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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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README.md
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README.md
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<div align="center">
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<a href="https://demo.ragflow.io/">
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<img src="https://github.com/infiniflow/ragflow/assets/12318111/f034fb27-b3bf-401b-b213-e1dfa7448d2a" width="320" alt="ragflow logo">
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<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
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</a>
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</div>
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<p align="center">
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<a href="./README.md">English</a> |
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<a href="./README_zh.md">简体中文</a>
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## 🌟 Key Features
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### 🍭 **"Quality in, quality out"**
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- Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
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- Finds "needle in a data haystack" of literally unlimited tokens.
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- Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
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- Finds "needle in a data haystack" of literally unlimited tokens.
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### 🍱 **Template-based chunking**
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- Intelligent and explainable.
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- Plenty of template options to choose from.
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- Intelligent and explainable.
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- Plenty of template options to choose from.
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### 🌱 **Grounded citations with reduced hallucinations**
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- Visualization of text chunking to allow human intervention.
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- Quick view of the key references and traceable citations to support grounded answers.
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- Visualization of text chunking to allow human intervention.
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- Quick view of the key references and traceable citations to support grounded answers.
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### 🍔 **Compatibility with heterogeneous data sources**
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- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
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- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
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### 🛀 **Automated and effortless RAG workflow**
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- Streamlined RAG orchestration catered to both personal and large businesses.
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- Configurable LLMs as well as embedding models.
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- Multiple recall paired with fused re-ranking.
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- Intuitive APIs for seamless integration with business.
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- Streamlined RAG orchestration catered to both personal and large businesses.
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- Configurable LLMs as well as embedding models.
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- Multiple recall paired with fused re-ranking.
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- Intuitive APIs for seamless integration with business.
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## 🔎 System Architecture
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> The core image is about 15 GB in size and may take a while to load.
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4. Check the server status after having the server up and running:
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```bash
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$ docker logs -f ragflow-server
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```
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*The following output confirms a successful launch of the system:*
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_The following output confirms a successful launch of the system:_
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```bash
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____ ______ __
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* Running on http://127.0.0.1:9380
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* Running on http://172.22.0.5:9380
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INFO:werkzeug:Press CTRL+C to quit
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```
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```
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5. In your web browser, enter the IP address of your server as prompted and log in to RAGFlow.
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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.
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> See [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information.
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*The show is now on!*
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_The show is now on!_
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## 🔧 Configurations
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README_zh.md
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README_zh.md
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<div align="center">
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<a href="https://demo.ragflow.io/">
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<img src="https://github.com/infiniflow/ragflow/assets/12318111/f034fb27-b3bf-401b-b213-e1dfa7448d2a" width="320" alt="ragflow logo">
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<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
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</a>
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</div>
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<p align="center">
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<a href="./README.md">English</a> |
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<a href="./README_zh.md">简体中文</a>
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## 🌟 主要功能
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### 🍭 **"Quality in, quality out"**
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- 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。
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- 真正在无限上下文(token)的场景下快速完成大海捞针测试。
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- 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。
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- 真正在无限上下文(token)的场景下快速完成大海捞针测试。
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### 🍱 **基于模板的文本切片**
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- 不仅仅是智能,更重要的是可控可解释。
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- 多种文本模板可供选择
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- 不仅仅是智能,更重要的是可控可解释。
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- 多种文本模板可供选择
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### 🌱 **有理有据、最大程度降低幻觉(hallucination)**
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- 文本切片过程可视化,支持手动调整。
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- 有理有据:答案提供关键引用的快照并支持追根溯源。
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- 文本切片过程可视化,支持手动调整。
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- 有理有据:答案提供关键引用的快照并支持追根溯源。
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### 🍔 **兼容各类异构数据源**
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- 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。
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- 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。
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### 🛀 **全程无忧、自动化的 RAG 工作流**
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- 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。
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- 大语言模型 LLM 以及向量模型均支持配置。
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- 基于多路召回、融合重排序。
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- 提供易用的 API,可以轻松集成到各类企业系统。
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- 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。
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- 大语言模型 LLM 以及向量模型均支持配置。
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- 基于多路召回、融合重排序。
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- 提供易用的 API,可以轻松集成到各类企业系统。
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## 🔎 系统架构
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> 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。
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4. 服务器启动成功后再次确认服务器状态:
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```bash
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$ docker logs -f ragflow-server
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```
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*出现以下界面提示说明服务器启动成功:*
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_出现以下界面提示说明服务器启动成功:_
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```bash
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* Running on http://127.0.0.1:9380
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* Running on http://172.22.0.5:9380
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INFO:werkzeug:Press CTRL+C to quit
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```
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```
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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。
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> 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)。
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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。
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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。
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> 详见 [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md)。
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*好戏开始,接着奏乐接着舞!*
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_好戏开始,接着奏乐接着舞!_
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> 详见 [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md)。
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_好戏开始,接着奏乐接着舞!_
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## 🔧 系统配置
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web/src/assets/logo-with-text.png
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BIN
web/src/assets/logo-with-text.png
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Binary file not shown.
After Width: | Height: | Size: 8.0 KiB |
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>
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<Space size={12} onClick={handleLogoClick} className={styles.logoWrapper}>
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<Logo className={styles.appIcon}></Logo>
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<span className={styles.appName}>RagFlow</span>
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<span className={styles.appName}>RAGFlow</span>
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</Space>
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<Space size={[0, 8]} wrap>
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<Radio.Group
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import { ReactComponent as MoonIcon } from '@/assets/svg/moon.svg';
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import { ReactComponent as TranslationIcon } from '@/assets/svg/translation.svg';
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import { BellOutlined, GithubOutlined } from '@ant-design/icons';
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import { GithubOutlined } from '@ant-design/icons';
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import { Space } from 'antd';
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import React from 'react';
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import User from '../user';
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<Circle>
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<GithubOutlined onClick={handleGithubCLick} />
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</Circle>
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<Circle>
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{/* <Circle>
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<TranslationIcon />
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</Circle>
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<Circle>
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<BellOutlined />
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</Circle>
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<Circle>
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<MoonIcon />
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</Circle>
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</Circle> */}
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<User></User>
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</Space>
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</div>
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<Form.Item
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label="Language"
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name="language"
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initialValue={'Chinese'}
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initialValue={'English'}
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rules={[{ required: true, message: 'Please input your language!' }]}
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>
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<Select placeholder="select your language">
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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.
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</p><p>
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Instead of chunking the résumé, we parse the résumé into structured data. As a HR, you can dump all the résumé you have,
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the you can list all the candidates that match the qualifications just by talk with <i>'RagFlow'</i>.
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the you can list all the candidates that match the qualifications just by talk with <i>'RAGFlow'</i>.
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</p>
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`,
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},
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getUploadFileListFromBase64,
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normFile,
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} from '@/utils/fileUtil';
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import { PlusOutlined, QuestionCircleOutlined } from '@ant-design/icons';
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import { PlusOutlined } from '@ant-design/icons';
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import {
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Button,
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Divider,
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Select,
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Space,
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Spin,
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Tooltip,
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Upload,
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UploadFile,
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} from 'antd';
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<Form.Item<FieldType>
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label={
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<div>
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<Space>
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Your photo
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</Space>
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<Space>Your photo</Space>
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<div>This will be displayed on your profile.</div>
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</div>
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}
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<Input disabled />
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</Form.Item>
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<p className={parentStyles.itemDescription}>
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Once registered, an account cannot be changed and can only be
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cancelled.
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Once registered, E-mail cannot be changed.
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</p>
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</Form.Item>
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<Form.Item
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