Merge pull request #48 from apconw/dev

update read me

Author: apconw

README-en.md

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+# Large Model Data Assistant
+
+[![中文文档](https://img.shields.io/badge/中文文档-点击查看-orange)](README.md)
+
+🌟 **Project Introduction**
+
+A lightweight, full - link supported, and easily customizable large model application project.
+
+**Compatible with large models such as DeepSeek and Qwen2.5**
+
+This is a one - stop large model application development project built on technologies like Dify, Ollama & Vllm, Sanic, and Text2SQL 📊. It features a modern UI crafted with Vue3, TypeScript, and Vite 5. It supports data graphical Q&A based on large models through ECharts 📈 and can handle tabular Q&A for CSV files 📂. Additionally, it can be easily integrated with third - party open - source RAG systems and retrieval systems 🌐 to support a wide range of general knowledge Q&A.
+
+As a lightweight large model application development project, Sanic - Web 🛠️ supports rapid iteration and expansion, facilitating the quick implementation of large model projects. 🚀
+
+## Architecture Diagram
+![image](./images/app-01.png)
+
+## 🎉 **Features**
+- **Core Technology Stack**: Dify + Ollama + RAG + (Qwen2.5/DeepSeek) + Text2SQL
+- **UI Framework**: Vue 3 + TypeScript + Vite 5
+- **Data Q&A**: Integrates ECharts and large models to achieve lightweight graphical data Q&A display via Text2SQL.
+- **Table Q&A**: Supports uploading CSV files and provides table data Q&A based on large model summarization, preprocessing, and Text2SQL.
+- **General Q&A**: Supports general data Q&A through integration with third - party RAG systems and public network retrieval.
+- **Application Architecture**: Serves as a lightweight, full - link, one - stop large model application development framework for easy expansion and implementation.
+- **Flexible Deployment**: Supports one - click deployment of all dependent components for large model application development using docker - compose, with zero configuration required.
+
+## Demo
+![image](./images/chat-04.gif)
+![image](./images/chat-05.png)
+![image](./images/chat-01.png)
+![image](./images/chat-02.png)
+
+## 💡 Environment Requirements
+
+Before you start, make sure your development environment meets the following minimum requirements:
+
+- **Operating System**: Windows 10/11, macOS M series, Centos/Ubuntu
+- **GPU**: For local deployment with Ollama, an NVIDIA GPU is recommended. Alternatively, you can use the CPU mode or purchase an API key from the public network.
+- **Memory**: 8GB+
+
+## 🔧 **Prerequisites**
+* Python 3.8+
+* Poetry 1.8.3+
+* Dify 0.7.1+
+* Mysql 8.0+
+* Node.js 18.12.x+
+* Pnpm 9.x
+
+## 📚 **Large Model Deployment**
+- [Refer to Ollama Deployment](https://qwen.readthedocs.io/en/latest/run_locally/ollama.html)
+- Models: Qwen2.5 7B model
+- Models: DeepSeek R1 7B model
+- [Alibaba Cloud Public Network API Key](http://aliyun.com/product/bailian)
+
+## ⚙️ **Dify Environment Configuration**
+1. **Install Dify**
+   - [Official Documentation](https://docs.dify.ai/en)
+   - To assist those new to large model applications, this project provides a one - click solution to start the Dify service for a quick experience.
+   - Local access address for Dify: http://localhost:18000. You need to register your own account and password.
+   ```bash
+   # Start the built - in Dify service
+   cd docker/dify/docker
+   docker - compose up -d
+
+2. **Configure Dify**
+- Add the Ollama large model provider in Dify and configure the Qwen2.5 and DeepSeek R1 models.
+- Import the docker/dify/数据问答_v1.1.2_deepseek.yml canvas from the project root directory.
+- Copy the API key corresponding to the canvas for use in the following steps.
+- After importing the canvas, manually select the locally configured large model and save the settings.
+  
+![image](./images/llm-setting.png)
+![image](./images/llm-setting-deepseek.png)
+![image](./images/import-convas.png)
+![image](./images/convas-api-key.png)
+
+## 🚀 Quick Start
+The specific steps are as follows:
+
+1. Clone the repository
+```bash
+git clone https://github.com/apconw/sanic - web.git
+```
+
+2. Start the service
+
+- Modify the environment variables starting with DIFY_ in the chat - service of docker - compose.
+- Set the DIFY_DATABASE_QA_API_KEY to the API key obtained from the Dify canvas.
+
+```bash
+# Start the front - end, back - end services, and middleware
+cd docker
+docker compose up -d
+```
+3. Configure Minio
+   
+- Access the MinIO service at http://localhost:19001/. The default account is admin, and the password is 12345678.
+- Create a bucket named filedata and configure the Access Key.
+- Modify the environment variables starting with MINIO_ in the chat - service of docker - compose and restart the service.
+
+```bash
+# Restart the front - end, back - end services, and middleware
+cd docker
+docker compose up -d
+```
+
+4. Initialize the data
+
+```bash
+# Install the dependency package
+pip install pymysql
+
+# For Mac or Linux users
+cd docker
+./init.sh
+
+# For Windows users
+cd common
+python initialize_mysql.py
+```
+
+5. Access the application
+- Front - end service: http://localhost:8081
+
+
+## 🛠️ Local Development
+- Clone the repository
+- Deploy large models: Refer to the above Large Model Deployment section to install Ollama and deploy the Qwen2.5 and DeepSeek R1 models.
+- Configure Dify: Refer to the above Dify Environment Configuration section to obtain the API key from the Dify canvas and modify the DIFY_DATABASE_QA_API_KEY in the .env.dev file.
+- Configure Minio: Modify the Minio - related keys in the .env.dev file.
+- Install dependencies and start the services
+
+1. **Back - end dependencies**
+- Install Poetry: [Refer to the official Poetry documentation](https://python - poetry.org/docs/)
+
+```bash
+# Install Poetry
+pip install poetry
+
+# Install dependencies at the root directory
+# Set up the domestic mirror
+poetry source add --priority = primary mirrors https://pypi.tuna.tsinghua.edu.cn/simple/
+poetry install --no - root
+```
+
+2. **Install middleware**
+```bash 
+cd docker
+docker compose up -d mysql minio
+```
+3. **Configure Minio**
+- Access the MinIO service at http://localhost:19001/. The default account is admin, and the password is 12345678.
+- Create a bucket named filedata and configure the Access Key.
+- Modify the Minio - related keys in the .env.dev file.
+
+4. **Initialize the data**
+- If you are using a local MySQL environment, you need to modify the database connection information in the initialize_mysql source code.
+```bash
+# For Mac or Linux users
+cd docker
+./init.sh
+
+# For Windows users
+cd common
+python initialize_mysql.py
+```
+
+5. **Front - end dependencies**
+- The front - end is based on the open - source project [chatgpt - vue3 - light - mvp](https://github.com/pdsuwwz/chatgpt - vue3 - light - mvp).
+
+```bash 
+# Install front - end dependencies and start the service
+cd web
+
+# Install pnpm globally
+npm install -g pnpm
+
+# Install dependencies
+pnpm i
+
+# Start the service
+pnpm dev
+```
+
+6. **Start the back - end service**
+```bash
+# Start the back - end service
+python serv.py
+```
+
+7. **Access the service**
+- Front - end service: http://localhost:2048
+
+## 🐳 Build Images
+- Execute the following commands to build the images:
+```bash
+# Build the front - end image
+make web - build
+
+# Build the back - end image
+make server - build
+```
+
+## 🌹 Support
+If you find this project useful, please give it a star on GitHub by clicking the [`Star`](https://github.com/apconw/sanic-web)  button. Your support is our motivation to keep improving. Thank you! ^_^
+
+
+## ⭐ Star History
+ [![Star History Chart](https://api.star-history.com/svg?repos=apconw/sanic-web&type=Date)](https://star-history.com/#apconw/sanic-web&Date)
+
+
+## QA Community
+- Join our large model application community to discuss and share experiences.
+- Follow the official WeChat account and click the "WeChat Group" menu to join.
+
+|               微信群                |
+| :---------------------------------: |
+| ![image](./images/wchat-search.png) |
+
+
+## License
+MIT License | Copyright © 2024 - PRESENT AiAdventurer