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# Quotes
Quotes database example, which demonstrates the Elasticsearch integration with
Pydantic models. This example features a React frontend and a FastAPI back end.
## What is this?
This directory contains a small application that demonstrates how easy it is
to set up a full-text and vector database using [Elasticsearch](https://www.elastic.co/elasticsearch),
while defining the data model with [Pydantic](https://docs.pydantic.dev/latest/).
The application includes a FastAPI back end and a React front end. It ingests a
dataset of famous quotes into in an Elasticsearch index, and for each quote it
generates an embedding using the
[all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
Sentence Transformers model.
The dataset has about 850 famous quotes, each with their author and tags. The
data is a subset of a
[Kaggle dataset](https://www.kaggle.com/datasets/akmittal/quotes-dataset) that
appears to have been generated from quotes that were scraped from the Goodreads
[popular quotes](https://www.goodreads.com/quotes) page.
## Requirements
Please make sure you have the following installed:
- Python 3.10 or newer
- Node.js 18 or newer
## 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:
```bash
git clone https://github.com/elastic/elasticsearch-py
cd examples/quotes
```
### Install the Node and Python dependencies
Run the following command to set up the JavaScript and Python environment and
install all the dependencies:
```bash
npm install
```
### Start a development Elasticsearch container
You can use [start-local](https://www.elastic.co/docs/deploy-manage/deploy/self-managed/local-development-installation-quickstart)
to start a small Elasticsearch instance.
Use this command to launch the instance (Docker and Docker Compose are required):
```bash
curl -fsSL https://elastic.co/start-local | sh
```
Once your Elasticsearch instead is deployed, create an environment variable called
`ELASTICSEARCH_URL`, making sure it includes the password generated by start-local.
Example:
```bash
export ELASTICSEARCH_URL=http://elastic:your-password-here@localhost:9200
```
### Create the quotes database
Run this command in your terminal:
```bash
npm run ingest
```
Note that the `ELASTICSEARCH_URL` variable must be defined in the terminal
session in which you run this command.
This task should take a minute or less. The GPU, if available, is used optimize
the generation of the embeddings.
### Start the back end
Run this command in your terminal:
```bash
npm run backend
```
Note that the `ELASTICSEARCH_URL` variable must be defined in the terminal
session in which you run this command.
### Start the front end
Open a second terminal window and run this command:
```bash
npm run dev
```
You can now navigate to `http://localhost:5173` on your web browser to access
the application.
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