cs-assistant 0.1.2

CS Assistant

CS Assistant

CS Assistant is a command-line tool that helps solve computer science problems by leveraging large language models. It can generate algorithmic explanations, pseudocode, code implementations in multiple programming languages, and visual diagrams.

Features

• Generate verbal explanations of algorithms in multiple languages
• Create pseudocode representations of solutions
• Implement complete code solutions in various programming languages (Python, Java, etc.)
• Generate Mermaid diagrams visualizing the solution
• Support for multiple LLM providers (OpenAI, Google, Anthropic)
• Configurable output directory and formatting options

Installation

Prerequisites

• Python 3.8 or higher
• API keys for at least one of the supported LLM providers (OpenAI, Google, Anthropic)

Option 1: Install from PyPI (Recommended)

Install the package directly from PyPI:

pip install cs-assistant

The first run will automatically create a .config/cs_assistant/.csarc template file in your home directory.

Option 2: Install from Source

1. Clone the repository
2. Run the setup script:

./setup.sh

This will:

• Create a virtual environment
• Install all dependencies
• Create a template .env file for your API keys

3. Activate the virtual environment:

source venv/bin/activate

4. Edit the .env file with your API keys (The first run of the application will also automatically create a .config/cs_assistant/.csarc template file)

Option 3: Manual installation

1. Clone the repository
2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the dependencies:

pip install -r requirements.txt

4. The first run of the application will automatically create a .config/cs_assistant/.csarc file in your home directory. Edit this file with your API keys:

OPENAI_API_KEY="your_openai_api_key_here"
GOOGLE_API_KEY="your_google_api_key_here"
ANTHROPIC_API_KEY="your_anthropic_api_key_here"

Usage

Basic Commands

If installed from PyPI:

# Create a default configuration file
cs-assistant create-config -o config.json

# Solve a coding problem
cs-assistant solve-issue "Write an algorithm to find the longest common subsequence of two strings"

# Solve a problem with specific configuration file
cs-assistant -c config.json solve-issue "Implement a binary search tree"

If installed from source:

# Create a default configuration file
python -m src.main create-config -o config.json

# Solve a coding problem
python -m src.main solve-issue "Write an algorithm to find the longest common subsequence of two strings"

# Solve a problem with specific configuration file
python -m src.main -c config.json solve-issue "Implement a binary search tree"

Command Line Options

usage: CS Assistant [-h] [-c CONFIG] [-o OUTPUT_DIRECTORY] {create-config,solve-issue} ...

options:
  -h, --help            show this help message and exit
  -c CONFIG, --config CONFIG
                        Path to the configuration file (config.json). If provided, command-line arguments will be ignored.
  -o OUTPUT_DIRECTORY, --output-directory OUTPUT_DIRECTORY
                        Directory where solution files will be saved.

Available commands:
  {create-config,solve-issue}
    create-config       Create a new configuration file with default settings.
    solve-issue         Solve a given coding issue.

Solve Issue Command Options

usage: CS Assistant solve-issue [-h] [-m LLM_MODEL] [-t TEMPERATURE] [-v | --verbal-algorithm | --no-verbal-algorithm]
                             [-l VERBAL_ALGORITHM_LANGUAGES [VERBAL_ALGORITHM_LANGUAGES ...]]
                             [-p | --verbal-algorithm-include-pseudocode | --no-verbal-algorithm-include-pseudocode]
                             [-d | --include-mermaid-diagram | --no-include-mermaid-diagram]
                             [-i | --code-implementations | --no-code-implementations]
                             [-L CODE_IMPLEMENTATIONS_LANGUAGES [CODE_IMPLEMENTATIONS_LANGUAGES ...]]
                             issue_description

positional arguments:
  issue_description     The description of the coding issue.

options:
  -h, --help            show this help message and exit
  -m LLM_MODEL, --llm-model LLM_MODEL
                        The identifier for the large language model to be used. Format: 'vendor/model_name' where vendor (e.g., 'google', 'openai') represents
                        the company providing the LLM.
  -t TEMPERATURE, --temperature TEMPERATURE
                        Controls the randomness of the model's output (0.0 to 1.0).

Verbal Algorithm Options:
  -v, --verbal-algorithm, --no-verbal-algorithm
                        Enable/disable verbal algorithm explanations.
  -l VERBAL_ALGORITHM_LANGUAGES [VERBAL_ALGORITHM_LANGUAGES ...], --verbal-algorithm-languages VERBAL_ALGORITHM_LANGUAGES [VERBAL_ALGORITHM_LANGUAGES ...]
                        List of languages for verbal explanations.
  -p, --verbal-algorithm-include-pseudocode, --no-verbal-algorithm-include-pseudocode
                        Include/exclude pseudocode in the output.

  -d, --include-mermaid-diagram, --no-include-mermaid-diagram
                        Include/exclude a Mermaid diagram in the output.

Code Implementation Options:
  -i, --code-implementations, --no-code-implementations
                        Enable/disable code implementation generation.
  -L CODE_IMPLEMENTATIONS_LANGUAGES [CODE_IMPLEMENTATIONS_LANGUAGES ...], --code-implementations-languages CODE_IMPLEMENTATIONS_LANGUAGES [CODE_IMPLEMENTATIONS_LANGUAGES ...]
                        List of programming languages for code implementations.

Example

# Generate a solution for finding prime numbers in Python and JavaScript
cs-assistant solve-issue -L Python JavaScript "Write an algorithm to find all prime numbers up to n using the Sieve of Eratosthenes"

Configuration

You can customize the behavior of CS Assistant using a configuration file. Create a default configuration with:

cs-assistant create-config -o config.json

This will generate a JSON file with default settings that you can modify:

{
  "solve_issue": {
    "llm_model": "google/models/gemini-2.5-pro-exp-03-25",
    "temperature": 0.7,
    "verbal_algorithm": {
      "languages": [
        "en"
      ],
      "include_pseudocode": true
    },
    "include_mermaid_diagram": true,
    "code_implementations": {
      "languages": [
        "Python"
      ]
    }
  },
  "output_directory": "cs-assistant-output"
}

Output

The tool creates a directory structure for each problem in the specified output directory:

cs-assistant-output/
└── find_prime_numbers/
    ├── verbal_algorithm.md      # Contains algorithm explanation and pseudocode
    ├── sieve_of_eratosthenes.py # Python implementation
    ├── SieveOfEratosthenes.js   # JavaScript implementation
    └── find_prime_numbers-diagram.md # Contains Mermaid diagram

API Keys

You need to set up API keys for the LLM providers you want to use. These can be provided in one of two ways:

1. In a .csarc file located at ~/.config/cs_assistant/.csarc
2. As environment variables

Example .csarc file:

OPENAI_API_KEY="sk-..."
GOOGLE_API_KEY="..."
ANTHROPIC_API_KEY="sk-ant-..."

The first run of the application will automatically create this file with template values that you can replace with your actual API keys.

License

See the LICENSE file for details.