DeepV-Ki addresses the challenge of maintaining and understanding code documentation by transforming code repositories into interactive, AI-powered wikis. It targets developers and technical users seeking to quickly grasp complex codebases, offering features like RAG-based code Q&A and automatic architecture diagram generation. The primary benefit is a significant reduction in documentation overhead and an improvement in code comprehension.

How It Works

This project employs an AI-driven approach to deeply analyze code structure, design patterns, and core logic. It automatically generates a professional, interactive wiki with navigation, detailed documentation, and architectural diagrams rendered using Mermaid. A key component is the Retrieval-Augmented Generation (RAG) system, enabling accurate, multi-turn Q&A directly on the codebase. This method streamlines documentation creation and enhances code exploration capabilities.

Quick Start & Requirements

• Prerequisites: Python 3.12+ (backend), Node.js 18+ (frontend), pnpm (package manager), uv (Python package manager, recommended).
• Setup: Clone the repository, navigate into the directory, copy .env.example to .env, and populate required API keys (e.g., OpenAI, GitLab).
• Run: Execute ./start_dev.sh for a unified development environment.
• Access: Frontend available at http://localhost:3000, backend API docs at http://localhost:8001/docs.
• Configuration: Detailed environment variable configuration is available in .env.example, including LLM API keys and GitLab OAuth settings.

Highlighted Details

• One-click wiki generation supporting 10+ languages.
• AI-powered deep code analysis for understanding structure and logic.
• Automatic generation of interactive Mermaid diagrams (flow, sequence, class).
• RAG-based code Q&A with multi-turn dialogue and streaming responses.
• Supports multiple LLM providers including OpenAI, Gemini, Azure, AWS Bedrock, and local Ollama.
• Compatible with GitHub, GitLab (SaaS/Self-hosted), Bitbucket, Gerrit, and private repositories.

Maintenance & Community

No specific details regarding maintainers, community channels (e.g., Discord, Slack), roadmap, or recent activity were found in the provided README content.

Licensing & Compatibility

• License: MIT License.
• Compatibility: The MIT license is permissive, generally allowing for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

The provided README does not explicitly detail any limitations, alpha status, known bugs, or unsupported platforms. Setup requires specific versions of Python and Node.js, along with necessary API keys for LLM integration.