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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
tags/v0.1.0
balibabu 1 年之前
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共有 8 個文件被更改，包括 82 次插入 和 75 次删除
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  1. 35
    29
      README.md
  2. 38
    28
      README_zh.md
  3. 二進制
      web/src/assets/logo-with-text.png
  4. 1
    1
      web/src/layouts/components/header/index.tsx
  5. 3
    8
      web/src/layouts/components/right-toolbar/index.tsx
  6. 1
    1
      web/src/pages/add-knowledge/components/knowledge-setting/configuration.tsx
  7. 1
    1
      web/src/pages/add-knowledge/components/knowledge-setting/utils.ts
  8. 3
    7
      web/src/pages/user-setting/setting-profile/index.tsx

+ 35
- 29
README.md 查看文件

@@ -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>
@@ -26,27 +25,32 @@
[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
@@ -65,11 +69,11 @@
### 🚀 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
> ```
>
@@ -92,7 +96,7 @@
$ 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
@@ -102,31 +106,33 @@
> 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
@@ -136,14 +142,14 @@ When it comes to system configurations, you will need to manage the following fi
- [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
> ```
@@ -171,4 +177,4 @@ See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
## 🙌 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.

+ 38
- 28
README_zh.md 查看文件

@@ -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>
@@ -26,27 +25,32 @@
[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

@@ -69,7 +73,7 @@

> 如需确认 `vm.max_map_count` 的大小：
>
> ```bash
> ```bash
> $ sysctl vm.max_map_count
> ```
>
@@ -102,32 +106,38 @@
> 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

@@ -137,14 +147,14 @@
- [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
> ```
@@ -172,4 +182,4 @@ $ docker compose up -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)。

二進制
web/src/assets/logo-with-text.png 查看文件


+ 1
- 1
web/src/layouts/components/header/index.tsx 查看文件

@@ -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

+ 3
- 8
web/src/layouts/components/right-toolbar/index.tsx 查看文件

@@ -1,6 +1,4 @@
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>

+ 1
- 1
web/src/pages/add-knowledge/components/knowledge-setting/configuration.tsx 查看文件

@@ -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">

+ 1
- 1
web/src/pages/add-knowledge/components/knowledge-setting/utils.ts 查看文件

@@ -81,7 +81,7 @@ export const TextMap = {
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>
`,
},

+ 3
- 7
web/src/pages/user-setting/setting-profile/index.tsx 查看文件

@@ -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

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