Setting up OpenAI API Key for Folder Analysis Project To configure your OpenAI API key and run the folder analysis project, follow the steps below.

Environment Setup

Set the OpenAI API Key in the .env file

root@DESKTOP-4N2JHAU ~/a/analysisproj (master)# tree -L 2 -a
.
├── .env

.env File

OPENAI_API_KEY=sk-*************

Install code LLAMA3-8B

./llama-server --port 9090 --hf-repo hugging-quants/Llama-3.2-3B-Instruct-Q4_K_M-GGUF --hf-file llama-3.2-3b-instruct-q4_k_m.gguf -c 4096 --n-gpu-layers 28


Custom Configuration
In your Rust project, configure the following constants for folder analysis and code summary generation:

main.rs

// Server port configuration
const SERVER_PORT: u16 = 3030;

// List of code file extensions to filter
const CODE_FILE_EXTENSIONS: &[&str] = &[
    "rs", "py", "js", "ts", "java", "cpp", "c", "go", "sh", "rb", "bat", "cs", "resx", "h", "md",
];

// GPT prompt for folder analysis (includes placeholders {})
const FOLDER_ANALYSIS_PROMPT: &str = "Based on the following folder names, identify potential source code directories written by the user. Return a JSON structure with the key 'analysis_key' and a list of directories that match the criteria:\n{folders}\n{extra_folders}";

// GPT prompt for code summarization
const FILE_SUMMARY_PROMPT: &str = "Generate a concise summary for the following code (no more than 100 words). Use professional software engineering terminology and retain the original variable names for easy analysis. Please describe in Traditional Chinese:\n{}";

// Project directory path
const PROJECT_PATH: &str = "/root/Ghost";
Running the Project
To execute the project, use the following command:

cargo run

This will start the folder analysis process, leveraging the OpenAI API for generating summaries and insights.

Demo Output Here are example outputs from running the analysis:

demo

By following these instructions, you can run the project and analyze folder structures with automated code summaries.

x213212/QPR

Set the OpenAI API Key in the .env file

demo