Intro to Algorithms Class Chatbot

Prerequisites for Development

• python 3.9 and pip
  • tensorflow
  • tensorflow_hub
  • tensorflow-text
  • (See details in "How to Run the Project" about installing other dependencies)
• Haystack (see installation instructions)
  • Apple M1 has extra dependencies
• Elasticsearch host (see details in "Miscellaneous" about elasticsearch)

Prerequisites for Deployment

• Docker
• Elasticsearch host (see details in "Miscellaneous" about elasticsearch)

How to Run the Project for Development

• Change directory to src/server
• Run shell script run.sh to prepare and store in your elasticsearch host the information of
  • i) question and answer (Q&A) pairs,
  • ii) algorithm general knowledge base and
  • iii) syllabus knowledge base.
• Install pipenv by pip install pipenv
• Run pipenv install to install backend dependencies listed in the Pipfile.lock
• Run python -m src.api.app to start the Flask API, which then initiates the NLP pipeline

Project Deployment

• Change directory to src/server
• Run shell script prod.sh to build an image of the API and the NLP pipeline. Then, the backend will be running in a docker container. It has been tested that in Linux x86 system the docker image can be successfully built.
• It is suggested that backend server should have a GPU for faster inferencing time.

Architecture

NLP pipeline visualization:

Performance of QueryClassifier

Precision = 1.00
Recall = 0.83
F1 = 0.91
Confusion Matrix:

Miscellaneous

• In Fall 2022, the backend is hosted on the Google Cloud Platform. We utilized a Nvidia Tesla P100 (225W, 16G RAM) and 2 vCPU with 7.5G RAM. This results in 5s response time for each request on average.
  • A elasticsearch host should be running locally or on the cloud. We used to have a free-trial cloud elasticsearch host provided by elastic.co. Alternatively, elasticsearch instance can also be hosted in a docker container.