docs: update english docs

docs: update readme

README.en.md

@@ -2,96 +2,105 @@
 
 ---
 
+[中文版](README.md)
+
 A Open Database-GPT Experiment, interact your data and environment using the local GPT, no data leaks, 100% privately, 100% security.
 
-![GitHub Repo stars](https://img.shields.io/github/stars/csunny/db-gpt?style=social)
+## Features
 
-一个数据库相关的GPT实验项目, 模型与数据全部本地化部署, 绝对保障数据的隐私安全。 同时此GPT项目可以直接本地部署连接到私有数据库, 进行私有数据处理。 
+- SQL Project
+  - SQL Generate
+  - SQL-diagnosis
+- Database-QA Based Knowledge 
 
-[DB-GPT](https://github.com/csunny/DB-GPT) 是一个实验性的开源应用，它基于[FastChat](https://github.com/lm-sys/FastChat)，并使用[vicuna-13b](https://huggingface.co/Tribbiani/vicuna-13b)作为基础模型。此外，此程序结合了[langchain](https://github.com/hwchase17/langchain)和[llama-index](https://github.com/jerryjliu/llama_index)基于现有知识库进行[In-Context Learning](https://arxiv.org/abs/2301.00234)来对其进行数据库相关知识的增强。它可以进行SQL生成、SQL诊断、数据库知识问答、数据处理等一系列的工作。 
+## Architecture Design
 
-
-## 项目方案
-<img src="https://github.com/csunny/DB-GPT/blob/main/asserts/DB-GPT.png" width="600" margin-left="auto" margin-right="auto" >
+<p align="center">
+  <img src="./assets/DB-GPT.png" width="600px" />
+</p>
 
 [DB-GPT](https://github.com/csunny/DB-GPT) is an experimental open-source application that builds upon the [FastChat](https://github.com/lm-sys/FastChat) model and uses vicuna as its base model. Additionally, it looks like this application incorporates langchain and llama-index embedding knowledge to improve Database-QA capabilities. 
 
 Overall, it appears to be a sophisticated and innovative tool for working with databases. If you have any specific questions about how to use or implement DB-GPT in your work, please let me know and I'll do my best to assist you.
 
+## Demo
 
-## 运行效果演示
 Run on an RTX 4090 GPU (The origin mov not sped up!, [YouTube地址](https://www.youtube.com/watch?v=1PWI6F89LPo))
-- 运行演示
 
-![](https://github.com/csunny/DB-GPT/blob/main/asserts/演示.gif)
+### Run
 
+<p align="center">
+  <img src="./assets/演示.gif" width="600px" />
+</p>
 
-- SQL生成示例
-首先选择对应的数据库, 然后模型即可根据对应的数据库Schema信息生成SQL
+### SQL Generate
 
-<img src="https://github.com/csunny/DB-GPT/blob/main/asserts/SQLGEN.png" width="600" margin-left="auto" margin-right="auto" >
+First, select the DataBase, you can use Schema to generate the SQL.。
 
-The Generated SQL is runable.
+<p align="center">
+  <img src="./assets/SQLGEN.png" width="600px" />
+</p>
 
-<img src="https://github.com/csunny/DB-GPT/blob/main/asserts/exeable.png" width="600" margin-left="auto" margin-right="auto" >
+<p align="center">
+  <img src="./assets/exeable.png" width="600px" />
+</p>
 
-- 数据库QA示例 
+### Database-QA
 
-<img src="https://github.com/csunny/DB-GPT/blob/main/asserts/DB_QA.png" margin-left="auto" margin-right="auto" width="600">
+<p align="center">
+  <img src="./assets/DB_QA.png" width="600px" />
+</p>
 
-基于默认内置知识库QA
+<p align="center">
+  <img src="./assets/VectorDBQA.png" width="600px" />
+</p>
 
-<img src="https://github.com/csunny/DB-GPT/blob/main/asserts/VectorDBQA.png" width="600" margin-left="auto" margin-right="auto" >
+## Deployment
 
-# Dependencies
-1. First you need to install python requirements.
-```
-python>=3.9
-pip install -r requirements.txt
+### 1. Python Requirement
+
+```bash
+$ python>=3.9
+$ pip install -r requirements.txt
 ```
+
 or if you use conda envirenment, you can use this command
-```
-cd DB-GPT
-conda env create -f environment.yml
+
+```bash
+$ conda env create -f environment.yml
 ```
 
-2. MySQL Install
+### 2. MySQL
 
 In this project examples, we connect mysql and run SQL-Generate. so you need install mysql local for test. recommand docker
-```
-docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa12345678 -dit mysql:latest
-```
-The password just for test, you can change this if necessary
 
-# Install
-1. 基础模型下载
-关于基础模型, 可以根据[vicuna](https://github.com/lm-sys/FastChat/blob/main/README.md#model-weights)合成教程进行合成。 
-如果此步有困难的同学，也可以直接使用[Hugging Face](https://huggingface.co/)上的模型进行替代. [替代模型](https://huggingface.co/Tribbiani/vicuna-7b)
-
-2. Run model server
-```
-cd pilot/server
-python vicuna_server.py
+```bash
+$ docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa12345678 -dit mysql:latest
 ```
 
-3. Run gradio webui
-```
-python webserver.py 
+### 3. LLM
+
+- [vicuna](https://github.com/lm-sys/FastChat/blob/main/README.md#model-weights)
+- [Hugging Face](https://huggingface.co/Tribbiani/vicuna-7b)
+
+```bash
+$ cd pilot/server
+$ python vicuna_server.py
 ```
 
-4. 基于阿里云部署指南
-[阿里云部署指南](https://open.oceanbase.com/blog/3278046208)
+Run gradio webui
 
-# Featurs
-- SQL-Generate
-- Database-QA Based Knowledge 
-- SQL-diagnosis
+```bash
+$ python webserver.py 
+```
 
-总的来说，它是一个用于数据库的复杂且创新的AI工具。如果您对如何在工作中使用或实施DB-GPT有任何具体问题，请联系我, 我会尽力提供帮助, 同时也欢迎大家参与到项目建设中, 做一些有趣的事情。
+## Thanks
 
-<img src="https://github.com/csunny/DB-GPT/blob/main/asserts/wechat.jpg" width="400" margin-left="auto" margin-right="auto" >
+- [FastChat](https://github.com/lm-sys/FastChat)
+- [vicuna-13b](https://huggingface.co/Tribbiani/vicuna-13b)
+- [langchain](https://github.com/hwchase17/langchain)
+- [llama-index](https://github.com/jerryjliu/llama_index) and [In-Context Learning](https://arxiv.org/abs/2301.00234)
 
-# Contribute
-[Contribute](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING)
-# Licence
-[MIT](https://github.com/csunny/DB-GPT/blob/main/LICENSE)
+## Licence
+
+The MIT License (MIT)

README.md

@@ -18,7 +18,7 @@
 ## 架构方案
 
 <p align="center">
-  <img src="./asserts/DB-GPT.png" width="740px" />
+  <img src="./assets/DB-GPT.png" width="600px" />
 </p>
 
 DB-GPT 基于[FastChat](https://github.com/lm-sys/FastChat) 构建大模型运行环境，并提供 vicuna 作为基础的大语言模型。此外，我们通过 langchain 和 llama-index 提供私域知识库问答能力。 
@@ -29,7 +29,7 @@ DB-GPT 基于[FastChat](https://github.com/lm-sys/FastChat) 构建大模型运
 ### 运行环境演示
 
 <p align="center">
-  <img src="./asserts/演示.gif" width="680px" />
+  <img src="./assets/演示.gif" width="600px" />
 </p>
 
 ### SQL 生成
@@ -37,30 +37,30 @@ DB-GPT 基于[FastChat](https://github.com/lm-sys/FastChat) 构建大模型运
 首先选择对应的数据库, 然后模型即可根据对应的数据库 Schema 信息生成 SQL。
 
 <p align="center">
-  <img src="./asserts/SQLGEN.png" width="680px" />
+  <img src="./assets/SQLGEN.png" width="600px" />
 </p>
 
 运行成功的效果如下面的演示：
 
 <p align="center">
-  <img src="./asserts/exeable.png" width="680px" />
+  <img src="./assets/exeable.png" width="600px" />
 </p>
 
 ### 数据库问答
 
 <p align="center">
-  <img src="./asserts/DB_QA.png" width="680px" />
+  <img src="./assets/DB_QA.png" width="600px" />
 </p>
 
 基于默认内置知识库。
 
 <p align="center">
-  <img src="./asserts/VectorDBQA.png" width="680px" />
+  <img src="./assets/VectorDBQA.png" width="600px" />
 </p>
 
 ## 部署
 
-### 1. 安装 Python 依赖的模块。
+### 1. 安装 Python
 
 ```bash
 $ python>=3.9
@@ -109,16 +109,23 @@ $ python webserver.py
 - [llama-index](https://github.com/jerryjliu/llama_index) 基于现有知识库进行[In-Context Learning](https://arxiv.org/abs/2301.00234)来对其进行数据库相关知识的增强。
 
 <!-- GITCONTRIBUTOR_START -->
+
 ## Contributors
 
-|[<img src="https://avatars.githubusercontent.com/u/17919400?v=4" width="100px;"/><br/><sub><b>csunny</b></sub>](https://github.com/csunny)<br/>|
+|[<img src="https://avatars.githubusercontent.com/u/17919400?v=4" width="100px;"/><br/><sub><b>csunny</b></sub>](https://github.com/csunny)<br/>|[<img src="https://avatars.githubusercontent.com/u/1011681?v=4" width="100px;"/><br/><sub><b>xudafeng</b></sub>](https://github.com/xudafeng)<br/>|
 | :---: | :---: |
 
 
-This project follows the git-contributor [spec](https://github.com/xudafeng/git-contributor), auto updated at `Sun May 14 2023 22:37:46 GMT+0800`.
+This project follows the git-contributor [spec](https://github.com/xudafeng/git-contributor), auto updated at `Sun May 14 2023 23:02:43 GMT+0800`.
 
 <!-- GITCONTRIBUTOR_END -->
 
+这是一个用于数据库的复杂且创新的工具，如有任何具体问题，请联系如下微信，我会尽力提供帮助，同时也欢迎参与到项目建设中。
+
+<p align="center">
+  <img src="./assets/wechat.jpg" width="320px" />
+</p>
+
 ## Licence
 
 The MIT License (MIT)