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npc0-hue 17832f2424 fix: Dify 1.8.1问题修复

本次提交整合了多个功能改进和问题修复：

主要功能：
- 批量工作流处理功能完善，支持 Excel 上传和进度跟踪
- 管理中心反向代理和转发配置优化
- 用户同步添加互斥锁，防止并发问题
- 计费系统和额度显示优化
- AI 绘图功能扩展

前端改进：
- 文本生成应用显示修复
- 批量任务进度展示优化
- 按钮样式和 CSS 优化，禁止换行
- 多语言支持完善（新增印尼语等）
- 构建镜像逻辑优化
- 批量处理进度管理器实现

后端改进：
- Docker Compose 配置升级
- 队列任务和 Worker Pool 优化
- Admin API 初始化和验证逻辑改进
- 数据库迁移和初始化完善
- 静态变量处理优化
- URL 签名助手实现
- Celery 扩展优化
- 代码和导入包问题修复（idea 自动调整代码位置）

技术改进：
- 兼容性修复 (flask-restx, jschardet)
- 钉钉 Web API 版本更新
- 代码格式化和导入包问题修复
- 日志处理优化
- 工作流循环管理优化

Docker 相关：
- Nginx 配置更新
- 容器启动脚本优化
- 镜像构建流程改进
- docker-compose.dify-plus.yaml 大幅更新

管理后台：
- 工作流批量处理 API 实现
- 工作池初始化
- 批量工作流服务实现
- 转发扩展配置
- 用户服务扩展

2025-10-17 23:04:25 +08:00

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Dify Backend API

Usage

Important

In the v1.3.0 release, poetry has been replaced with uv as the package manager for Dify API backend service.

Start the docker-compose stack

The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using docker-compose.

cd ../docker
cp middleware.env.example middleware.env
# change the profile to other vector database if you are not using weaviate
docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
cd ../api

Copy .env.example to .env
```
cp .env.example .env
```

Generate a SECRET_KEY in the .env file.

bash for Linux

sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env

bash for Mac

secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env

Create environment.

Dify API service uses UV to manage dependencies. First, you need to add the uv package manager, if you don't have it already.
```
pip install uv
# Or on macOS
brew install uv
```
Install dependencies
```
uv sync --dev
```
Run migrate

Before the first launch, migrate the database to the latest version.
```
uv run flask db upgrade
uv run flask extend_db upgrade
```

Start backend

uv run flask run --host 0.0.0.0 --port=5001 --debug

Start Dify web service.
Setup your application by visiting http://localhost:3000.
If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.

uv run celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation

Addition, if you want to debug the celery scheduled tasks, you can use the following command in another terminal:

uv run celery -A app.celery beat

Testing

Install dependencies for both the backend and the test environment
```
uv sync --dev
```

Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml, more can check Claude.md

uv run pytest                           # Run all tests
uv run pytest tests/unit_tests/         # Unit tests only
uv run pytest tests/integration_tests/  # Integration tests

# Code quality
../dev/reformat               # Run all formatters and linters
uv run ruff check --fix ./    # Fix linting issues
uv run ruff format ./         # Format code
uv run basedpyright .         # Type checking

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Dify Backend API

Usage

Testing

2.8 KiB

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