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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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Google's Agent to Agent Protocol Report

Key Points

Google's Agent2Agent (A2A) protocol standardizes communication between AI agents, promoting collaboration across diverse systems.
A2A facilitates message exchange for sharing context, instructions, and artifacts between agents.
The protocol complements Anthropic's Model Context Protocol (MCP) by providing a networking layer for agents.
A2A allows agents to negotiate content formats, supporting diverse media types such as iframes and video.
Google intends A2A to be an open, community-driven project to foster innovation and adoption.
Industry experts anticipate A2A will accelerate AI adoption by simplifying integrations and data exchange.

Overview

Google's Agent2Agent (A2A) protocol is designed to establish a standardized method for AI agents to communicate, irrespective of their origin, framework, or location. This initiative seeks to foster seamless collaboration and collective intelligence among AI agents, thereby enhancing the effectiveness of agentic solutions. A2A operates as a networking layer that complements other protocols such as Anthropic's Model Context Protocol (MCP), contributing to a more unified AI agent ecosystem.

Detailed Analysis

Purpose and Design

A2A addresses the challenge of integrating disparate AI systems by providing a common language for AI agents. It enables these agents to share context, replies, artifacts, and user instructions, facilitating collaborative problem-solving. The design of A2A supports flexible user experiences by allowing agents to negotiate content formats like iframes, videos, and web forms.

Technical Aspects

The protocol utilizes "parts" within messages, which are fully formed pieces of content with specified content types, enabling negotiation of the correct format needed between agents. A2A builds upon existing standards including HTTP, SSE, and JSON-RPC.

Community and Industry Impact

Google's vision for A2A is to create an open, community-driven project that encourages contributions and updates from the open-source community. Industry experts from companies like Deloitte, Accenture, EPAM, and New Relic believe A2A will accelerate AI adoption by simplifying integrations, facilitating data exchange, and fostering a more unified AI agent ecosystem. LangChain has also expressed interest in collaborating with Google Cloud on this shared protocol.

Relationship with MCP

A2A complements Anthropic's Model Context Protocol (MCP). While A2A provides a networking layer for agents to communicate, MCP functions as a plugin system, granting agents access to tools, context, and data.

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