Developer
0a9588abb7
- Add pagination support to findAll (page, limit query params) - Add findByTemplateId method to service - Add GET /by-template/:templateId endpoint to controller - Service already includes CRUD for QuestionBank and QuestionBankItem
208 lines
6.3 KiB
Markdown
208 lines
6.3 KiB
Markdown
# AuraK
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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.
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## ✨ Features
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- 🔐 **User System**: Complete user registration, login, and permission management
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- 🤖 **Multi-Model Support**: OpenAI-compatible interfaces + Google Gemini native support
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- 📚 **Intelligent Knowledge Base**: Document upload, chunking, vectorization, hybrid search
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- 💬 **Streaming Chat**: Real-time display of processing status and generated content
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- 🔍 **Citation Tracking**: Clear display of source documents and related segments for answers
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- 🌍 **Multi-Language Support**: Japanese, Chinese, and English for interface and AI responses
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- 👁️ **Vision Capabilities**: Supports multimodal models for image processing
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- ⚙️ **Flexible Configuration**: User-specific API keys and inference parameter customization
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- 🎯 **Dual-Mode Processing**: Fast mode (Tika) + High-precision mode (Vision Pipeline)
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- 💰 **Cost Management**: User quota management and cost estimation
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## 🏗️ Tech Stack
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### Frontend
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- **Framework**: React 19 + TypeScript + Vite
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- **Styling**: Tailwind CSS
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- **Icons**: Lucide React
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- **State Management**: React Context
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### Backend
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- **Framework**: NestJS + TypeScript
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- **AI Framework**: LangChain
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- **Database**: SQLite (metadata) + Elasticsearch (vector storage)
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- **File Processing**: Apache Tika + Vision Pipeline
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- **Authentication**: JWT
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- **Document Conversion**: LibreOffice + ImageMagick
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## 🏢 Internal Network Deployment
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This system supports deployment in internal networks. Main modifications include:
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- **External Resources**: KaTeX CSS moved from external CDN to local resources
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- **AI Models**: Supports configuring internal AI model services without external API access
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- **Build Configuration**: Dockerfiles can be configured to use internal image registries
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See [Internal Deployment Guide](INTERNAL_DEPLOYMENT_GUIDE.md) for detailed configuration instructions.
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## 🚀 Quick Start
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### Prerequisites
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- Node.js 18+
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- Yarn
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- Docker & Docker Compose
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### 1. Clone the Project
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```bash
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git clone <repository-url>
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cd simple-kb
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```
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### 2. Install Dependencies
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```bash
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yarn install
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```
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### 3. Start Basic Services
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```bash
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docker-compose up -d elasticsearch tika libreoffice
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```
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### 4. Configure Environment Variables
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```bash
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# Backend environment setup
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cp server/.env.sample server/.env
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# Edit server/.env file (set API keys, etc.)
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# Frontend environment setup
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cp web/.env.example web/.env
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# Edit web/.env file (modify frontend settings as needed)
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```
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See the comments in `server/.env.sample` and `web/.env.example` for detailed configuration.
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### 5. Start Development Server
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```bash
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yarn dev
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```
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Access http://localhost:5173 to get started!
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## 📖 User Guide
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### 1. User Registration/Login
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- Account registration is required for first-time use.
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- Each user has their own independent knowledge base and model settings.
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### 2. AI Model Configuration
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- Add AI models from "Model Management".
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- Supports OpenAI, DeepSeek, Claude and other compatible interfaces.
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- Supports Google Gemini native interface.
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- Configure LLM, Embedding, and Rerank models.
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### 3. Document Upload
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- Supports various formats: PDF, Word, PPT, Excel, etc.
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- Choose between Fast mode (text-only) or High-precision mode (image + text mixed).
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- Adjustable chunk size and overlap for documents.
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- Select embedding model for vectorization.
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### 4. Start Intelligent Q&A
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- Ask questions based on uploaded documents.
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- View search and generation process in real-time.
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- Check answer sources and related document fragments.
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## 🔧 Configuration Guide
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### Model Settings
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- **LLM Model**: Used for dialogue generation (e.g., GPT-4, Gemini-1.5-Pro)
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- **Embedding Model**: Used for document vectorization (e.g., text-embedding-3-small)
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- **Rerank Model**: Used for re-ranking search results (optional)
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### Inference Parameters
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- **Temperature**: Controls answer randomness (0-1)
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- **Max Tokens**: Maximum output length
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- **Top K**: Number of document segments to search
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- **Similarity Threshold**: Filters low-relevance content
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## 📁 Project Structure
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```
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simple-kb/
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├── web/ # Frontend application
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│ ├── components/ # React components
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│ ├── services/ # API services
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│ ├── contexts/ # React Context
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│ └── utils/ # Utility functions
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├── server/ # Backend application
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│ ├── src/
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│ │ ├── auth/ # Authentication module
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│ │ ├── chat/ # Chat module
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│ │ ├── knowledge-base/ # Knowledge base module
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│ │ ├── model-config/ # Model configuration module
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│ │ └── user/ # User module
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│ └── data/ # Data storage
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├── docs/ # Project documentation
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└── docker-compose.yml # Docker configuration
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```
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## 📚 Documentation
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- [System Design Document](docs/DESIGN.md)
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- [Current Implementation Status](docs/CURRENT_IMPLEMENTATION.md)
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- [API Documentation](docs/API.md)
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- [Deployment Guide](docs/DEPLOYMENT.md)
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- [RAG Feature Implementation](docs/rag_complete_implementation.md)
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## 🐳 Docker Deployment
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### Development Environment
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```bash
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# Start basic services
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docker-compose up -d elasticsearch tika
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# Local development
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yarn dev
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```
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### Production Environment
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```bash
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# Build and start all services
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docker-compose up -d
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```
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## 🤝 Contributing
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1. Fork the project
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2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
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3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
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4. Push to the branch (`git push origin feature/AmazingFeature`)
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5. Open a Pull Request
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## 📄 License
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This project is provided under the MIT license. See the [LICENSE](LICENSE) file for details.
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## 🙏 Acknowledgments
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- [LangChain](https://langchain.com/) - AI application development framework
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- [NestJS](https://nestjs.com/) - Node.js backend framework
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- [React](https://react.dev/) - Frontend UI framework
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- [Elasticsearch](https://www.elastic.co/) - Search and analytics engine
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- [Apache Tika](https://tika.apache.org/) - Document parsing tool
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## 📞 Support
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For questions or suggestions, please submit an [Issue](../../issues) or contact the maintainers.
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