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https://github.com/piyp791/playlist2vec
This repository contains code for playlist2vec.com, a website built to demo the vector search model described in our paper, "Representation, Exploration, & Recommendation of Playlists".
https://github.com/piyp791/playlist2vec
fastapi full-text-search nginx nodejs sequence-to-sequence-autoencoder vector-search
Last synced: 7 days ago
JSON representation
This repository contains code for playlist2vec.com, a website built to demo the vector search model described in our paper, "Representation, Exploration, & Recommendation of Playlists".
- Host: GitHub
- URL: https://github.com/piyp791/playlist2vec
- Owner: piyp791
- License: apache-2.0
- Created: 2024-12-05T11:21:39.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-28T15:59:51.000Z (8 days ago)
- Last Synced: 2024-12-28T16:27:27.647Z (8 days ago)
- Topics: fastapi, full-text-search, nginx, nodejs, sequence-to-sequence-autoencoder, vector-search
- Language: CSS
- Homepage: https://playlist2vec.com/
- Size: 2.13 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## ABOUT
This repository contains code for [playlist2vec.com](https://playlist2vec.com/), a website built to demo the vector search model described in our paper, "Representation, Exploration, & Recommendation of Playlists".
## FEATURES
- [x] Intuitive search interface and Spotify integration
- [x] Microservices architecture for seamless scalability.
- [x] Docker containers for a streamlined deployment.
- [x] Utilization of low-cost libraries ([SQLite](https://www.sqlite.org/) and [Usearch Vector Search](https://github.com/unum-cloud/usearch)) to minimize app footprint.
- [x] MMAP-based vector search enabling the application to operate efficiently on budget-friendly machines such as Raspberry Pi.
- [x] NGINX configuration for a robust traffic handling.
- [x] Rate limiting implemented to safeguard against DDoS attacks.
- [x] NGINX caching to optimize server resource usage.
- [ ] Auto-scaling capabilities using Kubernetes.
## INSTALLATION
*Note: This setup has been tested on Ubuntu 22.04 for both x86_64 and aarch64 architectures.*
### 1. Install Docker
1. Follow the instructions to install Docker from this link: [Install Docker On Ubuntu](https://docs.docker.com/engine/install/ubuntu/).
2. Make sure to complete the post-installation steps outlined [here](https://docs.docker.com/engine/install/linux-postinstall/).
### 2. Install Nginx
Install Nginx by following the guide available at: [How to Install Nginx on Ubuntu 22.04](https://www.digitalocean.com/community/tutorials/how-to-install-nginx-on-ubuntu-22-04).
### 3. Install NodeJS
You can install Node.js (v20.18.0) by referring to this tutorial: [How to install Node.js on Ubuntu 22.04](https://www.digitalocean.com/community/tutorials/how-to-install-node-js-on-ubuntu-22-04).
### 4. Download Code
Run the following command to clone the repository:
```
git clone https://github.com/piyp791/playlist2vec.git
```
### 5. Navigate To Project
Navigate to the project directory by running:
```
cd playlist2vec
```
### 6. Nginx Setup
1. Copy the `nginx/nginx.conf` file to `/etc/nginx/`.
2. Copy the `nginx/site_config` file to `/etc/nginx/sites-available/`.
3. Create a symbolic link from `sites-available` to `sites-enabled` with the following command:
```
sudo ln -s /etc/nginx/sites-available/ /etc/nginx/sites-enabled/
```
4. Remove the default configuration file by executing:
```
sudo rm /etc/nginx/sites-enabled/default
```
5. Create a cache directory for NGINX:
```
sudo mkdir /var/cache/nginx
```
6. Verify the configuration is correct by running:
```
sudo nginx -t
```
7. Restart NGINX with the command:
```
sudo systemctl restart nginx
```
### 7. Install the Application
From within the project directory, execute the build script:
```
./build.sh.
```
This will create an HTTP version of the website, which can be integrated with a service like a [Cloudflare tunnel](https://www.cloudflare.com/en-ca/products/tunnel/) for an HTTPS frontend.
### Note
By default, the build script configures the application to use the mini version of the corpus, which contains 377,000 items. In this configuration, the search index for the mini corpus is loaded into memory. In contrast, the search index built with full corpus is accessed in memory-mapped mode.
To change this setting, you can modify the `.env` file. Set the `IS_MINI` variable to `false` to use the full version of the corpus:
```
IS_MINI=false
```
### RUN IN DEVELOPMENT MODE (WITHOUT NGINX)
### Setting up Search Service:
```
cd search-service
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn src.server:app --port 3001
```
### Setting up Autocomplete Service:
```
cd autocomplete-service
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn src.server:app --port 3002
```
### Setting up Web-Server:
```
cd web-server
npm install
npm run start-dev-mode
```
### RUN TESTS
```
cd web-server
npm run test-dev
```
## CITATION
```
Papreja, P., Venkateswara, H., Panchanathan, S. (2020). Representation, Exploration and Recommendation of Playlists. In: Cellier, P., Driessens, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham. https://doi.org/10.1007/978-3-030-43887-6_50
```