⚡️ Speed up VectorDBQA.validate_search_type() by 7% in libs/langchain/langchain/chains/retrieval_qa/base.py

[codeflash-ai]

📄 VectorDBQA.validate_search_type() in libs/langchain/langchain/chains/retrieval_qa/base.py

📈 Performance improved by 7% (0.07x faster)

⏱️ Runtime went down from 1.92μs to 1.79μs

Explanation and details

(click to show)

The given code snippet is already very optimized and simple. However, we can speed it up a little bit by eliminating the local variable assignment.

Here, we are directly accessing the "search_type" value from the dictionary and combining both conditions into one line. This way, we eliminate the unnecessary variable assignment thus marginally speeding up the code execution. But the improvement might be negligible given that the original code was already optimized.

Correctness verification

The new optimized code was tested for correctness. The results are listed below.

🔘 (none found) − ⚙️ Existing Unit Tests

✅ 19 Passed − 🌀 Generated Regression Tests

(click to show generated tests)

# imports
import pytest  # used for our unit tests
from pydantic import (  # assuming BaseRetrievalQA is a subclass of BaseModel
    BaseModel, root_validator)

# function to test

class BaseRetrievalQA(BaseModel):
    pass
from langchain.chains.retrieval_qa.base import VectorDBQA

# unit tests

# Test valid search_type values
@pytest.mark.parametrize("valid_search_type", ["similarity", "mmr"])
def test_validate_search_type_valid(valid_search_type):
    # Prepare input dictionary with a valid search_type
    values = {"search_type": valid_search_type}
    # Expect the function to return the input dictionary unchanged
    assert VectorDBQA.validate_search_type(values) == values

# Test invalid search_type values
@pytest.mark.parametrize("invalid_search_type", ["invalid_type", "", None, 1, [], {}])
def test_validate_search_type_invalid(invalid_search_type):
    # Prepare input dictionary with an invalid search_type
    values = {"search_type": invalid_search_type}
    # Expect the function to raise ValueError for invalid search_type
    with pytest.raises(ValueError):
        VectorDBQA.validate_search_type(values)

# Test missing search_type key
def test_validate_search_type_missing():
    # Prepare input dictionary without a search_type key
    values = {"another_key": "another_value"}
    # Expect the function to return the input dictionary unchanged
    assert VectorDBQA.validate_search_type(values) == values

# Test empty dictionary
def test_validate_search_type_empty_dict():
    # Prepare an empty input dictionary
    values = {}
    # Expect the function to return the input dictionary unchanged
    assert VectorDBQA.validate_search_type(values) == values

# Test search_type with mixed data types
@pytest.mark.parametrize("mixed_type_search_type", [True, 1.0])
def test_validate_search_type_mixed_types(mixed_type_search_type):
    # Prepare input dictionary with a search_type of mixed data types
    values = {"search_type": mixed_type_search_type}
    # Expect the function to raise ValueError for non-string search_type
    with pytest.raises(ValueError):
        VectorDBQA.validate_search_type(values)

# Test search_type with special characters or whitespace
@pytest.mark.parametrize("special_char_search_type", [" similarity ", "similarity!"])
def test_validate_search_type_special_chars(special_char_search_type):
    # Prepare input dictionary with a search_type with special characters or whitespace
    values = {"search_type": special_char_search_type}
    # Expect the function to raise ValueError for search_type with special characters or whitespace
    with pytest.raises(ValueError):
        VectorDBQA.validate_search_type(values)

# Test search_type with similar but incorrect strings
@pytest.mark.parametrize("similar_search_type", ["similarty", "similarity_search"])
def test_validate_search_type_similar_strings(similar_search_type):
    # Prepare input dictionary with a search_type that is similar but incorrect
    values = {"search_type": similar_search_type}
    # Expect the function to raise ValueError for similar but incorrect search_type
    with pytest.raises(ValueError):
        VectorDBQA.validate_search_type(values)

# Test search_type with NoneType or empty collections
@pytest.mark.parametrize("none_or_empty_search_type", [None, [], {}])
def test_validate_search_type_none_or_empty(none_or_empty_search_type):
    # Prepare input dictionary with a search_type of NoneType or empty collections
    values = {"search_type": none_or_empty_search_type}
    # Expect the function to raise ValueError for NoneType or empty collections as search_type
    with pytest.raises(ValueError):
        VectorDBQA.validate_search_type(values)