension ({k: v for k, v in d.items() if v is not None}) consumed 60% of the total runtime. This operation involved:

1. Creating d.items() iterator
2. Checking each key-value pair for None
3. Building a new dictionary

The optimized version eliminates this expensive iteration entirely, resulting in a 118% speedup (from 50.7μs to 23.3μs).

Test Case Performance:
The optimization is particularly effective for cases with many None fields - the annotated tests show 158-203% improvements when most or all fields are None, as the optimized version skips unnecessary dictionary operations entirely.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 6 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from dataclasses import dataclass
from typing import List, Optional

# imports
import pytest  # used for our unit tests
from pinecone.db_data.dataclasses.search_query_vector import SearchQueryVector

# unit tests

# 1. Basic Test Cases

def test_as_dict_all_fields_none():
    # All fields are None, should return empty dict
    sqv = SearchQueryVector()
    codeflash_output = sqv.as_dict() # 1.52μs -> 503ns (203% faster)















#------------------------------------------------
from dataclasses import dataclass
from typing import List, Optional

# imports
import pytest  # used for our unit tests
from pinecone.db_data.dataclasses.search_query_vector import SearchQueryVector

# unit tests

# --- Basic Test Cases ---

def test_as_dict_all_fields_none():
    # All fields are None, expect empty dict
    sqv = SearchQueryVector()
    codeflash_output = sqv.as_dict() # 1.30μs -> 493ns (164% faster)



















def test_as_dict_mutation_sensitivity():
    # If as_dict returns fields with None, test should fail
    sqv = SearchQueryVector()
    codeflash_output = sqv.as_dict(); result = codeflash_output # 1.22μs -> 471ns (158% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from pinecone.db_data.dataclasses.search_query_vector import SearchQueryVector

def test_SearchQueryVector_as_dict():
    SearchQueryVector.as_dict(SearchQueryVector(values=[], sparse_values=[], sparse_indices=[]))

To edit these changes git checkout codeflash/optimize-SearchQueryVector.as_dict-mh6a4vln and push.