# Running Personal MAIC Deployment

## Preparation

Please make sure you have [Docker](https://docs.docker.com/engine/install) for your specific OS platform and [Python3](https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/?C=M&O=D)<sup class="tooltip" data-tooltip="We recommend to use miniconda to create an new environment instead of using the default Python3."><a role="button">[?]</a></sup> installed.

### Clone Repo

Clone the repo using [Git](https://git-scm.com/downloads) and navigate to the root directory of the project:
```bash
git clone git@github.com:THU-MAIC/MAIC-Core.git maic
cd maic
```

### Install pip dependencies

Pip install Requirements with Python >= 3.11:
```bash
pip install -r requirements.txt
```

### Initialize MongoDB

Download the MongoDB dump file from this [link](https://cloud.tsinghua.edu.cn/f/33d4e25fc62041e1b943/?dl=1) and unzip the dump file in the current directory.
Example of in linux bash (use other commands for other platforms):
```bash
# downlod dump file
wget -O dump.zip https://cloud.tsinghua.edu.cn/f/33d4e25fc62041e1b943/?dl=1
# unzip dump file
unzip dump.zip
```

Then, run the following commands to initialize the MongoDB database in docker:
```bash
# install mongo
docker pull mongo:4.4.4
# create mongo from ${your_dump_path}/dump:/mongo_dump
docker run -d --name algo-new-mongo -v ${your_dump_path}/mongo_dump:/dump -p 27017:27017 mongo:4.4.4
# initialize database
docker exec -it -u root algo-new-mongo sh -c "cd /dump && mongorestore ./"
```

### Initialize RabbitMQ

```bash
# install rabbitmq-server
docker pull rabbitmq:management
# start rabbitmq
docker run -d --name rabbitmq -p 5672:5672 -p 15672:15672 rabbitmq:3.9.15-management
```

## InClass Deployment
### Deploy InClass Worker

Run the following code in two separate terminals in the root directory of algo-backend:
```bash
# deploy zhipuai workers
python -m service.llm.zhipuai --zhipu_api_key <replace with your zhipuai apikey>
# deploy classroom_session workers
python -m service.inclass.main
```
### Running Commandline Interface

Run the following code in a new python console to initialize the course course **"TAGI" by Zhiyuan Liu**(modification needed):
```bash
python
# Run an inclass example
>>> from service.inclass.main import INCLASS_SERVICE
# Start the inclass service with specified session id, which can be replaced with your own session id created by PreClass service in the MongoDB database
>>> INCLASS_SERVICE.trigger("674ff0c87baecad6fd515719")
# Continue the inclass service with user input
>>> INCLASS_SERVICE.trigger("674ff0c87baecad6fd515719", user_input="Hello!")
```


## PreClass Deployment

PreClass is a service indenpendent from InClass, which helps to create your own class agenda by uploading your slides. 
We have provided an created inclass agenda from course **"TAGI" by Zhiyuan Liu**(modification needed).

### Deploy PreClass Worker

Run the following code in the root directory of algo-backend:
```bash
# deploy openai workers in a separate terminal
python -m service.llm.openai

# deploy main workers in a separate terminal
python -m service.preclass.main

# deploy processors
python -m service.preclass.processors.pptx2pdf
python -m service.preclass.processors.pdf2png
python -m service.preclass.processors.ppt2text
python -m service.preclass.processors.gen_description
python -m service.preclass.processors.gen_structure
python -m service.preclass.processors.gen_readscript
python -m service.preclass.processors.gen_showfile
python -m service.preclass.processors.gen_askquestion
```

### Running Commandline Interface
Make sure you have a slide file named "your_slide.pptx" in the root directory of maic. Run the following code in the python console:
```bash
# Run a preclass example
>>> from service.preclass.main import PRECLASS_MAIN
# Trigger a preclass job
>>> PRECLASS_MAIN.trigger("your_slide.pptx")
```