KnowledgeBase

AuraK

AuraK is a multi-tenant intelligent AI knowledge base platform. Built with React + NestJS, it's a full-stack RAG (Retrieval-Augmented Generation) system with external API support, RBAC, and tenant isolation.

✨ Features

• 🔐 User System: Complete user registration, login, and permission management
• 🤖 Multi-Model Support: OpenAI-compatible interfaces + Google Gemini native support
• 📚 Intelligent Knowledge Base: Document upload, chunking, vectorization, hybrid search
• 💬 Streaming Chat: Real-time display of processing status and generated content
• 🔍 Citation Tracking: Clear display of source documents and related segments for answers
• 🌍 Multi-Language Support: Japanese, Chinese, and English for interface and AI responses
• 👁️ Vision Capabilities: Supports multimodal models for image processing
• ⚙️ Flexible Configuration: User-specific API keys and inference parameter customization
• 🎯 Dual-Mode Processing: Fast mode (Tika) + High-precision mode (Vision Pipeline)
• 💰 Cost Management: User quota management and cost estimation

🏗️ Tech Stack

Frontend

• Framework: React 19 + TypeScript + Vite
• Styling: Tailwind CSS
• Icons: Lucide React
• State Management: React Context

Backend

• Framework: NestJS + TypeScript
• AI Framework: LangChain
• Database: SQLite (metadata) + Elasticsearch (vector storage)
• File Processing: Apache Tika + Vision Pipeline
• Authentication: JWT
• Document Conversion: LibreOffice + ImageMagick

🏢 Internal Network Deployment

This system supports deployment in internal networks. Main modifications include:

• External Resources: KaTeX CSS moved from external CDN to local resources
• AI Models: Supports configuring internal AI model services without external API access
• Build Configuration: Dockerfiles can be configured to use internal image registries

See Internal Deployment Guide for detailed configuration instructions.

🚀 Quick Start

Prerequisites

• Node.js 18+
• Yarn
• Docker & Docker Compose

1. Clone the Project

git clone <repository-url>
cd simple-kb

2. Install Dependencies

yarn install

3. Start Basic Services

docker-compose up -d elasticsearch tika libreoffice

4. Configure Environment Variables

# Backend environment setup
cp server/.env.sample server/.env
# Edit server/.env file (set API keys, etc.)

# Frontend environment setup
cp web/.env.example web/.env
# Edit web/.env file (modify frontend settings as needed)

See the comments in server/.env.sample and web/.env.example for detailed configuration.

5. Start Development Server

yarn dev

Access http://localhost:5173 to get started!

📖 User Guide

1. User Registration/Login

• Account registration is required for first-time use.
• Each user has their own independent knowledge base and model settings.

2. AI Model Configuration

• Add AI models from "Model Management".
• Supports OpenAI, DeepSeek, Claude and other compatible interfaces.
• Supports Google Gemini native interface.
• Configure LLM, Embedding, and Rerank models.

3. Document Upload

• Supports various formats: PDF, Word, PPT, Excel, etc.
• Choose between Fast mode (text-only) or High-precision mode (image + text mixed).
• Adjustable chunk size and overlap for documents.
• Select embedding model for vectorization.

4. Start Intelligent Q&A

• Ask questions based on uploaded documents.
• View search and generation process in real-time.
• Check answer sources and related document fragments.

🔧 Configuration Guide

Model Settings

• LLM Model: Used for dialogue generation (e.g., GPT-4, Gemini-1.5-Pro)
• Embedding Model: Used for document vectorization (e.g., text-embedding-3-small)
• Rerank Model: Used for re-ranking search results (optional)

Inference Parameters

• Temperature: Controls answer randomness (0-1)
• Max Tokens: Maximum output length
• Top K: Number of document segments to search
• Similarity Threshold: Filters low-relevance content

📁 Project Structure

simple-kb/
├── web/                 # Frontend application
│   ├── components/      # React components
│   ├── services/        # API services
│   ├── contexts/        # React Context
│   └── utils/          # Utility functions
├── server/             # Backend application
│   ├── src/
│   │   ├── auth/       # Authentication module
│   │   ├── chat/       # Chat module
│   │   ├── knowledge-base/ # Knowledge base module
│   │   ├── model-config/   # Model configuration module
│   │   └── user/       # User module
│   └── data/           # Data storage
├── docs/               # Project documentation
└── docker-compose.yml  # Docker configuration

📚 Documentation

• System Design Document
• Current Implementation Status
• API Documentation
• Deployment Guide
• RAG Feature Implementation

🐳 Docker Deployment

Development Environment

# Start basic services
docker-compose up -d elasticsearch tika

# Local development
yarn dev

Production Environment

# Build and start all services
docker-compose up -d

🤝 Contributing

1. Fork the project
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is provided under the MIT license. See the LICENSE file for details.

🙏 Acknowledgments

• LangChain - AI application development framework
• NestJS - Node.js backend framework
• React - Frontend UI framework
• Elasticsearch - Search and analytics engine
• Apache Tika - Document parsing tool

📞 Support

For questions or suggestions, please submit an Issue or contact the maintainers.