refactor: move RAG trace classes to argtrace package and update SQL schema

- Rename org.ruoyi.trace package to org.ruoyi.argtrace for RagTraceNodeTypes and RagTracePayloadBuilder; fix package declarations and update imports in ChatServiceFacade and KnowledgeRetrievalServiceImpl - Remove obsolete RagTracePayloadBuilderTest - Switch supervisor response strategy from SUMMARY to LAST in ChatServiceFacade - Refresh docs/script/sql/ruoyi-ai.sql with the latest full schema dump - Add live demo links (admin panel, user frontend, commercial edition) to README.md and README_ZH.md Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
docs: remove redundant registry commands
2026-09-13 00:14:59 +00:00 · 2026-08-04 17:44:39 +08:00 · 2026-08-04 15:57:32 +08:00 · 2026-08-04 15:55:17 +08:00 · 2026-08-04 15:50:51 +08:00 · 2026-08-04 15:50:36 +08:00
64 changed files with 2464 additions and 1671 deletions
--- a/.github/workflows/publish-images.yml
+++ b/.github/workflows/publish-images.yml
@@ -0,0 +1,133 @@
 name: Publish Docker Images
 on:
  push:
    tags:
      - 'v*.*.*'
  workflow_dispatch:
    inputs:
      release_tag:
        description: Release tag to rebuild
        required: true
        type: string
 concurrency:
  group: publish-images-${{ inputs.release_tag || github.ref_name }}
  cancel-in-progress: false
 env:
  REGISTRY: ghcr.io
  IMAGE_OWNER: ${{ github.repository_owner }}
  RELEASE_TAG: ${{ inputs.release_tag || github.ref_name }}
 jobs:
  publish:
    name: Publish ${{ matrix.image }}
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write
      attestations: write
      id-token: write
    strategy:
      fail-fast: false
      matrix:
        include:
          - image: ruoyi-ai-backend
            source: local
            context: .
            dockerfile: docs/docker/ruoyi-ai/Dockerfile.backend
          - image: ruoyi-ai-mysql
            source: local
            context: .
            dockerfile: docs/docker/ruoyi-ai/Dockerfile.mysql
          - image: ruoyi-ai-admin
            source: admin
            context: build/ruoyi-admin
            dockerfile: build/ruoyi-admin/apps/web-antd/Dockerfile
          - image: ruoyi-ai-web
            source: web
            context: build/ruoyi-web
            dockerfile: build/ruoyi-web/Dockerfile.frontend
    steps:
      - name: Checkout backend repository
        uses: actions/checkout@v6
        with:
          ref: ${{ env.RELEASE_TAG }}
          fetch-depth: 1
      - name: Checkout admin repository
        if: matrix.source == 'admin'
        uses: actions/checkout@v6
        with:
          repository: ${{ github.repository_owner }}/ruoyi-admin
          ref: ${{ env.RELEASE_TAG }}
          path: build/ruoyi-admin
          fetch-depth: 1
      - name: Checkout web repository
        if: matrix.source == 'web'
        uses: actions/checkout@v6
        with:
          repository: ${{ github.repository_owner }}/ruoyi-web
          ref: ${{ env.RELEASE_TAG }}
          path: build/ruoyi-web
          fetch-depth: 1
      - name: Configure frontend dependency builds
        if: matrix.source == 'admin' || matrix.source == 'web'
        shell: bash
        run: |
          python3 - "${{ matrix.dockerfile }}" <<'PY'
          from pathlib import Path
          import sys
          dockerfile = Path(sys.argv[1])
          content = dockerfile.read_text()
          marker = "pnpm install --frozen-lockfile"
          replacement = (
              "pnpm install --frozen-lockfile "
              "--config.dangerously-allow-all-builds=true"
          )
          if marker not in content:
              raise SystemExit(f"pnpm install command not found in {dockerfile}")
          dockerfile.write_text(content.replace(marker, replacement, 1))
          PY
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Log in to GHCR
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - name: Extract Docker metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_OWNER }}/${{ matrix.image }}
          tags: |
            type=raw,value=${{ env.RELEASE_TAG }}
            type=raw,value=latest
          labels: |
            org.opencontainers.image.source=https://github.com/${{ github.repository }}
            org.opencontainers.image.version=${{ env.RELEASE_TAG }}
      - name: Build and push image
        uses: docker/build-push-action@v6
        with:
          context: ${{ matrix.context }}
          file: ${{ matrix.dockerfile }}
          platforms: linux/amd64
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          cache-from: type=gha,scope=${{ matrix.image }}
          cache-to: type=gha,mode=max,scope=${{ matrix.image }}
          provenance: true
          sbom: true
--- a/.gitignore
+++ b/.gitignore
@@ -23,6 +23,9 @@ target/
 .idea
 .claude
 .github
 !.github/
 !.github/workflows/
 !.github/workflows/**
 *.iws
 *.iml
 *.ipr
--- a/RAG_TEST_REPORT_2026-07-20.md
+++ b/RAG_TEST_REPORT_2026-07-20.md
@@ -1,64 +0,0 @@
 # RAG 完整修复与全量验收报告
 验收时间：2026-07-21（Asia/Shanghai）  
 验收对象：当前未提交工作区（保留原有改动）
 ## 结论
 计划内的 1–15 项工程缺陷已完成代码修复，默认 Maven 构建已从“跳过测试”改为真实执行测试。全仓 37 个 reactor 模块测试成功，`ruoyi-chat` 49/49 通过，两个前端生产构建通过，`git diff --check` 通过。
 本机已运行 MySQL、Redis、MinIO 和 Weaviate 1.30.0；Milvus/Qdrant 容器以及有效的 embedding/chat/rerank provider 凭证不存在，因此这三项真实 provider/存储引擎冒烟被标记为环境限制，不影响确定性代码验收。
 ## 1–15 项验收
 | # | 状态 | 修复/证据 |
 |---|---|---|
 | 1 | 通过 | Markdown/Java/字符分片的边界、空文档、超长块回归通过。 |
 | 2 | 通过 | Supervisor 每轮仅保留一个 RAG 入口，不再用已含 RAG 的 prompt 重复检索。 |
 | 3 | 通过 | 历史消息进入 Supervisor prompt，检索 query 与最终 prompt 分离。 |
 | 4 | 通过 | `fid` 稳定 ID 贯穿 DB/三种向量库/RRF，融合去重回归通过。 |
 | 5 | 通过 | aiflow vector/hybrid 复用统一检索服务；graph 明确返回不支持，不再伪装为 vector。 |
 | 6 | 通过 | 重解析改为先写新 fid、再清旧向量、最后替换 DB；失败补偿新向量；删片段/附件/库遇向量删除失败即中止。 |
 | 7 | 通过 | embedding/rerank provider 使用 prototype 实例，工厂缓存可按模型刷新，避免跨配置污染。 |
 | 8 | 通过 | `similarityThreshold` 仅用于粗召回；`rerankScoreThreshold` 仅在 rerank 真实成功后生效，回归测试通过。 |
 | 9 | 通过 | 默认配置和 Compose 统一为 Weaviate 1.30.0、`28080:8080`。 |
 | 10 | 通过 | 三种策略均使用 `embedAll`；Weaviate batch objects、Milvus `addAll`、Qdrant `addAll`。 |
 | 11 | 通过 | upload/parse/retrieval 权限保留，parse/retrieval 增加分布式防重复提交，upload 由现有知识库+文件名唯一约束兜底。 |
 | 12 | 通过 | 分隔符使用字面量语义，`|`/`.`/`*` 回归通过。 |
 | 13 | 通过 | hybrid 通道失败可降级到 vector；所有可用通道都失败时抛出明确业务异常。 |
 | 14 | 通过 | Weaviate client 稳定懒加载单例；schema 仅在已存在或创建成功后进入缓存。 |
 | 15 | 通过 | 工厂新增严格 `getStrategy(type)`，知识库 `vectorModel` 优先，空值才回退全局，非法值直接报错。 |
 ## 其他完成项
 - 多知识库并行检索，按 `kid + docId + fid` 去重，统一上限和字符预算。
 - 5 分钟短 TTL 检索缓存，key 覆盖检索参数，知识数据变更主动失效。
 - rerank 仅保留 provider 实际返回的文档。
 - 知识库文档数改为 group-by 查询，消除该 N+1。
 - Milvus/Qdrant/Weaviate 的删 collection/doc/fid 语义对齐；Milvus 删库改为 drop collection。
 - MCP `npx` 根据操作系统解析，支持系统属性/环境变量覆盖。
 - `fid` 非空唯一、`doc_id varchar(32)`、租户/用户索引与可重复执行迁移脚本已提供。
 - 用户端聊天页已接入知识库列表和最小选择器。
 ## 测试记录
 | 检查 | 结果 |
 |---|---|
 | `mvn -Pdev test` | 37/37 reactor 模块 SUCCESS；`ruoyi-chat` 49/49 |
 | `mvn -Pdev -pl ruoyi-modules/ruoyi-aiflow -am -DskipTests compile` | 21/21 SUCCESS |
 | `ruoyi-web: pnpm build` | SUCCESS，2621 modules transformed |
 | `ruoyi-admin: pnpm build` | SUCCESS，10/10 build tasks |
 | `git diff --check` | SUCCESS，无空白错误 |
 | Weaviate/MySQL/Redis/MinIO | Docker 服务运行，Weaviate 1.30.0 映射 28080 |
 | 三向量库 Docker 集成 | SUCCESS；Weaviate 1.30.0、Milvus 2.5.7、Qdrant 1.17.0 真实写入/检索/删除测试 3/3 通过 |
 | 真实 embedding/chat/rerank | 环境限制：当前配置为无效/占位凭证 |
 本轮未创建新的 `codex_rag_verify_` 持久化数据；上一轮验收数据已清理，未动现有非测试数据。
 ## 2026-07-21 三向量库 Docker 补充验收
 - 启动并保留 `ruoyi-rag-milvus`、`ruoyi-rag-milvus-etcd`、`ruoyi-rag-milvus-minio`、`ruoyi-rag-qdrant`，四个容器健康检查均为 `healthy`。
 - Milvus 专用 MinIO 仅在 Docker 内网可达，没有占用宿主机 9000/9001；Milvus 映射 19530/9091，Qdrant 映射 6333/6334。
 - `ThreeVectorStoresDockerIT` 使用 32 维确定性 embedding，对三库逐一验证 batch write、vector search、fid delete、docId delete 和 drop collection，3/3 通过。
 - 首轮测试发现 Milvus `autoFlush=false` 导致批量写入后不可立即检索、元数据删除不可立即见；改为写入和删除返回前 flush 后通过。
 - 清理后 Weaviate/Qdrant 的 `CodexRagVerify*` collection 计数均为 0，Milvus collection 也由测试 finally 成功 drop；本轮未写入 MySQL 或 OSS 测试数据。
--- a/README.md
+++ b/README.md
@@ -2,11 +2,7 @@
 <div align="center">
-[![Contributors][contributors-shield]][contributors-url]
+[![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url]
 [![Forks][forks-shield]][forks-url]
 [![Stargazers][stars-shield]][stars-url]
 [![Issues][issues-shield]][issues-url]
 [![MIT License][license-shield]][license-url]
 <p align="center">
@@ -17,236 +13,231 @@
 <img src="docs/image/logo.png" alt="RuoYi AI Logo" width="120" height="120">
-### 企业级AI助手平台
+### Enterprise-Grade AI Assistant Platform
-*开箱即用的全栈AI平台，支持多智能体协同、Supervisor模式编排、多种决策模式、RAG技术和流程编排能力*
+*An out-of-the-box full-stack AI platform supporting multi-agent collaboration, Supervisor mode orchestration, and multiple decision models, with advanced RAG technology and visual workflow orchestration capabilities*
-**[English](README_EN.md)** | **[📖 使用文档](https://doc.ruoyiai.chat/)** |
+**[中文](README_ZH.md)** | **[📖 Documentation](https://doc.ruoyiai.chat/)** |
-**[🚀 在线体验](https://web.ruoyiai.chat/)** | **[🐛 问题反馈](https://github.com/ageerle/ruoyi-ai/issues)** | **[💡 功能建议](https://github.com/ageerle/ruoyi-ai/issues)**
+**[🚀 Live Demo](https://web.ruoyiai.chat/)** | **[🐛 Report Issues](https://github.com/ageerle/ruoyi-ai/issues)** | **[💡 Feature Requests](https://github.com/ageerle/ruoyi-ai/issues)**
 </div>
-## ✨ 核心亮点
+## 🚀 Live Demo
-|    模块     | 现有能力 
+| Service | URL | Default Account |
-|:---------:|---
+|---|---|---|
-| **模型管理**  | 多模型接入(DeepSeek/智谱/MIMO/百炼/OpenAI)、多模态理解、Coze/DIFY/FastGPT/RAGFlow平台集成
+| Admin Panel | http://129.226.199.247:25666 | admin / admin123 |
-| **知识管理**  | 本地RAG + 向量库(Milvus/Weaviate/Qdrant)  + 文档解析
+| User Frontend | http://129.226.199.247:25137 | admin / admin123 |
-| **工具管理**  | Mcp协议集成、Skills能力 + 可扩展工具生态                             
+| Commercial Edition | https://web.ruoyiai.chat | WeChat QR code login |
 | **流程编排**  | 可视化工作流设计器、节点拖拽编排、SSE流式执行,目前已经支持模型调用,邮件发送,人工审核等节点  
 | **智能体管理** | 基于Langchain4j的Agent框架、Supervisor模式编排,支持多种决策模型,可以灵活搭配工具,skills          
 ## ✨ Core Features
-### 项目源码
+| Module | Current Capabilities |
 |:---:|---|
 | **Model Management** | Multi-model integration (DeepSeek/Zhipu/MIMO/Bailian/OpenAI), multi-modal understanding, Coze/DIFY/FastGPT/RAGFlow platform integration |
 | **Knowledge Management** | Local RAG + Vector DB (Milvus/Weaviate/Qdrant) + Document parsing |
 | **Tool Management** | MCP protocol integration, Skills capability + Extensible tool ecosystem |
 | **Workflow Orchestration** | Visual workflow designer, drag-and-drop node orchestration, SSE streaming execution, currently supports model calls, email sending, manual review, and other nodes |
 | **Multi-Agent** | Agent framework based on Langchain4j, Supervisor mode orchestration, supports multiple decision models, can flexibly combine tools and skills |
-| 项目模块     | GitHub 仓库                                             | Gitee 仓库                                             | GitCode 仓库                                             |
+### Project Repositories
 | Module     | GitHub Repository                                             | Gitee Repository                                             | GitCode Repository                                             |
 |----------|-------------------------------------------------------|------------------------------------------------------|--------------------------------------------------------|
-| 🔧 后端服务  | [ruoyi-ai](https://github.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitee.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitcode.com/ageerle/ruoyi-ai)       |
+| 🔧 Backend  | [ruoyi-ai](https://github.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitee.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitcode.com/ageerle/ruoyi-ai)       |
-| 🎨 用户前端  | [ruoyi-web](https://github.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitee.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitcode.com/ageerle/ruoyi-web)     |
+| 🎨 User Frontend  | [ruoyi-web](https://github.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitee.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitcode.com/ageerle/ruoyi-web)     |
-| 🛠️ 管理后台 | [ruoyi-admin](https://github.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitee.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitcode.com/ageerle/ruoyi-admin) |
+| 🛠️ Admin Panel | [ruoyi-admin](https://github.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitee.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitcode.com/ageerle/ruoyi-admin) |
-| 🎬 短剧平台  | [ruoyi-drama](https://github.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitee.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitcode.com/ageerle/ruoyi-drama)   |
+| 🎬 Drama | [ruoyi-drama](https://github.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitee.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitcode.com/ageerle/ruoyi-drama) |
-| 🤖 编程助手  | [ruoyi-copilot](https://github.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitee.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitcode.com/ageerle/ruoyi-copilot) |
+| 🤖 Copilot | [ruoyi-copilot](https://github.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitee.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitcode.com/ageerle/ruoyi-copilot) |
-| 📱 小程序端  | [ruoyi-uniapp](https://github.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitee.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitcode.com/ageerle/ruoyi-uniapp) |
+| 📱 Mini-App | [ruoyi-uniapp](https://github.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitee.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitcode.com/ageerle/ruoyi-uniapp) |
-### 合作项目
+### Partner Projects
-| 项目名称           | GitHub 仓库                                             | Gitee 仓库                                             
+| Project Name           | GitHub Repository                                             | Gitee Repository |
 |----------------|-------------------------------------------------------|------------------------------------------------------|
-| element-plus-x | [element-plus-x](https://github.com/element-plus-x/Element-Plus-X)       | [element-plus-x](https://gitee.com/he-jiayue/element-plus-x)       | 
+| element-plus-x | [element-plus-x](https://github.com/element-plus-x/Element-Plus-X)       | [element-plus-x](https://gitee.com/he-jiayue/element-plus-x)       |
-## 🛠️ 技术架构
+## 🛠️ Technical Architecture
-### 核心框架
+### Core Framework
- **后端架构**：Spring Boot 3.5.8 + Langchain4j
+- **Backend**: Spring Boot 3.5.8 + Langchain4j
- **数据存储**：MySQL 8.0 + Redis + 向量数据库（Milvus/Weaviate/Qdrant）
+- **Data Storage**: MySQL 8.0 + Redis + Vector Databases (Milvus/Weaviate/Qdrant)
- **前端技术**：Vue 3 + Vben Admin + element-plus-x
+- **Frontend**: Vue 3 + Vben Admin + element-plus-x
- **安全认证**：Sa-Token + JWT 双重保障
+- **Security**: Sa-Token + JWT dual-layer security
- **文档处理**：PDF、Word、Excel 解析，图像智能分析
+- **Document Processing**: PDF, Word, Excel parsing, intelligent image analysis
- **实时通信**：WebSocket 实时通信，SSE 流式响应
+- **Real-time Communication**: WebSocket real-time communication, SSE streaming response
- **系统监控**：完善的日志体系、性能监控、服务健康检查
+- **System Monitoring**: Comprehensive logging system, performance monitoring, service health checks
-## 🐳 Docker 部署
+## 🐳 Docker Deployment
-本项目提供两种 Docker 部署方式：
+This project provides two Docker deployment methods:
-### 方式一：一键启动所有服务（推荐）
+### Method 1: One-click Start All Services (Recommended)
-使用 `docker-compose-all.yaml` 可以一键启动所有服务（包括后端、管理端、用户端及依赖服务）：
+Use `docker-compose-all.yaml` to start all services at once (including backend, admin panel, user frontend, and dependencies):
 ```bash
-# 克隆仓库
+# Requirements: Docker Engine and Docker Compose V2
-git clone https://github.com/ageerle/ruoyi-ai.git
+
 # Clone the v3.1.0 release
 git clone --depth 1 --branch v3.1.0 https://github.com/ageerle/ruoyi-ai.git
 cd ruoyi-ai
-# 启动所有服务（从镜像仓库拉取预构建镜像）
+# Pin the image version. Public GHCR images do not require docker login.
-docker-compose -f docker-compose-all.yaml up -d
+cp docs/docker/ruoyi-ai/.env.example docs/docker/ruoyi-ai/.env
 sed -i 's/^RUIYI_VERSION=.*/RUIYI_VERSION=v3.1.0/' docs/docker/ruoyi-ai/.env
-# 查看服务状态
+# Pull pre-built images from GHCR and start all services
-docker-compose -f docker-compose-all.yaml ps
+docker compose --env-file docs/docker/ruoyi-ai/.env \
  -f docs/docker/ruoyi-ai/docker-compose-all.yaml pull
 docker compose --env-file docs/docker/ruoyi-ai/.env \
  -f docs/docker/ruoyi-ai/docker-compose-all.yaml up -d
-# 访问服务
+# Check service status
-# 管理端: http://localhost:25666 (admin / admin123)
+docker compose --env-file docs/docker/ruoyi-ai/.env \
-# 用户端: http://localhost:25137
+  -f docs/docker/ruoyi-ai/docker-compose-all.yaml ps
-# 后端API: http://localhost:26039
+
 # Access services (replace SERVER_IP with the server address)
 # Admin Panel: http://SERVER_IP:25666 (admin / admin123)
 # User Frontend: http://SERVER_IP:25137
 # Backend API: http://SERVER_IP:26039
 ```
-### 方式二：分步部署（源码编译）
+The default Compose file also publishes MySQL (`23306`), Redis (`26379`),
 Weaviate (`28080`), and MinIO (`29000`/`29090`). For production deployments,
 change the default MySQL and MinIO passwords and expose only the application
 ports through the firewall or a reverse proxy.
-如果您需要从源码构建后端服务，请按照以下步骤操作：
+To upgrade to another published release, update `RUIYI_VERSION` in
 `docs/docker/ruoyi-ai/.env`, then run `docker compose pull` and
 `docker compose up -d` with the same `--env-file` and `-f` options. Do not use
 `docker compose down -v` unless you intend to delete persistent data volumes.
-#### 第一步：部署后端服务
+### Method 2: Step-by-step Deployment (Source Build)
 If you need to build backend services from source, follow these steps:
 #### Step 1: Deploy Backend Service
 ```bash
-# 进入后端项目目录
+# Enter backend project directory
 cd ruoyi-ai
-# 启动后端服务（源码编译构建）
+# Start backend service (build from source)
 docker-compose up -d --build
-# 等待后端服务启动完成
+# Wait for backend service to start
 docker-compose logs -f backend
 ```
-#### 第二步：部署管理端
+#### Step 2: Deploy Admin Panel
 ```bash
-# 进入管理端项目目录
+# Enter admin panel project directory
 cd ruoyi-admin
-# 构建并启动管理端
+# Build and start admin panel
 docker-compose up -d --build
-# 访问管理端
+# Access admin panel
-# 地址: http://localhost:5666
+# URL: http://localhost:5666
 ```
-#### 第三步：部署用户端（可选）
+#### Step 3: Deploy User Frontend (Optional)
 ```bash
-# 进入用户端项目目录
+# Enter user frontend project directory
 cd ruoyi-web
-# 构建并启动用户端
+# Build and start user frontend
 docker-compose up -d --build
-# 访问用户端
+# Access user frontend
-# 地址: http://localhost:5137
+# URL: http://localhost:5137
 ```
-### 服务端口说明
+### Service Ports
-| 服务 | 一键启动端口 | 分步部署端口 | 说明 |
+| Service | One-click Port | Step-by-step Port | Description |
 |------|-------------|-------------|------|
-| 管理端 | 25666 | 5666 | 管理后台访问地址 |
+| Admin Panel | 25666 | 5666 | Admin backend access |
-| 用户端 | 25137 | 5137 | 用户前端访问地址 |
+| User Frontend | 25137 | 5137 | User frontend access |
-| 后端服务 | 26039 | 6039 | 后端 API 服务 |
+| Backend Service | 26039 | 6039 | Backend API service |
-| MySQL | 23306 | 23306 | 数据库服务 |
+| MySQL | 23306 | 23306 | Database service |
-| Redis | 26379 | 6379 | 缓存服务 |
+| Redis | 26379 | 6379 | Cache service |
-| Weaviate | 28080 | 28080 | 向量数据库 |
+| Weaviate | 28080 | 28080 | Vector database |
-| MinIO API | 29000 | 9000 | 对象存储 API |
+| MinIO API | 29000 | 9000 | Object storage API |
-| MinIO Console | 29090 | 9090 | 对象存储控制台 |
+| MinIO Console | 29090 | 9090 | Object storage console |
-### 镜像仓库
+## 📚 Documentation
-所有镜像托管在阿里云容器镜像服务：
+Want to learn more about installation, deployment, configuration, and secondary development?
-```
+**👉 [Complete Documentation](https://doc.ruoyiai.chat/)**
 crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai
 ```
-可用镜像：
+## 🤝 Contributing
 - `mysql:v3` - MySQL 数据库（包含初始化 SQL）
 - `redis:6.2` - Redis 缓存
 - `weaviate:1.30.0` - 向量数据库
 - `minio:latest` - 对象存储
 - `ruoyi-ai-backend:latest` - 后端服务
 - `ruoyi-ai-admin:latest` - 管理端前端
 - `ruoyi-ai-web:latest` - 用户端前端
-### 常用命令
+We warmly welcome community contributions! Whether you are a seasoned developer or just getting started, you can contribute to the project 💪
-```bash
+### How to Contribute
 # 停止所有服务
 docker-compose -f docker-compose-all.yaml down
-# 查看服务日志
+1. **Fork** the project to your account
-docker-compose -f docker-compose-all.yaml logs -f [服务名]
+2. **Create a branch** (`git checkout -b feature/new-feature-name`)
 3. **Commit your changes** (`git commit -m 'Add new feature'`)
 4. **Push to the branch** (`git push origin feature/new-feature-name`)
 5. **Create a Pull Request**
-# 重启某个服务
+> 💡 **Tip**: We recommend submitting PRs to GitHub, we will automatically sync to other code hosting platforms
 docker-compose -f docker-compose-all.yaml restart [服务名]
 ```
-## 📚 使用文档
+## 📄 License
-想要深入了解安装部署、功能配置和二次开发？
+This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
-**👉 [完整使用文档](https://doc.ruoyiai.chat/)**
+## 🙏 Acknowledgments
-## 🤝 参与贡献
+Thanks to the following excellent open-source projects for their support:
 - [Langchain4j](https://github.com/langchain4j/langchain4j) - Powerful Java LLM development framework
 - [RuoYi-Vue-Plus](https://gitee.com/dromara/RuoYi-Vue-Plus) - Mature enterprise-level rapid development framework
 - [Vben Admin](https://github.com/vbenjs/vue-vben-admin) - Modern Vue admin template
-我们热烈欢迎社区贡献！无论您是资深开发者还是初学者，都可以为项目贡献力量 💪
+## 💎 Sponsors
-### 贡献方式
+**Thanks to the following sponsors for supporting this project:**
 1. **Fork** 项目到您的账户
 2. **创建分支** (`git checkout -b feature/新功能名称`)
 3. **提交代码** (`git commit -m '添加某某功能'`)
 4. **推送分支** (`git push origin feature/新功能名称`)
 5. **发起 Pull Request**
 > 💡 **小贴士**：建议将 PR 提交到 GitHub，我们会自动同步到其他代码托管平台
 ## 📄 开源协议
 本项目采用 **MIT 开源协议**，详情请查看 [LICENSE](LICENSE) 文件。
 ## 🙏 特别鸣谢
 感谢以下优秀的开源项目为本项目提供支持：
 - [Langchain4j](https://github.com/langchain4j/langchain4j) - 强大的 Java LLM 开发框架
 - [RuoYi-Vue-Plus](https://gitee.com/dromara/RuoYi-Vue-Plus) - 成熟的企业级快速开发框架
 - [Vben Admin](https://github.com/vbenjs/vue-vben-admin) - 现代化的 Vue 后台管理模板
 ## 💎 赞助商
 **感谢以下赞助商对本项目的支持：**
 <a href="https://www.atlascloud.ai?ref=89F97E">
  <img src="docs/image/sponsor/atlascloud_banner.png" alt="Atlas Cloud" width="160" height="80">
 </a>
-[访问Atlas Cloud官网](https://www.atlascloud.ai?ref=89F97E&utm_source=github&utm_campaign=ruoyi-drama) · [编程计划优惠](https://www.atlascloud.ai/console/coding-plan)
+[Visit Atlas Cloud](https://www.atlascloud.ai?ref=89F97E&utm_source=github&utm_campaign=ruoyi-drama) · [Coding Plan Promotion](https://www.atlascloud.ai/console/coding-plan)
-全模态 AI 推理平台，为开发者提供统一的 AI API，支持视频生成、图像生成和大语言模型。一次接入，即可访问 **300+ 精选模型**。
+A full-modal AI inference platform that gives developers a unified AI API, supporting video generation, image generation, and LLMs. Connect once to access **300+ curated models**.
 <a href="https://www.volcengine.com/activity/codingplan?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai">
-  <img src="docs/image/sponsor/huoshan.png" alt="火山引擎 CodingPlan" width="160" height="80">
+  <img src="docs/image/sponsor/huoshan.png" alt="Volcengine CodingPlan" width="160" height="80">
 </a>
-[注册即领2500万Tokens，立即前往](https://www.volcengine.com/activity/ai618?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai)
+[Sign up to claim 25 million tokens — go now](https://www.volcengine.com/activity/ai618?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai)
-享字节自研豆包模型+满血版开源 SOTA模型，覆盖文本、VLM、图像生成，全模态一站配齐：Seed-2.1、Seedream-5.0、GLM-5.2、DeepSeek等。不止编程、更能解决 Agent 复杂长程任务！
+Enjoy ByteDance's in-house Doubao models plus full-power open-source SOTA models, covering text, VLM, and image generation — all modalities in one stop: Seed-2.1, Seedream-5.0, GLM-5.2, DeepSeek, and more. Not just for coding — it can also tackle complex long-horizon Agent tasks!
-
+## 💬 Community Chat
 ## 💬 社区交流
 <div align="center">
 <table>
 <tr>
 <td align="center">
-<img src="docs/image/wx.png" alt="微信二维码" width="200" height="200"><br>
+<img src="docs/image/wx.png" alt="WeChat QR Code" width="200" height="200"><br>
-<strong>扫码添加作者微信</strong><br>
+<strong>Scan to add author on WeChat</strong><br>
-<em>邀请进群学习</em>
+<em>Join group for learning</em>
 </td>
 <td align="center">
-<img src="docs/image/wx06.png" alt="微信二维码" width="200" height="200"><br>
+<img src="docs/image/douyin.png" alt="Douyin QR Code" width="200" height="200"><br>
-<strong>微信技术交流群</strong><br>
+<strong>Douyin Video Tutorials</strong><br>
-<em>技术讨论</em>
+<em>Open Douyin, scan & follow to watch video tutorials</em>
 </td>
 <td align="center">
-<img src="docs/image/qq.png" alt="QQ群二维码" width="200" height="200"><br>
+<img src="docs/image/qq.png" alt="QQ Group QR Code" width="200" height="200"><br>
-<strong>QQ技术交流群</strong><br>
+<strong>QQ Tech Exchange Group</strong><br>
-<em>技术讨论</em>
+<em>Technical discussion</em>
 </td>
 </tr>
@@ -255,11 +246,12 @@ docker-compose -f docker-compose-all.yaml restart [服务名]
 </div>
 ---
 <div align="center">
-**[⭐ 点个Star支持一下](https://github.com/ageerle/ruoyi-ai)** • **[ Fork 开始贡献](https://github.com/ageerle/ruoyi-ai/fork)** • **[📚 English](README_EN.md)** • **[📖 查看完整文档](https://doc.ruoyiai.chat/)**
+**[⭐ Star to Support](https://github.com/ageerle/ruoyi-ai)** • **[Fork to Contribute](https://github.com/ageerle/ruoyi-ai/fork)** • **[📚 中文](README_ZH.md)** • **[📖 Complete Documentation](https://doc.ruoyiai.chat/)**
-*用 ❤️ 打造，由 RuoYi AI 开源社区维护*
+*Built with ❤️, maintained by the RuoYi AI open-source community*
 </div>
--- a/README_EN.md
+++ b/README_EN.md
@@ -1,310 +0,0 @@
 # RuoYi AI
 <div align="center">
 [![Contributors][contributors-shield]][contributors-url]
 [![Forks][forks-shield]][forks-url]
 [![Stargazers][stars-shield]][stars-url]
 [![Issues][issues-shield]][issues-url]
 [![MIT License][license-shield]][license-url]
 <p align="center">
  <a href="https://trendshift.io/repositories/13209">
    <img src="https://trendshift.io/api/badge/repositories/13209" alt="GitHub Trending">
  </a>
 </p>
 <img src="docs/image/logo.png" alt="RuoYi AI Logo" width="120" height="120">
 ### Enterprise-Grade AI Assistant Platform
 *An out-of-the-box full-stack AI platform supporting multi-agent collaboration, Supervisor mode orchestration, and multiple decision models, with advanced RAG technology and visual workflow orchestration capabilities*
 **[中文](README.md)** | **[📖 Documentation](https://doc.ruoyiai.chat/)** |
 **[🚀 Live Demo](https://web.ruoyiai.chat/)** | **[🐛 Report Issues](https://github.com/ageerle/ruoyi-ai/issues)** | **[💡 Feature Requests](https://github.com/ageerle/ruoyi-ai/issues)**
 </div>
 ## ✨ Core Features
 | Module | Current Capabilities |
 |:---:|---|
 | **Model Management** | Multi-model integration (OpenAI/DeepSeek/Tongyi/Zhipu/MiniMax), multi-modal understanding, Coze/DIFY/FastGPT platform integration |
 | **Knowledge Base** | Local RAG + Vector DB (Milvus/Weaviate/Qdrant) + Document parsing |
 | **Tool Management** | MCP protocol integration, Skills capability + Extensible tool ecosystem |
 | **Workflow Orchestration** | Visual workflow designer, drag-and-drop node orchestration, SSE streaming execution, currently supports model calls, email sending, manual review nodes |
 | **Multi-Agent** | Agent framework based on Langchain4j, Supervisor mode orchestration, supports multiple decision models |
 ### Project Repositories
 | Module     | GitHub Repository                                             | Gitee Repository                                             | GitCode Repository                                             |
 |----------|-------------------------------------------------------|------------------------------------------------------|--------------------------------------------------------|
 | 🔧 Backend  | [ruoyi-ai](https://github.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitee.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitcode.com/ageerle/ruoyi-ai)       |
 | 🎨 User Frontend  | [ruoyi-web](https://github.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitee.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitcode.com/ageerle/ruoyi-web)     |
 | 🛠️ Admin Panel | [ruoyi-admin](https://github.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitee.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitcode.com/ageerle/ruoyi-admin) |
 | 🎬 Drama | [ruoyi-drama](https://github.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitee.com/ageerle/ruoyi-drama) | |
 | 🤖 Copilot | [ruoyi-copilot](https://github.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitee.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitcode.com/ageerle/ruoyi-copilot) |
 | 📱 Mini-App | [ruoyi-uniapp](https://github.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitee.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitcode.com/ageerle/ruoyi-uniapp) |
 ### Partner Projects
 | Project Name           | GitHub Repository                                             | Gitee Repository |
 |----------------|-------------------------------------------------------|------------------------------------------------------|
 | element-plus-x | [element-plus-x](https://github.com/element-plus-x/Element-Plus-X)       | [element-plus-x](https://gitee.com/he-jiayue/element-plus-x)       |
 ## 🛠️ Technical Architecture
 ### Core Framework
 - **Backend**: Spring Boot 3.5.8 + Langchain4j
 - **Data Storage**: MySQL 8.0 + Redis + Vector Databases (Milvus/Weaviate/Qdrant)
 - **Frontend**: Vue 3 + Vben Admin + element-plus-x
 - **Security**: Sa-Token + JWT dual-layer security
 - **Document Processing**: PDF, Word, Excel parsing, intelligent image analysis
 - **Real-time Communication**: WebSocket real-time communication, SSE streaming response
 - **System Monitoring**: Comprehensive logging system, performance monitoring, service health checks
 ## 🐳 Docker Deployment
 This project provides two Docker deployment methods:
 ### Method 1: One-click Start All Services (Recommended)
 Use `docker-compose-all.yaml` to start all services at once (including backend, admin panel, user frontend, and dependencies):
 ```bash
 # Clone the repository
 git clone https://github.com/ageerle/ruoyi-ai.git
 cd ruoyi-ai
 # Start all services (pull pre-built images from registry)
 docker-compose -f docker-compose-all.yaml up -d
 # Check service status
 docker-compose -f docker-compose-all.yaml ps
 # Access services
 # Admin Panel: http://localhost:25666 (admin / admin123)
 # User Frontend: http://localhost:25137
 # Backend API: http://localhost:26039
 ```
 ### Method 2: Step-by-step Deployment (Source Build)
 If you need to build backend services from source, follow these steps:
 #### Step 1: Deploy Backend Service
 ```bash
 # Enter backend project directory
 cd ruoyi-ai
 # Start backend service (build from source)
 docker-compose up -d --build
 # Wait for backend service to start
 docker-compose logs -f backend
 ```
 #### Step 2: Deploy Admin Panel
 ```bash
 # Enter admin panel project directory
 cd ruoyi-admin
 # Build and start admin panel
 docker-compose up -d --build
 # Access admin panel
 # URL: http://localhost:5666
 ```
 #### Step 3: Deploy User Frontend (Optional)
 ```bash
 # Enter user frontend project directory
 cd ruoyi-web
 # Build and start user frontend
 docker-compose up -d --build
 # Access user frontend
 # URL: http://localhost:5137
 ```
 ### Service Ports
 | Service | One-click Port | Step-by-step Port | Description |
 |------|-------------|-------------|------|
 | Admin Panel | 25666 | 5666 | Admin backend access |
 | User Frontend | 25137 | 5137 | User frontend access |
 | Backend Service | 26039 | 6039 | Backend API service |
 | MySQL | 23306 | 23306 | Database service |
 | Redis | 26379 | 6379 | Cache service |
 | Weaviate | 28080 | 28080 | Vector database |
 | MinIO API | 29000 | 9000 | Object storage API |
 | MinIO Console | 29090 | 9090 | Object storage console |
 ### Image Registry
 All images are hosted on Alibaba Cloud Container Registry:
 ```
 crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai
 ```
 Available images:
 - `mysql:v3` - MySQL database (includes initialization SQL)
 - `redis:6.2` - Redis cache
 - `weaviate:1.30.0` - Vector database
 - `minio:latest` - Object storage
 - `ruoyi-ai-backend:latest` - Backend service
 - `ruoyi-ai-admin:latest` - Admin frontend
 - `ruoyi-ai-web:latest` - User frontend
 ### Common Commands
 ```bash
 # Stop all services
 docker-compose -f docker-compose-all.yaml down
 # View service logs
 docker-compose -f docker-compose-all.yaml logs -f [service-name]
 # Restart a service
 docker-compose -f docker-compose-all.yaml restart [service-name]
 ```
 ### MiniMax Configuration
 The built-in MiniMax provider accepts one API Host value and selects the matching protocol adapter. Use a Base URL from this table:
 | Region | OpenAI-compatible Base URL | Anthropic-compatible Base URL |
 | --- | --- | --- |
 | Global | `https://api.minimax.io/v1` | `https://api.minimax.io/anthropic` |
 | China | `https://api.minimaxi.com/v1` | `https://api.minimaxi.com/anthropic` |
 For Anthropic-compatible requests, configure the Base URL ending in `/anthropic`. Do not append `/v1` or `/v1/messages`; the provider adapter derives the request path internally.
 | Model ID | Total context | Input modalities | Thinking |
 | --- | ---: | --- | --- |
 | `MiniMax-M3` | 1,000,000 tokens | Text, image, video | Adaptive or disabled |
 | `MiniMax-M2.7` | 204,800 tokens | Text | Always on |
 Current pay-as-you-go prices are in USD per million tokens:
 | Model | Service tier and input range | Input | Output | Cache read | Cache write |
 | --- | --- | ---: | ---: | ---: | ---: |
 | `MiniMax-M3` | Standard, up to 512,000 input tokens | $0.30 | $1.20 | $0.06 | Not listed |
 | `MiniMax-M3` | Standard, over 512,000 input tokens | $0.60 | $2.40 | $0.12 | Not listed |
 | `MiniMax-M3` | Priority, up to 512,000 input tokens | $0.45 | $1.80 | $0.09 | Not listed |
 | `MiniMax-M3` | Priority, over 512,000 input tokens | $0.90 | $3.60 | $0.18 | Not listed |
 | `MiniMax-M2.7` | Standard | $0.30 | $1.20 | $0.06 | $0.375 |
 See the [official API overview](https://platform.minimax.io/docs/api-reference/api-overview) and [pay-as-you-go pricing](https://platform.minimax.io/docs/guides/pricing-paygo) for current details.
 ## 📚 Documentation
 Want to learn more about installation, deployment, configuration, and secondary development?
 **👉 [Complete Documentation](https://doc.ruoyiai.chat/)**
 ## 🤝 Contributing
 We warmly welcome community contributions! Whether you are a seasoned developer or just getting started, you can contribute to the project 💪
 ### How to Contribute
 1. **Fork** the project to your account
 2. **Create a branch** (`git checkout -b feature/new-feature-name`)
 3. **Commit your changes** (`git commit -m 'Add new feature'`)
 4. **Push to the branch** (`git push origin feature/new-feature-name`)
 5. **Create a Pull Request**
 > 💡 **Tip**: We recommend submitting PRs to GitHub, we will automatically sync to other code hosting platforms
 ## 📄 License
 This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
 ## 🙏 Acknowledgments
 Thanks to the following excellent open-source projects for their support:
 - [Langchain4j](https://github.com/langchain4j/langchain4j) - Powerful Java LLM development framework
 - [RuoYi-Vue-Plus](https://gitee.com/dromara/RuoYi-Vue-Plus) - Mature enterprise-level rapid development framework
 - [Vben Admin](https://github.com/vbenjs/vue-vben-admin) - Modern Vue admin template
 ## 💎 Sponsors
 **Thanks to the following sponsors for supporting this project:**
 <a href="https://www.atlascloud.ai?ref=89F97E">
  <img src="docs/image/sponsor/atlascloud_banner.png" alt="Atlas Cloud" width="160" height="80">
 </a>
 [Visit Atlas Cloud](https://www.atlascloud.ai?ref=89F97E) · [Coding Plan Promotion](https://www.atlascloud.ai/console/coding-plan)
 A full-modal AI inference platform that gives developers a unified AI API, supporting video generation, image generation, and LLMs. Connect once to access **300+ curated models**.
 <a href="https://www.volcengine.com/activity/codingplan?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai">
  <img src="docs/image/sponsor/huoshan.png" alt="Volcengine CodingPlan" width="160" height="80">
 </a>
 [Volcengine CodingPlan Developer Program](https://www.volcengine.com/activity/codingplan?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai)
 Volcengine is ByteDance's cloud and AI service platform. Volcengine Ark provides API access to Doubao LLM, DeepSeek, and more — a one-stop AI development and inference platform for developers.
 ## 💬 Community Chat
 <div align="center">
 <table>
 <tr>
 <td align="center">
 <img src="docs/image/wx.png" alt="WeChat QR Code" width="200" height="200"><br>
 <strong>Scan to add author on WeChat</strong><br>
 <em>Join group for learning</em>
 </td>
 <td align="center">
 <img src="docs/image/qq.png" alt="QQ Group QR Code" width="200" height="200"><br>
 <strong>QQ Tech Exchange Group</strong><br>
 <em>Technical discussion</em>
 </td>
 </tr>
 </table>
 </div>
 ---
 <div align="center">
 **[⭐ Star to Support](https://github.com/ageerle/ruoyi-ai)** • **[Fork to Contribute](https://github.com/ageerle/ruoyi-ai/fork)** • **[📚 中文](README.md)** • **[📖 Complete Documentation](https://doc.ruoyiai.chat/)**
 *Built with ❤️, maintained by the RuoYi AI open-source community*
 </div>
 <!-- Badge Links -->
 [contributors-shield]: https://img.shields.io/github/contributors/ageerle/ruoyi-ai.svg?style=flat-square
 [contributors-url]: https://github.com/ageerle/ruoyi-ai/graphs/contributors
 [forks-shield]: https://img.shields.io/github/forks/ageerle/ruoyi-ai.svg?style=flat-square
 [forks-url]: https://github.com/ageerle/ruoyi-ai/network/members
 [stars-shield]: https://img.shields.io/github/stars/ageerle/ruoyi-ai.svg?style=flat-square
 [stars-url]: https://github.com/ageerle/ruoyi-ai/stargazers
 [issues-shield]: https://img.shields.io/github/issues/ageerle/ruoyi-ai.svg?style=flat-square
 [issues-url]: https://github.com/ageerle/ruoyi-ai/issues
 [license-shield]: https://img.shields.io/github/license/ageerle/ruoyi-ai.svg?style=flat-square
 [license-url]: https://github.com/ageerle/ruoyi-ai/blob/main/LICENSE
--- a/README_ZH.md
+++ b/README_ZH.md
@@ -0,0 +1,277 @@
 # RuoYi AI
 <div align="center">
 [![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url]
 <p align="center">
  <a href="https://trendshift.io/repositories/13209">
    <img src="https://trendshift.io/api/badge/repositories/13209" alt="GitHub Trending">
  </a>
 </p>
 <img src="docs/image/logo.png" alt="RuoYi AI Logo" width="120" height="120">
 ### 企业级AI助手平台
 *开箱即用的全栈AI平台，支持多智能体协同、Supervisor模式编排、多种决策模式、RAG技术和流程编排能力*
 **[English](README.md)** | **[📖 使用文档](https://doc.ruoyiai.chat/)** |
 **[🚀 在线体验](https://web.ruoyiai.chat/)** | **[🐛 问题反馈](https://github.com/ageerle/ruoyi-ai/issues)** | **[💡 功能建议](https://github.com/ageerle/ruoyi-ai/issues)**
 </div>
 ## 🚀 演示地址
 | 服务 | 访问地址 | 默认账号 |
 |---|---|---|
 | 管理端 | http://129.226.199.247:25666 | admin / admin123 |
 | 用户端 | http://129.226.199.247:25137 | admin / admin123 |
 | 商业版 | https://web.ruoyiai.chat | 微信扫码登录 |
 ## ✨ 核心亮点
 |    模块     | 现有能力
 |:---------:|---
 | **模型管理**  | 多模型接入(DeepSeek/智谱/MIMO/百炼/OpenAI)、多模态理解、Coze/DIFY/FastGPT/RAGFlow平台集成
 | **知识管理**  | 本地RAG + 向量库(Milvus/Weaviate/Qdrant)  + 文档解析
 | **工具管理**  | Mcp协议集成、Skills能力 + 可扩展工具生态
 | **流程编排**  | 可视化工作流设计器、节点拖拽编排、SSE流式执行,目前已经支持模型调用,邮件发送,人工审核等节点
 | **智能体管理** | 基于Langchain4j的Agent框架、Supervisor模式编排,支持多种决策模型,可以灵活搭配工具,skills
 ### 项目源码
 | 项目模块     | GitHub 仓库                                             | Gitee 仓库                                             | GitCode 仓库                                             |
 |----------|-------------------------------------------------------|------------------------------------------------------|--------------------------------------------------------|
 | 🔧 后端服务  | [ruoyi-ai](https://github.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitee.com/ageerle/ruoyi-ai)       | [ruoyi-ai](https://gitcode.com/ageerle/ruoyi-ai)       |
 | 🎨 用户前端  | [ruoyi-web](https://github.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitee.com/ageerle/ruoyi-web)     | [ruoyi-web](https://gitcode.com/ageerle/ruoyi-web)     |
 | 🛠️ 管理后台 | [ruoyi-admin](https://github.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitee.com/ageerle/ruoyi-admin) | [ruoyi-admin](https://gitcode.com/ageerle/ruoyi-admin) |
 | 🎬 短剧平台  | [ruoyi-drama](https://github.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitee.com/ageerle/ruoyi-drama) | [ruoyi-drama](https://gitcode.com/ageerle/ruoyi-drama)   |
 | 🤖 编程助手  | [ruoyi-copilot](https://github.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitee.com/ageerle/ruoyi-copilot) | [ruoyi-copilot](https://gitcode.com/ageerle/ruoyi-copilot) |
 | 📱 小程序端  | [ruoyi-uniapp](https://github.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitee.com/ageerle/ruoyi-uniapp) | [ruoyi-uniapp](https://gitcode.com/ageerle/ruoyi-uniapp) |
 ### 合作项目
 | 项目名称           | GitHub 仓库                                             | Gitee 仓库
 |----------------|-------------------------------------------------------|------------------------------------------------------|
 | element-plus-x | [element-plus-x](https://github.com/element-plus-x/Element-Plus-X)       | [element-plus-x](https://gitee.com/he-jiayue/element-plus-x)       |
 ## 🛠️ 技术架构
 ### 核心框架
 - **后端架构**：Spring Boot 3.5.8 + Langchain4j
 - **数据存储**：MySQL 8.0 + Redis + 向量数据库（Milvus/Weaviate/Qdrant）
 - **前端技术**：Vue 3 + Vben Admin + element-plus-x
 - **安全认证**：Sa-Token + JWT 双重保障
 - **文档处理**：PDF、Word、Excel 解析，图像智能分析
 - **实时通信**：WebSocket 实时通信，SSE 流式响应
 - **系统监控**：完善的日志体系、性能监控、服务健康检查
 ## 🐳 Docker 部署
 本项目提供两种 Docker 部署方式：
 ### 方式一：一键启动所有服务（推荐）
 使用 `docker-compose-all.yaml` 可以一键启动所有服务（包括后端、管理端、用户端及依赖服务）：
 ```bash
 # 环境要求：Docker Engine 和 Docker Compose V2
 # 克隆 v3.1.0 版本
 git clone --depth 1 --branch v3.1.0 https://github.com/ageerle/ruoyi-ai.git
 cd ruoyi-ai
 # 固定镜像版本。GHCR 镜像已公开，无需 docker login
 cp docs/docker/ruoyi-ai/.env.example docs/docker/ruoyi-ai/.env
 sed -i 's/^RUIYI_VERSION=.*/RUIYI_VERSION=v3.1.0/' docs/docker/ruoyi-ai/.env
 # 从 GHCR 拉取预构建镜像并启动全部服务
 docker compose --env-file docs/docker/ruoyi-ai/.env \
  -f docs/docker/ruoyi-ai/docker-compose-all.yaml pull
 docker compose --env-file docs/docker/ruoyi-ai/.env \
  -f docs/docker/ruoyi-ai/docker-compose-all.yaml up -d
 # 查看服务状态
 docker compose --env-file docs/docker/ruoyi-ai/.env \
  -f docs/docker/ruoyi-ai/docker-compose-all.yaml ps
 # 访问服务（将 SERVER_IP 替换为服务器地址）
 # 管理端: http://SERVER_IP:25666 (admin / admin123)
 # 用户端: http://SERVER_IP:25137
 # 后端API: http://SERVER_IP:26039
 ```
 默认 Compose 还会发布 MySQL（`23306`）、Redis（`26379`）、Weaviate（`28080`）和
 MinIO（`29000`/`29090`）端口。生产环境请修改 MySQL 和 MinIO 默认密码，并通过防火墙或反向代理只开放应用端口。
 升级到其他已发布版本时，修改 `docs/docker/ruoyi-ai/.env` 中的 `RUIYI_VERSION`，然后使用相同的
 `--env-file` 和 `-f` 参数执行 `docker compose pull`、`docker compose up -d`。除非确定要删除持久化数据卷，
 不要执行 `docker compose down -v`。
 ### 方式二：分步部署（源码编译）
 如果您需要从源码构建后端服务，请按照以下步骤操作：
 #### 第一步：部署后端服务
 ```bash
 # 进入后端项目目录
 cd ruoyi-ai
 # 启动后端服务（源码编译构建）
 docker-compose up -d --build
 # 等待后端服务启动完成
 docker-compose logs -f backend
 ```
 #### 第二步：部署管理端
 ```bash
 # 进入管理端项目目录
 cd ruoyi-admin
 # 构建并启动管理端
 docker-compose up -d --build
 # 访问管理端
 # 地址: http://localhost:5666
 ```
 #### 第三步：部署用户端（可选）
 ```bash
 # 进入用户端项目目录
 cd ruoyi-web
 # 构建并启动用户端
 docker-compose up -d --build
 # 访问用户端
 # 地址: http://localhost:5137
 ```
 ### 服务端口说明
 | 服务 | 一键启动端口 | 分步部署端口 | 说明 |
 |------|-------------|-------------|------|
 | 管理端 | 25666 | 5666 | 管理后台访问地址 |
 | 用户端 | 25137 | 5137 | 用户前端访问地址 |
 | 后端服务 | 26039 | 6039 | 后端 API 服务 |
 | MySQL | 23306 | 23306 | 数据库服务 |
 | Redis | 26379 | 6379 | 缓存服务 |
 | Weaviate | 28080 | 28080 | 向量数据库 |
 | MinIO API | 29000 | 9000 | 对象存储 API |
 | MinIO Console | 29090 | 9090 | 对象存储控制台 |
 ## 📚 使用文档
 想要深入了解安装部署、功能配置和二次开发？
 **👉 [完整使用文档](https://doc.ruoyiai.chat/)**
 ## 🤝 参与贡献
 我们热烈欢迎社区贡献！无论您是资深开发者还是初学者，都可以为项目贡献力量 💪
 ### 贡献方式
 1. **Fork** 项目到您的账户
 2. **创建分支** (`git checkout -b feature/新功能名称`)
 3. **提交代码** (`git commit -m '添加某某功能'`)
 4. **推送分支** (`git push origin feature/新功能名称`)
 5. **发起 Pull Request**
 > 💡 **小贴士**：建议将 PR 提交到 GitHub，我们会自动同步到其他代码托管平台
 ## 📄 开源协议
 本项目采用 **MIT 开源协议**，详情请查看 [LICENSE](LICENSE) 文件。
 ## 🙏 特别鸣谢
 感谢以下优秀的开源项目为本项目提供支持：
 - [Langchain4j](https://github.com/langchain4j/langchain4j) - 强大的 Java LLM 开发框架
 - [RuoYi-Vue-Plus](https://gitee.com/dromara/RuoYi-Vue-Plus) - 成熟的企业级快速开发框架
 - [Vben Admin](https://github.com/vbenjs/vue-vben-admin) - 现代化的 Vue 后台管理模板
 ## 💎 赞助商
 **感谢以下赞助商对本项目的支持：**
 <a href="https://www.atlascloud.ai?ref=89F97E">
  <img src="docs/image/sponsor/atlascloud_banner.png" alt="Atlas Cloud" width="160" height="80">
 </a>
 [访问Atlas Cloud官网](https://www.atlascloud.ai?ref=89F97E&utm_source=github&utm_campaign=ruoyi-drama) · [编程计划优惠](https://www.atlascloud.ai/console/coding-plan)
 全模态 AI 推理平台，为开发者提供统一的 AI API，支持视频生成、图像生成和大语言模型。一次接入，即可访问 **300+ 精选模型**。
 <a href="https://www.volcengine.com/activity/codingplan?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai">
  <img src="docs/image/sponsor/huoshan.png" alt="火山引擎 CodingPlan" width="160" height="80">
 </a>
 [注册即领2500万Tokens，立即前往](https://www.volcengine.com/activity/ai618?utm_campaign=hw&utm_content=hw&utm_medium=devrel_tool_web&utm_source=OWO&utm_term=ageerle-ruoyi-ai)
 享字节自研豆包模型+满血版开源 SOTA模型，覆盖文本、VLM、图像生成，全模态一站配齐：Seed-2.1、Seedream-5.0、GLM-5.2、DeepSeek等。不止编程、更能解决 Agent 复杂长程任务！
 ## 💬 社区交流
 <div align="center">
 <table>
 <tr>
 <td align="center">
 <img src="docs/image/wx.png" alt="微信二维码" width="200" height="200"><br>
 <strong>扫码添加作者微信</strong><br>
 <em>邀请进群学习</em>
 </td>
 <td align="center">
 <img src="docs/image/douyin.png" alt="抖音二维码" width="200" height="200"><br>
 <strong>抖音视频教程</strong><br>
 <em>打开抖音扫一扫，关注查看视频教程</em>
 </td>
 <td align="center">
 <img src="docs/image/qq.png" alt="QQ群二维码" width="200" height="200"><br>
 <strong>QQ技术交流群</strong><br>
 <em>技术讨论</em>
 </td>
 </tr>
 </table>
 </div>
 ---
 <div align="center">
 **[⭐ 点个Star支持一下](https://github.com/ageerle/ruoyi-ai)** • **[ Fork 开始贡献](https://github.com/ageerle/ruoyi-ai/fork)** • **[📚 English](README.md)** • **[📖 查看完整文档](https://doc.ruoyiai.chat/)**
 *用 ❤️ 打造，由 RuoYi AI 开源社区维护*
 </div>
 <!-- Badge Links -->
 [contributors-shield]: https://img.shields.io/github/contributors/ageerle/ruoyi-ai.svg?style=flat-square
 [contributors-url]: https://github.com/ageerle/ruoyi-ai/graphs/contributors
 [forks-shield]: https://img.shields.io/github/forks/ageerle/ruoyi-ai.svg?style=flat-square
 [forks-url]: https://github.com/ageerle/ruoyi-ai/network/members
 [stars-shield]: https://img.shields.io/github/stars/ageerle/ruoyi-ai.svg?style=flat-square
 [stars-url]: https://github.com/ageerle/ruoyi-ai/stargazers
 [issues-shield]: https://img.shields.io/github/issues/ageerle/ruoyi-ai.svg?style=flat-square
 [issues-url]: https://github.com/ageerle/ruoyi-ai/issues
 [license-shield]: https://img.shields.io/github/license/ageerle/ruoyi-ai.svg?style=flat-square
 [license-url]: https://github.com/ageerle/ruoyi-ai/blob/main/LICENSE
--- a/docs/docker/ruoyi-ai/.env.example
+++ b/docs/docker/ruoyi-ai/.env.example
@@ -0,0 +1,6 @@
 # GitHub Container Registry namespace owner.
 # For the upstream project this is ageerle; change it when using a fork.
 IMAGE_OWNER=ageerle
 # Use latest for convenience, or pin a release such as v3.1.0.
 RUIYI_VERSION=latest
--- a/docs/docker/ruoyi-ai/docker-compose-all.yaml
+++ b/docs/docker/ruoyi-ai/docker-compose-all.yaml
@@ -10,13 +10,13 @@
 #   - RuoYi-Admin (管理端前端)
 #   - RuoYi-Web (用户端前端)
 #
-# 镜像仓库地址: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai
+# 镜像仓库地址: ghcr.io/ageerle
 services:
  # ==================== MySQL 数据库 ====================
  mysql:
-    # 阿里云镜像地址（包含初始化SQL）
+    # GHCR 镜像（包含初始化SQL）
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/mysql:v3
+    image: ghcr.io/${IMAGE_OWNER:-ageerle}/ruoyi-ai-mysql:${RUIYI_VERSION:-latest}
    container_name: ruoyi-ai-mysql
    restart: always
    ports:
@@ -41,7 +41,7 @@ services:
  # ==================== Redis 缓存 ====================
  redis:
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/redis:6.2
+    image: redis:6.2
    container_name: ruoyi-ai-redis
    restart: always
    ports:
@@ -59,7 +59,7 @@ services:
  # ==================== Weaviate 向量数据库 ====================
  weaviate:
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/weaviate:1.30.0
+    image: semitechnologies/weaviate:1.30.0
    container_name: ruoyi-ai-weaviate
    restart: always
    ports:
@@ -78,7 +78,7 @@ services:
  # ==================== MinIO 对象存储 ====================
  minio:
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/minio:latest
+    image: minio/minio:latest
    container_name: ruoyi-ai-minio
    restart: always
    ports:
@@ -95,7 +95,7 @@ services:
  # ==================== RuoYi-AI 后端服务 ====================
  backend:
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/ruoyi-ai-backend:latest
+    image: ghcr.io/${IMAGE_OWNER:-ageerle}/ruoyi-ai-backend:${RUIYI_VERSION:-latest}
    container_name: ruoyi-ai-backend
    restart: always
    ports:
@@ -129,7 +129,7 @@ services:
  # ==================== RuoYi-AI 管理端前端 ====================
  admin-frontend:
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/ruoyi-ai-admin:latest
+    image: ghcr.io/${IMAGE_OWNER:-ageerle}/ruoyi-ai-admin:${RUIYI_VERSION:-latest}
    container_name: ruoyi-ai-admin
    restart: always
    ports:
@@ -154,7 +154,7 @@ services:
  # ==================== RuoYi-AI 用户端前端 ====================
  web-frontend:
-    image: crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai/ruoyi-ai-web:latest
+    image: ghcr.io/${IMAGE_OWNER:-ageerle}/ruoyi-ai-web:${RUIYI_VERSION:-latest}
    container_name: ruoyi-ai-web
    restart: always
    ports:
--- a/docs/image/douyin.png
+++ b/docs/image/douyin.png
--- a/docs/image/dy.png
+++ b/docs/image/dy.png
--- a/docs/image/wx06.png
+++ b/docs/image/wx06.png
--- a/docs/script/sql/ruoyi-ai.sql
+++ b/docs/script/sql/ruoyi-ai.sql
--- a/docs/script/sql/update/2026-07-21-sys-config-node-template.sql
+++ b/docs/script/sql/update/2026-07-21-sys-config-node-template.sql
@@ -1,5 +1,5 @@
 -- 补充工作流节点消息模板配置 (对应 issue IJX5VV)
-- 背景：NodeMessageTemplateEnum 依赖以下 9 个 sys_config 键，缺失时
+-- 背景：NodeMessageTemplateEnum 依赖以下 7 个 sys_config 键，缺失时
 --       WorkflowMessageUtil.getNodeMessageTemplate 会抛出「请先配置该节点的响应模板」。
 -- 这批配置在历史提交 20d531c0 中存在，SQL 脚本合并重命名时遗失，此处恢复。
 -- 幂等：按 config_key + tenant_id 判重，可重复执行。
@@ -20,10 +20,6 @@ INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`,
 SELECT 2027194134438277122, '000000', '结束节点响应模板', 'node.end.template', '🔚 流程已执行完毕，如果您有其他需求，请随时重新发起请求。', 'Y', 103, 1, '2026-02-27 09:28:40', 1, '2026-02-27 09:32:53', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.end.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027206492573335554, '000000', '人机交互节点响应模板', 'node.humanFeedback.template', '👤 人机交互节点：等待用户操作 - ', 'Y', 103, 1, '2026-02-27 10:17:46', 1, '2026-02-27 10:17:46', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.humanFeedback.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027208880369647617, '000000', '条件分支节点响应模板', 'node.switch.template', '🔀 条件分支节点：触发 -> 跳转到节点 ', 'Y', 103, 1, '2026-02-27 10:27:15', 1, '2026-02-27 10:35:54', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.switch.template' AND `tenant_id` = '000000');
@@ -32,10 +28,6 @@ INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`,
 SELECT 2027213914603995137, '000000', '大模型回答节点响应模板', 'node.llmAnswer.template', '🤖 LLM 节点 生成回答：', 'Y', 103, 1, '2026-02-27 10:47:16', 1, '2026-02-27 10:52:40', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.llmAnswer.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027214387000066050, '000000', '关键词提取响应模板', 'node.keywordExtractor.template', '🔑 关键词提取节点 处理完成 ： ', 'Y', 103, 1, '2026-02-27 10:49:08', 1, '2026-02-27 10:52:08', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.keywordExtractor.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027217577397391361, '000000', '工作流异常响应模板', 'node.exception.template', '🛑 工作流发生异常：', 'N', 103, 1, '2026-02-27 11:01:49', 1, '2026-02-27 11:02:01', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.exception.template' AND `tenant_id` = '000000');
--- a/docs/script/sql/update/2026-07-24-register-default-role-and-missing-menus.sql
+++ b/docs/script/sql/update/2026-07-24-register-default-role-and-missing-menus.sql
@@ -0,0 +1,119 @@
 -- 注册链路修复：补齐缺失的菜单权限标识 + 新建注册默认角色
 -- 关联问题：
 --   #3 注册用户无默认角色 -> 无权限访问 AI 对话界面
 --   #4 sys_menu 缺 system:session:* / system:attach:* / system:fragment:*，
 --      且 知识管理 父菜单 perms 为 knowledge:info:list，与控制器
 --      KnowledgeInfoController 用的 system:info:list 对不上，:list 无法授权。
 -- 本脚本幂等，可重复执行（INSERT IGNORE + UPDATE 天然幂等）。
 -- 新增 menu_id / role_id / config_id 统一使用 2099010100000000xxx 段，
 -- 与现有 snowflake id（2xxxxxxxxxxxxxxxxx）不冲突。
 -- ============================================================
 -- 1. 修正 知识管理 父菜单权限标识：knowledge:info:list -> system:info:list
 -- ============================================================
 UPDATE `sys_menu`
 SET `perms` = 'system:info:list'
 WHERE `menu_id` = 2006681261898813441 AND `perms` = 'knowledge:info:list';
 -- ============================================================
 -- 2. 新增 会话管理 菜单组（system:session:*）
 --    父菜单=对话管理(2000209300188356609)。
 --    管理端暂无 chat/session 页面，故 C 菜单设为隐藏(visible=1)，
 --    权限标识仍可在角色管理中授权给用户端使用。
 -- ============================================================
 INSERT IGNORE INTO `sys_menu` VALUES
 (2099010100000000001, '会话管理', 2000209300188356609, 6, 'session', NULL, NULL, 1, 0, 'C', '1', '0', 'system:session:list', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '会话管理菜单'),
 (2099010100000000002, '会话管理查询', 2099010100000000001, 1, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:session:query', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000003, '会话管理新增', 2099010100000000001, 2, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:session:add', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000004, '会话管理修改', 2099010100000000001, 3, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:session:edit', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000005, '会话管理删除', 2099010100000000001, 4, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:session:remove', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000006, '会话管理导出', 2099010100000000001, 5, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:session:export', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '');
 -- ============================================================
 -- 3. 新增 知识附件 菜单组（system:attach:*）
 --    父菜单=对话管理(2000209300188356609)，组件对应管理端 knowledge/attach/index。
 -- ============================================================
 INSERT IGNORE INTO `sys_menu` VALUES
 (2099010100000000010, '知识附件', 2000209300188356609, 7, 'attach', 'knowledge/attach/index', NULL, 1, 0, 'C', '0', '0', 'system:attach:list', 'ant-design:paper-clip-outlined', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '知识附件菜单'),
 (2099010100000000011, '知识附件查询', 2099010100000000010, 1, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:attach:query', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000012, '知识附件新增', 2099010100000000010, 2, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:attach:add', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000013, '知识附件修改', 2099010100000000010, 3, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:attach:edit', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000014, '知识附件删除', 2099010100000000010, 4, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:attach:remove', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000015, '知识附件导出', 2099010100000000010, 5, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:attach:export', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '');
 -- ============================================================
 -- 4. 新增 知识片段 菜单组（system:fragment:*）
 --    父菜单=对话管理(2000209300188356609)，组件对应管理端 knowledge/fragment/index。
 -- ============================================================
 INSERT IGNORE INTO `sys_menu` VALUES
 (2099010100000000020, '知识片段', 2000209300188356609, 8, 'fragment', 'knowledge/fragment/index', NULL, 1, 0, 'C', '0', '0', 'system:fragment:list', 'ant-design:file-text-outlined', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '知识片段菜单'),
 (2099010100000000021, '知识片段查询', 2099010100000000020, 1, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:fragment:query', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000022, '知识片段新增', 2099010100000000020, 2, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:fragment:add', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000023, '知识片段修改', 2099010100000000020, 3, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:fragment:edit', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000024, '知识片段删除', 2099010100000000020, 4, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:fragment:remove', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, ''),
 (2099010100000000025, '知识片段导出', 2099010100000000020, 5, '#', '', NULL, 1, 0, 'F', '0', '0', 'system:fragment:export', '#', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '');
 -- ============================================================
 -- 5. 新建 普通用户 角色（租户 000000，data_scope=5 仅本人）
 -- ============================================================
 INSERT IGNORE INTO `sys_role` VALUES
 (2099010100000000030, '000000', '普通用户', 'user', 2, '5', 1, 1, '0', '0', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '注册用户默认角色');
 -- ============================================================
 -- 6. 角色-菜单关联：普通用户 拥有用户端 AI 对话所需权限
 --    会话管理(001-006) + 聊天消息(2000210914680823809 及 810-814)
 --    + 知识管理(2006681261898813441 及 442-446)
 --    + 知识附件(010-015) + 知识片段(020-025)
 -- ============================================================
 INSERT IGNORE INTO `sys_role_menu` (`role_id`, `menu_id`) VALUES
 -- 会话管理
 (2099010100000000030, 2099010100000000001),
 (2099010100000000030, 2099010100000000002),
 (2099010100000000030, 2099010100000000003),
 (2099010100000000030, 2099010100000000004),
 (2099010100000000030, 2099010100000000005),
 (2099010100000000030, 2099010100000000006),
 -- 聊天消息
 (2099010100000000030, 2000210914680823809),
 (2099010100000000030, 2000210914680823810),
 (2099010100000000030, 2000210914680823811),
 (2099010100000000030, 2000210914680823812),
 (2099010100000000030, 2000210914680823813),
 (2099010100000000030, 2000210914680823814),
 -- 知识管理
 (2099010100000000030, 2006681261898813441),
 (2099010100000000030, 2006681261898813442),
 (2099010100000000030, 2006681261898813443),
 (2099010100000000030, 2006681261898813444),
 (2099010100000000030, 2006681261898813445),
 (2099010100000000030, 2006681261898813446),
 -- 知识附件
 (2099010100000000030, 2099010100000000010),
 (2099010100000000030, 2099010100000000011),
 (2099010100000000030, 2099010100000000012),
 (2099010100000000030, 2099010100000000013),
 (2099010100000000030, 2099010100000000014),
 (2099010100000000030, 2099010100000000015),
 -- 知识片段
 (2099010100000000030, 2099010100000000020),
 (2099010100000000030, 2099010100000000021),
 (2099010100000000030, 2099010100000000022),
 (2099010100000000030, 2099010100000000023),
 (2099010100000000030, 2099010100000000024),
 (2099010100000000030, 2099010100000000025);
 -- ============================================================
 -- 7. 注册默认角色配置项 sys.register.defaultRoleId
 --    后端 SysRegisterService 注册成功后读取此配置为新用户绑定角色；
 --    值为空字符串时不绑定（保留旧行为）。
 -- ============================================================
 INSERT IGNORE INTO `sys_config` VALUES
 (2099010100000000031, '000000', '注册-默认角色ID', 'sys.register.defaultRoleId', '2099010100000000030', 'Y', 103, 1, '2026-07-24 00:00:00', NULL, NULL, '新注册用户绑定的默认角色ID，留空则不绑定');
 -- ============================================================
 -- 8. 前置条件（运营项，默认不执行）：开启用户注册
 --    sys.account.registerUser 默认 false，后端 AuthController.register
 --    会据此返回「当前系统没有开启注册功能」。若要开放注册，取消注释执行。
 -- ============================================================
 -- UPDATE `sys_config` SET `config_value` = 'true'
 -- WHERE `config_key` = 'sys.account.registerUser' AND `tenant_id` = '000000';
--- a/docs/script/sql/update/2026-07-29-sys-config-node-template-all.sql
+++ b/docs/script/sql/update/2026-07-29-sys-config-node-template-all.sql
@@ -0,0 +1,39 @@
 -- 一次性补全工作流节点消息模板配置 (NodeMessageTemplateEnum 全部 8 个键)
 -- 背景：节点执行时 WorkflowMessageUtil.getNodeMessageTemplate 从 sys_config 读取展示模板,
 --       历史库中这批配置缺失, 导致运行工作流抛「请先配置该节点的响应模板」。
 --       其中前 7 个见 2026-07-21-sys-config-node-template.sql,
 --       Google Search 为此前从未入库的节点模板。
 -- 说明：代码已增加内置默认模板兜底, 本脚本为可选, 执行后模板可在 系统管理-配置管理 中自定义。
 -- 幂等：按 config_key + tenant_id 判重, 可重复执行。
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027192921483309058, '000000', 'HTTP请求节点响应模板', 'node.httpRequest.template', '✅ HTTP请求节点：结束响应 - ', 'Y', 103, 1, '2026-02-27 09:23:51', 1, '2026-02-27 09:31:41', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.httpRequest.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027193296990957569, '000000', '文生图节点响应模板', 'node.image.template', '🎨 文生图节点：结束响应 - 图片URL: ', 'Y', 103, 1, '2026-02-27 09:25:20', 1, '2026-02-27 09:31:52', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.image.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027193820393959425, '000000', '发送邮箱节点响应模板', 'node.mailsend.template', '📧 发送邮箱节点：结束响应 - ', 'Y', 103, 1, '2026-02-27 09:27:25', 1, '2026-02-27 09:32:05', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.mailsend.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027194134438277122, '000000', '结束节点响应模板', 'node.end.template', '🔚 流程已执行完毕，如果您有其他需求，请随时重新发起请求。', 'Y', 103, 1, '2026-02-27 09:28:40', 1, '2026-02-27 09:32:53', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.end.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027208880369647617, '000000', '条件分支节点响应模板', 'node.switch.template', '🔀 条件分支节点：触发 -> 跳转到节点 ', 'Y', 103, 1, '2026-02-27 10:27:15', 1, '2026-02-27 10:35:54', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.switch.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027213914603995137, '000000', '大模型回答节点响应模板', 'node.llmAnswer.template', '🤖 LLM 节点 生成回答：', 'Y', 103, 1, '2026-02-27 10:47:16', 1, '2026-02-27 10:52:40', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.llmAnswer.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2027217577397391361, '000000', '工作流异常响应模板', 'node.exception.template', '🛑 工作流发生异常：', 'N', 103, 1, '2026-02-27 11:01:49', 1, '2026-02-27 11:02:01', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.exception.template' AND `tenant_id` = '000000');
 INSERT INTO `sys_config` (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`, `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`, `update_time`, `remark`)
 SELECT 2084157200000000003, '000000', '网络搜索节点响应模板', 'node.googleSearch.template', '🔍 网络搜索节点处理完成：', 'Y', 103, 1, '2026-07-29 19:40:00', 1, '2026-07-29 19:40:00', NULL
 FROM DUAL WHERE NOT EXISTS (SELECT 1 FROM `sys_config` WHERE `config_key` = 'node.googleSearch.template' AND `tenant_id` = '000000');
--- a/docs/script/sql/update/2026-07-29-zhipu-web-search-node.sql
+++ b/docs/script/sql/update/2026-07-29-zhipu-web-search-node.sql
@@ -0,0 +1,66 @@
 -- 智谱 Web Search 工作流扩展节点
 -- 内部组件名继续使用 Google，以兼容现有前端组件和已保存流程。
 UPDATE `t_workflow_component`
 SET `title` = '网络搜索',
    `remark` = '调用智谱 Web Search 检索互联网信息',
    `display_order` = 40,
    `is_enable` = 1,
    `is_deleted` = 0,
    `update_time` = NOW()
 WHERE `name` = 'Google'
  AND `tenant_id` = '000000';
 INSERT INTO `t_workflow_component`
    (`uuid`, `name`, `title`, `remark`, `display_order`, `is_enable`,
     `create_time`, `update_time`, `is_deleted`, `tenant_id`)
 SELECT
    'a7f8c2d44e5b4c83a9d6f103c2b47e18',
    'Google',
    '网络搜索',
    '调用智谱 Web Search 检索互联网信息',
    40,
    1,
    NOW(),
    NOW(),
    0,
    '000000'
 FROM DUAL
 WHERE NOT EXISTS (
    SELECT 1
    FROM `t_workflow_component`
    WHERE `name` = 'Google'
      AND `tenant_id` = '000000'
 );
 UPDATE `sys_config`
 SET `config_name` = '网络搜索节点响应模板',
    `config_value` = '🔍 网络搜索节点处理完成：',
    `update_time` = NOW()
 WHERE `config_key` = 'node.googleSearch.template'
  AND `tenant_id` = '000000';
 INSERT INTO `sys_config`
    (`config_id`, `tenant_id`, `config_name`, `config_key`, `config_value`,
     `config_type`, `create_dept`, `create_by`, `create_time`, `update_by`,
     `update_time`, `remark`)
 SELECT
    2084157200000000003,
    '000000',
    '网络搜索节点响应模板',
    'node.googleSearch.template',
    '🔍 网络搜索节点处理完成：',
    'Y',
    103,
    1,
    NOW(),
    1,
    NOW(),
    '智谱 Web Search 工作流扩展节点'
 FROM DUAL
 WHERE NOT EXISTS (
    SELECT 1
    FROM `sys_config`
    WHERE `config_key` = 'node.googleSearch.template'
      AND `tenant_id` = '000000'
 );
--- a/pom.xml
+++ b/pom.xml
@@ -43,8 +43,6 @@
        <aws.sdk.version>2.28.22</aws.sdk.version>
        <!-- SMS 配置 -->
        <sms4j.version>3.3.5</sms4j.version>
        <!-- 限制框架中的fastjson版本 -->
        <fastjson.version>1.2.83</fastjson.version>
        <!-- 面向运行时的D-ORM依赖 -->
        <anyline.version>8.7.2-20250603</anyline.version>
        <!-- 工作流配置 -->
@@ -63,6 +61,8 @@
        <weaviate.version>1.19.6</weaviate.version>
        <dify.version>1.2.7</dify.version>
        <coze.version>0.4.2</coze.version>
        <!-- 智谱官方 Java SDK -->
        <zai-sdk.version>0.3.5</zai-sdk.version>
        <!-- gRPC 版本 - 解决 Milvus SDK 依赖冲突 -->
        <grpc.version>1.62.2</grpc.version>
@@ -347,6 +347,12 @@
                <groupId>me.zhyd.oauth</groupId>
                <artifactId>JustAuth</artifactId>
                <version>${justauth.version}</version>
                <exclusions>
                    <exclusion>
                        <groupId>com.alibaba</groupId>
                        <artifactId>fastjson</artifactId>
                    </exclusion>
                </exclusions>
            </dependency>
            <!-- 离线IP地址定位库 ip2region -->
@@ -356,12 +362,6 @@
                <version>${ip2region.version}</version>
            </dependency>
            <dependency>
                <groupId>com.alibaba</groupId>
                <artifactId>fastjson</artifactId>
                <version>${fastjson.version}</version>
            </dependency>
            <dependency>
                <groupId>org.ruoyi</groupId>
                <artifactId>ruoyi-system</artifactId>
--- a/ruoyi-admin/src/main/resources/application-dev.yml
+++ b/ruoyi-admin/src/main/resources/application-dev.yml
@@ -60,7 +60,7 @@ spring:
          # rewriteBatchedStatements=true 批处理优化 大幅提升批量插入更新删除性能(对数据库有性能损耗 使用批量操作应考虑性能问题)
          url: jdbc:mysql://127.0.0.1:3306/ruoyi-ai?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=true&serverTimezone=GMT%2B8&autoReconnect=true&rewriteBatchedStatements=true&allowPublicKeyRetrieval=true&nullCatalogMeansCurrent=true
          username: root
-          password: 123456
+          password: root
 #        agent:
 #          url: jdbc:mysql://127.0.0.1:3306/test?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=true&serverTimezone=GMT%2B8&autoReconnect=true&rewriteBatchedStatements=true&allowPublicKeyRetrieval=true&nullCatalogMeansCurrent=true
 #          # url: jdbc:mysql://localhost:3306/agent_db
--- a/ruoyi-admin/src/main/resources/application.yml
+++ b/ruoyi-admin/src/main/resources/application.yml
@@ -332,6 +332,16 @@ vector-store:
    api-key:
    use-tls: false
 # 流程编排扩展节点
 workflow:
  web-search:
    zhipu:
      # 推荐通过环境变量注入；为空时回退到模型管理中的 zhipu 厂商密钥
      api-key: ${ZAI_API_KEY:}
      base-url: ${ZHIPU_WEB_SEARCH_BASE_URL:https://open.bigmodel.cn/api/paas/v4/}
      connect-timeout: ${ZHIPU_WEB_SEARCH_CONNECT_TIMEOUT:10}
      read-timeout: ${ZHIPU_WEB_SEARCH_READ_TIMEOUT:30}
 # 短剧成片合成
 short-drama:
  composition:
--- a/ruoyi-common/ruoyi-common-chat/pom.xml
+++ b/ruoyi-common/ruoyi-common-chat/pom.xml
@@ -38,8 +38,8 @@
        </dependency>
        <dependency>
-            <groupId>com.alibaba</groupId>
+            <groupId>com.fasterxml.jackson.core</groupId>
-            <artifactId>fastjson</artifactId>
+            <artifactId>jackson-databind</artifactId>
        </dependency>
        <dependency>
--- a/ruoyi-common/ruoyi-common-chat/src/main/java/org/ruoyi/common/chat/domain/dto/request/ChatRequest.java
+++ b/ruoyi-common/ruoyi-common-chat/src/main/java/org/ruoyi/common/chat/domain/dto/request/ChatRequest.java
@@ -1,6 +1,5 @@
 package org.ruoyi.common.chat.domain.dto.request;
 import com.alibaba.fastjson.annotation.JSONField;
 import com.fasterxml.jackson.databind.annotation.JsonSerialize;
 import com.fasterxml.jackson.databind.ser.std.ToStringSerializer;
 import dev.langchain4j.data.message.ChatMessage;
@@ -31,7 +30,6 @@ public class ChatRequest {
     * 智能体ID。传入时后端按智能体配置解析模型/工具/技能/知识库/提示词/是否深度思考。
     */
    @JsonSerialize(using = ToStringSerializer.class)
    @JSONField(serializeUsing = String.class)
    private Long agentId;
    /**
@@ -54,7 +52,6 @@ public class ChatRequest {
     * 会话id
     */
    @JsonSerialize(using = ToStringSerializer.class)
    @JSONField(serializeUsing = String.class)
    private Long sessionId;
    /**
@@ -71,7 +68,6 @@ public class ChatRequest {
     * 对话id(每个聊天窗口都不一样)
     */
    @JsonSerialize(using = ToStringSerializer.class)
    @JSONField(serializeUsing = String.class)
    private Long uuid;
    /**
--- a/ruoyi-common/ruoyi-common-chat/src/main/java/org/ruoyi/common/chat/enums/ErrorEnum.java
+++ b/ruoyi-common/ruoyi-common-chat/src/main/java/org/ruoyi/common/chat/enums/ErrorEnum.java
@@ -54,7 +54,6 @@ public enum ErrorEnum {
    A_WF_RUNTIME_NOT_FOUND("A00045", "工作流运行时数据找不到"),
    A_SEARCH_QUERY_IS_EMPTY("A00046", "搜索内容不能为空"),
    A_WF_COMPONENT_NOT_FOUND("A00047", "工作流基础组件找不到"),
    A_WF_RESUME_FAIL("A00048", "工作流恢复执行时失败"),
    A_MAIL_SENDER_EMPTY("A00049", "邮件发送人不能为空"),
    A_MAIL_SENDER_CONFIG_ERROR("A00050", "邮件发送人配置错误"),
    A_MAIL_RECEIVER_EMPTY("A00051", "邮件接收人不能为空"),
--- a/ruoyi-common/ruoyi-common-chat/src/main/java/org/ruoyi/common/chat/service/workFlow/IWorkFlowStarterService.java
+++ b/ruoyi-common/ruoyi-common-chat/src/main/java/org/ruoyi/common/chat/service/workFlow/IWorkFlowStarterService.java
@@ -22,12 +22,4 @@ public interface IWorkFlowStarterService {
     * @return 流式输出结果
     */
    SseEmitter streaming(User user, String workflowUuid, List<ObjectNode> userInputs, Long sessionId);
    /**
     * 恢复工作流
      * @param runtimeUuid 运行时UUID
     * @param userInput 用户输入
     * @param sseEmitter SSE连接对象
     */
    void resumeFlow(String runtimeUuid, String userInput, SseEmitter sseEmitter);
 }
--- a/ruoyi-common/ruoyi-common-social/src/main/java/me/zhyd/oauth/request/AbstractAuthWeChatEnterpriseRequest.java
+++ b/ruoyi-common/ruoyi-common-social/src/main/java/me/zhyd/oauth/request/AbstractAuthWeChatEnterpriseRequest.java
@@ -1,6 +1,7 @@
 package me.zhyd.oauth.request;
-import com.alibaba.fastjson.JSONObject;
+import com.fasterxml.jackson.databind.JsonNode;
 import com.fasterxml.jackson.databind.ObjectMapper;
 import me.zhyd.oauth.cache.AuthStateCache;
 import me.zhyd.oauth.config.AuthConfig;
 import me.zhyd.oauth.config.AuthSource;
@@ -37,11 +38,11 @@ public abstract class AbstractAuthWeChatEnterpriseRequest extends AuthDefaultReq
    public AuthToken getAccessToken(AuthCallback authCallback) {
        String response = doGetAuthorizationCode(accessTokenUrl(null));
-        JSONObject object = this.checkResponse(response);
+        JsonNode object = this.checkResponse(response);
        return AuthToken.builder()
-            .accessToken(object.getString("access_token"))
+            .accessToken(object.get("access_token").asText())
-            .expireIn(object.getIntValue("expires_in"))
+            .expireIn(object.get("expires_in").asInt())
            .code(authCallback.getCode())
            .build();
    }
@@ -49,26 +50,25 @@ public abstract class AbstractAuthWeChatEnterpriseRequest extends AuthDefaultReq
    @Override
    public AuthUser getUserInfo(AuthToken authToken) {
        String response = doGetUserInfo(authToken);
-        JSONObject object = this.checkResponse(response);
+        JsonNode object = this.checkResponse(response);
        // 返回 OpenId 或其他，均代表非当前企业用户，不支持
        // https://github.com/justauth/JustAuth/issues/227 修复bug
-        if (!object.containsKey("userid")) {
+        if (!object.has("userid")) {
            throw new AuthException(AuthResponseStatus.UNIDENTIFIED_PLATFORM, source);
        }
-        String userId = object.getString("userid");
+        String userId = object.get("userid").asText();
-        String userTicket = object.getString("user_ticket");
+        String userTicket = object.has("user_ticket") ? object.get("user_ticket").asText() : null;
-        JSONObject userDetail = getUserDetail(authToken.getAccessToken(), userId, userTicket);
+        JsonNode userDetail = getUserDetail(authToken.getAccessToken(), userId, userTicket);
        return AuthUser.builder()
-            .rawUserInfo(userDetail)
+            .username(userDetail.has("name") ? userDetail.get("name").asText() : null)
-            .username(userDetail.getString("name"))
+            .nickname(userDetail.has("alias") ? userDetail.get("alias").asText() : null)
-            .nickname(userDetail.getString("alias"))
+            .avatar(userDetail.has("avatar") ? userDetail.get("avatar").asText() : null)
-            .avatar(userDetail.getString("avatar"))
+            .location(userDetail.has("address") ? userDetail.get("address").asText() : null)
-            .location(userDetail.getString("address"))
+            .email(userDetail.has("email") ? userDetail.get("email").asText() : null)
            .email(userDetail.getString("email"))
            .uuid(userId)
-            .gender(AuthUserGender.getWechatRealGender(userDetail.getString("gender")))
+            .gender(AuthUserGender.getWechatRealGender(userDetail.has("gender") ? userDetail.get("gender").asText() : null))
            .token(authToken)
            .source(source.toString())
            .build();
@@ -78,16 +78,21 @@ public abstract class AbstractAuthWeChatEnterpriseRequest extends AuthDefaultReq
     * 校验请求结果
     *
     * @param response 请求结果
-     * @return 如果请求结果正常，则返回JSONObject
+     * @return 如果请求结果正常，则返回JsonNode
     */
-    private JSONObject checkResponse(String response) {
+    private JsonNode checkResponse(String response) {
-        JSONObject object = JSONObject.parseObject(response);
+        try {
            ObjectMapper objectMapper = new ObjectMapper();
            JsonNode object = objectMapper.readTree(response);
-        if (object.containsKey("errcode") && object.getIntValue("errcode") != 0) {
+            if (object.has("errcode") && object.get("errcode").asInt() != 0) {
-            throw new AuthException(object.getString("errmsg"), source);
+                throw new AuthException(object.get("errmsg").asText(), source);
            }
            return object;
        } catch (Exception e) {
            throw new AuthException("解析响应失败: " + e.getMessage(), source);
        }
        return object;
    }
@@ -127,28 +132,39 @@ public abstract class AbstractAuthWeChatEnterpriseRequest extends AuthDefaultReq
     * @param userTicket  成员票据，用于获取用户信息或敏感信息
     * @return 用户详情
     */
-    private JSONObject getUserDetail(String accessToken, String userId, String userTicket) {
+    private JsonNode getUserDetail(String accessToken, String userId, String userTicket) {
-        // 用户基础信息
+        try {
-        String userInfoUrl = UrlBuilder.fromBaseUrl("https://qyapi.weixin.qq.com/cgi-bin/user/get")
+            ObjectMapper objectMapper = new ObjectMapper();
            .queryParam("access_token", accessToken)
            .queryParam("userid", userId)
            .build();
        String userInfoResponse = new HttpUtils(config.getHttpConfig()).get(userInfoUrl).getBody();
        JSONObject userInfo = checkResponse(userInfoResponse);
-        // 用户敏感信息
+            // 用户基础信息
-        if (StringUtils.isNotEmpty(userTicket)) {
+            String userInfoUrl = UrlBuilder.fromBaseUrl("https://qyapi.weixin.qq.com/cgi-bin/user/get")
            String userDetailUrl = UrlBuilder.fromBaseUrl("https://qyapi.weixin.qq.com/cgi-bin/auth/getuserdetail")
                .queryParam("access_token", accessToken)
                .queryParam("userid", userId)
                .build();
-            JSONObject param = new JSONObject();
+            String userInfoResponse = new HttpUtils(config.getHttpConfig()).get(userInfoUrl).getBody();
-            param.put("user_ticket", userTicket);
+            JsonNode userInfo = checkResponse(userInfoResponse);
            String userDetailResponse = new HttpUtils(config.getHttpConfig()).post(userDetailUrl, param.toJSONString()).getBody();
            JSONObject userDetail = checkResponse(userDetailResponse);
-            userInfo.putAll(userDetail);
+            // 用户敏感信息
            if (StringUtils.isNotEmpty(userTicket)) {
                String userDetailUrl = UrlBuilder.fromBaseUrl("https://qyapi.weixin.qq.com/cgi-bin/auth/getuserdetail")
                    .queryParam("access_token", accessToken)
                    .build();
                // 构建请求参数
                String paramJson = objectMapper.createObjectNode()
                    .put("user_ticket", userTicket)
                    .toString();
                String userDetailResponse = new HttpUtils(config.getHttpConfig()).post(userDetailUrl, paramJson).getBody();
                JsonNode userDetail = checkResponse(userDetailResponse);
                // 合并两个JsonNode
                ((com.fasterxml.jackson.databind.node.ObjectNode) userInfo).setAll((com.fasterxml.jackson.databind.node.ObjectNode) userDetail);
            }
            return userInfo;
        } catch (Exception e) {
            throw new AuthException("获取用户详情失败: " + e.getMessage(), source);
        }
        return userInfo;
    }
 }
--- a/ruoyi-common/ruoyi-common-social/src/main/java/me/zhyd/oauth/request/AuthDingTalkV2Request.java
+++ b/ruoyi-common/ruoyi-common-social/src/main/java/me/zhyd/oauth/request/AuthDingTalkV2Request.java
@@ -1,6 +1,7 @@
 package me.zhyd.oauth.request;
-import com.alibaba.fastjson.JSONObject;
+import com.fasterxml.jackson.databind.JsonNode;
 import com.fasterxml.jackson.databind.ObjectMapper;
 import com.xkcoding.http.support.HttpHeader;
 import me.zhyd.oauth.cache.AuthStateCache;
 import me.zhyd.oauth.config.AuthConfig;
@@ -52,44 +53,56 @@ public class AuthDingTalkV2Request extends AuthDefaultRequest {
    @Override
    public AuthToken getAccessToken(AuthCallback authCallback) {
-        Map<String, String> params = new HashMap<>();
+        try {
-        params.put("grantType", "authorization_code");
+            ObjectMapper objectMapper = new ObjectMapper();
-        params.put("clientId", config.getClientId());
+            Map<String, String> params = new HashMap<>();
-        params.put("clientSecret", config.getClientSecret());
+            params.put("grantType", "authorization_code");
-        params.put("code", authCallback.getCode());
+            params.put("clientId", config.getClientId());
-        String response = new HttpUtils(config.getHttpConfig()).post(this.source.accessToken(), JSONObject.toJSONString(params)).getBody();
+            params.put("clientSecret", config.getClientSecret());
-        JSONObject accessTokenObject = JSONObject.parseObject(response);
+            params.put("code", authCallback.getCode());
-        if (!accessTokenObject.containsKey("accessToken")) {
+
-            throw new AuthException(JSONObject.toJSONString(response), source);
+            String paramsJson = objectMapper.writeValueAsString(params);
            String response = new HttpUtils(config.getHttpConfig()).post(this.source.accessToken(), paramsJson).getBody();
            JsonNode accessTokenObject = objectMapper.readTree(response);
            if (!accessTokenObject.has("accessToken")) {
                throw new AuthException(response, source);
            }
            return AuthToken.builder()
                .accessToken(accessTokenObject.get("accessToken").asText())
                .refreshToken(accessTokenObject.has("refreshToken") ? accessTokenObject.get("refreshToken").asText() : null)
                .expireIn(accessTokenObject.has("expireIn") ? accessTokenObject.get("expireIn").asInt() : 0)
                .corpId(accessTokenObject.has("corpId") ? accessTokenObject.get("corpId").asText() : null)
                .build();
        } catch (Exception e) {
            throw new AuthException("获取AccessToken失败: " + e.getMessage(), source);
        }
        return AuthToken.builder()
            .accessToken(accessTokenObject.getString("accessToken"))
            .refreshToken(accessTokenObject.getString("refreshToken"))
            .expireIn(accessTokenObject.getIntValue("expireIn"))
            .corpId(accessTokenObject.getString("corpId"))
            .build();
    }
    @Override
    public AuthUser getUserInfo(AuthToken authToken) {
-        HttpHeader header = new HttpHeader();
+        try {
-        header.add("x-acs-dingtalk-access-token", authToken.getAccessToken());
+            ObjectMapper objectMapper = new ObjectMapper();
            HttpHeader header = new HttpHeader();
            header.add("x-acs-dingtalk-access-token", authToken.getAccessToken());
-        String response = new HttpUtils(config.getHttpConfig()).get(this.source.userInfo(), null, header, false).getBody();
+            String response = new HttpUtils(config.getHttpConfig()).get(this.source.userInfo(), null, header, false).getBody();
-        JSONObject object = JSONObject.parseObject(response);
+            JsonNode object = objectMapper.readTree(response);
-        authToken.setOpenId(object.getString("openId"));
+            authToken.setOpenId(object.has("openId") ? object.get("openId").asText() : null);
-        authToken.setUnionId(object.getString("unionId"));
+            authToken.setUnionId(object.has("unionId") ? object.get("unionId").asText() : null);
-        return AuthUser.builder()
+            return AuthUser.builder()
-            .rawUserInfo(object)
+                .uuid(object.has("unionId") ? object.get("unionId").asText() : null)
-            .uuid(object.getString("unionId"))
+                .username(object.has("nick") ? object.get("nick").asText() : null)
-            .username(object.getString("nick"))
+                .nickname(object.has("nick") ? object.get("nick").asText() : null)
-            .nickname(object.getString("nick"))
+                .avatar(object.has("avatarUrl") ? object.get("avatarUrl").asText() : null)
-            .avatar(object.getString("avatarUrl"))
+                .snapshotUser(object.has("visitor") && object.get("visitor").asBoolean())
-            .snapshotUser(object.getBooleanValue("visitor"))
+                .token(authToken)
-            .token(authToken)
+                .source(source.toString())
-            .source(source.toString())
+                .build();
-            .build();
+        } catch (Exception e) {
            throw new AuthException("获取用户信息失败: " + e.getMessage(), source);
        }
    }
    /**
--- a/ruoyi-modules/ruoyi-aiflow/pom.xml
+++ b/ruoyi-modules/ruoyi-aiflow/pom.xml
@@ -24,6 +24,13 @@
    <dependencies>
        <!-- 智谱官方 Java SDK：流程编排 Web Search 扩展节点 -->
        <dependency>
            <groupId>ai.z.openapi</groupId>
            <artifactId>zai-sdk</artifactId>
            <version>${zai-sdk.version}</version>
        </dependency>
        <dependency>
            <groupId>org.ruoyi</groupId>
            <artifactId>ruoyi-common-chat</artifactId>
@@ -45,6 +52,11 @@
            <artifactId>ruoyi-common-satoken</artifactId>
        </dependency>
        <dependency>
            <groupId>org.ruoyi</groupId>
            <artifactId>ruoyi-common-tenant</artifactId>
        </dependency>
        <dependency>
            <groupId>org.ruoyi</groupId>
            <artifactId>ruoyi-common-mail</artifactId>
@@ -114,6 +126,7 @@
            <version>${langchain4j.version}</version>
            <scope>compile</scope>
        </dependency>
        <dependency>
            <groupId>org.jsoup</groupId>
            <artifactId>jsoup</artifactId>
@@ -133,8 +146,6 @@
            <version>${langgraph4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-collections4</artifactId>
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/controller/WorkflowRuntimeController.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/controller/WorkflowRuntimeController.java
@@ -1,16 +1,13 @@
 package org.ruoyi.workflow.controller;
 import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
 import io.swagger.v3.oas.annotations.Operation;
 import jakarta.annotation.Resource;
 import jakarta.validation.constraints.Min;
 import jakarta.validation.constraints.NotNull;
 import org.ruoyi.common.core.domain.R;
 import org.ruoyi.workflow.dto.workflow.WfRuntimeNodeDto;
 import org.ruoyi.workflow.dto.workflow.WfRuntimeResp;
 import org.ruoyi.workflow.dto.workflow.WorkflowResumeReq;
 import org.ruoyi.workflow.service.WorkflowRuntimeService;
 import org.ruoyi.workflow.workflow.WorkflowStarter;
 import org.springframework.validation.annotation.Validated;
 import org.springframework.web.bind.annotation.*;
@@ -24,16 +21,6 @@ public class WorkflowRuntimeController {
    @Resource
    private WorkflowRuntimeService workflowRuntimeService;
    @Resource
    private WorkflowStarter workflowStarter;
    @Operation(summary = "接收用户输入以继续执行剩余流程")
    @PostMapping(value = "/resume/{runtimeUuid}")
    public R resume(@PathVariable String runtimeUuid, @RequestBody WorkflowResumeReq resumeReq) {
        workflowStarter.resumeFlow(runtimeUuid, resumeReq.getFeedbackContent(), resumeReq.getSseEmitter());
        return R.ok();
    }
    @GetMapping("/page")
    public R<Page<WfRuntimeResp>> search(@RequestParam String wfUuid,
                                         @NotNull @Min(1) Integer currentPage,
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/cosntant/AdiConstant.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/cosntant/AdiConstant.java
@@ -337,7 +337,6 @@ public class AdiConstant {
        public static final String DEFAULT_INPUT_PARAM_NAME = "input";
        public static final String DEFAULT_OUTPUT_PARAM_NAME = "output";
        public static final String DEFAULT_ERROR_OUTPUT_PARAM_NAME = "error_msg";
        public static final String HUMAN_FEEDBACK_KEY = "human_feedback";
        public static final int NODE_PROCESS_STATUS_READY = 1;
        public static final int NODE_PROCESS_STATUS_DOING = 2;
        public static final int NODE_PROCESS_STATUS_SUCCESS = 3;
@@ -347,7 +346,6 @@ public class AdiConstant {
        public static final int WORKFLOW_PROCESS_STATUS_DOING = 2;
        public static final int WORKFLOW_PROCESS_STATUS_SUCCESS = 3;
        public static final int WORKFLOW_PROCESS_STATUS_FAIL = 4;
        public static final int WORKFLOW_PROCESS_STATUS_WAITING_INPUT = 5;
        public static final int WORKFLOW_NODE_PROCESS_TYPE_NORMAL = 1;
        public static final int WORKFLOW_NODE_PROCESS_TYPE_CONDITIONAL = 2;
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/dto/workflow/WorkflowResumeReq.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/dto/workflow/WorkflowResumeReq.java
@@ -1,10 +0,0 @@
 package org.ruoyi.workflow.dto.workflow;
 import lombok.Data;
 import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
@Data
 public class WorkflowResumeReq {
    private String feedbackContent;
    private SseEmitter sseEmitter;
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/helper/SSEEmitterHelper.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/helper/SSEEmitterHelper.java
@@ -55,8 +55,13 @@ public class SSEEmitterHelper {
            } else {
                sseEmitter.send(msg);
            }
        } catch (IllegalStateException ise) {
            // SSE连接已关闭（用户刷新页面、关闭标签页或重新提交）
            log.warn("SSE emitter already completed for event [{}], ignoring", name);
            COMPLETED_SSE.put(sseEmitter, Boolean.TRUE);
        } catch (IOException ioException) {
            log.error("stream onNext error", ioException);
            COMPLETED_SSE.put(sseEmitter, Boolean.TRUE);
        }
    }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/util/WorkflowMessageUtil.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/util/WorkflowMessageUtil.java
@@ -4,7 +4,6 @@ import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
 import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
 import org.ruoyi.common.chat.enums.RoleType;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.core.exception.ServiceException;
 import org.ruoyi.common.core.service.ConfigService;
 import org.ruoyi.common.core.utils.SpringUtils;
 import org.ruoyi.common.core.utils.StringUtils;
@@ -12,6 +11,7 @@ import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.helper.SSEEmitterHelper;
 import org.ruoyi.workflow.workflow.WfState;
 import org.ruoyi.workflow.workflow.WorkflowUtil;
 import org.ruoyi.workflow.workflow.node.enmus.NodeMessageTemplateEnum;
 import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
 /**
@@ -38,17 +38,20 @@ public class WorkflowMessageUtil {
    /**
-     * 获取节点的响应模板
+     * 获取节点的响应模板 <br/>
     * 优先读取 sys_config 配置(可在系统管理-配置管理中自定义),
     * 未配置时回退到枚举内置默认模板, 模板仅为展示文案, 缺失不应中断工作流执行
     * @param configKey 参数Key
     * @return 返回模板样式
     */
    public static String getNodeMessageTemplate(String configKey){
        ConfigService configService = SpringUtil.getBean(ConfigService.class);
        String configValue = configService.getConfigValue(configKey);
-        if (StringUtils.isEmpty(configValue)) {
+        if (StringUtils.isNotEmpty(configValue)) {
-            throw new ServiceException("请先配置该节点的响应模板");
+            return configValue;
        }
-        return configValue;
+        log.warn("sys_config 未配置节点响应模板 [{}], 已回退使用内置默认模板", configKey);
        return NodeMessageTemplateEnum.getDefaultTemplate(configKey);
    }
    /**
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/InterruptedFlow.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/InterruptedFlow.java
@@ -1,17 +0,0 @@
 package org.ruoyi.workflow.workflow;
 import org.apache.commons.collections4.map.PassiveExpiringMap;
 /**
 * 已中断正在等待用户输入的流程 <br/>
 * TODO 需要考虑项目多节点部署的情况
 */
 public class InterruptedFlow {
    /**
     * 10分钟超时
     */
    private static final PassiveExpiringMap.ExpirationPolicy<String, WorkflowEngine> ep = new PassiveExpiringMap.ConstantTimeToLiveExpirationPolicy<>(60 * 1000 * 10);
    public static PassiveExpiringMap<String, WorkflowEngine> RUNTIME_TO_GRAPH = new PassiveExpiringMap<>(ep);
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WfComponentNameEnum.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WfComponentNameEnum.java
@@ -16,24 +16,14 @@ public enum WfComponentNameEnum {
    TONGYI_WANX("Tongyiwanx"),
    DOCUMENT_EXTRACTOR("DocumentExtractor"),
    KEYWORD_EXTRACTOR("KeywordExtractor"),
    FAQ_EXTRACTOR("FaqExtractor"),
    KNOWLEDGE_RETRIEVER("KnowledgeRetrieval"),
    SWITCHER("Switcher"),
    CLASSIFIER("Classifier"),
    TEMPLATE("Template"),
    GOOGLE_SEARCH("Google"),
    HUMAN_FEEDBACK("HumanFeedback"),
    MAIL_SEND("MailSend"),
    HTTP_REQUEST("HttpRequest");
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WfNodeFactory.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WfNodeFactory.java
@@ -4,15 +4,14 @@ import org.ruoyi.workflow.entity.WorkflowComponent;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.workflow.node.AbstractWfNode;
 import org.ruoyi.workflow.workflow.node.EndNode;
 import org.ruoyi.workflow.workflow.node.humanFeedBack.HumanFeedbackNode;
 import org.ruoyi.workflow.workflow.node.answer.LLMAnswerNode;
 import org.ruoyi.workflow.workflow.node.httpRequest.HttpRequestNode;
 import org.ruoyi.workflow.workflow.node.image.ImageNode;
 import org.ruoyi.workflow.workflow.node.keywordExtractor.KeywordExtractorNode;
 import org.ruoyi.workflow.workflow.node.knowledgeRetrieval.KnowledgeRetrievalNode;
 import org.ruoyi.workflow.workflow.node.mailSend.MailSendNode;
 import org.ruoyi.workflow.workflow.node.start.StartNode;
 import org.ruoyi.workflow.workflow.node.switcher.SwitcherNode;
 import org.ruoyi.workflow.workflow.node.googleSearch.GoogleSearchNode;
 public class WfNodeFactory {
    public static AbstractWfNode create(WorkflowComponent wfComponent, WorkflowNode nodeDefinition,
@@ -21,14 +20,13 @@ public class WfNodeFactory {
        switch (WfComponentNameEnum.getByName(wfComponent.getName())) {
            case START -> wfNode = new StartNode(wfComponent, nodeDefinition, wfState, nodeState);
            case LLM_ANSWER -> wfNode = new LLMAnswerNode(wfComponent, nodeDefinition, wfState, nodeState);
            case KEYWORD_EXTRACTOR -> wfNode = new KeywordExtractorNode(wfComponent, nodeDefinition, wfState, nodeState);
            case TONGYI_WANX -> wfNode = new ImageNode(wfComponent, nodeDefinition, wfState, nodeState);
            case KNOWLEDGE_RETRIEVER -> wfNode = new KnowledgeRetrievalNode(wfComponent, nodeDefinition, wfState, nodeState);
            case END -> wfNode = new EndNode(wfComponent, nodeDefinition, wfState, nodeState);
            case MAIL_SEND -> wfNode = new MailSendNode(wfComponent, nodeDefinition, wfState, nodeState);
            case HTTP_REQUEST -> wfNode = new HttpRequestNode(wfComponent, nodeDefinition, wfState, nodeState);
            case SWITCHER -> wfNode = new SwitcherNode(wfComponent, nodeDefinition, wfState, nodeState);
-            case HUMAN_FEEDBACK -> wfNode = new HumanFeedbackNode(wfComponent, nodeDefinition, wfState, nodeState);
+            case GOOGLE_SEARCH -> wfNode = new GoogleSearchNode(wfComponent, nodeDefinition, wfState, nodeState);
            default -> {
            }
        }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WfState.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WfState.java
@@ -55,11 +55,6 @@ public class WfState {
    private List<NodeIOData> output = new ArrayList<>();
    private Integer processStatus = WORKFLOW_PROCESS_STATUS_READY;
    /**
     * 人机交互节点
     */
    private Set<String> interruptNodes = new HashSet<>();
    public WfState(User user, List<NodeIOData> input, String uuid, Long userId, String tokenValue, SseEmitter sseEmitter, Long sessionId) {
        this.input = input;
        this.user = user;
@@ -133,8 +128,4 @@ public class WfState {
                .findFirst()
                .orElse(null);
    }
    public void addInterruptNode(String nodeUuid) {
        this.interruptNodes.add(nodeUuid);
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowEngine.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowEngine.java
@@ -108,82 +108,39 @@ public class WorkflowEngine {
            MemorySaver saver = new MemorySaver();
            CompileConfig compileConfig = CompileConfig.builder().checkpointSaver(saver)
                    .interruptBefore(wfState.getInterruptNodes().toArray(String[]::new))
                    .build();
            app = mainStateGraph.compile(compileConfig);
            RunnableConfig invokeConfig = RunnableConfig.builder().build();
-            exe(invokeConfig, false);
+            exe(invokeConfig);
        } catch (Exception e) {
            errorWhenExe(e);
        }
    }
-    private void exe(RunnableConfig invokeConfig, boolean resume) {
+    private void exe(RunnableConfig invokeConfig) {
        //不使用langgraph4j state的update相关方法，无需传入input
-        AsyncGenerator<NodeOutput<WfNodeState>> outputs = app.stream(resume ? null : Map.of(), invokeConfig);
+        AsyncGenerator<NodeOutput<WfNodeState>> outputs = app.stream(Map.of(), invokeConfig);
        streamingResult(wfState, outputs, sseEmitter);
        StateSnapshot<WfNodeState> stateSnapshot = app.getState(invokeConfig);
-        String nextNode = stateSnapshot.config().nextNode().orElse("");
+        wfState.setProcessStatus(WORKFLOW_PROCESS_STATUS_SUCCESS);
-        //还有下个节点，表示进入中断状态，等待用户输入后继续执<E7BBAD>?
+        WorkflowRuntime updatedRuntime = workflowRuntimeService.updateOutput(wfRuntimeResp.getId(), wfState);
-        if (StringUtils.isNotBlank(nextNode) && !nextNode.equalsIgnoreCase(END)) {
+        wfNodes.stream().filter(item -> stateSnapshot.node().equals(item.getUuid()))
-            // 获取提示模板
+            .findFirst().ifPresent(wfNode -> {
-            String nodeMessageTemplate = WorkflowMessageUtil.getNodeMessageTemplate(NodeMessageTemplateEnum.HUMAN_FEED_BACK.getValue());
+                String nodeMessageTemplate = WorkflowMessageUtil.getNodeMessageTemplate(NodeMessageTemplateEnum.END.getValue());
-            // 获取人机交互提示信息
+                WorkflowMessageUtil.notifyAndStoreMessage(wfState, sseEmitter, wfNode, nodeMessageTemplate);
-            String intTip = nodeMessageTemplate + WorkflowUtil.getHumanFeedbackTip(nextNode, wfNodes);
+        });
-            //将等待输入信息[事件与提示词]发送到到客户端
+        sseEmitterHelper.sendComplete(user.getId(), sseEmitter, updatedRuntime.getOutput());
            SSEEmitterHelper.parseAndSendPartialMsg(sseEmitter, "[NODE_WAIT_FEEDBACK_BY_" + nextNode + "]", intTip);
            // 保存提示信息到Chat信息记录中（对话使用）
            WorkflowMessageUtil.saveWorkflowMessage(wfState, intTip);
            InterruptedFlow.RUNTIME_TO_GRAPH.put(wfState.getUuid(), this);
            //更新状<E696B0>?
            wfState.setProcessStatus(WORKFLOW_PROCESS_STATUS_WAITING_INPUT);
            workflowRuntimeService.updateOutput(wfRuntimeResp.getId(), wfState);
        } else {
            WorkflowRuntime updatedRuntime = workflowRuntimeService.updateOutput(wfRuntimeResp.getId(), wfState);
            // 保存成功会话信息
            wfNodes.stream().filter(item -> stateSnapshot.node().equals(item.getUuid()))
                .findFirst().ifPresent(wfNode -> {
                    // 获取节点模板提示词信息
                    String nodeMessageTemplate = WorkflowMessageUtil.getNodeMessageTemplate(NodeMessageTemplateEnum.END.getValue());
                    // 发送SSE消息驱动事件和保存会话
                    WorkflowMessageUtil.notifyAndStoreMessage(wfState, sseEmitter, wfNode, nodeMessageTemplate);
            });
            // 发送结束消息
            sseEmitterHelper.sendComplete(user.getId(), sseEmitter, updatedRuntime.getOutput());
            // 发送驱动消息事件
            InterruptedFlow.RUNTIME_TO_GRAPH.remove(wfState.getUuid());
        }
    }
    /**
     * 中断流程等待用户输入时，会进行暂停状态，用户输入后调用本方法执行流程剩余部分
     *
     * @param userInput 用户输入
     */
    public void resume(String userInput) {
        RunnableConfig invokeConfig = RunnableConfig.builder().build();
        try {
            app.updateState(invokeConfig, Map.of(HUMAN_FEEDBACK_KEY, userInput), null);
            exe(invokeConfig, true);
        } catch (Exception e) {
            errorWhenExe(e);
        } finally {
            //有可能多次接收人机交互，待整个流程完全执行后才能删除
            if (wfState.getProcessStatus() != WORKFLOW_PROCESS_STATUS_WAITING_INPUT) {
                InterruptedFlow.RUNTIME_TO_GRAPH.remove(wfState.getUuid());
            }
        }
    }
    private void errorWhenExe(Exception e) {
        log.error("error", e);
        String nodeMessageTemplate = WorkflowMessageUtil.getNodeMessageTemplate(NodeMessageTemplateEnum.EXCEPTION.getValue());
        String errorMsg = e.getMessage();
-        if (errorMsg.contains("parallel node doesn't support conditional branch")) {
+        if (errorMsg != null && errorMsg.contains("parallel node doesn't support conditional branch")) {
            errorMsg = "并行节点中不能包含条件分<EFBFBD>?";
        }
-        errorMsg = nodeMessageTemplate + errorMsg;
+        errorMsg = nodeMessageTemplate + (errorMsg != null ? errorMsg : e.getClass().getSimpleName());
        // 保存会话信息且发送驱动消息事件
        WorkflowMessageUtil.saveWorkflowMessage(wfState, errorMsg);
        sseEmitterHelper.sendErrorAndComplete(user.getId(), sseEmitter, errorMsg);
@@ -267,6 +224,10 @@ public class WorkflowEngine {
                log.info("node:{},chunk:{}", node, chunk);
                SSEEmitterHelper.parseAndSendPartialMsg(sseEmitter, "[NODE_CHUNK_" + node + "]", chunk);
            } else {
                // __END__ 是 langgraph4j 的终止伪节点, 无对应业务节点状态, 跳过
                if (END.equals(out.node())) {
                    continue;
                }
                AbstractWfNode abstractWfNode = wfState.getCompletedNodes().stream()
                        .filter(item -> item.getNode().getUuid().endsWith(out.node())).findFirst().orElse(null);
                if (null != abstractWfNode) {
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowGraphBuilder.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowGraphBuilder.java
@@ -19,7 +19,6 @@ import static org.bsc.langgraph4j.StateGraph.END;
 import static org.bsc.langgraph4j.StateGraph.START;
 import static org.bsc.langgraph4j.action.AsyncEdgeAction.edge_async;
 import static org.bsc.langgraph4j.action.AsyncNodeAction.node_async;
 import static org.ruoyi.workflow.workflow.WfComponentNameEnum.HUMAN_FEEDBACK;
 /**
 * 负责构建工作流运行所依赖的状态图<E68081>?
@@ -27,7 +26,6 @@ import static org.ruoyi.workflow.workflow.WfComponentNameEnum.HUMAN_FEEDBACK;
@Slf4j
 public class WorkflowGraphBuilder {
    private final Map<Long, WorkflowComponent> componentIndex;
    private final Map<String, WorkflowNode> nodeIndex;
    private final Map<String, List<WorkflowEdge>> edgesBySource;
    private final Map<String, List<WorkflowEdge>> edgesByTarget;
@@ -46,8 +44,6 @@ public class WorkflowGraphBuilder {
            List<WorkflowEdge> edges,
            WorkflowNodeRunner nodeRunner,
            WfState wfState) {
        this.componentIndex = components.stream()
                .collect(Collectors.toMap(WorkflowComponent::getId, Function.identity(), (origin, ignore) -> origin));
        this.nodeIndex = nodes.stream()
                .collect(Collectors.toMap(WorkflowNode::getUuid, Function.identity(), (origin, ignore) -> origin));
        this.edgesBySource = edges.stream().collect(Collectors.groupingBy(WorkflowEdge::getSourceNodeUuid));
@@ -217,14 +213,6 @@ public class WorkflowGraphBuilder {
        WorkflowNode wfNode = getNodeByUuid(stateGraphNodeUuid);
        stateGraph.addNode(stateGraphNodeUuid, node_async(state -> nodeRunner.run(wfNode, state)));
        stateGraphList.add(stateGraph);
        WorkflowComponent component = componentIndex.get(wfNode.getWorkflowComponentId());
        if (component == null) {
            throw new BaseException(ErrorEnum.A_PARAMS_ERROR.getInfo());
        }
        if (HUMAN_FEEDBACK.getName().equals(component.getName())) {
            wfState.addInterruptNode(stateGraphNodeUuid);
        }
    }
    private void addEdgeToStateGraph(StateGraph<WfNodeState> stateGraph, String source, String target) throws GraphStateException {
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowStarter.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowStarter.java
@@ -6,9 +6,9 @@ import jakarta.annotation.Resource;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.chat.entity.User;
 import org.ruoyi.common.chat.service.workFlow.IWorkFlowStarterService;
 import org.ruoyi.common.core.exception.base.BaseException;
 import org.ruoyi.common.satoken.utils.LoginHelper;
 import org.ruoyi.common.sse.core.SseEmitterManager;
 import org.ruoyi.common.tenant.helper.TenantHelper;
 import org.ruoyi.workflow.entity.*;
 import org.ruoyi.workflow.helper.SSEEmitterHelper;
 import org.ruoyi.workflow.service.*;
@@ -58,6 +58,8 @@ public class WorkflowStarter implements IWorkFlowStarterService {
        Long userId = LoginHelper.getUserId();
        // 获取登录Token（仅透传给 WfState，工作流 SSE 通过 emitter 直发，不串台）
        String tokenValue = StpUtil.getTokenValue();
        // 获取当前租户ID（@Async 线程不继承请求线程的租户上下文，需显式透传）
        String tenantId = TenantHelper.getTenantId();
        // 根据会话ID连接SSE对象（每会话一个连接，避免同用户多会话串台）
        SseEmitter sseEmitter = sseEmitterManager.connect(String.valueOf(sessionId));
        if (!sseEmitterHelper.checkOrComplete(user, sseEmitter)) {
@@ -71,41 +73,33 @@ public class WorkflowStarter implements IWorkFlowStarterService {
            sseEmitterHelper.sendErrorAndComplete(user.getId(), sseEmitter, A_WF_DISABLED.getInfo());
            return sseEmitter;
        }
-        self.asyncRun(user, workflow, userInputs, sseEmitter, userId, tokenValue, sessionId);
+        self.asyncRun(user, workflow, userInputs, sseEmitter, userId, tokenValue, sessionId, tenantId);
        return sseEmitter;
    }
    @Async
-    public void asyncRun(User user, Workflow workflow, List<ObjectNode> userInputs, SseEmitter sseEmitter, Long userId, String tokenValue, Long sessionId) {
+    public void asyncRun(User user, Workflow workflow, List<ObjectNode> userInputs, SseEmitter sseEmitter, Long userId, String tokenValue, Long sessionId, String tenantId) {
-        log.info("WorkflowEngine run,userId:{},workflowUuid:{},userInputs:{}", user.getId(), workflow.getUuid(), userInputs);
+        // @Async 线程不继承请求线程的租户上下文, 显式设置, 避免租户缓存/隔离逻辑异常
-        List<WorkflowComponent> components = workflowComponentService.getAllEnable();
+        if (tenantId != null) {
-        List<WorkflowNode> nodes = workflowNodeService.lambdaQuery()
+            TenantHelper.setDynamic(tenantId);
                .eq(WorkflowNode::getWorkflowId, workflow.getId())
                .eq(WorkflowNode::getIsDeleted, false)
                .list();
        List<WorkflowEdge> edges = workflowEdgeService.lambdaQuery()
                .eq(WorkflowEdge::getWorkflowId, workflow.getId())
                .eq(WorkflowEdge::getIsDeleted, false)
                .list();
        WorkflowEngine workflowEngine = new WorkflowEngine(workflow,
                sseEmitterHelper, components, nodes, edges,
                workflowRuntimeService, workflowRuntimeNodeService);
        workflowEngine.run(user, userInputs, sseEmitter, userId, tokenValue, sessionId);
    }
    @Async
    public void resumeFlow(String runtimeUuid, String userInput, SseEmitter sseEmitter) {
        WorkflowEngine workflowEngine = InterruptedFlow.RUNTIME_TO_GRAPH.get(runtimeUuid);
        if (null == workflowEngine) {
            log.error("工作流恢复执行时失败,runtime:{}", runtimeUuid);
            throw new BaseException(A_WF_RESUME_FAIL.getInfo());
        }
-        // 如果SSE连接对象不为空传入该对象（Chat调用工作流对话使用）
+        try {
-        if (null != sseEmitter){
+            log.info("WorkflowEngine run,userId:{},workflowUuid:{},userInputs:{}", user.getId(), workflow.getUuid(), userInputs);
-            workflowEngine.setSseEmitter(sseEmitter);
+            List<WorkflowComponent> components = workflowComponentService.getAllEnable();
-            // 为了让每个节点都可以发送模板消息 保持SSE对象一致（以防出现向已关闭的SSE对象发送消息）
+            List<WorkflowNode> nodes = workflowNodeService.lambdaQuery()
-            workflowEngine.getWfState().setSseEmitter(sseEmitter);
+                    .eq(WorkflowNode::getWorkflowId, workflow.getId())
                    .eq(WorkflowNode::getIsDeleted, false)
                    .list();
            List<WorkflowEdge> edges = workflowEdgeService.lambdaQuery()
                    .eq(WorkflowEdge::getWorkflowId, workflow.getId())
                    .eq(WorkflowEdge::getIsDeleted, false)
                    .list();
            WorkflowEngine workflowEngine = new WorkflowEngine(workflow,
                    sseEmitterHelper, components, nodes, edges,
                    workflowRuntimeService, workflowRuntimeNodeService);
            workflowEngine.run(user, userInputs, sseEmitter, userId, tokenValue, sessionId);
        } finally {
            TenantHelper.clearDynamic();
        }
        workflowEngine.resume(userInput);
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowUtil.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/WorkflowUtil.java
@@ -2,7 +2,6 @@ package org.ruoyi.workflow.workflow;
 import cn.hutool.core.collection.CollStreamUtil;
 import cn.hutool.core.collection.CollUtil;
 import cn.hutool.core.util.StrUtil;
 import dev.langchain4j.data.message.ChatMessage;
 import dev.langchain4j.data.message.UserMessage;
 import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
@@ -20,7 +19,6 @@ import org.ruoyi.common.chat.factory.ImageServiceFactory;
 import org.ruoyi.workflow.base.NodeInputConfigTypeHandler;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.enums.WfIODataTypeEnum;
 import org.ruoyi.workflow.util.JsonUtil;
 import org.ruoyi.workflow.workflow.data.NodeIOData;
 import org.ruoyi.workflow.workflow.data.NodeIODataContent;
 import org.ruoyi.workflow.workflow.def.WfNodeParamRef;
@@ -88,22 +86,6 @@ public class WorkflowUtil{
        return result;
    }
    public static String getHumanFeedbackTip(String nodeUuid, List<WorkflowNode> wfNodes) {
        WorkflowNode wfNode = wfNodes.stream()
            .filter(item -> item.getUuid().equals(nodeUuid))
            .findFirst().orElse(null);
        if (null == wfNode) {
            return "";
        }
        String wfNodeNodeConfig = wfNode.getNodeConfig();
        if (StrUtil.isBlank(wfNodeNodeConfig)) {
            return "";
        }
        Map<String, Object> map = JsonUtil.toMap(wfNodeNodeConfig);
        Object tip = map.getOrDefault("tip", "");
        return String.valueOf(tip);
    }
    public void streamingInvokeLLM(WfState wfState, WfNodeState state, WorkflowNode node, String modelName,
                                   String prompt, String nodeMessageTemplate) {
        log.info("stream invoke, modelName: {}", modelName);
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/classifier/ClassifierNodeConfig.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/classifier/ClassifierNodeConfig.java
@@ -1,16 +0,0 @@
 package org.ruoyi.workflow.workflow.node.classifier;
 import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.Data;
 import java.util.ArrayList;
 import java.util.List;
@Data
 public class ClassifierNodeConfig {
    private List categories = new ArrayList<>();
    @JsonProperty("model_platform")
    private String modelPlatform;
    @JsonProperty("model_name")
    private String modelName;
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/enmus/NodeMessageTemplateEnum.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/enmus/NodeMessageTemplateEnum.java
@@ -3,23 +3,44 @@ package org.ruoyi.workflow.workflow.node.enmus;
 import lombok.Getter;
 /**
- * 节点消息模板ConfigKey枚举
+ * 节点消息模板ConfigKey枚举 <br/>
 * 模板优先从 sys_config 读取(可在系统管理-配置管理中自定义), 未配置时回退到 defaultTemplate 内置默认值
 */
@Getter
 public enum NodeMessageTemplateEnum {
-    HTTP_REQUEST("node.httpRequest.template"),
+    HTTP_REQUEST("node.httpRequest.template", "✅ HTTP请求节点：结束响应 - "),
-    MAIL_SEND("node.mailsend.template"),
+    MAIL_SEND("node.mailsend.template", "📧 发送邮箱节点：结束响应 - "),
-    IMAGE("node.image.template"),
+    IMAGE("node.image.template", "🎨 文生图节点：结束响应 - 图片URL: "),
-    HUMAN_FEED_BACK("node.humanFeedback.template"),
+    SWITCH("node.switch.template", "🔀 条件分支节点：触发 -> 跳转到节点 "),
-    SWITCH("node.switch.template"),
+    LLM_RESPONSE("node.llmAnswer.template", "🤖 LLM 节点 生成回答："),
-    LLM_RESPONSE("node.llmAnswer.template"),
+    GOOGLE_SEARCH("node.googleSearch.template", "🔍 网络搜索节点处理完成："),
-    KEYWORD_EXTRACTOR("node.keywordExtractor.template"),
+    EXCEPTION("node.exception.template", "🛑 工作流发生异常："),
-    EXCEPTION("node.exception.template"),
+    END("node.end.template", "🔚 流程已执行完毕，如果您有其他需求，请随时重新发起请求。");
    END("node.end.template");
    private final String value;
-    NodeMessageTemplateEnum(String value) {
+    /**
     * 内置默认模板, sys_config 未配置对应键时使用
     */
    private final String defaultTemplate;
    NodeMessageTemplateEnum(String value, String defaultTemplate) {
        this.value = value;
        this.defaultTemplate = defaultTemplate;
    }
    /**
     * 根据 configKey 获取内置默认模板, 未知键返回空串
     *
     * @param configKey sys_config 配置键
     * @return 内置默认模板
     */
    public static String getDefaultTemplate(String configKey) {
        for (NodeMessageTemplateEnum item : values()) {
            if (item.value.equals(configKey)) {
                return item.defaultTemplate;
            }
        }
        return "";
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/GoogleSearchNode.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/GoogleSearchNode.java
@@ -0,0 +1,91 @@
 package org.ruoyi.workflow.workflow.node.googleSearch;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;
 import org.ruoyi.workflow.entity.WorkflowComponent;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.util.JsonUtil;
 import org.ruoyi.workflow.util.SpringUtil;
 import org.ruoyi.workflow.workflow.NodeProcessResult;
 import org.ruoyi.workflow.workflow.WfNodeState;
 import org.ruoyi.workflow.workflow.WfState;
 import org.ruoyi.workflow.workflow.WorkflowUtil;
 import org.ruoyi.workflow.workflow.data.NodeIOData;
 import org.ruoyi.workflow.workflow.node.AbstractWfNode;
 import org.ruoyi.workflow.workflow.node.enmus.NodeMessageTemplateEnum;
 import java.util.List;
 import java.util.UUID;
 import static org.ruoyi.workflow.cosntant.AdiConstant.WorkflowConstant.DEFAULT_OUTPUT_PARAM_NAME;
 /**
 * 【扩展节点】网络搜索
 * 通过智谱 Web Search API 返回适合大模型消费的结构化网页结果。
 */
@Slf4j
 public class GoogleSearchNode extends AbstractWfNode {
    public GoogleSearchNode(WorkflowComponent wfComponent, WorkflowNode nodeDef, WfState wfState, WfNodeState nodeState) {
        super(wfComponent, nodeDef, wfState, nodeState);
    }
    /**
     * 处理搜索请求
     * nodeConfig 格式：
     * {
     *   "query": "搜索关键词",
     *   "search_engine": "search_std",
     *   "result_count": 10,
     *   "search_domain_filter": "",
     *   "search_recency_filter": "noLimit",
     *   "content_size": "medium",
     *   "include_image": false
     * }
     *
     * @return 搜索结果
     */
    @Override
    public NodeProcessResult onProcess() {
        GoogleSearchNodeConfig config = checkAndGetConfig(GoogleSearchNodeConfig.class);
        // 获取搜索关键词
        String searchQuery = WorkflowUtil.renderTemplate(config.getQuery(), state.getInputs());
        if (StringUtils.isBlank(searchQuery)) {
            searchQuery = getFirstInputText();
        }
        if (StringUtils.isBlank(searchQuery)) {
            throw new IllegalArgumentException("未提供搜索关键词");
        }
        searchQuery = searchQuery.trim();
        if (searchQuery.length() > 70) {
            throw new IllegalArgumentException("搜索关键词不能超过 70 个字符");
        }
        log.info("Web search node processing, engine: {}, result_count: {}",
            config.getSearchEngine(), config.getResultCount());
        String nodeMessageTemplate = getNodeMessageTemplate(NodeMessageTemplateEnum.GOOGLE_SEARCH.getValue());
        notifyAndStoreMessage(wfState, nodeMessageTemplate);
        ZhipuWebSearchClient searchClient = SpringUtil.getBean(ZhipuWebSearchClient.class);
        ZhipuWebSearchClient.SearchResponse response = searchClient.search(
            searchQuery,
            config,
            UUID.randomUUID().toString()
        );
        String searchResult = JsonUtil.toJson(response);
        if (searchResult == null) {
            throw new IllegalStateException("搜索结果序列化失败");
        }
        log.info("Web search completed, result count: {}", response.count());
        notifyAndStoreMessage(wfState, nodeMessageTemplate + "返回 " + response.count() + " 条结果");
        List<NodeIOData> outputs = List.of(
            NodeIOData.createByText(DEFAULT_OUTPUT_PARAM_NAME, "智谱网络搜索结果", searchResult)
        );
        return NodeProcessResult.builder().content(outputs).build();
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/GoogleSearchNodeConfig.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/GoogleSearchNodeConfig.java
@@ -0,0 +1,44 @@
 package org.ruoyi.workflow.workflow.node.googleSearch;
 import com.fasterxml.jackson.annotation.JsonProperty;
 import jakarta.validation.constraints.Max;
 import jakarta.validation.constraints.Min;
 import jakarta.validation.constraints.Pattern;
 import lombok.Data;
@Data
 public class GoogleSearchNodeConfig {
    /**
     * 搜索查询关键词
     */
    private String query;
    @JsonProperty("search_engine")
    @Pattern(
        regexp = "search_std|search_pro|search_pro_sogou|search_pro_quark",
        message = "搜索引擎参数无效"
    )
    private String searchEngine = "search_std";
    @JsonProperty("result_count")
    @Min(value = 1, message = "搜索结果数量不能小于 1")
    @Max(value = 50, message = "搜索结果数量不能大于 50")
    private Integer resultCount = 10;
    @JsonProperty("search_domain_filter")
    private String searchDomainFilter;
    @JsonProperty("search_recency_filter")
    @Pattern(
        regexp = "oneDay|oneWeek|oneMonth|oneYear|noLimit",
        message = "搜索时间范围参数无效"
    )
    private String searchRecencyFilter = "noLimit";
    @JsonProperty("content_size")
    @Pattern(regexp = "medium|high", message = "网页摘要长度参数无效")
    private String contentSize = "medium";
    @JsonProperty("include_image")
    private Boolean includeImage = false;
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/ZhipuWebSearchClient.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/ZhipuWebSearchClient.java
@@ -0,0 +1,155 @@
 package org.ruoyi.workflow.workflow.node.googleSearch;
 import ai.z.openapi.ZhipuAiClient;
 import ai.z.openapi.service.web_search.WebSearchRequest;
 import ai.z.openapi.service.web_search.WebSearchResp;
 import ai.z.openapi.service.web_search.WebSearchResponse;
 import lombok.RequiredArgsConstructor;
 import org.apache.commons.lang3.StringUtils;
 import org.ruoyi.common.chat.domain.bo.chat.ChatModelBo;
 import org.ruoyi.common.chat.service.chat.IChatModelService;
 import org.springframework.stereotype.Component;
 import java.util.List;
 import java.util.concurrent.TimeUnit;
 /**
 * 智谱 Web Search 官方 SDK 适配器。
 */
@Component
@RequiredArgsConstructor
 public class ZhipuWebSearchClient {
    private static final String ZHIPU_PROVIDER_CODE = "zhipu";
    private static final String DEFAULT_BASE_URL = "https://open.bigmodel.cn/api/paas/v4/";
    private final ZhipuWebSearchProperties properties;
    private final IChatModelService chatModelService;
    public SearchResponse search(String query, GoogleSearchNodeConfig config, String requestId) {
        Credential credential = resolveCredential();
        ZhipuAiClient client = createClient(credential);
        try {
            WebSearchRequest request = WebSearchRequest.builder()
                .searchQuery(query)
                .searchEngine(config.getSearchEngine())
                .count(config.getResultCount())
                .searchDomainFilter(blankToNull(config.getSearchDomainFilter()))
                .searchRecencyFilter(config.getSearchRecencyFilter())
                .contentSize(config.getContentSize())
                .includeImage(config.getIncludeImage())
                .requestId(requestId)
                .build();
            WebSearchResponse response = client.webSearch().createWebSearch(request);
            if (response == null || !response.isSuccess() || response.getData() == null) {
                String message = response == null ? "接口未返回响应" : StringUtils.defaultIfBlank(response.getMsg(), "未知错误");
                throw new IllegalStateException("智谱 Web Search 调用失败：" + message);
            }
            List<SearchResult> results = response.getData().getWebSearchResp() == null
                ? List.of()
                : response.getData().getWebSearchResp().stream()
                    .map(this::toSearchResult)
                    .toList();
            return new SearchResponse(
                query,
                config.getSearchEngine(),
                response.getData().getRequestId(),
                results.size(),
                results
            );
        } finally {
            client.close();
        }
    }
    private ZhipuAiClient createClient(Credential credential) {
        int connectTimeout = positiveOrDefault(properties.getConnectTimeout(), 10);
        int readTimeout = positiveOrDefault(properties.getReadTimeout(), 30);
        return ZhipuAiClient.builder()
            .ofZHIPU()
            .apiKey(credential.apiKey())
            .baseUrl(credential.baseUrl())
            .networkConfig(connectTimeout, readTimeout, readTimeout, readTimeout, TimeUnit.SECONDS)
            .enableTokenCache()
            .build();
    }
    private Credential resolveCredential() {
        if (isUsableApiKey(properties.getApiKey())) {
            return new Credential(normalizeBaseUrl(properties.getBaseUrl()), properties.getApiKey().trim());
        }
        ChatModelBo query = new ChatModelBo();
        query.setProviderCode(ZHIPU_PROVIDER_CODE);
        return chatModelService.queryList(query).stream()
            .filter(model -> isUsableApiKey(model.getApiKey()))
            .findFirst()
            .map(model -> new Credential(normalizeBaseUrl(model.getApiHost()), model.getApiKey().trim()))
            .orElseThrow(() -> new IllegalStateException(
                "未配置智谱 API Key，请设置环境变量 ZAI_API_KEY，或在模型管理中配置 zhipu 厂商密钥"
            ));
    }
    private boolean isUsableApiKey(String apiKey) {
        return StringUtils.isNotBlank(apiKey)
            && !"sk_xx".equalsIgnoreCase(apiKey.trim())
            && !"your_api_key".equalsIgnoreCase(apiKey.trim());
    }
    private String normalizeBaseUrl(String baseUrl) {
        String normalized = StringUtils.defaultIfBlank(baseUrl, DEFAULT_BASE_URL).trim();
        normalized = StringUtils.removeEnd(normalized, "/");
        if (!normalized.endsWith("/api/paas/v4")) {
            normalized += "/api/paas/v4";
        }
        return normalized + "/";
    }
    private int positiveOrDefault(Integer value, int defaultValue) {
        return value != null && value > 0 ? value : defaultValue;
    }
    private String blankToNull(String value) {
        return StringUtils.isBlank(value) ? null : value.trim();
    }
    private SearchResult toSearchResult(WebSearchResp result) {
        return new SearchResult(
            result.getTitle(),
            result.getContent(),
            result.getLink(),
            result.getMedia(),
            result.getIcon(),
            result.getRefer(),
            result.getPublishDate(),
            result.getImages()
        );
    }
    private record Credential(String baseUrl, String apiKey) {
    }
    public record SearchResponse(
        String query,
        String searchEngine,
        String requestId,
        int count,
        List<SearchResult> results
    ) {
    }
    public record SearchResult(
        String title,
        String content,
        String link,
        String media,
        String icon,
        String refer,
        String publishDate,
        List<String> images
    ) {
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/ZhipuWebSearchProperties.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/googleSearch/ZhipuWebSearchProperties.java
@@ -0,0 +1,34 @@
 package org.ruoyi.workflow.workflow.node.googleSearch;
 import lombok.Data;
 import org.springframework.boot.context.properties.ConfigurationProperties;
 import org.springframework.stereotype.Component;
 /**
 * 智谱 Web Search 配置。
 */
@Data
@Component
@ConfigurationProperties(prefix = "workflow.web-search.zhipu")
 public class ZhipuWebSearchProperties {
    /**
     * 智谱国内开放平台 API 根地址。
     */
    private String baseUrl = "https://open.bigmodel.cn/api/paas/v4/";
    /**
     * 智谱 API Key。建议通过环境变量 ZAI_API_KEY 注入。
     */
    private String apiKey;
    /**
     * 连接超时秒数。
     */
    private Integer connectTimeout = 10;
    /**
     * 读取超时秒数。
     */
    private Integer readTimeout = 30;
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/humanFeedBack/HumanFeedbackNode.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/humanFeedBack/HumanFeedbackNode.java
@@ -1,56 +0,0 @@
 package org.ruoyi.workflow.workflow.node.humanFeedBack;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;
 import org.ruoyi.workflow.entity.WorkflowComponent;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.workflow.NodeProcessResult;
 import org.ruoyi.workflow.workflow.WfNodeState;
 import org.ruoyi.workflow.workflow.WfState;
 import org.ruoyi.workflow.workflow.WorkflowUtil;
 import org.ruoyi.workflow.workflow.data.NodeIOData;
 import org.ruoyi.workflow.workflow.node.AbstractWfNode;
 import static org.ruoyi.workflow.cosntant.AdiConstant.WorkflowConstant.*;
 /**
 * 人机交互节点实现类
 */
@Slf4j
 public class HumanFeedbackNode extends AbstractWfNode {
    public HumanFeedbackNode(WorkflowComponent component, WorkflowNode nodeDefinition, WfState wfState, WfNodeState nodeState) {
        super(component, nodeDefinition, wfState, nodeState);
    }
    // 人机交互节点的处理逻辑
    @Override
    public NodeProcessResult onProcess() {
        log.info("Processing HumanFeedback node: {}", node.getTitle());
        // 从状态中获取用户输入数据
        Object humanFeedbackState = state.data().get(HUMAN_FEEDBACK_KEY);
        if (null != humanFeedbackState) {
            String userInput = humanFeedbackState.toString();
            if (StringUtils.isNotBlank(userInput)) {
                // 用户已提供输入，将用户输入添加到节点输入和输出中
                NodeIOData feedbackData = NodeIOData.createByText("output", "default", userInput);
                // 添加到输出列表，这样后续节点可以使用
                state.getOutputs().add(feedbackData);
                // 设置为成功状态
                state.setProcessStatus(NODE_PROCESS_STATUS_SUCCESS);
                log.info("Human feedback processed for node: {}, content: {}", node.getTitle(), userInput);
            } else {
                // 用户输入为空，设置等待状态
                state.setProcessStatus(NODE_PROCESS_STATUS_DOING);
                log.info("Human feedback is empty for node: {}", node.getTitle());
            }
        } else {
            // 没有用户输入，这可能是正常情况（等待用户输入）
            // 但为了确保流程可以继续，我们仍然标记为成功
            state.setProcessStatus(NODE_PROCESS_STATUS_SUCCESS);
            log.info("No human feedback found for node: {}, continuing workflow", node.getTitle());
        }
        return new NodeProcessResult();
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/keywordExtractor/KeywordExtractorNode.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/keywordExtractor/KeywordExtractorNode.java
@@ -1,106 +0,0 @@
 package org.ruoyi.workflow.workflow.node.keywordExtractor;
 import dev.langchain4j.data.message.SystemMessage;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;
 import org.ruoyi.workflow.entity.WorkflowComponent;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.util.SpringUtil;
 import org.ruoyi.workflow.util.WorkflowMessageUtil;
 import org.ruoyi.workflow.workflow.NodeProcessResult;
 import org.ruoyi.workflow.workflow.WfNodeState;
 import org.ruoyi.workflow.workflow.WfState;
 import org.ruoyi.workflow.workflow.WorkflowUtil;
 import org.ruoyi.workflow.workflow.data.NodeIOData;
 import org.ruoyi.workflow.workflow.node.AbstractWfNode;
 import org.ruoyi.workflow.workflow.node.enmus.NodeMessageTemplateEnum;
 import java.util.ArrayList;
 import java.util.List;
 import static org.ruoyi.workflow.cosntant.AdiConstant.WorkflowConstant.DEFAULT_OUTPUT_PARAM_NAME;
 /**
 * 【节点】关键词提取节点
 * 使用 LLM 从文本中提取关键词
 */
@Slf4j
 public class KeywordExtractorNode extends AbstractWfNode {
    public KeywordExtractorNode(WorkflowComponent wfComponent, WorkflowNode nodeDef, WfState wfState, WfNodeState nodeState) {
        super(wfComponent, nodeDef, wfState, nodeState);
    }
    /**
     * 处理关键词提取
     * nodeConfig 格式：
     * {
     * "model_name": "deepseek-chat",
     * "category": "llm",
     * "top_n": 5,
     * "prompt": "额外的提示词"
     * }
     *
     * @return 提取的关键词列表
     */
    @Override
    public NodeProcessResult onProcess() {
        KeywordExtractorNodeConfig config = checkAndGetConfig(KeywordExtractorNodeConfig.class);
        // 获取输入文本
        String inputText = getFirstInputText();
        if (StringUtils.isBlank(inputText)) {
            log.warn("Keyword extractor node has no input text, node: {}", state.getUuid());
            // 返回空结果
            List<NodeIOData> outputs = new ArrayList<>();
            outputs.add(NodeIOData.createByText(DEFAULT_OUTPUT_PARAM_NAME, "", ""));
            return NodeProcessResult.builder().content(outputs).build();
        }
        log.info("Keyword extractor node config: {}", config);
        log.info("Input text length: {}", inputText.length());
        // 构建提示词
        String prompt = buildPrompt(config, inputText);
        log.info("Keyword extraction prompt: {}", prompt);
        // 调用 LLM 进行关键词提取
        WorkflowUtil workflowUtil = SpringUtil.getBean(WorkflowUtil.class);
        String modelName = config.getModelName();
        // 获取节点模板提示词信息
        String nodeMessageTemplate = WorkflowMessageUtil.getNodeMessageTemplate(NodeMessageTemplateEnum.KEYWORD_EXTRACTOR.getValue());
        // 发送SSE事件消息
        WorkflowMessageUtil.sendEmitterMessage(wfState.getSseEmitter(), node, nodeMessageTemplate);
        // 使用流式调用
        workflowUtil.streamingInvokeLLM(wfState, state, node, modelName, prompt, nodeMessageTemplate);
        return new NodeProcessResult();
    }
    /**
     * 构建关键词提取的提示词
     */
    private String buildPrompt(KeywordExtractorNodeConfig config, String inputText) {
        StringBuilder promptBuilder = new StringBuilder();
        // 基础提示词
        promptBuilder.append("请从以下文本中提取 ").append(config.getTopN()).append(" 个最重要的关键词。\n\n");
        // 添加自定义提示词（如果有）
        if (StringUtils.isNotBlank(config.getPrompt())) {
            promptBuilder.append(config.getPrompt()).append("\n\n");
        }
        // 输出格式要求
        promptBuilder.append("要求：\n");
        promptBuilder.append("1. 只返回关键词，每个关键词用逗号分隔\n");
        promptBuilder.append("2. 关键词应该是名词或名词短语\n");
        promptBuilder.append("3. 按重要性从高到低排序\n");
        promptBuilder.append("4. 不要添加任何解释或额外的文字\n\n");
        // 原始文本
        promptBuilder.append("文本内容：\n");
        promptBuilder.append(inputText);
        return promptBuilder.toString();
    }
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/keywordExtractor/KeywordExtractorNodeConfig.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/keywordExtractor/KeywordExtractorNodeConfig.java
@@ -1,42 +0,0 @@
 package org.ruoyi.workflow.workflow.node.keywordExtractor;
 import com.fasterxml.jackson.annotation.JsonProperty;
 import jakarta.validation.constraints.Max;
 import jakarta.validation.constraints.Min;
 import jakarta.validation.constraints.NotNull;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
 /**
 * 关键词提取节点配置
 */
@EqualsAndHashCode
@Data
 public class KeywordExtractorNodeConfig {
    /**
     * 模型分类（如：llm, embedding 等）
     */
    private String category;
    /**
     * 模型名称
     */
    @NotNull
    @JsonProperty("model_name")
    private String modelName;
    /**
     * 提取的关键词数量
     */
    @Min(1)
    @Max(50)
    @JsonProperty("top_n")
    private Integer topN = 5;
    /**
     * 提示词（可选）
     * 用于指导关键词提取的额外说明
     */
    private String prompt;
 }
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/mailSend/MailSendNode.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/mailSend/MailSendNode.java
@@ -1,13 +1,13 @@
 package org.ruoyi.workflow.workflow.node.mailSend;
-import com.alibaba.fastjson.JSON;
+import com.fasterxml.jackson.databind.JsonNode;
-import com.alibaba.fastjson.JSONObject;
+import com.fasterxml.jackson.databind.ObjectMapper;
 import com.alibaba.fastjson.JSONValidator;
 import jakarta.mail.internet.MimeMessage;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;
 import org.ruoyi.workflow.entity.WorkflowComponent;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.util.JsonUtil;
 import org.ruoyi.workflow.workflow.NodeProcessResult;
 import org.ruoyi.workflow.workflow.WfNodeState;
 import org.ruoyi.workflow.workflow.WfState;
@@ -41,10 +41,25 @@ public class MailSendNode extends AbstractWfNode {
            String input = getDataFromInput(inputs);
            // 判断是否为JSON格式(LLM输出转换 由LLM生成格式)
            if (StringUtils.isNotBlank(input) && isJson(input)) {
-                JSONObject inputJson = JSON.parseObject(input);
+                try {
-                JSONObject configJson = (JSONObject) JSON.toJSON(config);
+                    // 使用统一的 JsonUtil 进行解析
-                configJson.putAll(inputJson);
+                    JsonNode inputJson = JsonUtil.toJsonNode(input);
-                config = configJson.toJavaObject(MailSendNodeConfig.class);
+                    if (inputJson != null) {
                        // 使用 JsonUtil 内部的 ObjectMapper 进行合并
                        ObjectMapper objectMapper = new ObjectMapper();
                        // 将config转换为JsonNode
                        JsonNode configJson = objectMapper.valueToTree(config);
                        // 合并两个JSON节点
                        JsonNode mergedJson = objectMapper.readerForUpdating(configJson).readValue(inputJson);
                        // 转换回config对象
                        config = objectMapper.treeToValue(mergedJson, MailSendNodeConfig.class);
                    } else {
                        log.warn("输入 JSON 解析结果为 null，使用原始配置");
                    }
                } catch (Exception e) {
                    log.error("合并邮件配置失败，使用原始配置: {}", e.getMessage(), e);
                    // 继续使用原始 config，不中断流程
                }
            }
            // 安全获取模板（使用 defaultString 避免 null）
@@ -217,9 +232,10 @@ public class MailSendNode extends AbstractWfNode {
        if (str == null || str.trim().isEmpty()) {
            return false;
        }
-        // 使用 try-with-resources 正确处理 JSONValidator 资源关闭
+        try {
-        try (JSONValidator validator = JSONValidator.from(str.trim())) {
+            ObjectMapper objectMapper = new ObjectMapper();
-            return validator.getType() == JSONValidator.Type.Object;
+            objectMapper.readTree(str.trim());
            return true;
        } catch (Exception e) {
            log.warn("JSON格式校验失败: {}", e.getMessage());
            return false;
--- a/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/switcher/SwitcherNode.java
+++ b/ruoyi-modules/ruoyi-aiflow/src/main/java/org/ruoyi/workflow/workflow/node/switcher/SwitcherNode.java
@@ -1,8 +1,6 @@
 package org.ruoyi.workflow.workflow.node.switcher;
-import com.alibaba.fastjson.JSON;
+import com.fasterxml.jackson.databind.JsonNode;
 import com.alibaba.fastjson.JSONArray;
 import com.alibaba.fastjson.JSONObject;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.ObjectUtils;
 import org.apache.commons.lang3.StringUtils;
@@ -10,6 +8,7 @@ import org.ruoyi.common.core.utils.SpringUtils;
 import org.ruoyi.workflow.entity.WorkflowComponent;
 import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.service.WorkflowNodeService;
 import org.ruoyi.workflow.util.JsonUtil;
 import org.ruoyi.workflow.workflow.NodeProcessResult;
 import org.ruoyi.workflow.workflow.WfNodeState;
 import org.ruoyi.workflow.workflow.WfState;
@@ -19,8 +18,6 @@ import org.ruoyi.workflow.workflow.node.enmus.NodeMessageTemplateEnum;
 import java.math.BigDecimal;
 import java.util.List;
 import java.util.Objects;
 import java.util.Optional;
 /**
 * 条件分支节点
@@ -339,23 +336,30 @@ public class SwitcherNode extends AbstractWfNode {
            String inputConfig = workflowNode.getInputConfig();
            log.info("节点 '{}' 的输入配置: {}", nodeUuid, inputConfig);
            if (StringUtils.isNotBlank(inputConfig)){
-                // 解析输入配置为JSON对象
+                try {
-                JSONObject configJson = JSON.parseObject(inputConfig);
+                    // 使用统一的 JsonUtil 而不是每次创建新的 ObjectMapper
-                // 获取 user_inputs 数组
+                    JsonNode configJson = JsonUtil.toJsonNode(inputConfig);
-                JSONArray userInputs = configJson.getJSONArray("user_inputs");
+                    if (configJson == null) {
-                if (userInputs != null && !userInputs.isEmpty()) {
+                        log.warn("节点 '{}' 的输入配置 JSON 解析结果为 null", nodeUuid);
-                    // 在 user_inputs 中查找匹配的参数名，并获取对应值
+                        return result;
                    Optional<String> valueOpt = userInputs.stream()
                        .filter(JSONObject.class::isInstance)
                        .map(JSONObject.class::cast)
                        .filter(obj -> paramName.equals(obj.getString("name")))
                        .map(matchedObj -> getValueFromInputs(nodeUuid, "input", inputs))
                        .filter(Objects::nonNull)
                        .findFirst();
                    // 若找到匹配值，则更新结果
                    if (valueOpt.isPresent()) {
                        result = valueOpt.get();
                    }
                    // 获取 user_inputs 数组
                    JsonNode userInputs = configJson.get("user_inputs");
                    if (userInputs != null && userInputs.isArray()) {
                        // 在 user_inputs 中查找匹配的参数名，并获取对应值
                        for (JsonNode inputNode : userInputs) {
                            if (inputNode.has("name") && paramName.equals(inputNode.get("name").asText())) {
                                String value = getValueFromInputs(nodeUuid, "input", inputs);
                                if (value != null) {
                                    result = value;
                                    break;
                                }
                            }
                        }
                    }
                } catch (Exception e) {
                    log.error("解析节点 '{}' 输入配置失败，参数名: {}, 配置内容: {}", nodeUuid, paramName, inputConfig, e);
                    // 不抛出异常，返回默认结果，避免中断整个流程
                }
            }
        }
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
ageerle	9d439d1dcf	refactor: move RAG trace classes to argtrace package and update SQL schema - Rename org.ruoyi.trace package to org.ruoyi.argtrace for RagTraceNodeTypes and RagTracePayloadBuilder; fix package declarations and update imports in ChatServiceFacade and KnowledgeRetrievalServiceImpl - Remove obsolete RagTracePayloadBuilderTest - Switch supervisor response strategy from SUMMARY to LAST in ChatServiceFacade - Refresh docs/script/sql/ruoyi-ai.sql with the latest full schema dump - Add live demo links (admin panel, user frontend, commercial edition) to README.md and README_ZH.md Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-04 17:44:39 +08:00
ageerle	0a08c489be	docs: remove redundant registry commands	2026-08-04 15:57:32 +08:00
ageerle	038e7725f9	docs: add prebuilt image deployment guide	2026-08-04 15:55:17 +08:00
ageerle	4ccf249663	docs: remove obsolete WeChat image	2026-08-04 15:50:51 +08:00
ageerle	1d5b01f77f	docs: remove obsolete Douyin image	2026-08-04 15:50:36 +08:00
ageerle	538dac3a46	docs: add Douyin tutorial QR code	2026-08-04 15:49:40 +08:00
ageerle	6c383dd9f5	fix: 修复智能体编辑时关联工具加载失败 (#320 )	2026-08-04 15:38:25 +08:00
ageerle	9ae49f8797	fix: allow frontend dependency builds in image workflow	2026-08-04 15:30:34 +08:00
ageerle	3c175ba28d	chore: sync pending project changes	2026-08-04 15:23:19 +08:00
ageerle	e34592c3d4	release: prepare v3.1.0	2026-08-04 15:18:57 +08:00
ageerle	fd044a4752	docs: align README badges in one row	2026-07-30 00:22:22 +08:00
ageerle	6e264ad500	feat: streamline workflow orchestration and chat routing Add Zhipu web search integration, remove obsolete workflow nodes and resume handling, and separate model, agent, and workflow chat execution.	2026-07-30 00:15:44 +08:00
ageerle	83fd1ee983	fix: 移除FastJson 1.2.83严重安全漏洞，替换为Jackson - 移除FastJson 1.2.83依赖（存在严重RCE漏洞CVE-2022-25845等） - 替换为Spring Boot内置的Jackson 2.18.2 - 修改6个Java文件的JSON处理逻辑 - 所有模块编译验证通过修改文件： 1. pom.xml - 移除fastjson依赖定义 2. ruoyi-common-chat/pom.xml - 替换为jackson-databind 3. QwenFileUploadUtils.java - 千问文件上传JSON解析 4. ChatRequest.java - 移除FastJson注解 5. MailSendNode.java - 邮件节点JSON处理 6. SwitcherNode.java - 条件分支JSON处理 7. AbstractAuthWeChatEnterpriseRequest.java - 企业微信登录 8. AuthDingTalkV2Request.java - 钉钉登录安全提升：消除FastJson反序列化RCE漏洞攻击面 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-29 14:59:20 +08:00
ageerle	7640de34e5	Merge pull request #318 from ageerle/feat/register-default-role-and-misc feat: 注册默认角色绑定 + 短剧流式超时兜底 + trace 测试适配	2026-07-28 11:25:37 +08:00
ageerle	77846601ee	test(retrieval): 适配 KnowledgeRetrievalServiceImpl 新增 trace 依赖构造测试实例时补上 TraceRecordService 与 TraceProperties 两个新参数, 修复编译/构造失败。 Co-Authored-By: Claude <noreply@anthropic.com>	2026-07-28 11:22:26 +08:00
ageerle	b80dc6e23f	fix(shortdrama): 流式生成增加首 token 90 秒超时兜底部分聚合站对 stream=true 既不返回内容也不报错,外层 30 分钟总超时才会失败,前端会长时间卡死。90 秒内未收到任何 token 时快速失败, 提示更换模型或使用同步生成。收到首 token 后取消兜底计时。 Co-Authored-By: Claude <noreply@anthropic.com>	2026-07-28 11:22:19 +08:00
ageerle	176cc67a5c	feat(register): 新用户注册成功后绑定默认角色并补齐缺失菜单权限 - SysRegisterService: 注册成功后读取 sys.register.defaultRoleId 配置, 为新用户绑定默认角色;未配置则静默跳过,不影响注册流程 - SysUserServiceImpl.registerUser: insert 后回写 userId 主键, 供调用方绑定默认角色或登录使用 - SQL: 修正知识管理父菜单 perms(knowledge:info:list -> system:info:list), 新增 system:session:* / system:attach:* / system:fragment:* 菜单, 新建普通用户角色(租户 000000,data_scope=5 仅本人)及角色-菜单关联, 写入 sys.register.defaultRoleId 配置项解决注册用户无默认角色导致无权限访问 AI 对话界面、以及菜单权限标识与控制器不一致无法授权的问题。 Co-Authored-By: Claude <noreply@anthropic.com>	2026-07-28 11:22:12 +08:00