docs: improve translation and replace images with English versions

docs/dev_manual/APIKey_chat.md

@@ -1,51 +1,51 @@
 !!! Abstract ""
-    MaxKB 社区版提供系统应用 API 以及兼容 OpenAI 标准格式进行对话，专业版提供系统级别 API，详细情况见：[系统API ](../user_manual/X-Pack/system_API.md)。
+    The MaxKB community edition provides system application APIs and supports conversation in a format compatible with the OpenAI standard. The professional edition offers system-level APIs. For more details, refer to: [System API](../user_manual/X-Pack/system_API.md).
 
-## 1 应用API
+## 1 Application API
 
-### 1.1 打开API文档
+### 1.1 Open API Documentation
 
 !!! Abstract ""
-    在应用信息中复制并访问 swagger 地址。
+    Copy and access the swagger address from the application information.
 
-![API doc](../img/dev/app_swaagger.png)
+![API doc](../img/dev/app_swagger.png)
+
+### 1.2 API Key Authentication
 
-### 1.2 API Key认证
-
 ![APIKEY](../img/dev/app_apikey.png)
 
-### 1.3 获取应用信息
+### 1.3 Retrieve Application Information
 
 !!! Abstract ""
-    调用 profile 接口，获取应用详细信息（应用 id、name等）。
-  
-![ profile](../img/dev/app_profile.png)
+    Call the profile interface to get detailed application information (such as application ID, name, etc.).
+
+![profile](../img/dev/app_profile.png)
 
-### 1.4 打开会话
+### 1.4 Open a Session
 
 !!! Abstract ""
-    调用打开会话接口，输入上述步骤获取的应用 id，打开会话并获取会话 id。
+    Call the open session interface, enter the application ID obtained in the steps above, open a session, and obtain a session ID.
 
 ![appid](../img/dev/chat_open.png)
 
-### 1.5 进行对话
+### 1.5 Conduct a Conversation
 
 !!! Abstract ""
-    调用对话接口，输入会话 id 进行对话。
+    Call the conversation interface, and enter the session ID to conduct a conversation.
 
 ![chatid](../img/dev/app_chat.png)
 
-## 2 标准OpenAI 格式
+## 2 Standard OpenAI Format
 
 !!! Abstract ""
-    MaxKB 应用兼容 OpenAI API 格式，在OpenAI API 原有调用方式的基础上替换为 MaxKB 应用提供的 Base URL 以及 API Key 即可。
+    The MaxKB application is compatible with the OpenAI API format. You can replace the original OpenAI API Base URL and API Key with those provided by the MaxKB application.
 
 ![API doc](../img/dev/openai_baseurl.png)
 
 !!! Abstract ""
 
     ```
-    # 将url 和 Authorization 替换为 MaxKB 应用实际真实的 Base URL 和 API Key。
+    # Replace the URL and Authorization with the actual Base URL and API Key from the MaxKB application.
 
     curl https://maxkb.fit2cloud.com/api/application/xxxxxxxx-8c56-11ef-a99e-0242ac140003/chat/completions \
         -H "Content-Type: application/json" \
@@ -54,11 +54,9 @@
             "model": "gpt-3.5-turbo",
             "messages": [
                 {
-                  "role": "你是杭州飞致云信息科技有限公司旗下产品 MaxKB 知识库问答系统的智能小助手，你的工作是帮助 MaxKB 用户解答使用中遇到的问题，用户找你回答问题时，你要把主题放在 MaxKB 知识库问答系统身上。",
-                  "content": "MaxKB 是什么？"
+                  "role": "You are the intelligent assistant of MaxKB, a product under Hangzhou Fit2Cloud Technology. Your job is to assist MaxKB users in solving problems they encounter during usage. When users come to you with questions, make sure to focus on the MaxKB Q&A system.",
+                  "content": "What is MaxKB?"
                 }
             ]
         }'
     ```
-
-

docs/faq/GPU_runOllama.md

@@ -1,48 +1,47 @@
-# 如何让Ollama使用GPU运行LLM模型
+# How to Run LLM Models on Ollama Using GPU
 
 !!! Abstract ""
-    以 NVIDIA 为例，说明 Ollama GPU 模式运行大模型的具体操作。
+    Using NVIDIA as an example, this guide explains the specific steps to run large models on Ollama in GPU mode.
 
-
-## 1 安装英伟达容器安装包
+## 1 Install NVIDIA Container Toolkit
 
 !!! Abstract ""
-    以 Ubuntu 22.04 为例（其它系统请参考：[英伟达官方文档](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/arch-overview.html)）
+    Using Ubuntu 22.04 as an example (for other systems, please refer to the [NVIDIA official documentation](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/arch-overview.html))
 
-    - 配置 apt 源
+    - Configure the apt source
     ```
     curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
     && 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
     ```
-    - 更新源
+    - Update the source
     ```
     sudo apt-get update
     ```
-    - 安装工具包
+    - Install the toolkit
     ```
     sudo apt-get install -y nvidia-container-toolkit
     ```
 
-## 2 使用 GPU 运行 Ollama
+## 2 Run Ollama Using GPU
 
 !!! Abstract ""
     ```
-    # 以后台模式运行 ollama 容器，并允许容器访问主机上所有可用的 NVIDIA GPU
+    # Run the Ollama container in the background mode and allow the container to access all available NVIDIA GPUs on the host
     docker run --gpus all -d -v /opt/ai/ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
     ```
 
-## 3 使用 Ollama 下载模型
+## 3 Download Models Using Ollama
 
 !!! Abstract ""
     ```
-    # 在线下载并运行模型
+    # Download and run models online
     docker exec -it ollama ollama run qwen:7b
     ```
 
-## 4 在 MaxKB 中添加 Ollama 模型
+## 4 Add Ollama Models in MaxKB
 
 !!! Abstract ""
-    下载完成并运行模型服务后，即可在 MaxKB 中添加对应的模型并使用。
-![添加模型](../img/FAQ/addmodel.png)
+    Once the models are downloaded and the model service is running, you can add the corresponding models in MaxKB and use them.
+![Add Model](../img/FAQ/addmodel.png)

docs/faq/MaxKB_VS_Dify.md

@@ -0,0 +1,81 @@
+!!! Abstract ""
+
+    MaxKB and Dify are both open-source projects based on large language model technology, with differences in product positioning and capabilities:
+
+    - Different product positioning: Dify is positioned as a development platform for large model applications, falling into the middleware category; MaxKB is positioned as an intelligent Q&A assistant based on large models and RAG, serving as an out-of-the-box final application.
+    - Product capability comparison: The following table is a capability comparison with LangChain, Flowise and other products provided by Dify's official documentation; MaxKB is an application built on LangChain that fills the gaps in enterprise-level features such as Workflow and SSO that LangChain lacks.
+
+    <table style="width: 100%;height: 80%;">
+      <tr>
+        <th align="center">Features</th>
+        <th align="center">Dify.AI</th>
+        <th align="center">LangChain</th>
+        <th align="center">Flowise</th>
+        <th align="center">OpenAI Assistant API</th>
+        <th align="center">MaxKB(Built on LangChain)</th>
+      </tr>
+      <tr>
+        <td align="center">Programming Method</td>
+        <td align="center">API + Application-oriented</td>
+        <td align="center">Python Code</td>
+        <td align="center">Application-oriented</td>
+        <td align="center">API-oriented</td>
+        <td align="center">API + Application-oriented</td>
+      </tr>
+      <tr>
+        <td align="center">Supported LLMs</td>
+        <td align="center">Rich variety</td>
+        <td align="center">Rich variety</td>
+        <td align="center">Rich variety</td>
+        <td align="center">OpenAI only</td>
+        <td align="center">Rich variety</td>
+      </tr>
+      <tr>
+        <td align="center">RAG Engine</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+      </tr>
+      <tr>
+        <td align="center">Agent</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">❌</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+      </tr>
+      <tr>
+        <td align="center">Workflow</td>
+        <td align="center">✅</td>
+        <td align="center">❌</td>
+        <td align="center">✅</td>
+        <td align="center">❌</td>
+        <td align="center">✅</td>
+      </tr>
+      <tr>
+        <td align="center">Observability</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">❌</td>
+        <td align="center">❌</td>
+        <td align="center">✅</td>
+      </tr>
+      <tr>
+        <td align="center">Enterprise Features (SSO/Access Control)</td>
+        <td align="center">✅</td>
+        <td align="center">❌</td>
+        <td align="center">❌</td>
+        <td align="center">❌</td>
+        <td align="center">✅</td>
+      </tr>
+      <tr>
+        <td align="center">Self-hosted Deployment</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">❌</td>
+        <td align="center">✅</td>
+      </tr>
+    </table>

docs/faq/Offline_install_OllamaModel.md

@@ -1,27 +1,26 @@
-# 如何使用Ollama离线部署LLM大语言模型
+# How to Deploy LLM Large Language Models Offline Using Ollama
 
+!!! Abstract ""
+    Using the qwen:0.5b model as an example, this guide details the process and steps for deploying LLM large language models offline with Ollama.
 
-!!! Abstract "" 
-    以 qwen:0.5b 模型为例，详细说明 Ollama 离线部署 LLM 大语言模型的过程和步骤。
-
-### 1 下载模型
+### 1 Download the Model
 
 !!! Abstract ""
-    访问 huggingface 并下载 qwen1_5-0_5b-chat-q5_k_m.gguf 模型文件。
+    Visit Hugging Face and download the qwen1_5-0_5b-chat-q5_k_m.gguf model file.
     ```
     https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/tree/main
     ```
-![下载模型](../img/FAQ/downModel.png)
+![Download Model](../img/FAQ/downModel.png)
 
-### 2 上传模型
+### 2 Upload the Model
 
 !!! Abstract ""
-    将下载好的 Qwen1.5-0.5B-Chat-GGUF 模型文件上传到 Ollama 所在服务器。
+    Upload the downloaded Qwen1.5-0.5B-Chat-GGUF model file to the server where Ollama is located.
 
-### 3 创建Ollama Modelfile
+### 3 Create Ollama Modelfile
 
 !!! Abstract ""
-    创建一个名为 Modelfile 的文件，内容如下：
+    Create a file named Modelfile with the following content:
     ```
     FROM ./qwen1_5-0_5b-chat-q5_k_m.gguf
 
@@ -34,25 +33,24 @@
     PARAMETER stop "<|im_start|>"
     PARAMETER stop "<|im_end|>"
     ```
-    说明：不同模型的 Modelfile 内容不同，可参考 Ollama 官网 [参数设置](https://ollama.com/library/qwen:0.5b) 。
-
-![模型参数模版](../img/FAQ/modelSetting.png)
+    Note: The content of the Modelfile varies for different models. You can refer to the Ollama official website for [parameter settings](https://ollama.com/library/qwen:0.5b).
 
+![Model Parameter Template](../img/FAQ/modelSetting.png)
 
-## 4 在 Ollama 中创建模型
+## 4 Create the Model in Ollama
 
 !!! Abstract ""
-    执行以下命令，创建模型：
+    Execute the following command to create the model:
     ```
     ollama create qwen:0.5b -f Modelfile
     ```
-    执行以下命令，确认模型创建成功：
+    Execute the following command to confirm successful model creation:
     ```
     ollama list
     ```
 
-![ollama查看模型列表](../img/FAQ/ollamaList.png)
+![View Model List in Ollama](../img/FAQ/ollamaList.png)
 
-## 5 在 MaxKB 中添加已创建的私有模型
+## 5 Add the Created Private Model in MaxKB
 
-![MaxKB中添加模型](../img/FAQ/MaxKBaddModel.png)
+![Add Model in MaxKB](../img/FAQ/MaxKBaddModel.png)

docs/faq/maxkb_In_halo.md

@@ -1,7 +1,7 @@
-# 将 MaxKB 小助手集成到 Halo 中
+# Integrate MaxKB Assistant into Halo
 
 !!! Abstract ""
-    [Halo](https://github.com/halo-dev/halo) [ˈheɪloʊ]，强大易用的开源建站工具。通过 MaxKB 小助手插件，可以将文章内容同步至 MaxKB 知识库，并可通过浮窗模式为网站提供智能问答。       
-    有关 MaxKB 小助手插件的使用说明，请查看 [官方文档](https://www.halo.run/store/apps/app-aWHcE)。
+    [Halo](https://github.com/halo-dev/halo) [ˈheɪloʊ] is a powerful and user-friendly open-source site-building tool. Through the MaxKB Assistant plugin, you can synchronize article content to the MaxKB knowledge base and provide intelligent Q&A on the website through a floating window mode.
+    For instructions on using the MaxKB Assistant plugin, please refer to the [official documentation](https://www.halo.run/store/apps/app-aWHcE).
 
-![halo](../img/FAQ/halo.png)
+![halo](../img/FAQ/halo.png)