diff --git a/docs/zh/tools/ai/_toc.yaml b/docs/zh/tools/ai/_toc.yaml
index 918c7cad6982d6b661a6290bb6c1b7a1c206f2d1..753633ff54f0ee533bf3958ab9224eae4a1d42c0 100644
--- a/docs/zh/tools/ai/_toc.yaml
+++ b/docs/zh/tools/ai/_toc.yaml
@@ -1,13 +1,18 @@
 label: AI
 sections:
-  - label: 智能交互平台
+  - label: openEuler Intelligence
     sections: 
-      - href: ./euler_copilot/intelligent_assistant/_toc.yaml
-      - href: ./euler_copilot/intelligent_vulnerability_patching/_toc.yaml
+      - href: 
+          upstream: https://gitee.com/openeuler/euler-copilot-framework/blob/master/docs_for_openEuler/openEuler_intelligence/intelligent_assistant/_toc.yaml
+      - href:
+          upstream: https://gitee.com/openeuler/euler-copilot-framework/blob/master/docs_for_openEuler/openEuler_intelligence/intelligent_vulnerability_patching/_toc.yaml
   - label: 智能底座
     sections:
-      - href: ./intelligent_foundation/sysHAX/_toc.yaml
+      - href:
+          upstream: https://gitee.com/openeuler/euler-copilot-framework/blob/master/docs_for_openEuler/intelligent_foundation/sysHAX/_toc.yaml
   - label: AI全栈
     sections:
-      - href: ./ai_full_stack/ai_container_image_userguide/_toc.yaml
-      - href: ./ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml
+      - href:
+          upstream: https://gitee.com/openeuler/euler-copilot-framework/blob/master/docs_for_openEuler/ai_full_stack/ai_container_image_userguide/_toc.yaml
+      - href:
+          upstream: https://gitee.com/openeuler/euler-copilot-framework/blob/master/docs_for_openEuler/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml
diff --git a/docs/zh/tools/ai/ai_full_stack/ai_container_image_userguide/_toc.yaml b/docs/zh/tools/ai/ai_full_stack/ai_container_image_userguide/_toc.yaml
deleted file mode 100644
index 8782a2915974178dadfa7b97efcb19df99069a72..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/ai_full_stack/ai_container_image_userguide/_toc.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-label: AI容器镜像用户指南
-isManual: true
-description: openEuler AI 容器镜像封装了 AI 框架等软件，提高 AI 应用开发或使用效率
-sections:
-  - label: AI容器镜像用户指南
-    href: ./ai-container-image-user-guide.md
diff --git a/docs/zh/tools/ai/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md b/docs/zh/tools/ai/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md
deleted file mode 100644
index b433f2f4dcb8d609d014e8ecf3f77d37d5fe5773..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/ai_full_stack/ai_container_image_userguide/ai-container-image-user-guide.md
+++ /dev/null
@@ -1,101 +0,0 @@
-# openEuler AI 容器镜像用户指南
-
-## 简介
-
-openEuler AI 容器镜像封装了不同硬件算力的 SDK 以及 AI 框架、大模型应用等软件，用户只需要在目标环境中加载镜像并启动容器，即可进行 AI 应用开发或使用，大大减少了应用部署和环境配置的时间，提升效率。
-
-## 获取镜像
-
-目前，openEuler 已发布支持 Ascend 和 NVIDIA 平台的容器镜像，获取路径如下：
-
-- [openeuler/cann](https://hub.docker.com/r/openeuler/cann) 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CANN 系列软件，适用于 Ascend 环境。
-- [openeuler/cuda](https://hub.docker.com/r/openeuler/cuda) 存放 SDK 类镜像，在 openEuler 基础镜像之上安装 CUDA 系列软件，适用于 NVIDIA 环境。
-- [openeuler/pytorch](https://hub.docker.com/r/openeuler/pytorch) 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 PyTorch，根据安装的 SDK 软件内容区分适用平台。
-- [openeuler/tensorflow](https://hub.docker.com/r/openeuler/tensorflow) 存放 AI 框架类镜像，在 SDK 镜像基础之上安装 TensorFlow，根据安装的 SDK 软件内容区分适用平台。
-- [openeuler/llm](https://hub.docker.com/r/openeuler/tensorrt) 存放模型应用类镜像，在 AI 框架镜像之上包含特定大模型及工具链，根据安装的 SDK 软件内容区分适用平台。
-
-详细的 AI 容器镜像分类和镜像 tag 的规范说明见[oEEP-0014](https://gitee.com/openeuler/TC/blob/master/oEEP/oEEP-0014%20openEuler%20AI容器镜像软件栈规范.md)。
-
-由于 AI 容器镜像的体积一般较大，推荐用户在启动容器前先通过如下命令将镜像拉取到开发环境中。
-
-```sh
-docker pull image:tag
-```
-
-其中，`image`为仓库名，如`openeuler/cann`，`tag`为目标镜像的 TAG，待镜像拉取完成后即可启动容器。注意，使用`docker pull`命令需按照下文方法安装`docker`软件。
-
-## 启动容器
-
-1. 在环境中安装`docker`，官方安装方法见[Install Docker Engine](https://docs.docker.com/engine/install/)，也可直接通过如下命令进行安装。
-
-    ```sh
-    yum install -y docker
-    ```
-
-    或
-
-    ```sh
-    apt-get install -y docker
-    ```
-
-2. NVIDIA环境安装`nvidia-container`
-
-    1）配置yum或apt repo
-    - 使用yum安装时，执行：
-
-    ```sh
-    curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo | \
-    sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
-    ```
-
-    - 使用apt安装时，执行：
-
-    ```sh
-    curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-    ```
-
-    ```sh
-    curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-    ```
-
-    2）安装`nvidia-container-toolkit`,`nvidia-container-runtime`，执行：
-
-    ```sh
-    # yum安装
-    yum install -y nvidia-container-toolkit nvidia-container-runtime
-    ```
-
-    ```sh
-    # apt安装
-    apt-get install -y nvidia-container-toolkit nvidia-container-runtime
-    ```
-
-    3）配置docker
-
-    ```sh
-    nvidia-ctk runtime configure --runtime=docker
-    systemctl restart docker
-    ```
-
-    非NVIDIA环境不执行此步骤。
-
-3. 确保环境中安装`driver`及`firmware`，用户可从[NVIDIA](https://www.nvidia.com/)或[Ascend](https://www.hiascend.com/)官网获取正确版本进行安装。安装完成后 Ascend 平台使用`npu-smi`命令、NVIDIA 平台使用`nvidia-smi`进行测试，正确显示硬件信息则说明安装正常。
-
-4. 完成上述操作后，即可使用`docker run`命令启动容器。
-
-```sh
-# Ascend环境启动容器
-docker run --rm --network host \
-           --device /dev/davinci0:/dev/davinci0 \
-           --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc \
-           -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-           -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-           -ti image:tag
-```
-
-```sh
-# NVIDIA环境启动容器
-docker run --gpus all -d -ti image:tag
-```
diff --git a/docs/zh/tools/ai/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml b/docs/zh/tools/ai/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml
deleted file mode 100644
index bed3b23bcec452195f1b6a830c6c7e3a017413b0..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/ai_full_stack/ai_large_model_service_images_userguide/_toc.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-label: AI大模型服务镜像使用指南
-isManual: true
-description: 支持百川、chatglm、星火等AI大模型的容器化封装
-sections:
-  - label: AI大模型服务镜像使用指南
-    href: ./llm-service-image-user-guide.md
diff --git a/docs/zh/tools/ai/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md b/docs/zh/tools/ai/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md
deleted file mode 100644
index c7c492b104b74e25ac87980c2d8580885a43df0e..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/ai_full_stack/ai_large_model_service_images_userguide/llm-service-image-user-guide.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# 支持百川、chatglm、星火等AI大模型的容器化封装
-
-已配好相关依赖，分为CPU和GPU版本，降低使用门槛，开箱即用。
-
-## 拉取镜像(CPU版本)
-
-```bash
-docker pull openeuler/llm-server:1.0.0-oe2203sp3
-```
-
-## 拉取镜像(GPU版本)
-
-```bash
-docker pull icewangds/llm-server:1.0.0
-```
-
-## 下载模型, 并转换为gguf格式
-
-```bash
-# 安装huggingface
-pip install huggingface-hub
-
-# 下载你想要部署的模型
-export HF_ENDPOINT=https://hf-mirror.com
-huggingface-cli download --resume-download baichuan-inc/Baichuan2-13B-Chat --local-dir /root/models/Baichuan2-13B-Chat --local-dir-use-symlinks False
-
-# gguf格式转换
-cd /root/models/
-git clone https://github.com/ggerganov/llama.cpp.git
-python llama.cpp/convert-hf-to-gguf.py ./Baichuan2-13B-Chat
-# 生成的gguf格式的模型路径 /root/models/Baichuan2-13B-Chat/ggml-model-f16.gguf
-```
-
-## 启动方式
-
-需要Docker v25.0.0及以上版本。
-
-若使用GPU镜像，需要OS上安装nvidia-container-toolkit，安装方式见<https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html>。
-
-docker-compose.yaml:
-
-```yaml
-version: '3'
-services:
-  model:
-    image: <image>:<tag>   #镜像名称与tag
-    restart: on-failure:5
-    ports:
-      - 8001:8000    #监听端口号，修改“8001”以更换端口
-    volumes:
-      - /root/models:/models  # 大模型挂载目录
-    environment:
-      - MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf  # 容器内的模型文件路径
-      - MODEL_NAME=baichuan13b  # 自定义模型名称
-      - KEY=sk-12345678  # 自定义API Key
-      - CONTEXT=8192  # 上下文大小
-      - THREADS=8    # CPU线程数，仅CPU部署时需要
-    deploy: # 指定GPU资源, 仅GPU部署时需要
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-```
-
-```bash
-docker-compose -f docker-compose.yaml up
-```
-
-docker run:
-
-```text
-cpu部署: docker run -d --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 openeuler/llm-server:1.0.0-oe2203sp3
-
-gpu部署: docker run -d --gpus all --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 icewangds/llm-server:1.0.0
-```
-
-## 调用大模型接口测试，成功返回则表示大模型服务已部署成功
-
-```bash
-curl -X POST http://127.0.0.1:8001/v1/chat/completions \
-     -H "Content-Type: application/json" \
-     -H "Authorization: Bearer sk-12345678" \
-     -d '{
-           "model": "baichuan13b",
-           "messages": [
-             {"role": "system", "content": "你是一个社区助手，请回答以下问题。"},
-             {"role": "user", "content": "你是谁?"}
-           ],
-           "stream": false,
-           "max_tokens": 1024
-         }'
-```
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/_toc.yaml b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/_toc.yaml
deleted file mode 100644
index e616c2efa3d78f28d1c0fed63f753bf519380b56..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/_toc.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-label: openEuler智能助手
-isManual: true
-description: 部署和使用openEuler智能助手
-sections:
-  - label: 快速上手
-    sections: 
-      - label: web问答
-        sections: 
-          - label: 部署指南
-            sections:
-              - label: 无网络环境下部署指南
-                href: ./quick_start/smart_web/deploy_guide/offline.md
-              - label: 网络环境下部署指南
-                href: ./quick_start/smart_web/deploy_guide/online.md
-          - label: 使用指导
-            sections: 
-              - label: 前言
-                href: ./quick_start/smart_web/user_guide/introduction.md
-              - label: 注册与登录
-                href: ./quick_start/smart_web/user_guide/registration_and_login.md
-              - label: 智能问答使用
-                href: ./quick_start/smart_web/user_guide/qa_guide.md
-      - label: shell问答
-        sections: 
-          - label: 使用指导
-            sections:
-              - label: API Key 获取
-                href: ./quick_start/smart_shell/user_guide/API_key.md
-              - label: 命令行助手使用
-                href: ./quick_start/smart_shell/user_guide/shell.md
-  - label: 进阶使用
-    sections:
-      - label: 智能诊断
-        sections:
-          - label: 部署指南
-            href: ./advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md
-          - label: 使用指导
-            href: ./advance/smart_diagnosis/user_guide/diagnosis_guidance.md
-      - label: 智能调优
-        sections:
-          - label: 部署指南
-            href: ./advance/smart_tuning/deploy_guide/tune_deployment.md
-          - label: 使用指导
-            href: ./advance/smart_tuning/user_guide/tune_guidance.md
-      - label: 知识库管理
-        sections:
-          - label: 部署指南
-            href: ./advance/knowledge_base/deploy_guide/witChainD_deployment.md
-          - label: 使用指导
-            href: ./advance/knowledge_base/user_guide/witChainD_guidance.md
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diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/knowledge_base/deploy_guide/witChainD_deployment.md
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+++ /dev/null
@@ -1,122 +0,0 @@
-# 本地资产库构建指南
-
-本平台设计的目的为了为企业（用户）提供一个可视化入口实现对本地文档资产的管理，功能方面分为以下几个部分：
-
-- 用户管理：
-  
-  您可以通过账号登入witchainD平台并在平台配置大模型，为后续某些文档解析模式提供能力。
-- 资产管理
-  
-  通过指定资产名称、资产简介、资产默认解析方法、资产默认分块大小和embedding模型等条目创建、删除、导入资产、编辑资产或资产信息
-- 文档管理
-    - 支持用户上传限定大小和限定数量的文件，也允许用户下载自己上传的文件，用户可以通过点击资产卡片的形式进入资产，此时文档以条目的形式展示。
-    - 支持txt、md、xlsx、docx和doc以及pdf文档的文档解析
-    - 文档解析方式有三种general、ocr和enhanced模式，general模式下只提取文字和表格，ocr模式下不仅提取文字和表格嗨提取部分文档的图片内容，enhanced在ocr的基础上对图片中提取的内容进行总结。
-
-- 文档解析结果管理：
-  
-  通过指定chunk的类别和关键字过滤目标chunk或者可以指定chunk是否启用，来评判或消除chunk对检索造成的影响。
-- 任务管理
-  
-  查看当前导入导出资产和文档解析任务的状态、取消或移除导入导出资产库和文档解析任务
-- 检索增强：
-  
-  本平台最终解析的结果通过向量化、关键字的形式对外呈现检索结果，也提供了token压缩和问题补全等技术增强结果命中的概率，也使用了上下文随机关联的形式补全检索结果。
-
-## 登录管理平台
-
-请在浏览器中输入 `https://$(wichaind域名)`访问 EulerCopilot 的 WitChainD 网页,
-登入界面如下，输入账号(admin)、密码(123456)点击登录按钮登录系统。
-
-![witchaind登录界面](./pictures/witChainD/witchaind登录界面.png)
-
-## 新建资产
-
-### 1. 查看资产库
-
-进入资产卡片显示页面，卡片展示了资产的名称、简介、文档篇数、创建时间和资产ID。
-
-![查看资产库界面](./pictures/witChainD/查看资产库界面.png)
-
-可通过红框中的按钮将卡片展示的资产转换为条目显示。
-
-### 2. 新建资产库
-
-点击新建资产，会跳出资产信息配置的框图
-
-![新建资产库界面](./pictures/witChainD/新建资产库界面.png)
-
-填写资产库名称、资产库描述（可选）、语言、嵌入模型、默认解析方法和默认文件分块大小后，点击确定。
-![新建资产库填写展示界面](./pictures/witChainD/新建资产库填写展示界面.png)
-
-资产库建立完毕之后会显示是否添加文档，点击确定
-
-![资产库创建完成界面](./pictures/witChainD/资产库创建完成界面.png)
-
-点击确定完成后进入资产库
-
-![资产库创建完成界面](./pictures/witChainD/进入资产库界面.png)
-
-## 导入文档
-
-### 单次导入
-
-点击导入文档按钮跳出文档上传框，选择本地文件并勾选进行上传
-
-![导入文档界面](./pictures/witChainD/导入文档界面.png)
-
-![选择文件](./pictures/witChainD/选择文件.png)
-
-### 批量导入
-
-点击1批量导入资产，2选择本地资产，3勾选本地资产，最后点击确定进行资产导入。
-
-![批量导入界面](./pictures/witChainD/批量导入界面.png)
-
-资产导入中
-
-![批量正在导入界面](./pictures/witChainD/批量正在导入界面.png)
-
-资产导入成功
-
-![批量导入成功界面](./pictures/witChainD/批量导入成功界面.png)
-
-## 解析文档
-
-等待解析中，点击取消可以停止文档解析。
-
-![解析中界面](./pictures/witChainD/解析中界面.png)
-
-解析完成后，解析状态会显示解析成功。
-
-![解析成功界面](./pictures/witChainD/解析成功界面.png)
-
-文档解析方式有三种general、ocr和enhanced模式，请根据需要选择合适的文档解析方法
-
-![解析方法选择界面](./pictures/witChainD/解析方法选择界面.png)
-
-解析完毕可以通过点击文件名进入文档解析结果展示详情，可以查看文档解析结果，如下图所示：
-
-![解析详情界面](./pictures/witChainD/解析详情界面.png)
-
-可以通过1过滤文档解析的片段、表格和图片等内容，通过2可以通过关键字检索模糊检索对应的片段，通过3可以设定是否在检索中是否启用片段，如下图所示：
-
-![解析结果过滤界面](./pictures/witChainD/解析结果过滤界面.png)
-
-## 导出资产
-
-点击回到首页
-
-![回到首页](./pictures/witChainD/回到首页.png)
-
-再点击导出资产
-
-![导出资产界面](./pictures/witChainD/导出资产界面.png)
-
-显示资产正在导出中，如下图所示：
-
-![正在导出界面](./pictures/witChainD/正在导出界面.png)
-
-导出成功点击下载，显示下载成功
-
-![导出成功](./pictures/witChainD/导出成功.png)
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diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/knowledge_base/user_guide/witChainD_guidance.md
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@@ -1,87 +0,0 @@
-# witChainD 使用指南——知识库管理
-
-完成 witChainD 部署之后，即可使用 witChainD 进行知识库管理管理。
-
-下文会从页面的维度进行 witChainD 的功能介绍。
-
-## 1. 资产库管理界面
-
-该页面为资产库管理界面，用户登录后将会进入该界面。
-
-![资产库管理界面](./pictures/资产库管理界面.png)
-
-**支持操作：**
-
-- 配置模型：点击右上角的设置按键，可以修改模型相关的配置。
-
-  ![模型配置](./pictures/模型配置.png)
-
-- 新增资产库：点击新建资产库按钮新建，支持自定义名称、描述、语言、嵌入模型、解析方法、文件分块大小、文档类别。注意：重复名称会自动将名称修改成资产库id。
-
-  ![新增资产库](./pictures/新增资产库.png)
-
-- 编辑资产库：点击资产库的编辑按钮编辑，支持修改名称、描述、语言、解析方法、文件分块大小、文档类别。注意：不能修改成重复名称。
-
-  ![编辑资产库](./pictures/编辑资产库0.png)
-
-  ![编辑资产库](./pictures/编辑资产库.png)
-
-- 导出资产库：点击资产库的导出按钮导出，导出完成后需要按任务列表中的下载任务下载对应资产库到本地。
-  
-  ![导出资产库](./pictures/导出资产库.png)
-
-- 批量导入资产库：点击批量导入，上传本地文件后选中即可导入。
-
-  ![批量导入资产库](./pictures/批量导入资产库.png)
-
-- 搜索资产库：在搜索栏中键入文本，可以搜索得到名称包含对应文本的资产库。
-
-## 2. 文档管理界面
-
-在资产管理界面点击对应资产库，可以进入文档管理界面。
-
-![文档管理界面](./pictures/文档管理界面.png)
-
-**支持操作：**
-
-- 导入文档：点击导入文档，从本地上传文件导入，导入后会自动以该资产库默认配置开始解析。
-
-  ![导入文档](./pictures/导入文档.png)
-
-- 解析文档：点击操作中的解析，对文档进行解析。也可以选中多个文档批量解析。
-
-  ![文档解析](./pictures/文档解析.png)
-  
-  ![文档解析2](./pictures/文档解析2.png)
-  
-  ![解析完成](./pictures/解析完成.png)
-
-- 编辑文档配置：点击编辑对文档配置进行编辑，支持编辑文档名称、解析方法、类别、文件分块大小。
-
-  ![编辑文档配置](./pictures/编辑文档配置.png)
-
-- 下载文档：点击下载即可将文档下载至本地，也可以选中多个文档批量下载。
-
-- 删除文档：点击删除即可将文档从资产库中删除，也可以选中多个文档批量删除。
-
-- 搜索文档：点击文档名称旁的搜索键，在弹出的搜索框中键入搜索的文本，可以搜索得到名称包含这些文本的文档。
-
-  ![搜索文档](./pictures/搜索文档.png)
-
-- 编辑资产库配置：支持编辑资产库名称、描述、语言、默认解析方法、文件分块大小、文档信息类别。
-
-  ![编辑资产库配置](./pictures/编辑资产库配置.png)
-
-## 3. 解析结果管理界面
-
-点击解析完成的文档，可以进入文档的解析结果管理界面。界面中会按照顺序显示文档解析后的文本块内容预览，每个文本块会附带一个标签，表示该文本块中的信息来源于文档中的段落、列表或者是图片。右侧的开关表示该文本块是否被启用。
-
-![文本块结果预览](./pictures/文本块结果预览.png)
-
-**支持操作**：
-
-- 关闭/启用文本块：点击文本块右侧的开关即可关闭/启用对应文本块，也可以选中多个文本块批量关闭/启用。
-
-  ![批量启用](./pictures/批量启用.png)
-
-- 搜索文本块：在搜索框中键入内容，可以查找包含对应内容的文本块。
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md
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--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_diagnosis/deploy_guide/diagnosis_deployment.md
+++ /dev/null
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-# 智能诊断部署指南
-
-## 准备工作
-
-+ 提前安装 [EulerCopilot 命令行（智能 Shell）客户端](../../../quick_start/smart_shell/user_guide/shell.md)
-
-+ 被诊断机器不能安装 crictl 和 isula，只能有 docker 一个容器管理工具
-
-+ 在需要被诊断的机器上安装 gala-gopher 和 gala-anteater
-
-## 部署 gala-gopher
-
-### 1. 准备 BTF 文件
-
-**如果Linux内核支持 BTF，则不需要准备 BTF 文件。**可以通过以下命令来查看Linux内核是否已经支持 BTF：
-
-```bash
-cat /boot/config-$(uname -r) | grep CONFIG_DEBUG_INFO_BTF
-```
-
-如果输出结果为`CONFIG_DEBUG_INFO_BTF=y`，则表示内核支持BTF。否则表示内核不支持BTF。
-如果内核不支持BTF，需要手动制作BTF文件。步骤如下：
-
-1. 获取当前Linux内核版本的 vmlinux 文件
-
-   vmlinux 文件存放在 `kernel-debuginfo` 包里面，存放路径为 `/usr/lib/debug/lib/modules/$(uname -r)/vmlinux`。
-
-   例如，对于 `kernel-debuginfo-5.10.0-136.65.0.145.oe2203sp1.aarch64`，对应的vmlinux路径为`/usr/lib/debug/lib/modules/5.10.0-136.65.0.145.oe2203sp1.aarch64/vmlinux`。
-
-2. 制作 BTF 文件
-
-   基于获取到 vmlinux 文件来制作 BTF 文件。这一步可以在自己的环境里操作。首先，需要安装相关的依赖包：
-
-   ```bash
-   # 说明：dwarves 包中包含 pahole 命令，llvm 包中包含 llvm-objcopy 命令
-   yum install -y llvm dwarves
-   ```
-
-   执行下面的命令行，生成 BTF 文件。
-
-   ```bash
-   kernel_version=4.19.90-2112.8.0.0131.oe1.aarch64  # 说明：这里需要替换成目标内核版本，可通过 uname -r 命令获取
-   pahole -J vmlinux
-   llvm-objcopy --only-section=.BTF --set-section-flags .BTF=alloc,readonly --strip-all vmlinux ${kernel_version}.btf
-   strip -x ${kernel_version}.btf
-   ```
-
-   生成的 BTF 文件名称为`<kernel_version>.btf`格式，其中 `<kernel_version>`为目标机器的内核版本，可通过 `uname -r` 命令获取。
-
-### 2. 下载 gala-gopher 容器镜像
-
-#### 在线下载
-
-gala-gopher 容器镜像已归档到 <https://hub.oepkgs.net/> 仓库中，可通过如下命令获取。
-
-```bash
-# 获取 aarch64 架构的镜像
-docker pull hub.oepkgs.net/a-ops/gala-gopher-profiling-aarch64:latest
-# 获取 x86_64 架构的镜像
-docker pull hub.oepkgs.net/a-ops/gala-gopher-profiling-x86_64:latest
-```
-
-#### 离线下载
-
-若无法通过在线下载的方式下载容器镜像，可联系我（何秀军 00465007）获取压缩包。
-
-拿到压缩包后，放到目标机器上，解压并加载容器镜像，命令行如下：
-
-```bash
-tar -zxvf gala-gopher-profiling-aarch64.tar.gz
-docker load < gala-gopher-profiling-aarch64.tar
-```
-
-### 3. 启动 gala-gopher 容器
-
-容器启动命令：
-
-```shell
-docker run -d --name gala-gopher-profiling --privileged --pid=host --network=host -v /:/host -v /etc/localtime:/etc/localtime:ro -v /sys:/sys -v /usr/lib/debug:/usr/lib/debug -v /var/lib/docker:/var/lib/docker -v /tmp/$(uname -r).btf:/opt/gala-gopher/btf/$(uname -r).btf -e GOPHER_HOST_PATH=/host gala-gopher-profiling-aarch64:latest
-```
-
-启动配置参数说明：
-
-+ `-v /tmp/$(uname -r).btf:/opt/gala-gopher/btf/$(uname -r).btf` ：如果内核支持 BTF，则删除该配置即可。如果内核不支持 BTF，则需要将前面准备好的 BTF 文件拷贝到目标机器上，并将 `/tmp/$(uname -r).btf` 替换为对应的路径。
-+ `gala-gopher-profiling-aarch64-0426` ：gala-gopher容器对应的tag，替换成实际下载的tag。
-
-探针启动：
-
-+ `container_id` 为需要观测的容器 id
-+ 分别启动 sli 和 container 探针
-
-```bash
-curl -X PUT http://localhost:9999/sli -d json='{"cmd":{"check_cmd":""},"snoopers":{"container_id":[""]},"params":{"report_period":5},"state":"running"}'
-```
-
-```bash
-curl -X PUT http://localhost:9999/container -d json='{"cmd":{"check_cmd":""},"snoopers":{"container_id":[""]},"params":{"report_period":5},"state":"running"}'
-```
-
-探针关闭
-
-```bash
-curl -X PUT http://localhost:9999/sli -d json='{"state": "stopped"}'
-```
-
-```bash
-curl -X PUT http://localhost:9999/container -d json='{"state": "stopped"}'
-```
-
-## 部署 gala-anteater
-
-源码部署：
-
-```bash
-# 请指定分支为 930eulercopilot
-git clone https://gitee.com/GS-Stephen_Curry/gala-anteater.git
-```
-
-安装部署请参考 <https://gitee.com/openeuler/gala-anteater>
-(请留意python版本导致执行setup.sh install报错)
-
-镜像部署：
-
-```bash
-docker pull hub.oepkgs.net/a-ops/gala-anteater:2.0.2
-```
-
-`/etc/gala-anteater/config/gala-anteater.yaml` 中 Kafka 和 Prometheus 的 `server` 和 `port` 需要按照实际部署修改，`model_topic`、`meta_topic`、`group_id` 自定义
-
-```yaml
-Kafka:
-  server: "xxxx"
-  port: "xxxx"
-  model_topic: "xxxx" # 自定义，与rca配置中保持一致
-  meta_topic: "xxxx" # 自定义，与rca配置中保持一致
-  group_id: "xxxx" # 自定义，与rca配置中保持一致
-  # auth_type: plaintext/sasl_plaintext, please set "" for no auth
-  auth_type: ""
-  username: ""
-  password: ""
-
-Prometheus:
-  server: "xxxx"
-  port: "xxxx"
-  steps: "5"
-```
-
-gala-anteater 中模型的训练依赖于 gala-gopher 采集的数据，因此请保证 gala-gopher 探针正常运行至少24小时，在运行 gala-anteater。
-
-## 部署 gala-ops
-
-每个中间件的大致介绍：
-
-kafka ： 一个数据库中间件， 分布式数据分流作用， 可以配置为当前的管理节点。
-
-prometheus：性能监控， 配置需要监控的生产节点 ip list。
-
-直接通过yum install安装kafka和prometheus，可参照安装脚本 <https://portrait.gitee.com/openeuler/gala-docs/blob/master/deploy/download_offline_res.sh#>
-
-只需要参照其中 kafka 和 prometheus 的安装即可
-
-## 部署 euler-copilot-rca
-
-镜像拉取
-
-```bash
-docker pull hub.oepkgs.net/a-ops/euler-copilot-rca:0.9.1
-```
-
-+ 修改 `config/config.json` 文件，配置 gala-gopher 镜像的 `container_id` 以及 `ip`，Kafka 和 Prometheus 的 `ip` 和 `port`（与上述 gala-anteater 配置保持一致）
-
-```yaml
-"gopher_container_id": "xxxx", # gala-gopher的容器id
-    "remote_host": "xxxx" # gala-gopher的部署机器ip
-  },
-  "kafka": {
-    "server": "xxxx",
-    "port": "xxxx",
-    "storage_topic": "usad_intermediate_results",
-    "anteater_result_topic": "xxxx",
-    "rca_result_topic": "xxxx",
-    "meta_topic": "xxxx"
-  },
-  "prometheus": {
-    "server": "xxxx",
-    "port": "xxxx",
-    "steps": 5
-  },
-```
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_diagnosis/user_guide/diagnosis_guidance.md
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-# 智能插件：智能诊断
-
-部署智能诊断工具后，可以通过 EulerCopilot 智能体框架实现对本机进行诊断。
-在智能诊断模式提问，智能体框架服务可以调用本机的诊断工具诊断异常状况、分析并生成报告。
-
-## 操作步骤
-
-**步骤1** 切换到“智能插件”模式
-
-```bash
-copilot -p
-```
-
-![切换到智能插件模式](./pictures/shell-plugin-diagnose-switch-mode.png)
-
-**步骤2** 异常事件检测
-
-```bash
-帮我进行异常事件检测
-```
-
-按下 `Ctrl + O` 键提问，然后在插件列表中选择“智能诊断”。
-
-![异常事件检测](./pictures/shell-plugin-diagnose-detect.png)
-
-**步骤3** 查看异常事件详情
-
-```bash
-查看 XXX 容器的异常事件详情
-```
-
-![查看异常事件详情](./pictures/shell-plugin-diagnose-detail.png)
-
-**步骤4** 执行异常事件分析
-
-```bash
-请对 XXX 容器的 XXX 指标执行 profiling 分析
-```
-
-![异常事件分析](./pictures/shell-plugin-diagnose-profiling.png)
-
-**步骤5** 查看异常事件分析报告
-
-等待 5 至 10 分钟，然后查看分析报告。
-
-```bash
-查看 <profiling-id> 对应的 profiling 报告
-```
-
-![执行优化脚本](./pictures/shell-plugin-diagnose-report.png)
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--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_tuning/deploy_guide/tune_deployment.md
+++ /dev/null
@@ -1,126 +0,0 @@
-# 智能调优部署指南
-
-## 准备工作
-
-+ 提前安装 [EulerCopilot 命令行（智能 Shell）客户端](../../../quick_start/smart_shell/user_guide/shell.md))
-
-+ 被调优机器需要为 openEuler 22.03 LTS-SP3
-
-+ 在需要被调优的机器上安装依赖
-
-```bash
-yum install -y sysstat perf
-```
-
-+ 被调优机器需要开启 SSH 22端口
-
-## 编辑配置文件
-
-修改values.yaml文件的tune部分，将 `enable` 字段改为 `True` ，并配置大模型设置、
-Embedding模型文件地址、以及需要调优的机器和对应机器上的 mysql 的账号名以及密码
-
-```bash
-vim /home/euler-copilot-framework/deploy/chart/agents/values.yaml
-```
-
-```yaml
-tune:
-    # 【必填】是否启用智能调优Agent
-    enabled: true
-    # 镜像设置
-    image:
-      # 镜像仓库。留空则使用全局设置。
-      registry: ""
-      # 【必填】镜像名称
-      name: euler-copilot-tune
-      # 【必填】镜像标签
-      tag: "0.9.1"
-      # 拉取策略。留空则使用全局设置。
-      imagePullPolicy: ""
-    # 【必填】容器根目录只读
-    readOnly: false
-    # 性能限制设置
-    resources: {}
-    # Service设置
-    service:
-      # 【必填】Service类型，ClusterIP或NodePort
-      type: ClusterIP
-      nodePort: 
-    # 大模型设置
-    llm:
-      # 【必填】模型地址（需要包含v1后缀）
-      url: 
-      # 【必填】模型名称
-      name: ""
-      # 【必填】模型API Key
-      key: ""
-      # 【必填】模型最大Token数
-      max_tokens: 8096
-    # 【必填】Embedding模型文件地址
-    embedding: ""
-    # 待优化机器信息
-    machine:
-      # 【必填】IP地址
-      ip: ""
-      # 【必填】Root用户密码
-      # 注意：必需启用Root用户以密码形式SSH登录
-      password: ""
-    # 待优化应用设置
-    mysql:
-      # 【必填】数据库用户名
-      user: "root"
-      # 【必填】数据库密码
-      password: ""
-```
-
-## 安装智能调优插件
-
-```bash
-helm install -n euler-copilot agents .
-```
-
-如果之前有执行过安装，则按下面指令更新插件服务
-
-```bash
-helm upgrade-n euler-copilot agents .
-```
-
-如果 framework未重启，则需要重启framework配置
-
-```bash
-kubectl delete pod framework-deploy-service-bb5b58678-jxzqr -n eulercopilot
-```
-
-## 测试
-
-+ 查看 tune 的 pod 状态
-
-  ```bash
-  NAME                                             READY   STATUS    RESTARTS   AGE
-  authhub-backend-deploy-authhub-64896f5cdc-m497f   2/2     Running   0          16d
-  authhub-web-deploy-authhub-7c48695966-h8d2p       1/1     Running   0          17d
-  pgsql-deploy-databases-86b4dc4899-ppltc           1/1     Running   0          17d
-  redis-deploy-databases-f8866b56-kj9jz             1/1     Running   0          17d
-  mysql-deploy-databases-57f5f94ccf-sbhzp           2/2     Running   0          17d
-  framework-deploy-service-bb5b58678-jxzqr          2/2     Running   0          16d
-  rag-deploy-service-5b7887644c-sm58z               2/2     Running   0          110m
-  web-deploy-service-74fbf7999f-r46rg               1/1     Running   0          2d
-  tune-deploy-agents-5d46bfdbd4-xph7b               1/1     Running   0          2d
-  ```
-
-+ pod启动失败排查办法
-    + 检查 euler-copilot-tune 目录下的 openapi.yaml 中 `servers.url` 字段，确保调优服务的启动地址被正确设置
-    + 检查 `$plugin_dir` 插件文件夹的路径是否配置正确，该变量位于 `deploy/chart/euler_copilot/values.yaml` 中的 `framework`模块，如果插件目录不存在，需新建该目录，并需要将该目录下的 euler-copilot-tune 文件夹放到 `$plugin_dir` 中。
-    + 检查sglang的地址和key填写是否正确，该变量位于 `vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml`
-
-    ```yaml
-      # 用于Function Call的模型
-      scheduler:
-        # 推理框架类型
-        backend: sglang
-        # 模型地址
-        url: ""
-        # 模型 API Key
-        key: ""
-      # 数据库设置
-    ```
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diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/advance/smart_tuning/user_guide/tune_guidance.md
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+++ /dev/null
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-# 智能插件：智能调优
-
-部署智能调优工具后，可以通过 EulerCopilot 智能体框架实现对本机进行调优。
-在智能调优模式提问，智能体框架服务可以调用本机的调优工具采集性能指标，并生成性能分析报告和性能优化建议。
-
-## 操作步骤
-
-**步骤1** 切换到“智能调优”模式
-
-```bash
-copilot -t
-```
-
-![切换到智能调优模式](./pictures/shell-plugin-tuning-switch-mode.png)
-
-**步骤2** 采集性能指标
-
-```bash
-帮我进行性能指标采集
-```
-
-![性能指标采集](./pictures/shell-plugin-tuning-metrics-collect.png)
-
-**步骤3** 生成性能分析报告
-
-```bash
-帮我生成性能分析报告
-```
-
-![性能分析报告](./pictures/shell-plugin-tuning-report.png)
-
-**步骤4** 生成性能优化建议
-
-```bash
-请生成性能优化脚本
-```
-
-![性能优化脚本](./pictures/shell-plugin-tuning-script-gen.png)
-
-**步骤5** 选择“执行命令”，运行优化脚本
-
-![执行优化脚本](./pictures/shell-plugin-tuning-script-exec.png)
-
-- 脚本内容如图：
-  ![优化脚本内容](./pictures/shell-plugin-tuning-script-view.png)
-
-## 远程调优
-
-如果需要对其他机器进行远程调优，请在上文示例的问题前面加上对应机器的 IP 地址。
-
-例如：`请对 192.168.1.100 这台机器进行性能指标采集。`
-
-进行远程调优前请确保目标机器已部署智能调优工具，同时请确保 EulerCopilot 智能体框架能够访问目标机器。
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_shell/user_guide/API_key.md
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-# 获取 API Key
-
-## 前言
-
-EulerCopilot 命令行助手使用 API Key 来验证用户身份，并获取 API 访问权限。
-因此，开始使用前，您需要先获取 API Key。
-
-## 注意事项
-
-- 请妥善保管您的 API Key，不要泄露给他人。
-- API Key 仅用于命令行助手与 DevStation 桌面端，不用于其他用途。
-- 每位用户仅可拥有一个 API Key，重复创建 API Key 将导致旧密钥失效。
-- API Key 仅在创建时显示一次，请务必及时保存。若密钥丢失，您需要重新创建。
-- 若您在使用过程中遇到“请求过于频繁”的错误，您的 API Key 可能已被他人使用，请及时前往官网刷新或撤销 API Key。
-
-## 获取方法
-
-1. 登录 EulerCopilot 网页端。
-2. 点击右上角头像，选择“API KEY”。
-3. 点击“新建”按钮。
-4. **请立即保存 API Key，它仅在创建时显示一次，请勿泄露给他人。**
-
-## 管理 API Key
-
-1. 登录 EulerCopilot 网页端。
-2. 点击右上角头像，选择“API KEY”。
-3. 点击“刷新”按钮，刷新 API Key；点击“撤销”按钮，撤销 API Key。
-
-- 刷新 API Key 后，旧密钥失效，请立即保存新生成的 API Key。
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-# 命令行助手使用指南
-
-## 简介
-
-EulerCopilot 命令行助手是一个命令行（Shell）AI 助手，您可以通过它来快速生成 Shell 命令并执行，从而提高您的工作效率。除此之外，基于 Gitee AI 在线服务的标准版本还内置了 openEuler 的相关知识，可以助力您学习与使用 openEuler 操作系统。
-
-## 环境要求
-
-- 操作系统：openEuler 22.03 LTS SP3，或者 openEuler 24.03 LTS 及以上版本
-- 命令行软件：
-    - Linux 桌面环境：支持 GNOME、KDE、DDE 等桌面环境的内置终端
-    - 远程 SSH 链接：支持兼容 xterm-256 与 UTF-8 字符集的终端
-
-## 安装
-
-EulerCopilot 命令行助手支持通过 OEPKGS 仓库进行安装。
-
-### 配置 OEPKGS 仓库
-
-```bash
-sudo dnf config-manager --add-repo https://repo.oepkgs.net/openeuler/rpm/`sed 's/release //;s/[()]//g;s/ /-/g' /etc/openEuler-release`/extras/`uname -m`
-```
-
-```bash
-sudo dnf clean all
-```
-
-```bash
-sudo dnf makecache
-```
-
-### 安装命令行助手
-
-```bash
-sudo dnf install eulercopilot-cli
-```
-
-若遇到 `Error: GPG check FAILED` 错误，使用 `--nogpgcheck` 跳过检查。
-
-```bash
-sudo dnf install --nogpgcheck eulercopilot-cli
-```
-
-## 初始化
-
-```bash
-copilot --init
-```
-
-然后根据提示输入 API Key 完成配置。
-
-![shell-init](./pictures/shell-init.png)
-
-初次使用前请先退出终端或重新连接 SSH 会话使配置生效。
-
-- **查看助手帮助页面**
-
-  ```bash
-  copilot --help
-  ```
-
-  ![shell-help](./pictures/shell-help.png)
-
-## 使用
-
-在终端中输入问题，按下 `Ctrl + O` 提问。
-
-### 快捷键
-
-- 输入自然语言问题后，按下 `Ctrl + O` 可以直接向 AI 提问。
-- 直接按下 `Ctrl + O` 可以自动填充命令前缀 `copilot`，输入参数后按下 `Enter` 即可执行。
-
-### 智能问答
-
-命令行助手初始化完成后，默认处于智能问答模式。
-命令提示符**左上角**会显示当前模式。
-若当前模式不是“智能问答”，执行 `copilot -c` (`copilot --chat`) 切换到智能问答模式。
-
-![chat-ask](./pictures/shell-chat-ask.png)
-
-AI 回答完毕后，会根据历史问答生成推荐问题，您可以复制、粘贴到命令行中进行追问。输入追问的问题后，按下 `Enter` 提问。
-
-![chat-next](./pictures/shell-chat-continue.png)
-
-![chat-next-result](./pictures/shell-chat-continue-result.png)
-
-智能问答模式下支持连续追问，每次追问最多可以关联3条历史问答的上下文。
-
-输入 `exit` 可以退出智能问答模式，回到 Linux 命令行。
-
-![chat-exit](./pictures/shell-chat-exit.png)
-
-- 若问答过程中遇到程序错误，可以按下 `Ctrl + C` 立即退出当前问答，再尝试重新提问。
-
-### Shell 命令
-
-AI 会根据您的问题返回 Shell 命令，EulerCopilot 命令行助手可以解释、编辑或执行这些命令，并显示命令执行结果。
-
-![shell-cmd](./pictures/shell-cmd.png)
-
-命令行助手会自动提取 AI 回答中的命令，并显示相关操作。您可以通过键盘上下键选择操作，按下 `Enter` 确认。
-
-![shell-cmd-interact](./pictures/shell-cmd-interact.png)
-
-#### 解释
-
-如果 AI 仅返回了一条命令，选择解释后会直接请求 AI 解释命令，并显示回答。
-若 AI 回答了多条命令，选择后会显示命令列表，您每次可以选择**一条**请求 AI 解释。
-
-![shell-cmd-explain-select](./pictures/shell-cmd-explain-select.png)
-
-完成解释后，您可以继续选择其他操作。
-
-![shell-cmd-explain-result](./pictures/shell-cmd-explain-result.png)
-
-#### 编辑
-
-![shell-cmd-edit](./pictures/shell-cmd-edit.png)
-
-选择一条命令进行编辑，编辑完成后按下 `Enter` 确认。
-
-![shell-cmd-edit-result](./pictures/shell-cmd-edit-result.png)
-
-完成编辑后，您可以继续编辑其他命令或选择其他操作。
-
-#### 执行
-
-如果 AI 仅返回了一条命令，选择执行后会直接执行命令，并显示执行结果。
-若 AI 回答了多条命令，选择后会显示命令列表，您每次可以选择**多条**命令来执行。
-
-您可以通过键盘上下键移动光标，按下 `空格键` 选择命令，按下 `Enter` 执行所选命令。
-被选中的命令会显示**蓝色高亮**，如图所示。
-
-![shell-cmd-exec-multi-select](./pictures/shell-cmd-exec-multi-select.png)
-
-若不选择任何命令，直接按下 `Enter`，则会跳过执行命令，直接进入下一轮问答。
-
-按下 `Enter` 后，被选中的命令会从上到下依次执行。
-
-![shell-cmd-exec-result](./pictures/shell-cmd-exec-result.png)
-
-若执行过程中遇到错误，命令行助手会显示错误信息，并**终止执行命令**，进入下一轮问答。
-您可以在下一轮问答中提示 AI 更正命令，或要求 AI 重新生成命令。
-
-## 卸载
-
-```bash
-sudo dnf remove eulercopilot-cli
-```
-
-然后使用以下命令删除配置文件。
-
-```bash
-rm ~/.config/eulercopilot/config.json
-```
-
-卸载完成后请重启终端或重新连接 SSH 会话使配置还原。
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md
deleted file mode 100644
index 8167907479569db279a838dc9dcf3e17597abbfd..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/deploy_guide/offline.md
+++ /dev/null
@@ -1,620 +0,0 @@
-# **EulerCopilot 智能助手部署指南**
-
-版本信息
-当前版本：v0.9.5
-更新日期：2025年4月1日
-
-## 产品概述
-
-EulerCopilot 是一款智能问答工具，使用 EulerCopilot 可以解决操作系统知识获取的便捷性，并且为OS领域模型赋能开发者及运维人员。作为获取操作系统知识，使能操作系统生产力工具 (如 A-Ops / A-Tune / x2openEuler / EulerMaker / EulerDevOps / StratoVirt / iSulad 等)，颠覆传统命令交付方式，由传统命令交付方式向自然语义进化，并结合智能体任务规划能力，降低开发、使用操作系统特性的门槛。
-
-本指南提供基于自动化脚本的EulerCopilot智能助手系统部署说明，支持一键自动部署和手动分步部署两种方式。
-
-### 组件介绍
-
-| 组件                          | 端口            | 说明                  |
-| ----------------------------- | --------------- | -------------------- |
-| euler-copilot-framework       | 8002 (内部端口) | 智能体框架服务         |
-| euler-copilot-web             | 8080            | 智能体前端界面        |
-| euler-copilot-rag             | 9988 (内部端口) | 检索增强服务           |
-| authhub-backend-service       | 11120 (内部端口) | 鉴权服务后端          |
-| authhub-web-service           | 8000            | 鉴权服务前端          |
-| mysql                         | 3306 (内部端口) | MySQL数据库           |
-| redis                         | 6379 (内部端口) | Redis数据库           |
-| minio                         | 9000 (内部端口) 9001(外部部端口) | minio数据库       |
-| mongo                         | 27017 (内部端口)         | mongo数据库           |
-| postgres                      | 5432 (内部端口) | 向量数据库             |
-| secret_inject                 | 无              | 配置文件安全复制工具   |
-
-### 软件要求
-
-|     类型        |      版本要求                         |  说明                                |
-|----------------| -------------------------------------|--------------------------------------|
-| 操作系统    | openEuler 22.03 LTS 及以上版本         | 无                                   |
-| K3s        | >= v1.30.2，带有 Traefik Ingress 工具   | K3s 提供轻量级的 Kubernetes 集群，易于部署和管理 |
-| Helm       | >= v3.15.3                           | Helm 是一个 Kubernetes 的包管理工具，其目的是快速安装、升级、卸载 EulerCopilot 服务 |
-| python     | >=3.9.9                              | python3.9.9 以上版本为模型的下载和安装提供运行环境 |
-
----
-
-### 硬件规格
-
-| 硬件资源      |  最小配置                  |    推荐配置               |
-|--------------|----------------------------|------------------------------|
-| CPU          | 4 核心                     | 16 核心及以上                 |
-| RAM          | 4 GB                       | 64 GB                        |
-| 存储         | 32 GB                      | 64G                         |
-| 大模型名称    | deepseek-llm-7b-chat      | DeepSeek-R1-Llama-8B        |
-| 显存 (GPU)   |  NVIDIA RTX A4000 8GB   | NVIDIA A100 80GB * 2         |
-
-**关键说明**：
-
-- 纯CPU环境，建议通过调用 OpenAI 接口或使用自带的模型部署方式来实现功能。
-- 如果k8s集群环境，则不需要单独安装k3s，要求version >= 1.28
-
----
-
-### 部署视图
-
-![部署图](./pictures/部署视图.png)
-
----
-
-### 域名配置
-
-需准备以下两个服务域名：
-
-- authhub认证服务：authhub.eulercopilot.local
-- EulerCopilot web服务：www.eulercopilot.local
-
-```bash
-# 本地Windows主机中进行配置
-# 打开 C:\Windows\System32\drivers\etc\hosts 添加记录
-# 替换127.0.0.1为目标服务器的IP
-127.0.0.1 authhub.eulercopilot.local
-127.0.0.1 www.eulercopilot.local
-```
-
-## 快速开始
-
-### 1. 资源获取
-
-- **获取部署脚本**
-  [EulerCopilot 的官方Git仓库](https://gitee.com/openeuler/euler-copilot-framework)
-  切换至dev分支点击下载ZIP, 并上传至目标服务器
-
-  ```bash
-  unzip euler-copilot-framework.tar -d /home
-  ```
-
-- **资源清单**
-
-    - **下载地址**
-    [EulerCopilot 资源仓库](https://repo.oepkgs.net/openEuler/rpm/openEuler-22.03-LTS/contrib/eulercopilot)
-    - **镜像文件**
-
-    ```bash
-    # 使用脚本自动下载所有镜像（需在联网环境执行）
-    cd /home/euler-copilot-framework/deploy/scripts/9-other-script/
-    # 执行镜像下载（版本号可替换）
-    ./save_images.sh 0.9.5  # 自动保存到目录/home/eulercopilot/images
-    # 上传至目标服务器
-    scp /home/eulercopilot/images/* root@target-server:/home/eulercopilot/images/
-    ```
-
-    - **模型部署文件**：`bge-m3-Q4_K_M.gguf`, `deepseek-llm-7b-chat-Q4_K_M.gguf`, `ollama-linux-arm64.tgz/ollama-linux-amd64.tgz`
-    - **工具包**：`helm-v3.15.0-linux-arm64.tar.gz/helm-v3.15.0-linux-amd64.tar.gz`, `k3s-airgap-images-arm64.tar.zst/k3s-airgap-images-amd64.tar.zst`,`k3s-arm64/k3s-amd64`, `k3s-install.sh`
-  
-- **关键说明**
-
-    - **网络要求**
-        - 手动下载需确保存在可访问外网文件的Windows环境，全部下载完成后传输至离线环境
-        - 脚本下载需在联网服务器执行，仅完成镜像下载，完成传输至离线环境
-    - **确保目标服务器存在以下目录**
-
-      ```bash
-      /home/eulercopilot/
-      ├── images/      # 存放镜像文件
-      ├── models/      # 存放模型文件
-      └── tools/       # 存放工具包
-      ```
-
-### 2. 部署EulerCopilot
-
-#### 一键部署
-
-```bash
-cd /home/euler-copilot-framework/deploy/scripts
-```
-
-```bash
-bash deploy.sh
-```
-
-```bash
-# 输入0进行一键自动部署
-==============================
-        主部署菜单
-==============================
-0) 一键自动部署
-1) 手动分步部署
-2) 重启服务
-3) 卸载所有组件并清除数据
-4) 退出程序
-==============================
-请输入选项编号（0-3）: 0
-```
-
----
-
-#### 手动分步部署
-
-```bash
-# 选择1 -> 1 进入手动分步部署
-==============================
-        主部署菜单
-==============================
-0) 一键自动部署
-1) 手动分步部署
-2) 重启服务
-3) 卸载所有组件并清除数据
-4) 退出程序
-==============================
-请输入选项编号（0-3）: 1
-```
-
-```bash
-# 输入选项编号（0-9），逐步部署
-==============================
-       手动分步部署菜单
-==============================
-1) 执行环境检查脚本
-2) 安装k3s和helm
-3) 安装Ollama
-4) 部署Deepseek模型
-5) 部署Embedding模型
-6) 安装数据库
-7) 安装AuthHub
-8) 安装EulerCopilot
-9) 返回主菜单
-==============================
-请输入选项编号（0-9）:
-```
-
----
-
-**关键说明**：
-
-1. 安装部署前需要预先准备好部署所需的资源
-2. 在部署过程中，您需要输入 Authhub 域名和 EulerCopilot 域名, 不输入则使用默认域名`authhub.eulercopilot.local`, `www.eulercopilot.local`。
-
-#### 重启服务
-
-```bash
-# 输入选项重启服务
-==============================
-        服务重启菜单
-==============================
-可重启的服务列表：
-1) authhub-backend
-2) authhub
-3) framework
-4) minio
-5) mongo
-6) mysql
-7) pgsql
-8) rag
-9) rag-web
-10) redis
-11) web
-12) 返回主菜单
-==============================
-请输入要重启的服务编号（1-12）:
-```
-
-#### 卸载所有组件
-
-```bash
-sudo ./deploy.sh
-# 选择2进行完全卸载
-==============================
-        主部署菜单
-==============================
-0) 一键自动部署
-1) 手动分步部署
-2) 卸载所有组件并清除数据
-3) 退出程序
-==============================
-请输入选项编号（0-3）: 2
-```
-
----
-
-**关键说明**：
-
-- 在部署过程中，您需要输入 Authhub 域名和 EulerCopilot 域名, 不输入则使用默认域名`authhub.eulercopilot.local`, `www.eulercopilot.local`。
-- 资源不足时可参考 FAQ 中的评估资源可用性解决
-- 查看组件日志
-
-```bash
-kubectl logs <pod名称> -n euler-copilot
-```
-
-- 查看服务状态
-
-```bash
-kubectl get pod -n euler-copilot
-```
-
-- 大模型配置修改并更新EulerCopilot
-
-```bash
-cd /home/euler-copilot-framework/deploy/chart/euler-copilot
-```
-
-```bash
-vim values.yaml
-```
-
-```bash
-helm upgrade euler-copilot -n euler-copilot .
-```
-
-## 验证安装
-
-恭喜您，**EulerCopilot** 已成功部署！为了开始您的体验，请在浏览器中输入 `https://您的EulerCopilot域名` 链接访问 EulerCopilot 的网页界面：
-
-首次访问时，您需要点击页面上的 **立即注册** 按钮来创建一个新的账号，并完成登录过程。
-
-![Web登录界面](./pictures/WEB登录界面.png)
-![Web 界面](./pictures/WEB界面.png)
-
-## 构建专有领域智能问答
-
-点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witChainD_deployment.md)
-**知识库登录默认账号 `admin`, 密码 `123456`**
-
----
-
-## 附录
-
-### 大模型准备
-
-#### GPU 环境
-
-可直接使用部署的deepseek大模型参考以下方式进行部署
-
-1. 下载模型文件
-
-   ```bash
-   huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat
-   ```
-
-2. 创建终端 control
-
-   ```bash
-   screen -S control
-   ```
-
-   ```bash
-   python3 -m fastchat.serve.controller
-   ```
-
-   按 Ctrl A+D 置于后台
-
-3. 创建新终端 api
-
-   ```bash
-   screen -S api
-   ```
-
-   ```bash
-   python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000  --api-keys sk-123456
-   ```
-
-   按 Ctrl A+D 置于后台
-   如果当前环境的 Python 版本是 3.12 或者 3.9 可以创建 python3.10 的 conda 虚拟环境
-
-   ```bash
-   mkdir -p /root/py310
-   ```
-
-   ```bash
-   conda create --prefix=/root/py310 python==3.10.14
-   ```
-
-   ```bash
-   conda activate /root/py310
-   ```
-
-4. 创建新终端 worker
-
-   ```bash
-   screen -S worker
-   ```
-
-   ```bash
-   screen -r worker
-   ```
-
-   安装 fastchat 和 vllm
-
-   ```bash
-   pip install fschat vllm
-   ```
-
-   安装依赖：
-
-   ```bash
-   pip install fschat[model_worker]
-   ```
-
-   ```bash
-   python3 -m fastchat.serve.vllm_worker --model-path /root/models/Qwen1.5-14B-Chat/ --model-name qwen1.5 --num-gpus 8 --gpu-memory-utilization=0.7 --dtype=half
-   ```
-
-   按 Ctrl A+D 置于后台
-
-5. 按照如下方式修改配置的大模型参数，并更新服务。
-
-   ```bash
-   vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml
-   ```
-
-   修改如下部分
-
-   ```yaml
-   # 模型设置
-   models:
-     # 用于问答的大模型；需要为OpenAI兼容接口
-     answer:
-       # [必填] 接口URL（无需带上“v1”后缀）
-       url: http://172.168.178.107:11434
-       # [必填] 接口API Key；默认置空
-       key: sk-123456
-       # [必填] 模型名称
-       name: deepseek-llm-7b-chat:latest
-       # [必填] 模型最大上下文数；建议>=8192
-       ctx_length: 8192
-       # 模型最大输出长度，建议>=2048
-       max_tokens: 2048
-       # 用于Function Call的模型；建议使用特定推理框架
-     functioncall:
-       # 推理框架类型，默认为ollama
-       # 可用的框架类型：["vllm", "sglang", "ollama", "openai"]
-       backend:
-       # 模型地址；不填则与问答模型一致
-     url: ollama
-       # API Key；不填则与问答模型一致
-       key:
-       # 模型名称；不填则与问答模型一致
-       name:
-       # 模型最大上下文数；不填则与问答模型一致
-       ctx_length:
-       # 模型最大输出长度；不填则与问答模型一致
-       max_tokens:
-     # 用于数据向量化（Embedding）的模型
-     embedding:
-       # 推理框架类型，默认为openai
-       # [必填] Embedding接口类型：["openai", "mindie"]
-       type: openai
-       # [必填] Embedding URL（需要带上“v1”后缀）
-       url: http://172.168.178.107:11434
-       # [必填] Embedding 模型API Key
-       key: sk-123456
-       # [必填] Embedding 模型名称
-       name: bge-m3:latest
-   ```
-
-   ```bash
-   # 更新服务
-   helm upgrade -n euler-copilot euler-copilot .
-   # 重启framework服务
-   kubectl get pod -n euler-copilot
-   kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot
-   ```
-
-#### NPU 环境
-
-NPU 环境部署可参考链接 [MindIE安装指南](https://www.hiascend.com/document/detail/zh/mindie/10RC2/whatismindie/mindie_what_0001.html)
-
-### FAQ
-
-#### 1. 解决 Hugging Face 连接错误
-
-如果遇到如下连接错误：
-
-```text
-urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object>: Failed to establish a new connection: [Errno 101] Network is unreachable
-```
-
-尝试以下解决方案：
-
-- 更新 `huggingface_hub` 包到最新版本。
-
-  ```bash
-  pip3 install -U huggingface_hub
-  ```
-
-- 如果网络问题依旧存在，可以尝试使用镜像站点作为端点。
-
-  ```bash
-  export HF_ENDPOINT=https://hf-mirror.com
-  ```
-
-#### 2. 在 RAG 容器中调用问答接口
-
-进入对应的 RAG Pod 后，可以通过 `curl` 命令发送 POST 请求来获取问答结果。请确保在请求体中提供具体的问题文本。
-
-```bash
-curl -k -X POST "http://localhost:9988/kb/get_answer" \
-     -H "Content-Type: application/json" \
-     -d '{
-           "question": "您的问题",
-           "kb_sn": "default_test",
-           "fetch_source": true
-         }'
-```
-
-#### 3. 解决 `helm upgrade` 错误
-
-当 Kubernetes 集群不可达时，您可能会遇到类似下面的错误信息：
-
-```text
-Error: UPGRADE FAILED: Kubernetes cluster unreachable
-```
-
-确保设置了正确的 KUBECONFIG 环境变量指向有效的配置文件。
-
-```bash
-echo "export KUBECONFIG=/etc/rancher/k3s/k3s.yaml" >> /root/.bashrc
-source /root/.bashrc
-```
-
-#### 4. 查看 Pod 日志失败
-
-如果您遇到查看 Pod 日志时权限被拒绝的问题，检查是否正确配置了代理设置，并将本机 IP 地址添加到 `no_proxy` 环境变量中。
-
-```bash
-cat /etc/systemd/system/k3s.service.env
-```
-
-编辑文件并确保包含：
-
-```bash
-no_proxy=XXX.XXX.XXX.XXX
-```
-
-#### 5. GPU环境中大模型流式回复问题
-
-对于某些服务执行 curl 大模型时无法进行流式回复的情况，尝试修改请求中的 `"stream"` 参数为 `false`。此外，确认已安装兼容版本的 Pydantic 库。
-
-```bash
-pip install pydantic==1.10.13
-```
-
-#### 6. sglang 模型部署指南
-
-按照以下步骤部署基于 sglang 的模型：
-
-```bash
-# 1. 激活名为 `myenv` 的 Conda 环境，该环境基于 Python 3.10 创建：
-conda activate myenv
-
-# 2. 安装 sglang 及其所有依赖项，指定版本为 0.3.0
-pip install "sglang[all]==0.3.0"
-
-# 3. 从特定索引安装 flashinfer，确保与您的 CUDA 和 PyTorch 版本兼容
-pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
-
-# 4. 使用 sglang 启动服务器，配置如下：
-python -m sglang.launch_server \
-    --served-model-name Qwen2.5-32B \
-    --model-path Qwen2.5-32B-Instruct-AWQ \
-    --host 0.0.0.0 \
-    --port 8001 \
-    --api-key "sk-12345" \
-    --mem-fraction-static 0.5 \
-    --tp 8
-```
-
-- 验证安装
-
-  ```bash
-  pip show sglang
-  pip show flashinfer
-  ```
-
-**注意事项：**
-
-- API Key：请确保 `--api-key` 参数中的 API 密钥是正确的
-- 模型路径： 确保 `--model-path` 参数中的路径是正确的，并且模型文件存在于该路径下。
-- CUDA 版本：确保你的系统上安装了 CUDA 12.1 和 PyTorch 2.4，因为 `flashinfer` 包依赖于这些特定版本。
-- 线程池大小：根据你的GPU资源和预期负载调整线程池大小。如果你有 8 个 GPU，那么可以选择 --tp 8 来充分利用这些资源。
-
-#### 7. 获取 Embedding
-
-使用 curl 发送 POST 请求以获取 embedding 结果：
-
-```bash
-curl -k -X POST http://localhost:11434/v1/embeddings \
-     -H "Content-Type: application/json" \
-     -d {"input": "The food was delicious and the waiter...", "model": "bge-m3", "encoding_format": "float"}
-```
-
-#### 8. 生成证书
-
-为了生成自签名证书，首先下载 [mkcert](https://github.com/FiloSottile/mkcert/releases)工具，然后运行以下命令：
-
-```bash
-mkcert -install
-mkcert example.com 
-```
-
-最后，将生成的证书和私钥拷贝到 values.yaml 中, 并应用至 Kubernetes Secret。
-
-```bash
-vim /home/euler-copilot-framework_openeuler/deploy/chart_ssl/traefik-secret.yaml
-```
-
-```bash
-kubectl apply -f traefik-secret.yaml
-```
-
-#### 9. 问题排查方法
-
-1. **获取集群事件信息**
-
-   为了更好地定位 Pod 失败的原因，请首先检查 Kubernetes 集群中的事件 (Events)。这可以提供有关 Pod 状态变化的上下文信息。
-
-   ```bash
-   kubectl get events -n euler-copilot
-   ```
-
-2. **验证镜像拉取状态**
-
-   确认容器镜像是否成功拉取。如果镜像未能正确加载，可能是由于网络问题或镜像仓库配置错误。
-
-   ```bash
-   k3s crictl images
-   ```
-
-3. **审查 Pod 日志**
-
-   检查相关 Pod 的日志，以寻找可能的错误信息或异常行为。这对于诊断应用程序级别的问题特别有用。
-
-   ```bash
-   kubectl logs rag-deploy-service-5b7887644c-sm58z -n euler-copilot
-   ```
-
-4. **评估资源可用性**
-
-   确保 Kubernetes 集群有足够的资源（如 CPU、内存和存储）来支持 Pod 的运行。资源不足可能导致镜像拉取失败或其他性能问题，或使得 Pod 状态从 Running 变为 Pending 或 Completed。可查看磁盘空间并保证至少有 30% 的可用空间。这有助于维持 Pod 的稳定运行状态。参考该链接挂载空间较大的磁盘[How to move k3s data to another location](https://mrkandreev.name/snippets/how_to_move_k3s_data_to_another_location/)
-
-   ```bash
-   kubectl top nodes
-   ```
-
-5. **确认 k3s 版本兼容性**
-
-   如果遇到镜像拉取失败且镜像大小为 0 的问题，请检查您的 k3s 版本是否符合最低要求（v1.30.2 或更高）。较低版本可能存在不兼容的问题。
-
-   ```bash
-   k3s -v
-   ```
-
-6. **检查配置**
-
-   检查 `values.yaml` 文件中关于 OIDC 配置和域名配置是否填写正确，确保配置无误后更新服务。
-
-   ```bash
-   cat /home/euler-copilot-framework/deploy/chart/euler_copilot
-   ```
-
-   ```bash
-   vim values.yaml | grep oidc
-   ```
-
-   ```bash
-   helm upgrade euler-copilot -n euler-copilot .
-   ```
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md
deleted file mode 100644
index 3e946173658dffbc296f616d94bfbd92a3d70890..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/deploy_guide/online.md
+++ /dev/null
@@ -1,596 +0,0 @@
-# **EulerCopilot 智能助手部署指南**
-
-版本信息
-当前版本：v0.9.5
-更新日期：2025年4月1日
-
-## 产品概述
-
-EulerCopilot 是一款智能问答工具，使用 EulerCopilot 可以解决操作系统知识获取的便捷性，并且为OS领域模型赋能开发者及运维人员。作为获取操作系统知识，使能操作系统生产力工具 (如 A-Ops / A-Tune / x2openEuler / EulerMaker / EulerDevOps / StratoVirt / iSulad 等)，颠覆传统命令交付方式，由传统命令交付方式向自然语义进化，并结合智能体任务规划能力，降低开发、使用操作系统特性的门槛。
-
-本指南提供基于自动化脚本的EulerCopilot智能助手系统部署说明，支持一键自动部署和手动分步部署两种方式。
-
-### 组件介绍
-
-| 组件                          | 端口            | 说明                  |
-| ----------------------------- | --------------- | -------------------- |
-| euler-copilot-framework       | 8002 (内部端口) | 智能体框架服务         |
-| euler-copilot-web             | 8080            | 智能体前端界面        |
-| euler-copilot-rag             | 9988 (内部端口) | 检索增强服务           |
-| authhub-backend-service       | 11120 (内部端口) | 鉴权服务后端          |
-| authhub-web-service           | 8000            | 鉴权服务前端          |
-| mysql                         | 3306 (内部端口) | MySQL数据库           |
-| redis                         | 6379 (内部端口) | Redis数据库           |
-| minio                         | 9000 (内部端口) 9001(外部部端口) | minio数据库       |
-| mongo                         | 27017 (内部端口)         | mongo数据库           |
-| postgres                      | 5432 (内部端口) | 向量数据库             |
-| secret_inject                 | 无              | 配置文件安全复制工具   |
-
-### 软件要求
-
-|     类型        |      版本要求                         |  说明                                |
-|----------------| -------------------------------------|--------------------------------------|
-| 操作系统    | openEuler 22.03 LTS 及以上版本         | 无                                   |
-| K3s        | >= v1.30.2，带有 Traefik Ingress 工具   | K3s 提供轻量级的 Kubernetes 集群，易于部署和管理 |
-| Helm       | >= v3.15.3                           | Helm 是一个 Kubernetes 的包管理工具，其目的是快速安装、升级、卸载 EulerCopilot 服务 |
-| python     | >=3.9.9                              | python3.9.9 以上版本为模型的下载和安装提供运行环境 |
-
----
-
-### 硬件规格
-
-| 硬件资源      |  最小配置                  |    推荐配置               |
-|--------------|----------------------------|------------------------------|
-| CPU          | 4 核心                     | 16 核心及以上                 |
-| RAM          | 4 GB                       | 64 GB                        |
-| 存储         | 32 GB                      | 64G                         |
-| 大模型名称    | deepseek-llm-7b-chat      | DeepSeek-R1-Llama-8B        |
-| 显存 (GPU)   |  NVIDIA RTX A4000 8GB   | NVIDIA A100 80GB * 2         |
-
-**关键说明**：
-
-- 纯CPU环境，建议通过调用 OpenAI 接口或使用自带的模型部署方式来实现功能。
-- 如果k8s集群环境，则不需要单独安装k3s，要求version >= 1.28
-
----
-
-### 部署视图
-
-![部署图](./pictures/部署视图.png)
-
----
-
-### 域名配置
-
-需准备以下两个服务域名：
-
-- authhub认证服务：authhub.eulercopilot.local
-- EulerCopilot web服务：www.eulercopilot.local
-
-```bash
-# 本地Windows主机中进行配置
-# 打开 C:\Windows\System32\drivers\etc\hosts 添加记录
-# 替换127.0.0.1为目标服务器的IP
-127.0.0.1 authhub.eulercopilot.local
-127.0.0.1 www.eulercopilot.local
-```
-
-## 快速开始
-
-### 1. 获取部署脚本
-
-- 从 EulerCopilot 的官方Git仓库 [euler-copilot-framework](https://gitee.com/openeuler/euler-copilot-framework) 下载最新的部署仓库
-- 如果您正在使用 Kubernetes，则不需要安装 k3s 工具。
-
-```bash
-# 下载目录以 home 为例
-cd /home
-```
-
-```bash
-git clone https://gitee.com/openeuler/euler-copilot-framework.git -b dev
-```
-
-```bash
-cd euler-copilot-framework/deploy/scripts
-```
-
-```bash
-# 为脚本文件添加可执行权限
-chmod -R +x ./*
-```
-
-### 2. 部署EulerCopilot
-
-#### **一键部署**
-
-```bash
-cd /home/euler-copilot-framework/deploy/scripts
-```
-
-```bash
-bash deploy.sh
-```
-
-```bash
-# 输入0进行一键自动部署
-==============================
-        主部署菜单
-==============================
-0) 一键自动部署
-1) 手动分步部署
-2) 重启服务
-3) 卸载所有组件并清除数据
-4) 退出程序
-==============================
-请输入选项编号（0-3）: 0
-```
-
----
-
-#### **分步部署**
-
-```bash
-# 选择1 -> 1 进入手动分步部署
-==============================
-        主部署菜单
-==============================
-0) 一键自动部署
-1) 手动分步部署
-2) 重启服务
-3) 卸载所有组件并清除数据
-4) 退出程序
-==============================
-请输入选项编号（0-3）: 1
-```
-
-```bash
-# 输入选项编号（0-9），逐步部署
-==============================
-       手动分步部署菜单
-==============================
-1) 执行环境检查脚本
-2) 安装k3s和helm
-3) 安装Ollama
-4) 部署Deepseek模型
-5) 部署Embedding模型
-6) 安装数据库
-7) 安装AuthHub
-8) 安装EulerCopilot
-9) 返回主菜单
-==============================
-请输入选项编号（0-9）:
-```
-
----
-
-#### **重启服务**
-
-```bash
-# 输入选项重启服务
-==============================
-        服务重启菜单
-==============================
-可重启的服务列表：
-1) authhub-backend
-2) authhub
-3) framework
-4) minio
-5) mongo
-6) mysql
-7) pgsql
-8) rag
-9) rag-web
-10) redis
-11) web
-12) 返回主菜单
-==============================
-请输入要重启的服务编号（1-12）:
-```
-
-#### **卸载所有组件**
-
-```bash
-sudo ./deploy.sh
-# 选择2进行完全卸载
-==============================
-        主部署菜单
-==============================
-0) 一键自动部署
-1) 手动分步部署
-2) 卸载所有组件并清除数据
-3) 退出程序
-==============================
-请输入选项编号（0-3）: 2
-```
-
----
-
-**关键说明**：
-
-- 在部署过程中，您需要输入 Authhub 域名和 EulerCopilot 域名, 不输入则使用默认域名`authhub.eulercopilot.local`, `www.eulercopilot.local`。
-- 资源不足时可参考 FAQ 中的评估资源可用性解决
-- 查看组件日志
-
-```bash
-kubectl logs <pod名称> -n euler-copilot
-```
-
-- 查看服务状态
-
-```bash
-kubectl get pod -n euler-copilot
-```
-
-- 大模型配置修改并更新EulerCopilot
-
-```bash
-cd /home/euler-copilot-framework/deploy/chart/euler-copilot
-```
-
-```bash
-vim values.yaml
-```
-
-```bash
-helm upgrade euler-copilot -n euler-copilot .
-```
-
-## 验证安装
-
-恭喜您，**EulerCopilot** 已成功部署！为了开始您的体验，请在浏览器中输入 `https://您的EulerCopilot域名` 链接访问 EulerCopilot 的网页界面：
-
-首次访问时，您需要点击页面上的 **立即注册** 按钮来创建一个新的账号，并完成登录过程。
-
-![Web登录界面](./pictures/WEB登录界面.png)
-![Web 界面](./pictures/WEB界面.png)
-
-## 构建专有领域智能问答
-
-点击知识库，可登录本地知识库管理页面，详细信息请参考文档 [本地资产库构建指南](../../../advance/knowledge_base/deploy_guide/witChainD_deployment.md)
-**知识库登录默认账号 `admin`, 密码 `123456`**
-
----
-
-## 附录
-
-### 大模型准备
-
-#### GPU 环境
-
-可直接使用部署的deepseek大模型参考以下方式进行部署
-
-1. 下载模型文件：
-
-   ```bash
-   huggingface-cli download --resume-download Qwen/Qwen1.5-14B-Chat --local-dir Qwen1.5-14B-Chat
-   ```
-
-2. 创建终端 control
-
-   ```bash
-   screen -S control
-   ```
-
-   ```bash
-   python3 -m fastchat.serve.controller
-   ```
-
-   按 Ctrl A+D 置于后台
-
-3. 创建新终端 api
-
-   ```bash
-   screen -S api
-   ```
-
-   ```bash
-   python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 30000  --api-keys sk-123456
-   ```
-
-   按 Ctrl A+D 置于后台
-   如果当前环境的 Python 版本是 3.12 或者 3.9 可以创建 python3.10 的 conda 虚拟环境
-
-   ```bash
-   mkdir -p /root/py310
-   ```
-
-   ```bash
-   conda create --prefix=/root/py310 python==3.10.14
-   ```
-
-   ```bash
-   conda activate /root/py310
-   ```
-
-4. 创建新终端 worker
-
-   ```bash
-   screen -S worker
-   ```
-
-   ```bash
-   screen -r worker
-   ```
-
-   安装 fastchat 和 vllm
-
-   ```bash
-   pip install fschat vllm
-   ```
-
-   安装依赖：
-
-   ```bash
-   pip install fschat[model_worker]
-   ```
-
-   ```bash
-   python3 -m fastchat.serve.vllm_worker --model-path /root/models/Qwen1.5-14B-Chat/ --model-name qwen1.5 --num-gpus 8 --gpu-memory-utilization=0.7 --dtype=half
-   ```
-
-   按 Ctrl A+D 置于后台
-
-5. 按照如下方式修改配置的大模型参数，并更新服务。
-
-   ```bash
-   vim /home/euler-copilot-framework/deploy/chart/euler_copilot/values.yaml
-   ```
-
-   修改如下部分
-
-   ```yaml
-   # 模型设置
-   models:
-     # 用于问答的大模型；需要为OpenAI兼容接口
-     answer:
-       # [必填] 接口URL（无需带上“v1”后缀）
-       url: http://172.168.178.107:11434
-       # [必填] 接口API Key；默认置空
-       key: sk-123456
-       # [必填] 模型名称
-       name: deepseek-llm-7b-chat:latest
-       # [必填] 模型最大上下文数；建议>=8192
-       ctx_length: 8192
-       # 模型最大输出长度，建议>=2048
-       max_tokens: 2048
-       # 用于Function Call的模型；建议使用特定推理框架
-     functioncall:
-       # 推理框架类型，默认为ollama
-       # 可用的框架类型：["vllm", "sglang", "ollama", "openai"]
-       backend:
-       # 模型地址；不填则与问答模型一致
-     url: ollama
-       # API Key；不填则与问答模型一致
-       key:
-       # 模型名称；不填则与问答模型一致
-       name:
-       # 模型最大上下文数；不填则与问答模型一致
-       ctx_length:
-       # 模型最大输出长度；不填则与问答模型一致
-       max_tokens:
-     # 用于数据向量化（Embedding）的模型
-     embedding:
-       # 推理框架类型，默认为openai
-       # [必填] Embedding接口类型：["openai", "mindie"]
-       type: openai
-       # [必填] Embedding URL（需要带上“v1”后缀）
-       url: http://172.168.178.107:11434
-       # [必填] Embedding 模型API Key
-       key: sk-123456
-       # [必填] Embedding 模型名称
-       name: bge-m3:latest
-   ```
-
-   ```bash
-   # 更新服务
-   helm upgrade -n euler-copilot euler-copilot .
-   # 重启framework服务
-   kubectl get pod -n euler-copilot
-   kubectl delete pod framework-deploy-65b669fc58-q9bw7 -n euler-copilot
-   ```
-
-#### NPU 环境
-
-NPU 环境部署可参考链接 [MindIE安装指南](https://www.hiascend.com/document/detail/zh/mindie/10RC2/whatismindie/mindie_what_0001.html)
-
-### FAQ
-
-#### 1. 解决 Hugging Face 连接错误
-
-如果遇到如下连接错误：
-
-```text
-urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object>: Failed to establish a new connection: [Errno 101] Network is unreachable
-```
-
-尝试以下解决方案：
-
-- 更新 `huggingface_hub` 包到最新版本。
-
-  ```bash
-  pip3 install -U huggingface_hub
-  ```
-
-- 如果网络问题依旧存在，可以尝试使用镜像站点作为端点。
-
-  ```bash
-  export HF_ENDPOINT=https://hf-mirror.com
-  ```
-
-#### 2. 在 RAG 容器中调用问答接口
-
-进入对应的 RAG Pod 后，可以通过 `curl` 命令发送 POST 请求来获取问答结果。请确保在请求体中提供具体的问题文本。
-
-```bash
-curl -k -X POST "http://localhost:9988/kb/get_answer" \
-     -H "Content-Type: application/json" \
-     -d '{
-           "question": "您的问题",
-           "kb_sn": "default_test",
-           "fetch_source": true
-         }'
-```
-
-#### 3. 解决 `helm upgrade` 错误
-
-当 Kubernetes 集群不可达时，您可能会遇到类似下面的错误信息：
-
-```text
-Error: UPGRADE FAILED: Kubernetes cluster unreachable
-```
-
-确保设置了正确的 KUBECONFIG 环境变量指向有效的配置文件。
-
-```bash
-echo "export KUBECONFIG=/etc/rancher/k3s/k3s.yaml" >> /root/.bashrc
-source /root/.bashrc
-```
-
-#### 4. 查看 Pod 日志失败
-
-如果您遇到查看 Pod 日志时权限被拒绝的问题，检查是否正确配置了代理设置，并将本机 IP 地址添加到 `no_proxy` 环境变量中。
-
-```bash
-cat /etc/systemd/system/k3s.service.env
-```
-
-编辑文件并确保包含：
-
-```bash
-no_proxy=XXX.XXX.XXX.XXX
-```
-
-#### 5. GPU环境中大模型流式回复问题
-
-对于某些服务执行 curl 大模型时无法进行流式回复的情况，尝试修改请求中的 `"stream"` 参数为 `false`。此外，确认已安装兼容版本的 Pydantic 库。
-
-```bash
-pip install pydantic==1.10.13
-```
-
-#### 6. sglang 模型部署指南
-
-按照以下步骤部署基于 sglang 的模型：
-
-```bash
-# 1. 激活名为 `myenv` 的 Conda 环境，该环境基于 Python 3.10 创建：
-conda activate myenv
-
-# 2. 安装 sglang 及其所有依赖项，指定版本为 0.3.0
-pip install "sglang[all]==0.3.0"
-
-# 3. 从特定索引安装 flashinfer，确保与您的 CUDA 和 PyTorch 版本兼容
-pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
-
-# 4. 使用 sglang 启动服务器，配置如下：
-python -m sglang.launch_server \
-    --served-model-name Qwen2.5-32B \
-    --model-path Qwen2.5-32B-Instruct-AWQ \
-    --host 0.0.0.0 \
-    --port 8001 \
-    --api-key "sk-12345" \
-    --mem-fraction-static 0.5 \
-    --tp 8
-```
-
-- 验证安装
-
-  ```bash
-  pip show sglang
-  pip show flashinfer
-  ```
-
-**注意事项：**
-
-- API Key：请确保 `--api-key` 参数中的 API 密钥是正确的
-- 模型路径： 确保 `--model-path` 参数中的路径是正确的，并且模型文件存在于该路径下。
-- CUDA 版本：确保你的系统上安装了 CUDA 12.1 和 PyTorch 2.4，因为 `flashinfer` 包依赖于这些特定版本。
-- 线程池大小：根据你的GPU资源和预期负载调整线程池大小。如果你有 8 个 GPU，那么可以选择 --tp 8 来充分利用这些资源。
-
-#### 7. 获取 Embedding
-
-使用 curl 发送 POST 请求以获取 embedding 结果：
-
-```bash
-curl -k -X POST http://localhost:11434/v1/embeddings \
-     -H "Content-Type: application/json" \
-     -d {"input": "The food was delicious and the waiter...", "model": "bge-m3", "encoding_format": "float"}
-```
-
-#### 8. 生成证书
-
-为了生成自签名证书，首先下载 [mkcert](https://github.com/FiloSottile/mkcert/releases)工具，然后运行以下命令：
-
-```bash
-mkcert -install
-mkcert example.com 
-```
-
-最后，将生成的证书和私钥拷贝到 values.yaml 中, 并应用至 Kubernetes Secret。
-
-```bash
-vim /home/euler-copilot-framework_openeuler/deploy/chart_ssl/traefik-secret.yaml
-```
-
-```bash
-kubectl apply -f traefik-secret.yaml
-```
-
-#### 9. 问题排查方法
-
-1. **获取集群事件信息**
-
-   为了更好地定位 Pod 失败的原因，请首先检查 Kubernetes 集群中的事件 (Events)。这可以提供有关 Pod 状态变化的上下文信息。
-
-   ```bash
-   kubectl get events -n euler-copilot
-   ```
-
-2. **验证镜像拉取状态**
-
-   确认容器镜像是否成功拉取。如果镜像未能正确加载，可能是由于网络问题或镜像仓库配置错误。
-
-   ```bash
-   k3s crictl images
-   ```
-
-3. **审查 Pod 日志**
-
-   检查相关 Pod 的日志，以寻找可能的错误信息或异常行为。这对于诊断应用程序级别的问题特别有用。
-
-   ```bash
-   kubectl logs rag-deploy-service-5b7887644c-sm58z -n euler-copilot
-   ```
-
-4. **评估资源可用性**
-
-   确保 Kubernetes 集群有足够的资源（如 CPU、内存和存储）来支持 Pod 的运行。资源不足可能导致镜像拉取失败或其他性能问题，或使得 Pod 状态从 Running 变为 Pending 或 Completed。可查看磁盘空间并保证至少有 30% 的可用空间。这有助于维持 Pod 的稳定运行状态。参考该链接挂载空间较大的磁盘[How to move k3s data to another location](https://mrkandreev.name/snippets/how_to_move_k3s_data_to_another_location/)
-
-   ```bash
-   kubectl top nodes
-   ```
-
-5. **确认 k3s 版本兼容性**
-
-   如果遇到镜像拉取失败且镜像大小为 0 的问题，请检查您的 k3s 版本是否符合最低要求（v1.30.2 或更高）。较低版本可能存在不兼容的问题。
-
-   ```bash
-   k3s -v
-   ```
-
-6. **检查配置**
-
-   检查 `values.yaml` 文件中关于 OIDC 配置和域名配置是否填写正确，确保配置无误后更新服务。
-
-   ```bash
-   cat /home/euler-copilot-framework/deploy/chart/euler_copilot
-   ```
-
-   ```bash
-   vim values.yaml | grep oidc
-   ```
-
-   ```bash
-   helm upgrade euler-copilot -n euler-copilot .
-   ```
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@@ -1,68 +0,0 @@
-# 前言
-
-## 概述
-
-本文档介绍了 EulerCopilot 的使用方法，对 EulerCopilot 线上服务的 Web 界面的各项功能做了详细介绍，同时提供了常见的问题解答，详细请参考对应手册。
-
-## 读者对象
-
-本文档主要适用于 EulerCopilot 的使用人员。使用人员必须具备以下经验和技能：
-
-- 熟悉 openEuler 操作系统相关情况。
-- 有 AI 对话使用经验。
-
-## 修改记录
-
-| 文档版本 | 发布日期     | 修改说明         |
-|--------|------------|----------------|
-| 03     | 2024-09-19 | 更新新版界面。 |
-| 02     | 2024-05-13 | 优化智能对话操作指引。 |
-| 01     | 2024-01-28 | 第一次正式发布。 |
-
-## 介绍
-
-### 免责声明
-
-- 使用过程中涉及的非工具本身验证功能所用的用户名和密码，不作他用，且不会被保存在系统环境中。
-- 在您进行对话或操作前应当确认您为应用程序的所有者或已获得所有者的充足授权同意。
-- 对话结果中可能包含您所分析应用的内部信息和相关数据，请妥善管理。
-- 除非法律法规或双方合同另有规定，openEuler 社区对分析结果不做任何明示或暗示的声明和保证，不对分析结果的适销性、满意度、非侵权性或特定用途适用性等作出任何保证或者承诺。
-- 您根据分析记录所采取的任何行为均应符合法律法规的要求，并由您自行承担风险。
-- 未经所有者授权，任何个人或组织均不得使用应用程序及相关分析记录从事任何活动。openEuler 社区不对由此造成的一切后果负责，亦不承担任何法律责任。必要时，将追究其法律责任。
-
-### EulerCopilot 简介
-
-EulerCopilot 是一个基于 openEuler 操作系统的人工智能助手，可以帮助用户解决各种技术问题，提供技术支持和咨询服务。它使用了最先进的自然语言处理技术和机器学习算法，能够理解用户的问题并提供相应的解决方案。
-
-### 场景内容
-
-1. OS 领域通用知识：EulerCopilot 可以咨询 Linux 常规知识、上游信息和工具链介绍和指导。
-2. openEuler 专业知识：EulerCopilot 可以咨询 openEuler 社区信息、技术原理和使用指导。
-3. openEuler 扩展知识：EulerCopilot 可以咨询 openEuler 周边硬件特性知识和ISV、OSV相关信息。
-4. openEuler 应用案例：EulerCopilot 可以提供 openEuler 技术案例、行业应用案例。
-5. shell 命令生成：EulerCopilot 可以帮助用户生成单条 shell 命令或者复杂命令。
-
-总之，EulerCopilot 可以应用于各种场景，帮助用户提高工作效率和了解 Linux、openEuler 等的相关知识。
-
-### 访问和使用
-
-EulerCopilot 通过网址访问 Web 网页进行使用。账号注册与登录请参考[注册与登录](./registration_and_login.md)。使用方法请参考[智能问答使用指南](./qa_guide.md)。
-
-### 界面说明
-
-#### 界面分区
-
-EulerCopilot 界面主要由如图 1 所示的区域组成，各个区域的作用如表 1 所示。
-
-- 图 1 EulerCopilot 界面
-
-![Copilot 界面](./pictures/main-page-sections.png)
-
-- 表 1 EulerCopilot 首页界面分区说明
-
-| 区域 | 名称       | 说明                                                            |
-|-----|------------|----------------------------------------------------------------|
-| 1   | 设置管理区        | 提供账号登录和退出操作入口和明亮/黑暗模式切换功能                |
-| 2   | 对话管理区       | 用于用户新建对话、对话历史记录管理和对话历史记录批量删除操作        |
-| 3   | 对话区           | 用于用户和 EulerCopilot 的对话聊天               |
-| 4   | 服务协议和隐私政策区 | 提供查看服务协议和隐私政策入口                               |
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--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/user_guide/qa_guide.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# 智能问答使用指南
-
-## 开始对话
-
-在对话区下侧输入框即可输入对话想要提问的内容，输入 `Shift + Enter` 可进行换行，输入 `Enter` 即可发送对话提问内容，或者单击“发送”也可发送对话提问内容。
-
-> **说明**
->
-> 对话区位于页面的主体部分，如图 1 所示。
-
-- 图 1 对话区
-  ![对话区](./pictures/chat-area.png)
-
-### 多轮连续对话
-
-EulerCopilot 智能问答支持多轮连续对话。只需要在同一个对话中继续追问即可使用，如图 2 所示。
-
-- 图 2 多轮对话
-  ![多轮对话](./pictures/context-support.png)
-
-### 重新生成
-
-如遇到 AI 生成的内容有误或不完整的特殊情况，可以要求 AI 重新回答问题。单击对话回答左下侧的“重新生成”文字，可让 EulerCopilot 重新回答用户问题，重新回答后，在对话回答右下侧，会出现回答翻页的图标![向前翻页](./pictures/icon-arrow-prev.png)和![向后翻页](./pictures/icon-arrow-next.png)，单击![向前翻页](./pictures/icon-arrow-prev.png)或![向后翻页](./pictures/icon-arrow-next.png)可查看不同的回答，如图 3 所示。
-
-- 图 3 重新生成
-  ![重新生成](./pictures/regenerate.png)
-
-### 推荐问题
-
-在 AI 回答的下方，会展示一些推荐的问题，单击即可进行提问，如图 4 所示。
-
-- 图 4 推荐问题
-  ![推荐问题](./pictures/recommend-questions.png)
-
-## 自定义背景知识
-
-EulerCopilot 支持上传文件功能。上传文件后，AI 会将上传的文件内容作为背景知识，在回答问题时，会结合背景知识进行回答。上传的背景知识只会作用于当前对话，不会影响其他对话。
-
-### 上传文件
-
-**步骤1** 单击对话区左下角的“上传文件”按钮，如图 5 所示。
-
-- 图 5 上传文件按钮
-  ![上传文件](./pictures/file-upload-btn.png)
-
-> **说明**
->
-> 鼠标悬停到“上传文件”按钮上，会显示提示允许上传文件的规格和格式，如图 6 所示。
-
-- 图 6 鼠标悬停显示上传文件规格提示
-  ![上传文件提示](./pictures/file-upload-btn-prompt.png)
-
-**步骤2** 在弹出的文件选择框中，选择需要上传的文件，单击“打开”，即可上传文件。最多上传10个文件，总大小限制为64MB。接受 PDF、docx、doc、txt、md、xlsx。
-
-开始上传后，对话区下方会显示上传进度，如图 7 所示。
-
-- 图 7 同时上传的所有文件排列在问答输入框下方
-  ![上传文件](./pictures/file-upload-uploading.png)
-
-文件上传完成后会自动解析，如图 8 所示，解析完成后，对话区下方会显示每个文件的大小信息。
-
-- 图 8 文件上传至服务器后将显示“正在解析”
-  ![文件解析](./pictures/file-upload-parsing.png)
-
-文件上传成功后，左侧历史记录区会显示上传的文件数量，如图 9 所示。
-
-- 图 9 对话历史记录标签上会展示上传文件数量
-  ![历史记录标记](./pictures/file-upload-history-tag.png)
-
-### 针对文件提问
-
-文件上传完成后，即可针对文件进行提问，提问方式同普通对话模式，如图 10 所示。
-回答结果如图 11 所示。
-
-- 图 10 询问与上传的文件相关的问题
-  ![针对文件提问](./pictures/file-upload-ask-against-file.png)
-
-- 图 11 AI 以上传的为背景知识进行回答
-  ![根据自定义背景知识回答](./pictures/file-upload-showcase.png)
-
-## 管理对话
-
-> **说明**
->
-> 对话管理区在页面左侧。
-
-### 新建对话
-
-单击“新建对话”按钮即可新建对话，如图 12 所示。
-
-- 图 12 “新建对话”按钮在页面左上方
-  ![新建对话](./pictures/new-chat.png)
-
-### 对话历史记录搜索
-
-在页面左侧历史记录搜索输入框输入关键词，然后单击![搜索](./pictures/icon-search.png)即可进行对话历史记录搜索如图 13 所示。
-
-- 图 13 对话历史记录搜索框
-  ![对话历史记录搜索](./pictures/search-history.png)
-
-### 对话历史记录单条管理
-
-历史记录的列表位于历史记录搜索栏的下方，在每条对话历史记录的右侧，单击![编辑](./pictures/icon-edit.png)即可编辑对话历史记录的名字，如图 14 所示。
-
-- 图 14 点击“编辑”图标重命名历史记录
-  ![重命名历史记录](./pictures/rename-session.png)
-
-在对话历史记录名字重新书写完成后，单击右侧![确认](./pictures/icon-confirm.png)即可完成重命名，或者单击右侧![取消](./pictures/icon-cancel.png)放弃本次重命名，如图 15 所示。
-
-- 图 15 完成/取消重命名历史记录
-  ![完成/取消重命名历史记录](./pictures/rename-session-confirmation.png)
-
-另外，单击对话历史记录右侧的删除图标，如图 16 所示，即可对删除单条对话历史记录进行二次确认，在二次确认弹出框，如图 17 所示，单击“确认”，可确认删除单条对话历史记录，或者单击“取消”，取消本次删除。
-
-- 图 16 点击“垃圾箱”图标删除单条历史记录
-  ![删除单条历史记录](./pictures/delete-session.png)
-
-- 图 17 二次确认后删除历史记录
-  ![删除单条历史记录二次确认](./pictures/delete-session-confirmation.png)
-
-### 对话历史记录批量删除
-
-首先单击“批量删除”，如图 18 所示。
-
-- 图 18 批量删除功能在历史记录搜索框右上方
-  ![批量删除](./pictures/bulk-delete.png)
-
-然后可对历史记录进行选择删除，如图 19 所示。单击“全选”，即对所有历史记录选中，单击单条历史记录或历史记录左侧的选择框，可对单条历史记录进行选中。
-
-- 图 19 在左侧勾选要批量删除历史记录
-  ![批量删除历史记录选择](./pictures/bulk-delete-multi-select.png)
-
-最后需要对批量删除历史记录进行二次确认，如图 20 所示，单击“确认”，即删除，单击“取消”，即取消本次删除。
-
-- 图 20 二次确认后删除所选的历史记录
-  ![批量删除二次确认](./pictures/bulk-delete-confirmation.png)
-
-## 反馈与举报
-
-在对话记录区，对话回答的右下侧，可进行对话回答反馈，如图 21 所示，单击![满意](./pictures/icon-thumb-up.png)，可给对话回答点赞；单击![不满意](./pictures/icon-thumb-down.png)，可以给对话回答反馈不满意的原因。
-
-- 图 21 点赞和不满意反馈
-  ![点赞和不满意反馈](./pictures/feedback.png)
-
-对于反馈不满意原因，如图 22 所示，在单击![不满意](./pictures/icon-thumb-down.png)之后，对话机器人会展示反馈内容填写的对话框，可选择相关的不满意原因的选项。
-
-- 图 22 回答不满意反馈
-  ![回答不满意反馈](./pictures/feedback-illegal.png)
-
-其中单击选择“存在错误信息”，需要填写参考答案链接和描述，如图 23 所示。
-
-- 图 23 回答不满意反馈——存在错误信息
-  ![回答不满意反馈——存在错误信息](./pictures/feedback-misinfo.png)
-
-### 举报
-
-如果发现 AI 返回的内容中有违规信息，可以点击右下角按钮举报，如图 24 所示。点击举报后选择举报类型并提交，若没有合适的选项，请选择“其他”并输入原因，如图 25 所示。
-
-- 图 24 举报按钮在对话块的右下角
-  ![举报1](./pictures/report.png)
-
-- 图 25 点击后可选择举报类型
-  ![举报2](./pictures/report-options.png)
-
-## 查看服务协议和隐私政策
-
-单击文字“服务协议”，即可查看服务协议，单击文字“隐私政策”，即可查看隐私政策，如图 26、图 27 所示。
-
-- 图 26 服务协议和隐私政策入口在页面底部信息栏
-  ![服务协议和隐私政策入口](./pictures/privacy-policy-entry.png)
-
-- 图 27 点击后显示服务协议或隐私政策弹窗
-  ![服务协议和隐私政策](./pictures/privacy-policy.png)
-
-## 附录
-
-### 用户信息导出说明
-
-EulerCopilot 后台存在用户信息导出功能，如用户需要，需主动通过 <contact@openeuler.io> 邮箱联系我们，运维会将导出的用户信息通过邮箱回送给用户。
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md b/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md
deleted file mode 100644
index 8f5cd63781e1f320af33ffe4baf276f65f4696fa..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/euler_copilot/intelligent_assistant/quick_start/smart_web/user_guide/registration_and_login.md
+++ /dev/null
@@ -1,60 +0,0 @@
-# 登录 EulerCopilot
-
-本章节介绍如何登录已部署的 EulerCopilot 网页端的具体步骤。
-
-## 浏览器要求
-
-浏览器要求如表 1 所示。
-
-- 表 1 浏览器要求
-
-| 浏览器类型 | 最低版本 | 推荐版本 |
-| ----- | ----- | ----- |
-| Google Chrome | 72 | 121 或更高版本 |
-| Mozilla Firefox | 89 | 122 或更高版本 |
-| Apple Safari | 11.0 | 16.3 或更高版本 |
-
-## 操作步骤
-
-**步骤1** 打开本地 PC 机的浏览器，在地址栏输入部署指南中配置好的域名，按下 `Enter`。在未登录状态，进入 EulerCopilot，会出现登录提示弹出框，如图 1 所示。
-
-- 图 1 未登录
-
-![未登录](./pictures/login-popup.png)
-
-**步骤2** 登录 EulerCopilot（已注册账号）。
-
-打开登录界面，如图 2 所示。
-
-- 图 2 登录 EulerCopilot
-
-![登录 EulerCopilot](./pictures/authhub-login.png)
-
-## 注册 EulerCopilot 账号
-
-**步骤1**  在登录信息输入框右下角单击“立即注册”，如图 3 所示。
-
-- 图 3 立即注册
-
-![立即注册](./pictures/authhub-login-click2signup.png)
-
-**步骤2** 进入账号注册页，根据页面提示填写相关内容，如图 4 所示。
-
-- 图 4 账号注册
-
-![账号注册](./pictures/authhub-signup.png)
-
-**步骤3** 按页面要求填写账号信息后，单击“注册”，即可注册成功。注册后即可返回登录。
-
-## 退出登录
-
-**步骤1** 单击![退出登录](./pictures/icon-user.png)后，会出现“退出登录”下拉框，如图 5 所示。
-
-> **说明**
-> 账号管理区位于页面的右上角部分，如图 5 所示。
-
-- 图 5 账号管理区
-
-![账号管理区](./pictures/logout.png)
-
-**步骤2** 单击“退出登录”即可退出登录，如图 5 所示。
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_vulnerability_patching/_toc.yaml b/docs/zh/tools/ai/euler_copilot/intelligent_vulnerability_patching/_toc.yaml
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+++ /dev/null
@@ -1,6 +0,0 @@
-label: 智能化漏洞修补用户指南
-isManual: true
-description: 支持kernel仓库的漏洞修补
-sections:
-  - label: 智能化漏洞修补用户指南
-    href: ./intelligent-vulnerability-patching-user-guide.md
diff --git a/docs/zh/tools/ai/euler_copilot/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md b/docs/zh/tools/ai/euler_copilot/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md
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--- a/docs/zh/tools/ai/euler_copilot/intelligent_vulnerability_patching/intelligent-vulnerability-patching-user-guide.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# openEuler 智能化漏洞修补用户指南
-
-## 简介
-
-智能化漏洞修补提供了对openEuler的kernel仓库（[https://gitee.com/openeuler/kernel](https://gitee.com/openeuler/kernel)）进行智能化修补的能力，当前提供了CVE影响范围分析和补丁PR创建的功能。在代表CVE的issue下面评论/analyze和/create_pr命令来执行功能。
-
-## 功能入口
-
-在src-openEuler的kernel仓库（[https://gitee.com/src-openeuler/kernel.git](https://gitee.com/src-openeuler/kernel.git)）中，对代表CVE的issue下面进行评论。
-
-![CVE截图](pictures/代表CVE的issue.png)
-
-## /analyze命令
-
-`/analyze`命令提供了对CVE影响范围进行分析的能力。通过在issue下面评论`/analyze`，即可自动对当前维护范围内的openEuler版本进行分析，判断每一个openEuler版本是否引入该CVE，是否修复该CVE。
-
-![/analyze命令](pictures/analyze命令.png)
-
-> [!NOTE]说明  
-> /analyze命令无参数
-
-CVE是否引入存在如下几种情况：
-
-* 无影响
-* 受影响
-
-CVE是否修复存在如下几种情况：
-
-* 未修复
-* 已修复
-
-在评论的最后，会贴上引入补丁链接与修复补丁链接。
-
-## /create_pr命令
-
-`/create_pr`命令提供了对CVE的补丁进行智能化修复的能力。通过在issue下面评论`/create_pr <branch_name>`，即可自动获得漏洞补丁，并通过创建PR来合入openEuler下的linux仓库（[https://gitee.com/openeuler/kernel.git](https://gitee.com/openeuler/kernel.git)）中。
-![/create_pr命令](pictures/create_pr命令.png)
-
-`/create_pr`命令存在参数，包括如下几种情况：
-
-```shell
-# 对OLK-5.10分支创建补丁PR
-/create_pr OLK-5.10
-
-# 对OLK-5.10、OLK-6.6分支创建PR
-/create_pr OLK-5.10 OLK-6.6
-
-# 对当前所有的上游分支，包括openEuler-1.0-LTS、OLK-5.10、OLK-6.6三个分支
-/create_pr
-```
-
-返回结果如下：
-
-* pr创建成功
-* 没有修复补丁
-* 无法修复，存在冲突
-
-如果补丁代码与修复分支存在冲突的话，会提示`无法修复，存在冲突`。该能力将会在后续的版本中进行迭代强化。
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diff --git a/docs/zh/tools/ai/intelligent_foundation/sysHAX/_toc.yaml b/docs/zh/tools/ai/intelligent_foundation/sysHAX/_toc.yaml
deleted file mode 100644
index 4336146e16b5e642686555ba704c654cac564b5a..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/intelligent_foundation/sysHAX/_toc.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-label: sysHAX用户指南
-isManual: true
-description: 异构协同加速运行
-sections:
-  - label: sysHAX部署指南
-    href: ./deploy_guide/sysHax-deployment-guide.md
diff --git a/docs/zh/tools/ai/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md b/docs/zh/tools/ai/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md
deleted file mode 100644
index 06025bedca3484c6e8176c13f985ce9743f6bcb6..0000000000000000000000000000000000000000
--- a/docs/zh/tools/ai/intelligent_foundation/sysHAX/deploy_guide/sysHax-deployment-guide.md
+++ /dev/null
@@ -1,151 +0,0 @@
-# sysHAX部署指南
-
-sysHAX当前处于快速迭代阶段，基于vllm v0.6.6+npu进行验证。vllm上游发布的正式支持npu的版本为v0.7.1rc1，而当前用的vllm版本处于验证阶段，未合入主线。因此，在当前创新版本中暂不以源码形式发布，而是以容器化的形式为大家提供技术尝鲜。也欢迎开发者在使用过程中有任何问题和建议，可以在sig-Intelligence组中进行充分交流。
-
-vllm是一款**高吞吐、低内存占用**的**大语言模型（LLM）推理与服务引擎**，支持**CPU 计算加速**，提供高效的算子下发机制，包括：
-
-- **Schedule（调度）**：优化任务分发，提高并行计算效率
-- **Prepare Input（准备数据）**：高效的数据预处理，加速输入构建
-- **Ray 框架**：利用分布式计算提升推理吞吐
-- **Sample（模型后处理）**：优化采样策略，提升生成质量
-- **框架后处理**：融合多种优化策略，提升整体推理性能
-
-该引擎结合**高效计算调度与优化策略**，为 LLM 推理提供**更快、更稳定、更可扩展**的解决方案。
-
-## 环境准备
-
-| 服务器型号  | Atlas 800T/I A2 训练/推理服务器                      |
-| --------------- | --------------------------------------------------------- |
-| 操作系统    | openEuler 22.03 LTS及以上                              |
-| NPU驱动版本 | Ascend-hdk-910b-npu-driver_24.1.rc3_linux-aarch64.run |
-| 固件版本    | Ascend-hdk-910b-firmware_7.5.0.1.129.run              |
-
-### **安装驱动固件**
-
-- 创建驱动运行用户HwHiAiUser（运行驱动进程的用户），安装驱动时无需指定运行用户，默认即为HwHiAiUser。
-
-```shell
-groupadd -g 1000 HwHiAiUser
-useradd -g HwHiAiUser -u 1000 -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash
-```
-
-- 将驱动包和固件包上传到服务器任意目录如“/home”。
-- 执行如下命令，增加驱动和固件包的可执行权限。
-
-```shell
-chmod +x Ascend-hdk-910b-npu-driver_24.1.rc3_linux-aarch64.run
-chmod +x Ascend-hdk-910b-firmware_7.5.0.1.129.run
-```
-
-- 安装驱动
-
-```shell
-./Ascend-hdk-910b-npu-driver_24.1.rc3_linux-aarch64.run --full --install-for-all
-
-# 若执行上述安装命令出现类似如下回显信息
-# [ERROR]The list of missing tools: lspci,ifconfig,
-# 请执行yum install -y net-tools pciutils
-
-# 若系统出现如下关键回显信息，则表示驱动安装成功。
-# Driver package installed successfully!
-```
-
-- 安装固件
-
-```shell
-./Ascend-hdk-910b-firmware_7.5.0.1.129.run --full
-
-# 若系统出现如下关键回显信息，表示固件安装成功。
-# Firmware package installed successfully! Reboot now or after driver installation for the installation/upgrade to take effect
-```
-
-- 执行reboot命令重启系统。
-- 执行npu-smi info查看驱动加载是否成功。
-
-## 容器部署场景
-
-### 部署Ascend-Docker（容器引擎插件）
-
-- 参考版本："Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run"
-
-```shell
-# 将软件包”Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run”上传到服务器任意目录（如“/home”）。
-chmod +x Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run
-```
-
-```shell
-./Ascend-docker-runtime_6.0.RC3.1_linux-aarch64.run --install
-# 安装完成后，若显示类似如下信息，则说明软件安装成功:
-xxx install success
-```
-
-- 执行systemctl restart docker命令重启docker，使容器引擎插件在docker配置文件中添加的内容生效。
-
-### 容器场景vllm搭建
-
-```shell
-docker pull hub.oepkgs.net/neocopilot/vllm@sha256:c72a0533b8f34ebd4d352ddac3a969d57638c3d0c9c4af9b78c88400c6edff7a
-
-# /home路径不要全部映射，防止覆盖/home/HwHiAiUser
-docker run -itd \
-    -p 1111:22 \
-    --name vllm_oe \
-    --shm-size 16G \
-    --device /dev/davinci0 \
-    --device /dev/davinci1 \
-    --device /dev/davinci2 \
-    --device /dev/davinci3 \
-    --device /dev/davinci4 \
-    --device /dev/davinci5 \
-    --device /dev/davinci6 \
-    --device /dev/davinci7 \
-    --device /dev/davinci_manager \
-    --device /dev/devmm_svm \
-    --device /dev/hisi_hdc \
-    -v /usr/local/dcmi:/usr/local/dcmi \
-    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-    -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-    -w /home \
-    hub.oepkgs.net/neocopilot/vllm:0.6.6-aarch64-910B-oe2203-sp3 bash
-
-# 启动vllm，模型自行下载
-vllm serve /home/models/DeepSeek-R1-Distill-Llama-70B --distributed-executor-backend ray --tensor-parallel-size 8 --block-size 32 --preemption_mode swap
-```
-
-## 纯CPU推理环境部署
-
-- 模型文件准备
-
-1. 准备模型文件，放在`/home/model/`路径下
-
-    - **注意**：当前镜像版本支持DeepSeek 7B、32B以及Qwen系列模型
-
-2. 拉取镜像，镜像地址：docker pull hub.oepkgs.net/neocopilot/syshax/vllm-cpu@sha256:3983071e1928b9fddc037a51f2fc6b044d41a35d5c1e75ff62c8c5e6b1c157a3
-
-3. 启动容器：
-
-```bash
-docker run --name vllm_server_sysHAX \
-    -p 7001:7001 \
-    -v /home/model:/home/model/ \
-    -itd hub.oepkgs.net/neocopilot/syshax/vllm-cpu:0.1.2.4 bash
-```
-
-- 在容器中启动服务
-
-```bash
-cd /home/vllm_syshax
-python3 vllm/entrypoints/openai/api_server.py \
-    --model /home/model/DeepSeek-R1-Distill-Qwen-7B \
-    --served-model-name=ds7b \
-    --host 0.0.0.0 \
-    --port 7001 \
-    --dtype=half \
-    --swap_space=16 \
-    --block_size=16 \
-    --preemption_mode=swap \
-    --max_model_len=8192 &
-```
-
-**注意**：`--model`使用实际模型路径，`--served-model-name`可自己指定模型名字，端口两个需要对应，可不用7001
-部署完成，然后向7001端口发送请求即可，请求需满足OpenAPI格式。