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Covidex: A Search Engine for the COVID-19 Open Research Dataset

Build Status License: MIT

This repository contains the API server, neural models, and UI client for Covidex, a neural search engine for the COVID-19 Open Research Dataset (CORD-19). For a description of our system, check out this paper: Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset.

We also provide neural search infrastructure for searching domain-specific scholarly literature via Cydex. This paper details the abstractions developed on top of Covidex to facilitate domain-specific search: Cydex: Neural Search Infrastructure for the Scholarly Literature.

Environment Setup

API Server

  1. Install CUDA 10.1
  • For Ubuntu, follow these instructions
  • For Debian run sudo apt-get install nvidia-cuda-toolkit
  1. Install Anaconda (currently version 2020.02)
wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh
bash Anaconda3-2020.02-Linux-x86_64.sh
  1. Install Java 11 and Maven
sudo apt-get install openjdk-11-jre openjdk-11-jdk maven
  1. Create an Anaconda environment for Python 3.7
conda create -n covidex python=3.7
  1. Activate the Anaconda environment
conda activate covidex
  1. Install Python dependencies from inside api/
cd api
pip install -r api/requirements.txt

  1. Setup index and environment variables

    • Build Anserini indices for your dataset. We provide instructions for setting up Covidex with both CORD-19 and the ACL Anthology. Instructions to add support for new datasets is found under docs/adding-datasets.md

    • Set up environment variables by copying over the defaults from api/.env.sample into a new api/.env file, and modifying as needed. This requires setting the correct index and schema locations, CUDA devices, and enabling/disabling various services (highlighting, related search, neural ranking, etc.). Set DEVELOPMENT=False for production deployments.

UI Client

  1. Install Node.js 14+ and Yarn.

  2. Install dependencies from inside /client

yarn install

Local Deployment

Serve the UI from inside /client. The client will be running at localhost:3000.

yarn start

Separately, run the API server from inside /api. The server wil be running at localhost:8000.

uvicorn app.main:app --reload --port=8000

Production deployment

We provide a script under scripts/deploy-prod.sh to start the API server and serve the UI build files. This assumes the environment is set up correctly and api/.env contains DEVELOPMENT=False.

Start the server (deploys to port 8000 by default):

sh scripts/deploy-prod.sh

Optional: set the environment variable PORT to use a different port:

PORT=8080 sh scripts/deploy-prod.sh

Route port 80 to 8000 (or whatever port we deploy to). By default, the deployment script will use 8000.

sudo iptables -t nat -A PREROUTING -p tcp --dport 80 -j REDIRECT --to-port 8000

If we're having trouble accessing the service, check that there aren't any conflicting rules:

sudo iptables -t nat -L -n -v

If there are conflicting rules, we should delete them:

sudo iptables -t nat -D PREROUTING -p tcp --dport 80 -j REDIRECT --to-port UNWANTED_PORT

Log files are available under api/logs. New files are created daily based on UTC time. All filenames have the date appended, except for the current one, which will be named search.log or related.log.

Testing

Run all API tests:

TESTING=true pytest api

How do I cite this work?

@inproceedings{zhang2020covidex,
  title = "Covidex: Neural Ranking Models and Keyword Search Infrastructure for the {COVID}-19 Open Research Dataset",
  author = "Zhang, Edwin  and
    Gupta, Nikhil  and
    Tang, Raphael  and
    Han, Xiao  and
    Pradeep, Ronak  and
    Lu, Kuang  and
    Zhang, Yue  and
    Nogueira, Rodrigo  and
    Cho, Kyunghyun  and
    Fang, Hui  and
    Lin, Jimmy",
  booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
  month = nov,
  year = "2020",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/2020.sdp-1.5",
  doi = "10.18653/v1/2020.sdp-1.5",
  pages = "31--41",
}