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
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@ -1,10 +1,9 @@
<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>
@ -28,25 +27,30 @@
## 🌟 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
@ -102,10 +106,12 @@
> 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
____ ______ __
@ -119,14 +125,14 @@
* 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

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<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>
@ -28,25 +27,30 @@
## 🌟 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
@ -102,10 +106,12 @@
> 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
____ ______ __
@ -119,15 +125,19 @@
* 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!*
_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_
## 🔧 系统配置

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@ -55,7 +55,7 @@ const RagHeader = () => {
>
<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

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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';
@ -21,15 +19,12 @@ const RightToolBar = () => {
<Circle>
<GithubOutlined onClick={handleGithubCLick} />
</Circle>
<Circle>
{/* <Circle>
<TranslationIcon />
</Circle>
<Circle>
<BellOutlined />
</Circle>
<Circle>
<MoonIcon />
</Circle>
</Circle> */}
<User></User>
</Space>
</div>

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@ -62,7 +62,7 @@ const ConfigurationForm = ({ form }: { form: FormInstance }) => {
<Form.Item
label="Language"
name="language"
initialValue={'Chinese'}
initialValue={'English'}
rules={[{ required: true, message: 'Please input your language!' }]}
>
<Select placeholder="select your language">

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@ -81,7 +81,7 @@ export const TextMap = {
The résumé comes in a variety of formats, just like a persons 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>
`,
},

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@ -8,7 +8,7 @@ import {
getUploadFileListFromBase64,
normFile,
} from '@/utils/fileUtil';
import { PlusOutlined, QuestionCircleOutlined } from '@ant-design/icons';
import { PlusOutlined } from '@ant-design/icons';
import {
Button,
Divider,
@ -17,7 +17,6 @@ import {
Select,
Space,
Spin,
Tooltip,
Upload,
UploadFile,
} from 'antd';
@ -108,9 +107,7 @@ const UserSettingProfile = () => {
<Form.Item<FieldType>
label={
<div>
<Space>
Your photo
</Space>
<Space>Your photo</Space>
<div>This will be displayed on your profile.</div>
</div>
}
@ -177,8 +174,7 @@ const UserSettingProfile = () => {
<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