Quotes

Quotes database with vector search, implemented with Elasticsearch, FastAPI and React.

What is this?

This repository contains a small application that demonstrates how easy it is to set up a vector database using Elasticsearch and the Elasticsearch-DSL client for Python.

The application includes a FastAPI back end and a React front end written in TypeScript. The quotes are stored in an Elasticsearch index, and for each quote an embedding is generated using the all-MiniLM-L6-v2 Sentence Transformers model.

The dataset used by this application has about 37,000 famous quotes, each with their author and tags. The data originates from a Kaggle dataset that appears to have been generated from quotes that were scraped from the Goodreads popular quotes page.

Requirements

Please make sure you have the following installed:

• Python 3.10 or newer
• Node.js 18 or newer
• Docker

How To Run

Follow these steps to install this demo on your computer:

Clone this repository

Run the following command to install a copy of this project on your computer:

git clone https://github.com/miguelgrinberg/quotes
cd quotes

Install the Node and Python dependencies

Run the following command to set up the JavaScript and Python environment and install all the dependencies:

npm install

Start a development Elasticsearch container

Run this command to start a single-node Elasticsearch instance:

docker run -p 9200:9200 -d --name elasticsearch \
  -e "discovery.type=single-node" \
  -e "xpack.security.enabled=false" \
  -e "xpack.security.http.ssl.enabled=false" \
  -e "xpack.license.self_generated.type=basic" \
  -v "./backend/data:/usr/share/elasticsearch/data" \
  docker.elastic.co/elasticsearch/elasticsearch:8.15.0

Create the quotes database

Run this command in your terminal:

npm run ingest

Note that depending on your computer speed and wether you have a GPU or not this may take a few minutes.

Start the back end

Run this command in your terminal:

npm run backend

Start the front end

Open a second terminal window and run this command:

npm start

You can now navigate to http://localhost:3000 on your web browser to access the application.