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沃巴蒂 ff22ed3a8b feat(deepdata): 引入修改密码、文件预览组件与清洗算子增强，deepflow 改名为 deep-flow

本次同步 09-12 至 09-28 的代码变更，主要内容：

新增功能
- 修改密码：后端新增 ChangePasswordRequest DTO 与UserController/UserService
  对应接口，前端新增 ChangePasswordDialog 并在 Header 接入入口
- 文件预览组件重构：file-preview 目录拆分出 VideoPreview（支持 blobUrl 直传）、
  ImagePreview、AudioPreview，index.tsx 统一调度各类型预览器
- 新增 my_ops 算子模块：markitdown_parser、mineru_pdf_parser、text_quality_evaluator
- 新增 tools/minio-file-server MinIO 文件服务

功能增强
- 数据清洗：cleaning_task_service/scheduler/routes 重构，
  data_juicer_executor 与 wrapper 增强，OperatorTable/LogsTable/TaskDetail 同步调整
- 数据标注：annotation sync.py、project.py 接口与schema 更新
- 数据采集、算子市场、首页与菜单路由相关页面调整
- DataJuicer 算子接入（useOperatorOperations、dj_ops_schema）

结构调整
- deepflow/ 更名为 deep-flow/（419 个文件重命名）
- 新增 helm 部署编排、GitHub Actions 之外的自建镜像构建脚本

未纳入本次提交
- scripts-backup/、runtime/python-executor-backup/、components_backup/（备份目录）
- *.backup*、__pycache__ 产物
- zh_core_web_sm-3.8.0-py3-none-any.whl（46MB 构建产物）
- DataX/（内嵌独立仓库，远程亦未收录其源码）

2026-10-03 22:44:56 +08:00

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FAQ

Where's the name DeepFlow come from?
Which models does DeepFlow support?
How do I view complete model output?
How do I enable debug logging?
How do I troubleshoot issues?

Where's the name DeepFlow come from?

DeepFlow is short for Deep Exploration and Efficient Research Flow. It is named after the deer, which is a symbol of gentleness and elegance. We hope DeepFlow can bring a gentle and elegant deep research experience to you.

Which models does DeepFlow support?

Please refer to the Configuration Guide for more details.

How do I view complete model output?

If you want to see the complete model output, including system prompts, tool calls, and LLM responses:

Enable debug logging by setting DEBUG=True in your .env file
Enable LangChain verbose logging by adding these to your .env:
```
LANGCHAIN_VERBOSE=true
LANGCHAIN_DEBUG=true
```

Use LangSmith tracing for visual debugging (recommended for production):

LANGSMITH_TRACING=true
LANGSMITH_API_KEY="your-api-key"
LANGSMITH_PROJECT="your-project-name"

For detailed instructions, see the Debugging Guide.

How do I enable debug logging?

To enable debug logging:

Open your .env file
Set DEBUG=True
Restart your application

For Docker Compose:

docker compose restart

For development:

uv run main.py

You'll now see detailed logs including:

System prompts sent to LLMs
Model responses
Tool execution details
Workflow state transitions

See the Debugging Guide for more options.

How do I troubleshoot issues?

When encountering issues:

Check the logs: Enable debug logging as described above
Review configuration: Ensure your .env and conf.yaml are correct
Check existing issues: Search GitHub Issues for similar problems
Enable verbose logging: Use LANGCHAIN_VERBOSE=true for detailed output
Use LangSmith: For visual debugging, enable LangSmith tracing

For Docker-specific issues:

# View logs
docker compose logs -f

# Check container status
docker compose ps

# Restart services
docker compose restart

For more detailed troubleshooting steps, see the Debugging Guide.

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