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.
This commit is contained in:
Sun-ZhenXing
97
apps/deeptutor/.env.example
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97
apps/deeptutor/.env.example
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# DeepTutor Configuration
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# Copy this file to .env and fill in your API keys
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#! ==================================================
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#! General Settings
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#! ==================================================
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# Timezone (default: UTC)
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TZ=UTC
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# User and Group ID for file permissions (default: 1000)
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# Adjust if your host user has a different UID/GID
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PUID=1000
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PGID=1000
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# Global registry prefix (optional)
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# Example: registry.example.com/ or leave empty for Docker Hub/GHCR
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GLOBAL_REGISTRY=
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#! ==================================================
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#! DeepTutor Version
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#! ==================================================
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# Image version (default: latest)
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# Available tags: latest, v0.5.x
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# See: https://github.com/HKUDS/DeepTutor/pkgs/container/deeptutor
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DEEPTUTOR_VERSION=latest
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#! ==================================================
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#! Port Configuration
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#! ==================================================
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# Backend port (internal: 8001)
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BACKEND_PORT=8001
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# Host port override for backend
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DEEPTUTOR_BACKEND_PORT_OVERRIDE=8001
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# Frontend port (internal: 3782)
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FRONTEND_PORT=3782
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# Host port override for frontend
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DEEPTUTOR_FRONTEND_PORT_OVERRIDE=3782
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#! ==================================================
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#! API Base URLs
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#! ==================================================
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# Internal API base URL (used by frontend to communicate with backend)
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NEXT_PUBLIC_API_BASE=http://localhost:8001
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# External API base URL (for cloud deployment, set to your public URL)
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# Example: https://your-server.com:8001
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# For local deployment, use the same as NEXT_PUBLIC_API_BASE
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NEXT_PUBLIC_API_BASE_EXTERNAL=http://localhost:8001
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#! ==================================================
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#! LLM API Keys (Required)
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#! ==================================================
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# OpenAI API Key (Required)
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# Get from: https://platform.openai.com/api-keys
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OPENAI_API_KEY=sk-your-openai-api-key-here
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# OpenAI Base URL (default: https://api.openai.com/v1)
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# For OpenAI-compatible APIs (e.g., Azure OpenAI, custom endpoints)
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OPENAI_BASE_URL=https://api.openai.com/v1
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# Default LLM Model (default: gpt-4o)
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# Options: gpt-4o, gpt-4-turbo, gpt-4, gpt-3.5-turbo, etc.
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DEFAULT_MODEL=gpt-4o
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#! ==================================================
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#! Additional LLM API Keys (Optional)
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#! ==================================================
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# Anthropic API Key (Optional, for Claude models)
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# Get from: https://console.anthropic.com/
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ANTHROPIC_API_KEY=
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# Perplexity API Key (Optional, for web search)
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# Get from: https://www.perplexity.ai/settings/api
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PERPLEXITY_API_KEY=
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# DashScope API Key (Optional, for Alibaba Cloud models)
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# Get from: https://dashscope.console.aliyun.com/
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DASHSCOPE_API_KEY=
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#! ==================================================
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#! Resource Limits
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#! ==================================================
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# CPU limits (default: 4.00 cores limit, 1.00 cores reservation)
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DEEPTUTOR_CPU_LIMIT=4.00
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DEEPTUTOR_CPU_RESERVATION=1.00
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# Memory limits (default: 8G limit, 2G reservation)
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DEEPTUTOR_MEMORY_LIMIT=8G
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DEEPTUTOR_MEMORY_RESERVATION=2G
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248
apps/deeptutor/README.md
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248
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
|
||||
- **Co-Writer** — Interactive idea generation and writing assistance
|
||||
- **Smart Notebook** — Organize and retrieve learning materials efficiently
|
||||
- **Guided Learning** — Personalized learning paths and progress tracking
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||||
- **Multi-Agent System** — Specialized agents for different learning tasks
|
||||
- **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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|
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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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|
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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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|
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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
|
||||
- **Research** — Deep research with multi-agent collaboration
|
||||
- **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
|
||||
- **Export** — Download your work as PDF or Markdown
|
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- **Multi-language** — Support for multiple languages
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|
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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 |
|
||||
| ------------------------ | ---------- | ------------------------- |
|
||||
| `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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||||
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DeepTutor supports multiple LLM providers:
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|
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- **OpenAI** — GPT-4, GPT-4 Turbo, GPT-3.5 Turbo
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- **Anthropic** — Claude 3 (Opus, Sonnet, Haiku)
|
||||
- **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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||||
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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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||||
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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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||||
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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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||||
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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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||||
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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
|
||||
- **Data Privacy** — User data is stored in Docker volumes; ensure proper backup and security
|
||||
- **Resource Limits** — Set appropriate CPU and memory limits to prevent resource exhaustion
|
||||
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||||
## Updates
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||||
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||||
To update to the latest version:
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||||
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||||
```bash
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# Pull the latest image
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docker compose pull
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||||
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||||
# Recreate containers
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||||
docker compose up -d
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||||
```
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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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||||
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||||
```bash
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||||
docker compose up -d
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||||
```
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||||
|
||||
## Advanced Usage
|
||||
|
||||
### Custom Agent Configuration
|
||||
|
||||
Create `config/agents.yaml` to customize agent behaviors:
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||||
|
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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.
|
||||
|
||||
### Cloud Deployment
|
||||
|
||||
For cloud deployment, additional configuration is needed:
|
||||
|
||||
1. Set public URL in `.env`:
|
||||
|
||||
```env
|
||||
NEXT_PUBLIC_API_BASE_EXTERNAL=https://your-domain.com:8001
|
||||
```
|
||||
|
||||
2. Configure reverse proxy (nginx/Caddy) for HTTPS
|
||||
3. Ensure proper firewall rules
|
||||
4. Consider using environment-specific secrets management
|
||||
|
||||
### Using Different Embedding Models
|
||||
|
||||
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).
|
||||
|
||||
## Links
|
||||
|
||||
- **GitHub:** <https://github.com/HKUDS/DeepTutor>
|
||||
- **Documentation:** <https://hkuds.github.io/DeepTutor/>
|
||||
- **Issues:** <https://github.com/HKUDS/DeepTutor/issues>
|
||||
- **Discussions:** <https://github.com/HKUDS/DeepTutor/discussions>
|
||||
|
||||
## License
|
||||
|
||||
DeepTutor is licensed under the Apache-2.0 License. See the [official repository](https://github.com/HKUDS/DeepTutor) for details.
|
||||
248
apps/deeptutor/README.zh.md
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248
apps/deeptutor/README.zh.md
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@@ -0,0 +1,248 @@
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||||
# DeepTutor
|
||||
|
||||
中文说明 | [English](README.md)
|
||||
|
||||
## 概述
|
||||
|
||||
DeepTutor 是一个 AI 驱动的个性化学习助手，通过多智能体系统将任何文档转化为交互式学习体验。它可以帮助您解决问题、生成题目、进行研究、协作写作、整理笔记，并引导您完成学习路径。
|
||||
|
||||
**项目地址：** <https://github.com/HKUDS/DeepTutor>
|
||||
**许可证：** Apache-2.0
|
||||
**文档：** <https://hkuds.github.io/DeepTutor/>
|
||||
|
||||
## 功能特性
|
||||
|
||||
- **问题求解** — 提供详细的分步解决方案和可视化图表
|
||||
- **题目生成** — 根据您的知识水平生成自适应题目
|
||||
- **研究助手** — 通过多智能体协作进行深度研究
|
||||
- **协作写作** — 交互式创意生成和写作辅助
|
||||
- **智能笔记** — 高效组织和检索学习材料
|
||||
- **引导学习** — 个性化学习路径和进度跟踪
|
||||
- **多智能体系统** — 针对不同学习任务的专业智能体
|
||||
- **RAG 集成** — 使用 LightRAG 和 RAG-Anything 进行知识检索
|
||||
- **代码执行** — 内置代码练习环境
|
||||
|
||||
## 快速开始
|
||||
|
||||
### 前置要求
|
||||
|
||||
- Docker 和 Docker Compose
|
||||
- OpenAI API 密钥（必需）
|
||||
- 可选：Anthropic、Perplexity 或 DashScope API 密钥
|
||||
|
||||
### 安装步骤
|
||||
|
||||
1. **克隆仓库**
|
||||
|
||||
```bash
|
||||
git clone <your-compose-anything-repo>
|
||||
cd apps/deeptutor
|
||||
```
|
||||
|
||||
2. **配置环境变量**
|
||||
|
||||
```bash
|
||||
cp .env.example .env
|
||||
# 编辑 .env 文件并添加您的 API 密钥
|
||||
```
|
||||
|
||||
**必需配置：**
|
||||
- `OPENAI_API_KEY` — 您的 OpenAI API 密钥
|
||||
|
||||
**可选配置：**
|
||||
- `ANTHROPIC_API_KEY` — 用于 Claude 模型
|
||||
- `PERPLEXITY_API_KEY` — 用于网络搜索
|
||||
- `DASHSCOPE_API_KEY` — 用于阿里云模型
|
||||
- 如需调整端口（默认：后端 8001，前端 3782）
|
||||
- 云端部署时设置 `NEXT_PUBLIC_API_BASE_EXTERNAL`
|
||||
|
||||
3. **可选：自定义智能体配置**
|
||||
|
||||
创建 `config/agents.yaml` 文件以自定义智能体行为（详见[文档](https://hkuds.github.io/DeepTutor/guide/config.html)）。
|
||||
|
||||
4. **启动服务**
|
||||
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
首次运行需要约 30-60 秒初始化。
|
||||
|
||||
5. **访问应用**
|
||||
|
||||
- **前端界面：** <http://localhost:3782>
|
||||
- **后端 API：** <http://localhost:8001>
|
||||
- **API 文档：** <http://localhost:8001/docs>
|
||||
|
||||
## 使用方法
|
||||
|
||||
### 创建知识库
|
||||
|
||||
1. 访问 <http://localhost:3782/knowledge>
|
||||
2. 点击"新建知识库"
|
||||
3. 上传文档（支持 PDF、DOCX、TXT、Markdown、HTML 等）
|
||||
4. 等待处理完成
|
||||
|
||||
### 学习模式
|
||||
|
||||
- **求解（Solve）** — 获取问题的分步解决方案
|
||||
- **题目（Question）** — 基于学习材料生成练习题
|
||||
- **研究（Research）** — 通过多智能体协作进行深度研究
|
||||
- **协作写作（Co-Writer）** — 交互式写作和创意生成
|
||||
- **笔记（Notebook）** — 组织和管理学习材料
|
||||
- **引导（Guide）** — 遵循个性化学习路径
|
||||
|
||||
### 高级功能
|
||||
|
||||
- **代码执行** — 在界面中直接练习编码
|
||||
- **可视化图表** — 为复杂概念自动生成图表
|
||||
- **导出** — 将您的工作下载为 PDF 或 Markdown
|
||||
- **多语言支持** — 支持多种语言
|
||||
|
||||
## 配置说明
|
||||
|
||||
### 环境变量
|
||||
|
||||
主要环境变量（所有选项见 [.env.example](.env.example)）：
|
||||
|
||||
| 变量 | 默认值 | 描述 |
|
||||
| ------------------------ | -------- | -------------------- |
|
||||
| `OPENAI_API_KEY` | （必需） | 您的 OpenAI API 密钥 |
|
||||
| `DEFAULT_MODEL` | `gpt-4o` | 默认 LLM 模型 |
|
||||
| `BACKEND_PORT` | `8001` | 后端服务器端口 |
|
||||
| `FRONTEND_PORT` | `3782` | 前端应用端口 |
|
||||
| `DEEPTUTOR_CPU_LIMIT` | `4.00` | CPU 限制（核心数） |
|
||||
| `DEEPTUTOR_MEMORY_LIMIT` | `8G` | 内存限制 |
|
||||
|
||||
### 端口说明
|
||||
|
||||
- **8001** — 后端 API 服务器
|
||||
- **3782** — 前端 Web 界面
|
||||
|
||||
### 数据卷
|
||||
|
||||
- `deeptutor_data` — 用户数据、知识库和学习材料
|
||||
- `./config` — 自定义智能体配置（可选）
|
||||
|
||||
## 资源要求
|
||||
|
||||
**最低配置：**
|
||||
|
||||
- CPU：1 核心
|
||||
- 内存：2GB
|
||||
- 磁盘：2GB + 知识库所需空间
|
||||
|
||||
**推荐配置：**
|
||||
|
||||
- CPU：4 核心
|
||||
- 内存：8GB
|
||||
- 磁盘：10GB+
|
||||
|
||||
## 支持的模型
|
||||
|
||||
DeepTutor 支持多个 LLM 提供商：
|
||||
|
||||
- **OpenAI** — GPT-4、GPT-4 Turbo、GPT-3.5 Turbo
|
||||
- **Anthropic** — Claude 3（Opus、Sonnet、Haiku）
|
||||
- **Perplexity** — 用于网络搜索集成
|
||||
- **DashScope** — 阿里云模型
|
||||
- **OpenAI 兼容 API** — 任何与 OpenAI 格式兼容的 API
|
||||
|
||||
## 故障排查
|
||||
|
||||
### 后端启动失败
|
||||
|
||||
- 验证 `.env` 中的 `OPENAI_API_KEY` 是否正确设置
|
||||
- 查看日志：`docker compose logs -f`
|
||||
- 确保端口 8001 和 3782 未被占用
|
||||
- 验证数据卷有足够的磁盘空间
|
||||
|
||||
### 前端无法连接后端
|
||||
|
||||
- 确认后端正在运行：访问 <http://localhost:8001/docs>
|
||||
- 云端部署时，将 `NEXT_PUBLIC_API_BASE_EXTERNAL` 设置为您的公网 URL
|
||||
- 检查防火墙设置
|
||||
|
||||
### 知识库处理失败
|
||||
|
||||
- 确保有足够的内存（推荐 8GB+）
|
||||
- 检查文档格式是否支持
|
||||
- 查看日志了解具体错误
|
||||
|
||||
### API 速率限制
|
||||
|
||||
- 在提供商控制台监控 API 使用情况
|
||||
- 考虑升级 API 计划
|
||||
- 为不同任务使用不同模型
|
||||
|
||||
## 安全提示
|
||||
|
||||
- **API 密钥** — 妥善保管您的 API 密钥，切勿提交到版本控制系统
|
||||
- **网络暴露** — 生产环境部署时，使用 HTTPS 和适当的身份验证
|
||||
- **数据隐私** — 用户数据存储在 Docker 卷中，请确保适当的备份和安全措施
|
||||
- **资源限制** — 设置合适的 CPU 和内存限制以防止资源耗尽
|
||||
|
||||
## 更新
|
||||
|
||||
更新到最新版本：
|
||||
|
||||
```bash
|
||||
# 拉取最新镜像
|
||||
docker compose pull
|
||||
|
||||
# 重新创建容器
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
更新到特定版本，编辑 `.env` 中的 `DEEPTUTOR_VERSION` 并运行：
|
||||
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
## 高级用法
|
||||
|
||||
### 自定义智能体配置
|
||||
|
||||
创建 `config/agents.yaml` 以自定义智能体行为：
|
||||
|
||||
```yaml
|
||||
agents:
|
||||
solver:
|
||||
model: gpt-4o
|
||||
temperature: 0.7
|
||||
researcher:
|
||||
model: gpt-4-turbo
|
||||
max_tokens: 4000
|
||||
```
|
||||
|
||||
详细配置选项请参见[官方文档](https://hkuds.github.io/DeepTutor/guide/config.html)。
|
||||
|
||||
### 云端部署
|
||||
|
||||
云端部署需要额外配置：
|
||||
|
||||
1. 在 `.env` 中设置公网 URL：
|
||||
|
||||
```env
|
||||
NEXT_PUBLIC_API_BASE_EXTERNAL=https://your-domain.com:8001
|
||||
```
|
||||
|
||||
2. 配置反向代理（nginx/Caddy）以支持 HTTPS
|
||||
3. 确保适当的防火墙规则
|
||||
4. 考虑使用特定环境的密钥管理
|
||||
|
||||
### 使用不同的嵌入模型
|
||||
|
||||
DeepTutor 默认使用 `text-embedding-3-large`。要使用不同的嵌入模型，请参考[官方文档](https://hkuds.github.io/DeepTutor/guide/config.html)。
|
||||
|
||||
## 相关链接
|
||||
|
||||
- **GitHub：** <https://github.com/HKUDS/DeepTutor>
|
||||
- **文档：** <https://hkuds.github.io/DeepTutor/>
|
||||
- **问题反馈：** <https://github.com/HKUDS/DeepTutor/issues>
|
||||
- **讨论区：** <https://github.com/HKUDS/DeepTutor/discussions>
|
||||
|
||||
## 许可证
|
||||
|
||||
DeepTutor 使用 Apache-2.0 许可证。详情请参见[官方仓库](https://github.com/HKUDS/DeepTutor)。
|
||||
68
apps/deeptutor/docker-compose.yaml
Normal file
68
apps/deeptutor/docker-compose.yaml
Normal file
@@ -0,0 +1,68 @@
|
||||
# DeepTutor: AI-Powered Personalized Learning Assistant
|
||||
# https://github.com/HKUDS/DeepTutor
|
||||
# Transform any document into an interactive learning experience with multi-agent intelligence
|
||||
|
||||
x-defaults: &defaults
|
||||
restart: unless-stopped
|
||||
logging:
|
||||
driver: json-file
|
||||
options:
|
||||
max-size: 100m
|
||||
max-file: "3"
|
||||
|
||||
services:
|
||||
deeptutor:
|
||||
<<: *defaults
|
||||
image: ${GLOBAL_REGISTRY:-ghcr.io}/hkuds/deeptutor:${DEEPTUTOR_VERSION:-latest}
|
||||
ports:
|
||||
- "${DEEPTUTOR_BACKEND_PORT_OVERRIDE:-8001}:${BACKEND_PORT:-8001}"
|
||||
- "${DEEPTUTOR_FRONTEND_PORT_OVERRIDE:-3782}:${FRONTEND_PORT:-3782}"
|
||||
volumes:
|
||||
- deeptutor_data:/app/data
|
||||
- ./config:/app/config:ro
|
||||
environment:
|
||||
- TZ=${TZ:-UTC}
|
||||
# Backend port
|
||||
- BACKEND_PORT=${BACKEND_PORT:-8001}
|
||||
# Frontend port
|
||||
- FRONTEND_PORT=${FRONTEND_PORT:-3782}
|
||||
# API base URLs
|
||||
- NEXT_PUBLIC_API_BASE=${NEXT_PUBLIC_API_BASE:-http://localhost:8001}
|
||||
- NEXT_PUBLIC_API_BASE_EXTERNAL=${NEXT_PUBLIC_API_BASE_EXTERNAL:-http://localhost:8001}
|
||||
# LLM API Keys
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY}
|
||||
- OPENAI_BASE_URL=${OPENAI_BASE_URL:-https://api.openai.com/v1}
|
||||
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
|
||||
- PERPLEXITY_API_KEY=${PERPLEXITY_API_KEY:-}
|
||||
- DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY:-}
|
||||
# Default LLM model
|
||||
- DEFAULT_MODEL=${DEFAULT_MODEL:-gpt-4o}
|
||||
# User ID and Group ID for permission management
|
||||
- PUID=${PUID:-1000}
|
||||
- PGID=${PGID:-1000}
|
||||
healthcheck:
|
||||
test:
|
||||
[
|
||||
"CMD",
|
||||
"curl",
|
||||
"-f",
|
||||
"http://localhost:${BACKEND_PORT:-8001}/health",
|
||||
"||",
|
||||
"exit",
|
||||
"1",
|
||||
]
|
||||
interval: 30s
|
||||
timeout: 10s
|
||||
retries: 3
|
||||
start_period: 60s
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
cpus: ${DEEPTUTOR_CPU_LIMIT:-4.00}
|
||||
memory: ${DEEPTUTOR_MEMORY_LIMIT:-8G}
|
||||
reservations:
|
||||
cpus: ${DEEPTUTOR_CPU_RESERVATION:-1.00}
|
||||
memory: ${DEEPTUTOR_MEMORY_RESERVATION:-2G}
|
||||
|
||||
volumes:
|
||||
deeptutor_data:
|
||||
Reference in New Issue
Block a user