⚡️ Speed up method ResultDataResponse.serialize_results by 14% in src/backend/base/langflow/api/v1/schemas.py

[codeflash-ai]

📄 ResultDataResponse.serialize_results in src/backend/base/langflow/api/v1/schemas.py

✨ Performance Summary:

• Speed Increase: 📈 14% (0.14x faster)
• Runtime Reduction: ⏱️ From 926 microseconds down to 811 microseconds (best of 201 runs)

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📝 Explanation and details

Certainly! Here's the optimized version of your Python program.

Optimizations Made.

1. isinstance(value, list | tuple) was changed to isinstance(value, (list, tuple)) to use the more standard tuple of types syntax.
2. Combined the if-elif statements for better readability and slight performance enhancement by eliminating redundant checks.
3. Removed the redundant extra serialize_field(value) call for BaseModel by directly calling value.model_dump() within the check.
4. Simplified the serialize_results method by directly returning the result within the conditional statement.

These changes make the code more readable and slightly more efficient by reducing the number of conditional checks and method calls.

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✅ Correctness verification

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

Test	Status	Details
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 23 Passed	See below
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Coverage	87.5%

🌀 Generated Regression Tests Details

Click to view details

from __future__ import annotations

# imports
import pytest  # used for our unit tests
from langchain_core.documents import Document
from langflow.api.v1.schemas import ResultDataResponse
from pydantic import BaseModel, field_serializer
from pydantic.v1 import BaseModel as V1BaseModel

# unit tests

# Mock classes for testing
class MockDocument(Document):
    def to_json(self):
        return '{"mock": "document"}'

class SimpleModel(BaseModel):
    name: str
    age: int

class NestedModel(BaseModel):
    inner: SimpleModel

class SimpleV1Model(V1BaseModel):
    title: str
    year: int

    def to_json(self):
        return '{"title": "%s", "year": %d}' % (self.title, self.year)

# Pydantic BaseModel Instances
def test_pydantic_base_model_instances():
    simple_model = SimpleModel(name="John", age=30)
    nested_model = NestedModel(inner=simple_model)

# Pydantic v1 BaseModel Instances
def test_pydantic_v1_base_model_instances():
    simple_v1_model = SimpleV1Model(title="Test", year=2023)

# Document Instances




from __future__ import annotations

# imports
import pytest  # used for our unit tests
from langchain_core.documents import Document
from langflow.api.v1.schemas import ResultDataResponse
from pydantic import BaseModel, field_serializer
from pydantic.v1 import BaseModel as V1BaseModel

# unit tests

# Custom Document class for testing
class CustomDocument:
    def __init__(self, content):
        self.content = content

    def to_json(self):
        return f'{{"content": "{self.content}"}}'

# Pydantic models for testing
class SimpleModel(BaseModel):
    name: str
    age: int

class Address(BaseModel):
    city: str
    zip: str

class User(BaseModel):
    name: str
    address: Address

class SimpleV1Model(V1BaseModel):
    name: str
    age: int

class AddressV1(V1BaseModel):
    city: str
    zip: str

class UserV1(V1BaseModel):
    name: str
    address: AddressV1

# Test cases

def test_basic_types():
    codeflash_output = ResultDataResponse.serialize_results("hello")
    codeflash_output = ResultDataResponse.serialize_results(123)
    codeflash_output = ResultDataResponse.serialize_results(45.67)
    codeflash_output = ResultDataResponse.serialize_results(True)

def test_lists_and_tuples():
    codeflash_output = ResultDataResponse.serialize_results([1, "two", 3.0])
    codeflash_output = ResultDataResponse.serialize_results((1, "two", 3.0))
    codeflash_output = ResultDataResponse.serialize_results([1, [2, 3], "four"])

def test_dictionaries():
    codeflash_output = ResultDataResponse.serialize_results({"key": "value", "number": 123})
    codeflash_output = ResultDataResponse.serialize_results({"outer": {"inner": "value"}})
    codeflash_output = ResultDataResponse.serialize_results({"list": [1, 2, 3]})

def test_pydantic_models():
    simple_model = SimpleModel(name="John", age=30)
    codeflash_output = ResultDataResponse.serialize_results(simple_model)

    nested_model = User(name="Alice", address=Address(city="Wonderland", zip="12345"))
    codeflash_output = ResultDataResponse.serialize_results(nested_model)

def test_pydantic_v1_models():
    simple_v1_model = SimpleV1Model(name="John", age=30)
    codeflash_output = ResultDataResponse.serialize_results(simple_v1_model)

    nested_v1_model = UserV1(name="Alice", address=AddressV1(city="Wonderland", zip="12345"))
    codeflash_output = ResultDataResponse.serialize_results(nested_v1_model)

def test_custom_document():
    document = CustomDocument(content="Sample content")
    codeflash_output = ResultDataResponse.serialize_results(document)


def test_edge_cases():
    codeflash_output = ResultDataResponse.serialize_results([])
    codeflash_output = ResultDataResponse.serialize_results({})
    codeflash_output = ResultDataResponse.serialize_results(None)

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