⚡️ Speed up method ComponentToolkit._should_skip_output by 1,823% in PR #6028 (PlaygroundPage)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #6028

If you approve this dependent PR, these changes will be merged into the original PR branch PlaygroundPage.

This PR will be automatically closed if the original PR is merged.

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📄 1,823% (18.23x) speedup for ComponentToolkit._should_skip_output in src/backend/base/langflow/base/tools/component_tool.py

⏱️ Runtime : 41.4 microseconds → 2.15 microseconds (best of 36 runs)

📝 Explanation and details

To optimize the program for better performance, we can focus on refactoring the logic to efficiently check conditions in the _should_skip_output function. As part of the optimization, we will use frozenset for TOOL_TYPES_SET since it provides faster membership tests and reduce the number of checks by short-circuiting. Here's the rewritten implementation.

Explanation of the Optimizations.

1. Using frozenset for TOOL_TYPES_SET:

   • The frozenset is an immutable set providing faster membership tests (O(1) time complexity) compared to a regular set.
   • It can be initialized once as a class variable to avoid redundant conversions.

2. Short-circuiting Logic.

   • Combined the conditions using short-circuiting (or) which means if the first condition (i.e., not output.tool_mode) is True, it will not check the remaining conditions, thus improving performance.
   • Used self.TOOL_TYPES_SET_FROZEN.intersection(output.types) for efficient set intersection to check if any tool type exists in output.types.

These changes aim to enhance the performance by reducing the number of operations and leveraging faster data structures.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 4 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

from __future__ import annotations

from typing import Any

import pandas as pd
# imports
import pytest  # used for our unit tests
from langflow.base.tools.component_tool import ComponentToolkit
from langflow.custom.custom_component.component import Component
from pydantic import BaseModel, Field, model_serializer, model_validator


class Output(BaseModel):
    types: list[str] = Field(default=[])
    """List of output types for the field."""

    selected: str | None = Field(default=None)
    """The selected output type for the field."""

    name: str = Field(description="The name of the field.")
    """The name of the field."""

    hidden: bool | None = Field(default=None)
    """Dictates if the field is hidden."""

    display_name: str | None = Field(default=None)
    """The display name of the field."""

    method: str | None = Field(default=None)
    """The method to use for the output."""

    value: Any | None = Field(default=None)
    """The result of the Output. Dynamically updated as execution occurs."""

    cache: bool = Field(default=True)

    required_inputs: list[str] | None = Field(default=None)
    """List of required inputs for this output."""

    allows_loop: bool = Field(default=False)
    """Specifies if the output allows looping."""

    tool_mode: bool = Field(default=True)
    """Specifies if the output should be used as a tool"""

    def to_dict(self):
        return self.model_dump(by_alias=True, exclude_none=True)

    def add_types(self, type_: list[Any]) -> None:
        if self.types is None:
            self.types = []
        self.types.extend([t for t in type_ if t not in self.types])

    def set_selected(self) -> None:
        if not self.selected and self.types:
            self.selected = self.types[0]

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        result = handler(self)
        if self.value == UNDEFINED:
            result["value"] = UNDEFINED.value

        return result

    @model_validator(mode="after")
    def validate_model(self):
        if self.value == UNDEFINED.value:
            self.value = UNDEFINED
        if self.name is None:
            msg = "name must be set"
            raise ValueError(msg)
        if self.display_name is None:
            self.display_name = self.name
        return self

TOOL_OUTPUT_NAME = "component_as_tool"

TOOL_TYPES_SET = {"Tool", "BaseTool", "StructuredTool"}
from langflow.base.tools.component_tool import ComponentToolkit


# unit tests
@pytest.fixture
def component_toolkit():
    component = Component()  # Assuming Component can be instantiated like this
    return ComponentToolkit(component)


















from __future__ import annotations

from typing import Any

# imports
import pytest  # used for our unit tests
from langflow.base.tools.component_tool import ComponentToolkit
from pydantic import BaseModel, Field


# function to test
class Output(BaseModel):
    types: list[str] = Field(default=[])
    selected: str | None = Field(default=None)
    name: str = Field(description="The name of the field.")
    hidden: bool | None = Field(default=None)
    display_name: str | None = Field(default=None)
    method: str | None = Field(default=None)
    value: Any | None = Field(default=None)
    cache: bool = Field(default=True)
    required_inputs: list[str] | None = Field(default=None)
    allows_loop: bool = Field(default=False)
    tool_mode: bool = Field(default=True)

    def to_dict(self):
        return self.dict(exclude_none=True)

    def add_types(self, type_: list[Any]) -> None:
        if self.types is None:
            self.types = []
        self.types.extend([t for t in type_ if t not in self.types])

    def set_selected(self) -> None:
        if not self.selected and self.types:
            self.selected = self.types[0]

TOOL_OUTPUT_NAME = "component_as_tool"
TOOL_TYPES_SET = {"Tool", "BaseTool", "StructuredTool"}

class Component:
    pass
from langflow.base.tools.component_tool import ComponentToolkit


# unit tests
def test_tool_mode_false():
    # Output with tool_mode set to False
    output = Output(name="test_output", tool_mode=False)
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_tool_mode_true_name_not_matching():
    # Output with tool_mode set to True and name not matching TOOL_OUTPUT_NAME
    output = Output(name="test_output", tool_mode=True, types=["SomeType"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_name_matching_tool_output_name():
    # Output with name exactly matching TOOL_OUTPUT_NAME
    output = Output(name="component_as_tool", tool_mode=True)
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_name_not_matching_tool_output_name():
    # Output with name not matching TOOL_OUTPUT_NAME
    output = Output(name="different_tool", tool_mode=True)
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_types_containing_single_tool_type():
    # Output with types containing a single tool type
    output = Output(name="test_output", tool_mode=True, types=["Tool"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_types_containing_multiple_tool_types():
    # Output with types containing multiple tool types
    output = Output(name="test_output", tool_mode=True, types=["Tool", "BaseTool"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_types_containing_no_tool_types():
    # Output with types containing no tool types
    output = Output(name="test_output", tool_mode=True, types=["SomeType"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_types_empty_list():
    # Output with types as an empty list
    output = Output(name="test_output", tool_mode=True, types=[])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)


def test_name_empty_string():
    # Output with name as an empty string
    output = Output(name="", tool_mode=True, types=["SomeType"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)


def test_large_number_of_types():
    # Output with a large number of types
    types = ["Type" + str(i) for i in range(1000)]
    output = Output(name="test_output", tool_mode=True, types=types)
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)


def test_tool_mode_false_name_matching():
    # Output with tool_mode set to False and name matching TOOL_OUTPUT_NAME
    output = Output(name="component_as_tool", tool_mode=False)
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_tool_mode_true_name_not_matching_types_containing_tool_type():
    # Output with tool_mode set to True, name not matching TOOL_OUTPUT_NAME, but types containing a tool type
    output = Output(name="different_tool", tool_mode=True, types=["Tool"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)

def test_tool_mode_false_name_not_matching_types_containing_tool_type():
    # Output with tool_mode set to False, name not matching TOOL_OUTPUT_NAME, and types containing a tool type
    output = Output(name="different_tool", tool_mode=False, types=["Tool"])
    toolkit = ComponentToolkit(component=Component())
    codeflash_output = toolkit._should_skip_output(output)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.