About

smarta is an open-source Artificially Intelligent Teaching Assistant that uses vector space modeling to understand textbooks and syllabi.

Installation and Usage

You too can clone and host this chatbot. As a prerequisite, you need to have a Slack app and a MongoDB Atlas instance.

1. Set up a virtual environment python3 -m venv botvenv
2. Activate the virtual environment source ./botvenv/bin/activate. Once you're done using the venv, enter deactivate into the command line.
3. Install the requirements pip install -r requirements.txt
4. Create a .env file and enter the below information:

SLACK_SIGNING_SECRET=#STRING
SLACK_OAUTH_TOKEN=#STRING
PORT=#INT
DUMP_FILE_PATH=#STRING
MONGO_PASS=#STRING
MONGO_DB=#STRING

If you're on a hosting service like Heroku or Digital Ocean, you'll have to go to settings and configure the environment variables there.

5. Run the app python app.py

Hosting

As a forewarning, after any of these scripts are used, give the server some time (20-30 secs locally, perhaps more on public hosting services) to initialize. This will allow locally required resources to be fetched from any remote servers.

Hosting Locally (ngrok)

You can use ngrok to host locally ./ngrok http <PORT>

Deploying to Production

Use gunicorn -b 0.0.0.0:<PORT> app:flask_app

Preloading

Startup may take too long, causing workers to time out and quit. In that case, either manually increase timeout time or preload: gunicorn -b 0.0.0.0:<PORT> --preload app:flask_app gunicorn -b 0.0.0.0:<PORT> -t <TIME_SECS> app:flask_app

See more here.

Deploying to Heroku/DIgitalOcean/AWS Elastic Bean Stalk

This repo automatically deploys for most major hosting services. Just connect my repo https://github.com/FurkanToprak/smarTA.

• Note: This server has some considerable boot time, so you may have to find good ways to bind the WSGI server to your desired PORT (through gunicorn).

Relevant Endpoints

Make sure your Slack App is configured to listen to events from https://<YOUR_URL>/slack/events

Add to your Slack Workspace

To install smarta, just go to https://<YOUR_URL>/slack/install and click the button identical to the one below! The button generated is unique and secure.

Architecture

This chatbot is powered with the Bolt framework for python. This app listens to event hooks from Slack and responds using its knowledge base (stored in MongoDB) and its NLP Components. This app occupies whichever port specified in the .env file.

NLP Components

This slackbot indexes the inputted files by topic. The user provides a topic and the user input is fuzzy-matched against the inputted topics. After a section of the textbook is selected, a vector space model is used to calculate the semantic similarity of queries and paragraphs of the textbook. The vector space model uses embeddings that were trained and tested on the Stanford Question Answering Dataset

Database Schemas

The below architecture is used for the MongoDB database.

• [DB] smartav1
  • [Collection] Users

    _id: ObjectId()
    questions: string[],
    isAdmin: boolean,
    teamID: string,
    state: number,
    relevantTopics: string[],
    chosenTopic: number,
    

  • [Collection] Workspaces

    _id: ObjectId(),
    files: { topic: string, content: string }[],
    teamID: string,
    goodBot: number,
    badBot: number,