feat: Add Chinese documentation and Docker Compose configurations for DeepTutor and llama.cpp
- Created README.zh.md for DeepTutor with comprehensive features, installation steps, and usage instructions in Chinese. - Added docker-compose.yaml for DeepTutor to define services, environment variables, and resource limits. - Introduced .env.example for llama.cpp with configuration options for server settings and resource management. - Added README.md and README.zh.md for llama.cpp detailing features, prerequisites, quick start guides, and API documentation. - Implemented docker-compose.yaml for llama.cpp to support various server configurations (CPU, CUDA, ROCm) and CLI usage.
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Sun-ZhenXing
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apps/deeptutor/README.md
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# DeepTutor
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[中文说明](README.zh.md) | English
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## Overview
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DeepTutor is an AI-powered personalized learning assistant that transforms any document into an interactive learning experience with multi-agent intelligence. It helps you solve problems, generate questions, conduct research, collaborate on writing, organize notes, and guides you through learning paths.
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**Project:** <https://github.com/HKUDS/DeepTutor>
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**License:** Apache-2.0
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**Documentation:** <https://hkuds.github.io/DeepTutor/>
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## Features
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- **Problem Solving** — Detailed step-by-step solutions with visual diagrams
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- **Question Generation** — Adaptive questions based on your knowledge level
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- **Research Assistant** — Deep research with multi-agent collaboration
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- **Co-Writer** — Interactive idea generation and writing assistance
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- **Smart Notebook** — Organize and retrieve learning materials efficiently
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- **Guided Learning** — Personalized learning paths and progress tracking
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- **Multi-Agent System** — Specialized agents for different learning tasks
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- **RAG Integration** — LightRAG and RAG-Anything for knowledge retrieval
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- **Code Execution** — Built-in code playground for practice
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## Quick Start
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### Prerequisites
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- Docker and Docker Compose
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- OpenAI API key (required)
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- Optional: Anthropic, Perplexity, or DashScope API keys
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### Installation
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1. **Clone this repository**
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```bash
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git clone <your-compose-anything-repo>
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cd apps/deeptutor
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```
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2. **Configure environment**
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```bash
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cp .env.example .env
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# Edit .env and add your API keys
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```
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**Required configuration:**
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- `OPENAI_API_KEY` — Your OpenAI API key
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**Optional configuration:**
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- `ANTHROPIC_API_KEY` — For Claude models
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- `PERPLEXITY_API_KEY` — For web search
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- `DASHSCOPE_API_KEY` — For Alibaba Cloud models
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- Adjust ports if needed (default: 8001 for backend, 3782 for frontend)
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- Set `NEXT_PUBLIC_API_BASE_EXTERNAL` for cloud deployments
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3. **Optional: Custom agent configuration**
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Create a `config/agents.yaml` file to customize agent behaviors (see [documentation](https://hkuds.github.io/DeepTutor/guide/config.html) for details).
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4. **Start the service**
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```bash
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docker compose up -d
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```
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First run takes approximately 30-60 seconds to initialize.
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5. **Access the application**
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- **Frontend:** <http://localhost:3782>
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- **Backend API:** <http://localhost:8001>
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- **API Documentation:** <http://localhost:8001/docs>
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## Usage
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### Create Knowledge Base
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1. Navigate to <http://localhost:3782/knowledge>
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2. Click "New Knowledge Base"
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3. Upload documents (supports PDF, DOCX, TXT, Markdown, HTML, etc.)
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4. Wait for processing to complete
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### Learning Modes
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- **Solve** — Get step-by-step solutions to problems
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- **Question** — Generate practice questions based on your materials
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- **Research** — Deep research with multi-agent collaboration
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- **Co-Writer** — Interactive writing and idea generation
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- **Notebook** — Organize and manage your learning materials
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- **Guide** — Follow personalized learning paths
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### Advanced Features
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- **Code Execution** — Practice coding directly in the interface
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- **Visual Diagrams** — Automatic diagram generation for complex concepts
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- **Export** — Download your work as PDF or Markdown
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- **Multi-language** — Support for multiple languages
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## Configuration
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### Environment Variables
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Key environment variables (see [.env.example](.env.example) for all options):
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| Variable | Default | Description |
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| ------------------------ | ---------- | ------------------------- |
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| `OPENAI_API_KEY` | (required) | Your OpenAI API key |
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| `DEFAULT_MODEL` | `gpt-4o` | Default LLM model |
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| `BACKEND_PORT` | `8001` | Backend server port |
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| `FRONTEND_PORT` | `3782` | Frontend application port |
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| `DEEPTUTOR_CPU_LIMIT` | `4.00` | CPU limit (cores) |
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| `DEEPTUTOR_MEMORY_LIMIT` | `8G` | Memory limit |
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### Ports
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- **8001** — Backend API server
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- **3782** — Frontend web interface
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### Volumes
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- `deeptutor_data` — User data, knowledge bases, and learning materials
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- `./config` — Custom agent configurations (optional)
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## Resource Requirements
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**Minimum:**
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- CPU: 1 core
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- Memory: 2GB
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- Disk: 2GB + space for knowledge bases
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**Recommended:**
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- CPU: 4 cores
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- Memory: 8GB
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- Disk: 10GB+
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## Supported Models
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DeepTutor supports multiple LLM providers:
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- **OpenAI** — GPT-4, GPT-4 Turbo, GPT-3.5 Turbo
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- **Anthropic** — Claude 3 (Opus, Sonnet, Haiku)
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- **Perplexity** — For web search integration
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- **DashScope** — Alibaba Cloud models
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- **OpenAI-compatible APIs** — Any API compatible with OpenAI format
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## Troubleshooting
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### Backend fails to start
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- Verify `OPENAI_API_KEY` is set correctly in `.env`
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- Check logs: `docker compose logs -f`
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- Ensure ports 8001 and 3782 are not in use
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- Verify sufficient disk space for volumes
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### Frontend cannot connect to backend
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- Confirm backend is running: visit <http://localhost:8001/docs>
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- For cloud deployments, set `NEXT_PUBLIC_API_BASE_EXTERNAL` to your public URL
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- Check firewall settings
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### Knowledge base processing fails
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- Ensure sufficient memory (recommended 8GB+)
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- Check document format is supported
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- Review logs for specific errors
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### API rate limits
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- Monitor your API usage on provider dashboards
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- Consider upgrading your API plan
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- Use different models for different tasks
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## Security Notes
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- **API Keys** — Keep your API keys secure, never commit them to version control
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- **Network Exposure** — For production deployments, use HTTPS and proper authentication
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- **Data Privacy** — User data is stored in Docker volumes; ensure proper backup and security
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- **Resource Limits** — Set appropriate CPU and memory limits to prevent resource exhaustion
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## Updates
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To update to the latest version:
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```bash
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# Pull the latest image
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docker compose pull
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# Recreate containers
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docker compose up -d
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```
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To update to a specific version, edit `DEEPTUTOR_VERSION` in `.env` and run:
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```bash
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docker compose up -d
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```
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## Advanced Usage
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### Custom Agent Configuration
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Create `config/agents.yaml` to customize agent behaviors:
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```yaml
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agents:
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solver:
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model: gpt-4o
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temperature: 0.7
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researcher:
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model: gpt-4-turbo
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max_tokens: 4000
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```
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See [official documentation](https://hkuds.github.io/DeepTutor/guide/config.html) for detailed configuration options.
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### Cloud Deployment
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For cloud deployment, additional configuration is needed:
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1. Set public URL in `.env`:
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```env
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NEXT_PUBLIC_API_BASE_EXTERNAL=https://your-domain.com:8001
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```
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2. Configure reverse proxy (nginx/Caddy) for HTTPS
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3. Ensure proper firewall rules
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4. Consider using environment-specific secrets management
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### Using Different Embedding Models
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DeepTutor uses `text-embedding-3-large` by default. To use different embedding models, refer to the [official documentation](https://hkuds.github.io/DeepTutor/guide/config.html).
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## Links
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- **GitHub:** <https://github.com/HKUDS/DeepTutor>
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- **Documentation:** <https://hkuds.github.io/DeepTutor/>
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- **Issues:** <https://github.com/HKUDS/DeepTutor/issues>
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- **Discussions:** <https://github.com/HKUDS/DeepTutor/discussions>
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## License
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DeepTutor is licensed under the Apache-2.0 License. See the [official repository](https://github.com/HKUDS/DeepTutor) for details.
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