Prompt-Code

# LLM System Prompt Semantic Processing Solution A solution for semantic processing of system prompts based on LLM, designed to convert natural language into structured semantic label data. ## Features - Semantic label conversion using LLM - Support for field and value extraction - Build contextual relationship graph structures - SQLite data persistence storage ## Installation 1. Create and activate a Python virtual environment: Windows: ```bash # Create virtual environment python -m venv .venv # Activate virtual environment .\.venv\Scripts\activate # Confirm Python interpreter location where python ``` Linux/Mac: ```bash # Create virtual environment python3 -m venv .venv # Activate virtual environment source .venv/bin/activate # Confirm Python interpreter location which python ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` ## Usage 1. Ensure the virtual environment is activated, indicated by (venv) in the terminal prompt. 2. Configure the OpenAI API key: Modify the .env file. 3. Run the main program: ```bash python main.py ``` ## Project Structure ``` project_root/ ├── src/ # Source code ├── tests/ # Test cases ├── config/ # Configuration files ├── data/ # Data files ├── .venv/ # Python virtual environment └── main.py # Main entry point ``` ## Development Guide 1. After activating the virtual environment, install development dependencies: ```bash pip install -r requirements.txt ``` 2. Run tests: ```bash pytest tests/ ``` ## License MIT License