OpenRepoWiki

OpenRepoWiki is a tool that automatically generates a comprehensive wiki page for any given GitHub repository. I hate reading code, but I want to learn how to build stuffs from websites to databases. That's why I built OpenRepoWiki, where we can understand the purpose of that files and folders of a particular repository.

Features

• Automated Wiki Generation: Creates a summarized overview of a repository's purpose, functionality, and core components.
• Codebase Analysis: Analyzes the code structure, identifies key files and functions, and explains their roles within the project.
• Link To That Code Block: The sky-blue highlighted code block will point to the Github link where it referenced.

Installation

Requirements

• Either Google AI Studio or Deepseek API Key
• PostgreSQL (For storing the summarized repository information)
• Github API Key (To get more quota requesting the repository data)
• Amazon S3 (You can ignore the parameters if you are going to use it locally. You need to use certificate for your Database if you are going to host it.)
• Docker (If you are hosting locally)

Configuration (Local)

1. Copy .env.example to .env
2. Configure all the variables given in .env
3. Run docker compose up or docker compose up -d to hide the output

Configuration (Cloud)

1. Create PostgreSQL instance
2. Copy .env.example to .env
3. Configure all the variables given in .env
4. Install all the dependencies (npm install)
5. Initialize the database by typing (npm run db:init). If this does not work, you can install database manager GUI, connect to the database then manually execute SQL src/db/migrations/create_tables.sql
6. Build the server (npm run build)
7. Run (npm start)

Ollama Configuration Guide

• It's recommended if you can run bigger LLM than 14b parameter.
• You do not need to provide the API KEY
• Set LLM_PROVIDER to Ollama (It is going to connect to default ollama endpoint)
• Set LLM_MODELNAME to the model name you can see from Ollama using the command ollama ls
• It is recommended to set TOKEN_PROCESSING_CHARACTER_LIMIT between 10000-20000 (Approx 300-600 lines of code) if you are using low param LLM (ex. 8b, 14b)

Example:

LLM_PROVIDER=ollama
LLM_APIKEY=
LLM_MODELNAME=qwen2.5:14b

Additional Information

Caution

Before using this, it can easily use 1 million input / output tokens per Repository. Hence it is recommended to use cheaper LLM.

• If you are going to host it locally, you will only need to configure the Docker PostgreSQL container, Github API Key, and Google AI Studio or Deepseek API Key

Requirements and Documentation

Refer Documentation