feat: add logo-with-text.png (#184)

* feat: alter "RagFlow" to "RAGFlow"

* feat: move logo style to style tag

* feat: add logo-with-text.png

* feat: hide TranslationIcon

README.md

 <div align="center">
 <a href="https://demo.ragflow.io/">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/f034fb27-b3bf-401b-b213-e1dfa7448d2a" width="320" alt="ragflow logo">
+<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
 </a>
 </div>
 
-
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> 
 [RAGFlow](http://demo.ragflow.io) is an open-source, Retrieval-Augmented Generation engine built on large language models (LLM), deep document understanding, and multiple recall. It offers a streamlined RAG workflow for businesses of any scale, providing truthful responses with solid citations through a generative AI knowledge management platform.
 
 ## 🌟 Key Features
- 
+
 ### 🍭 **"Quality in, quality out"**
-  - Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
-  - Finds "needle in a data haystack" of literally unlimited tokens.
+
+- Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
+- Finds "needle in a data haystack" of literally unlimited tokens.
 
 ### 🍱 **Template-based chunking**
-  - Intelligent and explainable.
-  - Plenty of template options to choose from.
+
+- Intelligent and explainable.
+- Plenty of template options to choose from.
 
 ### 🌱 **Grounded citations with reduced hallucinations**
-  - Visualization of text chunking to allow human intervention.
-  - Quick view of the key references and traceable citations to support grounded answers.
+
+- Visualization of text chunking to allow human intervention.
+- Quick view of the key references and traceable citations to support grounded answers.
 
 ### 🍔 **Compatibility with heterogeneous data sources**
-  - Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
+
+- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
 
 ### 🛀 **Automated and effortless RAG workflow**
-  - Streamlined RAG orchestration catered to both personal and large businesses.
-  - Configurable LLMs as well as embedding models.
-  - Multiple recall paired with fused re-ranking.
-  - Intuitive APIs for seamless integration with business.
+
+- Streamlined RAG orchestration catered to both personal and large businesses.
+- Configurable LLMs as well as embedding models.
+- Multiple recall paired with fused re-ranking.
+- Intuitive APIs for seamless integration with business.
 
 ## 🔎 System Architecture
 
 
 ### 🚀 Start up the server
 
-1. Ensure `vm.max_map_count` > 65535: 
+1. Ensure `vm.max_map_count` > 65535:
 
    > To check the value of `vm.max_map_count`:
    >
-   > ```bash 
+   > ```bash
    > $ sysctl vm.max_map_count
    > ```
    >
    $ git clone https://github.com/infiniflow/ragflow.git
    ```
 
-3. Build the pre-built Docker images and start up the server: 
+3. Build the pre-built Docker images and start up the server:
 
    ```bash
    $ cd ragflow/docker
    > The core image is about 15 GB in size and may take a while to load.
 
 4. Check the server status after having the server up and running:
+
    ```bash
    $ docker logs -f ragflow-server
    ```
-   *The following output confirms a successful launch of the system:*
+
+   _The following output confirms a successful launch of the system:_
 
    ```bash
-       ____                 ______ __               
+       ____                 ______ __
       / __ \ ____ _ ____ _ / ____// /____  _      __
      / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ / 
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/  
-                 /____/                             
-     
+    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
+   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
+                 /____/
+
     * Running on all addresses (0.0.0.0)
     * Running on http://127.0.0.1:9380
     * Running on http://172.22.0.5:9380
     INFO:werkzeug:Press CTRL+C to quit
-    ```
+   ```
 
 5. In your web browser, enter the IP address of your server as prompted and log in to RAGFlow.
 6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
+
    > See [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information.
-   
-   *The show is now on!*
 
+   _The show is now on!_
 
 ## 🔧 Configurations
 
 - [service_conf.yaml](./docker/service_conf.yaml): Configures the back-end services.
 - [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up.
 
-You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file. 
+You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file.
 
 > The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service configurations, and you are REQUIRED to ensure that all environment settings listed in the [./docker/README](./docker/README.md) file are aligned with the corresponding configurations in the [service_conf.yaml](./docker/service_conf.yaml) file.
 
 To update the default serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` to `<YOUR_SERVING_PORT>:80`.
 
 > Updates to all system configurations require a system reboot to take effect:
-> 
+>
 > ```bash
 > $ docker-compose up -d
 > ```
 
 ## 🙌 Contributing
 
-RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our [Contribution Guidelines](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md) first. 
+RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our [Contribution Guidelines](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md) first.

README_zh.md

 <div align="center">
 <a href="https://demo.ragflow.io/">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/f034fb27-b3bf-401b-b213-e1dfa7448d2a" width="320" alt="ragflow logo">
+<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
 </a>
 </div>
 
-
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> 
 [RAGFlow](http://demo.ragflow.io) 是一款基于大型语言模型（LLM）以及深度文档理解构建的开源检索增强型生成引擎（Retrieval-Augmented Generation Engine）。RAGFlow 可以为各种规模的企业提供一套精简的 RAG 工作流程,通过生成式 AI （Generative AI）知识管理平台提供可靠的问答以及有理有据的引用。
 
 ## 🌟 主要功能
- 
+
 ### 🍭 **"Quality in, quality out"**
-  - 基于深度文档理解，能够从各类复杂格式的非结构化数据中提取真知灼见。
-  - 真正在无限上下文（token）的场景下快速完成大海捞针测试。
+
+- 基于深度文档理解，能够从各类复杂格式的非结构化数据中提取真知灼见。
+- 真正在无限上下文（token）的场景下快速完成大海捞针测试。
 
 ### 🍱 **基于模板的文本切片**
-  - 不仅仅是智能，更重要的是可控可解释。
-  - 多种文本模板可供选择
+
+- 不仅仅是智能，更重要的是可控可解释。
+- 多种文本模板可供选择
 
 ### 🌱 **有理有据、最大程度降低幻觉（hallucination）**
-  - 文本切片过程可视化，支持手动调整。
-  - 有理有据：答案提供关键引用的快照并支持追根溯源。
+
+- 文本切片过程可视化，支持手动调整。
+- 有理有据：答案提供关键引用的快照并支持追根溯源。
 
 ### 🍔 **兼容各类异构数据源**
-  - 支持丰富的文件类型，包括 Word 文档、PPT、excel 表格、txt 文件、图片、PDF、影印件、复印件、结构化数据, 网页等。
+
+- 支持丰富的文件类型，包括 Word 文档、PPT、excel 表格、txt 文件、图片、PDF、影印件、复印件、结构化数据, 网页等。
 
 ### 🛀 **全程无忧、自动化的 RAG 工作流**
-  - 全面优化的 RAG 工作流可以支持从个人应用乃至超大型企业的各类生态系统。
-  - 大语言模型 LLM 以及向量模型均支持配置。
-  - 基于多路召回、融合重排序。
-  - 提供易用的 API，可以轻松集成到各类企业系统。
+
+- 全面优化的 RAG 工作流可以支持从个人应用乃至超大型企业的各类生态系统。
+- 大语言模型 LLM 以及向量模型均支持配置。
+- 基于多路召回、融合重排序。
+- 提供易用的 API，可以轻松集成到各类企业系统。
 
 ## 🔎 系统架构
 
 
    > 如需确认 `vm.max_map_count` 的大小：
    >
-   > ```bash 
+   > ```bash
    > $ sysctl vm.max_map_count
    > ```
    >
    > 核心镜像文件大约 15 GB，可能需要一定时间拉取。请耐心等待。
 
 4. 服务器启动成功后再次确认服务器状态：
+
    ```bash
    $ docker logs -f ragflow-server
    ```
-   *出现以下界面提示说明服务器启动成功：*
+
+   _出现以下界面提示说明服务器启动成功：_
 
    ```bash
-       ____                 ______ __               
+       ____                 ______ __
       / __ \ ____ _ ____ _ / ____// /____  _      __
      / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ / 
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/  
-                 /____/                             
-     
+    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
+   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
+                 /____/
+
     * Running on all addresses (0.0.0.0)
     * Running on http://127.0.0.1:9380
     * Running on http://172.22.0.5:9380
     INFO:werkzeug:Press CTRL+C to quit
-    ```
+   ```
 
 5. 根据刚才的界面提示在你的浏览器中输入你的服务器对应的 IP 地址并登录 RAGFlow。
    > 上面这个例子中，您只需输入 http://172.22.0.5 即可：端口 9380 已通过 Docker 端口映射被设置成 80（默认的 HTTP 服务端口）。
-7. 在 [service_conf.yaml](./docker/service_conf.yaml) 文件的 `user_default_llm` 栏配置 LLM factory，并在 `API_KEY` 栏填写和你选择的大模型相对应的 API key。
+6. 在 [service_conf.yaml](./docker/service_conf.yaml) 文件的 `user_default_llm` 栏配置 LLM factory，并在 `API_KEY` 栏填写和你选择的大模型相对应的 API key。
+
+   > 详见 [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md)。
+
+   _好戏开始，接着奏乐接着舞！_
+
    > 详见 [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md)。
-   
-   *好戏开始，接着奏乐接着舞！*
 
+   _好戏开始，接着奏乐接着舞！_
 
 ## 🔧 系统配置
 
 - [service_conf.yaml](./docker/service_conf.yaml)：配置各类后台服务。
 - [docker-compose-CN.yml](./docker/docker-compose-CN.yml): 系统依赖该文件完成启动。
 
-请务必确保 [.env](./docker/.env) 文件中的变量设置与 [service_conf.yaml](./docker/service_conf.yaml) 文件中的配置保持一致！ 
+请务必确保 [.env](./docker/.env) 文件中的变量设置与 [service_conf.yaml](./docker/service_conf.yaml) 文件中的配置保持一致！
 
 > [./docker/README](./docker/README.md) 文件提供了环境变量设置和服务配置的详细信息。请**一定要**确保 [./docker/README](./docker/README.md) 文件当中列出来的环境变量的值与 [service_conf.yaml](./docker/service_conf.yaml) 文件当中的系统配置保持一致。
 
 如需更新默认的 HTTP 服务端口(80), 可以在 [docker-compose-CN.yml](./docker/docker-compose-CN.yml) 文件中将配置 `80:80` 改为 `<YOUR_SERVING_PORT>:80`。
 
 > 所有系统配置都需要通过系统重启生效：
-> 
+>
 > ```bash
 > $ docker compose up -f docker-compose-CN.yml -d
 > ```
 
 ## 🙌 贡献指南
 
-RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的[贡献者指南](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md)。 
+RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的[贡献者指南](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md)。

web/src/layouts/components/header/index.tsx

     >
       <Space size={12} onClick={handleLogoClick} className={styles.logoWrapper}>
         <Logo className={styles.appIcon}></Logo>
-        <span className={styles.appName}>RagFlow</span>
+        <span className={styles.appName}>RAGFlow</span>
       </Space>
       <Space size={[0, 8]} wrap>
         <Radio.Group

web/src/layouts/components/right-toolbar/index.tsx

-import { ReactComponent as MoonIcon } from '@/assets/svg/moon.svg';
-import { ReactComponent as TranslationIcon } from '@/assets/svg/translation.svg';
-import { BellOutlined, GithubOutlined } from '@ant-design/icons';
+import { GithubOutlined } from '@ant-design/icons';
 import { Space } from 'antd';
 import React from 'react';
 import User from '../user';
         <Circle>
           <GithubOutlined onClick={handleGithubCLick} />
         </Circle>
-        <Circle>
+        {/* <Circle>
           <TranslationIcon />
         </Circle>
-        <Circle>
-          <BellOutlined />
-        </Circle>
         <Circle>
           <MoonIcon />
-        </Circle>
+        </Circle> */}
         <User></User>
       </Space>
     </div>

web/src/pages/add-knowledge/components/knowledge-setting/configuration.tsx

       <Form.Item
         label="Language"
         name="language"
-        initialValue={'Chinese'}
+        initialValue={'English'}
         rules={[{ required: true, message: 'Please input your language!' }]}
       >
         <Select placeholder="select your language">

web/src/pages/add-knowledge/components/knowledge-setting/utils.ts

     The résumé comes in a variety of formats, just like a person’s personality, but we often have to organize them into structured data that makes it easy to search.
     </p><p>
     Instead of chunking the résumé, we parse the résumé into structured data. As a HR, you can dump all the résumé you have, 
-    the you can list all the candidates that match the qualifications just by talk with <i>'RagFlow'</i>.
+    the you can list all the candidates that match the qualifications just by talk with <i>'RAGFlow'</i>.
     </p>
     `,
   },

web/src/pages/user-setting/setting-profile/index.tsx

   getUploadFileListFromBase64,
   normFile,
 } from '@/utils/fileUtil';
-import { PlusOutlined, QuestionCircleOutlined } from '@ant-design/icons';
+import { PlusOutlined } from '@ant-design/icons';
 import {
   Button,
   Divider,
   Select,
   Space,
   Spin,
-  Tooltip,
   Upload,
   UploadFile,
 } from 'antd';
           <Form.Item<FieldType>
             label={
               <div>
-                <Space>
-                  Your photo
-                </Space>
+                <Space>Your photo</Space>
                 <div>This will be displayed on your profile.</div>
               </div>
             }
               <Input disabled />
             </Form.Item>
             <p className={parentStyles.itemDescription}>
-              Once registered, an account cannot be changed and can only be
-              cancelled.
+              Once registered, E-mail cannot be changed.
             </p>
           </Form.Item>
           <Form.Item