What is Coding Assistant

Coding Assistant is an AI-powered coding helper that reads your local code to provide context-aware assistance, explanations, and suggestions directly related to your project files.

Use cases

Use cases include debugging code, understanding complex code structures, generating code snippets, and improving coding efficiency through intelligent suggestions.

How to use

To use Coding Assistant, point it to your project folder and start asking questions. Ensure you have Python 3.11 or higher and the ‘tree’ command installed. Clone the repository, install dependencies, configure API keys, and run the application.

Key features

Key features include local code analysis, context-aware suggestions, customizable LLM provider, and easy setup with Python dependencies.

Where to use

Coding Assistant can be used in software development environments, educational settings for coding, and by individual developers looking for enhanced coding support.

Coding Assistant

Your Local AI Pair Programmer: Point this AI assistant to your project folder and start asking questions!

It reads your code locally to provide truly context-aware help, explanations, and suggestions directly related to your files. Code smarter, locally.

Prerequisites 📝

• Python: Version 3.11 or higher recommended.
• tree command: The get_tree_folders tool relies on this.
  • Linux (Debian/Ubuntu): sudo apt update && sudo apt install tree
  • macOS (using Homebrew): brew install tree

Installation ⚙️

1. Clone the repository:

   git clone https://github.com/Bessouat40/coding-assistant
   cd coding-assistant
   

2. Install Python dependencies:

   pip install -r requirements.txt
   

Configuration 🔑

1. API Keys & Settings: Sensitive information like LLM API keys should be stored in a .env file in the project’s root directory. - Copy the example file or create a new file named .env:

   cp .env.example .env
   

• Edit the .env file and add your keys:

  # .env
  MISTRAL_API_KEY=your_mistral_api_key_here
  # GOOGLE_API_KEY=your_google_api_key_here
  # Add other variables if needed, e.g., MAX_CONTEXT_TOKENS=7000
  

2. LLM Provider: Change LLM provider if you want :

• Go to api/utils.py and modify loadl_llm function. Uncomment the line with the provider you need.
  By default, it’s set to Google :

def load_llm(logger):
    try:
        model = ChatOllama(model="llama3.1:8b")
        # model = ChatMistralAI(model="codestral-latest")
        # model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001")
        logger.info(f"ChatGoogleGenerativeAI model '{model.model}' initialized successfully.")
        return model
    except Exception as e:
        logger.error(f"Failed to initialize the LLM model: {e}")
        raise RuntimeError(f"Could not initialize LLM: {e}") from e

Running the Application ▶️

A convenience script launch_assistant.sh is provided to start all components.

1. Make the script executable:

   chmod +x launch_assistant.sh
   

2. Run the script:

   ./launch_assistant.sh
   

This script will:

• Load environment variables from .env.
• Start the MCP Tool Server (agent_tools.py) in the background (logs to mcp_server.log).
• Start the FastAPI Backend (api.py) in the background (logs to fastapi_api.log).
• Start the Streamlit UI (streamlit_app.py) in the background (logs to streamlit_ui.log).
• Print the URLs and PIDs for each component.

You can view logs in the specified .log files for debugging. Press Ctrl+C in the terminal where you ran the script to stop all components gracefully.

Usage 🧑‍💻

1. Access the UI: Open your web browser and navigate to the Streamlit URL (usually http://localhost:8501).
2. Set Project Directory: In the sidebar, enter the absolute path to the local project directory you want the assistant to work with. Click “Set Directory”.
3. Chat: Use the chat input at the bottom to ask questions about the code in the specified directory. Examples:
   • “Explain the purpose of the run_agent function in api.py.”
   • “Show me the file structure of this project.” (Uses tree)
   • “What arguments does the generate_response_api function take?”
   • “Read the contents of prompt.py.” (Uses cat)
   • “Can you suggest improvements to the error handling in streamlit_app.py?”
4. Follow-up: The assistant remembers the context of your current chat session. Ask follow-up questions naturally.
5. Clear History: Use the “Clear Chat History” button in the sidebar to start a fresh conversation.