LogoLogo
  • 👋Welcome to Arcee AI Docs
  • Arcee Orchestra
    • Introduction to Arcee Orchestra
    • Getting Started
    • Workflows
      • Workflow Components
        • Model Node
        • Code Node
        • Integrations
        • Knowledge Retrieval
        • Conditional Node
      • Passing Variables
      • API Invocation
        • List Available Workflows API
        • Workflow Execution API
        • Workflow Execution Steps API
        • Execution History API
        • Workflow Diagram API
        • API Code Examples
        • Upload Workflow JSON API
        • Workflow Runs API
    • Workflow Library
      • Research Automation
      • Real Time Financial Analysis
      • Blog Writer
      • Code Improvement
      • Energy Domain Assistant
    • Chat Interface
    • FAQ
  • ARCEE CONDUCTOR
    • Introduction to Arcee Conductor
    • Getting Started
    • Features & Functionality
      • Auto Mode
      • Auto Reasoning Mode
      • Auto Tools Mode
      • Compare
      • Direct Model Invocation
      • Usage
      • API
    • Arcee Small Language Models
      • Model Selection
      • Model Performance
    • Pricing
Powered by GitBook
On this page
  • Demonstration
  • Workflow JSON
  • Input/Output Example
  1. Arcee Orchestra
  2. Workflow Library

Code Improvement

PreviousBlog WriterNextEnergy Domain Assistant

Last updated 26 days ago

The code improvement workflow starts with inputting a code snippet, then uses two models to generate improvements: one for making the code more Pythonic and another for identifying and suggesting security improvements. Finally, all improvements are compiled into a Google Doc.

Demonstration

Workflow JSON

To download the workflow, select the JSON file above, then use command/control + S and save with the .json extension.

Input/Output Example

Any python code, such as:

import subprocess
import sys


def install_package(package_name):
  try:
    __import__(package_name)
  except ImportError:
    print(f"{package_name} not found. Installing...")
    subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])


install_package("yfinance")

The workflow output is a Google Doc, which has content similar to the following:

Original Code

import subprocess
import sys


def install_package(package_name):
  try:
    __import__(package_name)
  except ImportError:
    print(f"{package_name} not found. Installing...")
    subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])


install_package("yfinance")

Pythonic Improvements

import importlib
import subprocess
import sys


def install_package(package_name):
    try:
        importlib.import_module(package_name)
    except ImportError:
        print(f"{package_name} not found. Installing...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])


install_package("yfinance")
import importlib.util
import subprocess
import sys


def install_package(package_name):
    if importlib.util.find_spec(package_name) is None:
        print(f"{package_name} not found. Installing...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])


install_package("yfinance")

Explanation:

- importlib.import_module vs. importlib.util.find_spec: The importlib.import_module function attempts to import the module, which can be slow and unnecessary if we only want to check for its existence. importlib.util.find_spec is more efficient as it only checks if the module specification exists without performing the import.

- Readability: Using importlib.util.find_spec makes the code more readable and Pythonic, as it clearly expresses the intent to check for the module's existence without side effects.

Security Improvements

1. Remote Code Execution: The subprocess.check_call function can be risky if the package_name is derived from user input. This can be mitigated by sanitizing the input and using a whitelist of trusted packages.

2. Package Integrity: Verify the package's integrity by using a trusted package index or checking digital signatures.

3. Error Handling: Implement comprehensive error handling to manage various failure scenarios.

4. Virtual Environments: Encourage the use of virtual environments to isolate package installations.

5. Update pip: Ensure pip is up-to-date to benefit from the latest security patches.

subprocess.check_callpackage_namepipimport subprocess
import sys
import pkg_resources


def install_package(package_name, source=None):
    # Check if the package is already installed
    if package_name in {pkg.key for pkg in pkg_resources.working_set}:
        print(f"{package_name} is already installed.")
        return


    # Ensure the package name is safe and trusted
    trusted_packages = {'yfinance', 'numpy', 'pandas', 'requests'}  # Example trusted packages
    if package_name not in trusted_packages:
        raise ValueError(f"Untrusted package: {package_name}")


    # Check if the package source is trusted
    trusted_sources = ['https://pypi.org/simple']
    if source is not None and source not in trusted_sources:
        raise ValueError(f"Untrusted source for {package_name}")


    try:
        # Install the package with a trusted source
        subprocess.check_call([sys.executable, "-m", "pip", "install", package_name, "--index-url", source or trusted_sources[0]])
    except subprocess.CalledProcessError as e:
        print(f"Error installing {package_name}: {e}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")


# Example usage:
install_package("yfinance", "https://pypi.org/simple")

Explanation:

- Input Validation: The trusted_packages set ensures that only known and trusted packages can be installed. This prevents potential command injection attacks.

- Trusted Sources: The trusted_sources list restricts the package installation to trusted indices, reducing the risk of installing malicious packages.

- Error Handling: Comprehensive error handling is implemented to catch and report specific errors, such as subprocess.CalledProcessError, and handle unexpected exceptions.

- Virtual Environments: While not explicitly shown in the code, it is recommended to run this script within a virtual environment to isolate the package installation.

- Update pip: Ensure that pip is up-to-date by running pip install --upgrade pip periodically.

trusted_packagestrusted_sourcessubprocess.CalledProcessErrorpippip install --upgrade pipBy implementing these improvements, the code becomes more Pythonic, efficient, and secure.

Build a Code Review Workflow in Arcee Orchestra
17KB
CodeImprovement.json
JSON for Code Improvement Workflow
Code Improvement Workflow
Page cover image