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
This commit is contained in:
balibabu
2024-04-01 10:54:11 +08:00
committed by GitHub
parent 0a9f589f9b
commit 286b1421cf
8 changed files with 84 additions and 77 deletions

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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>
@ -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!*
> See [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information.
_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.