Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/m0wer/rssfilter

Filter your current RSS feeds with AI customized recommendations.
https://github.com/m0wer/rssfilter

ai llm recommendation-system rss

Last synced: about 2 months ago
JSON representation

Filter your current RSS feeds with AI customized recommendations.

Host: GitHub
URL: https://github.com/m0wer/rssfilter
Owner: m0wer
License: mit
Created: 2024-03-02T09:52:07.000Z (10 months ago)
Default Branch: master
Last Pushed: 2024-09-28T09:52:19.000Z (3 months ago)
Last Synced: 2024-10-11T18:14:46.250Z (2 months ago)
Topics: ai, llm, recommendation-system, rss
Language: Python
Homepage: https://rssfilter.sgn.space/
Size: 197 KB
Stars: 20
Watchers: 1
Forks: 1
Open Issues: 12
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

[![build](https://github.com/m0wer/rssfilter/actions/workflows/docker.yaml/badge.svg)](https://github.com/m0wer/rssfilter/actions/workflows/docker.yaml)
[![pre-commit](https://github.com/m0wer/rssfilter/actions/workflows/pre-commit.yaml/badge.svg)](https://github.com/m0wer/rssfilter/actions/workflows/pre-commit.yaml)
[![build](https://github.com/m0wer/rssfilter/actions/workflows/test.yaml/badge.svg)](https://github.com/m0wer/rssfilter/actions/workflows/test.yaml)
[![build](https://github.com/m0wer/rssfilter/actions/workflows/monitor.yaml/badge.svg)](https://github.com/m0wer/rssfilter/actions/workflows/monitor.yaml)

# RSS filter

RSS feeds recommendation system based on user read articles. Replaces the feed
URLs with the backend URL and uses the backend to filter out unwanted items
and track user read articles. Uses LLM embeddings and machine learning to
recommend similar articles.

This is a simple RSS filter that filters out unwanted items from an RSS feed.
It is written in Python and uses the `feedparser` library to parse the feed.

It works by tracking the users read articles, computing their embeddings,
clusyering them, and then recommending similar articles from the feed.
It also includes random articles from the feed to allow for discovery of new
topics. This starts working only after a user has read a few articles (10 by
default).

Embedding models allow for a new era of recommendation systems, where a large
user base is not required, since recommendations are based on the content of
the articles, not on other users behavior.

## Self-hosting

You can self-host this project by running the following command:

```shell
cp .env.example .env
docker-compose -f docker-compose.yml up
```

If you don't have or want to use the GPU, first run:

```shell
sed -i 's/^.*devices:.*$/#&/' docker-compose.yaml
```

Test it with:

```shell
curl -X 'GET' \
'http://localhost/api/v1/feed/1/https%3A%2F%2Fnews.ycombinator.com%2Frss' \
-H 'accept: application/json'
```

To use the self-hosted frontend, you should change `apiBaseUrl` in
`frontend/static/app.js` to match the backend URL.

## Development

### Backend

#### Dependencies

To install the required libraries, run the following command in the backend or frontend:

```shell
pip install -r requirements.txt
```

#### Running the backend

```shell
cd backend
python -m uvicorn app.main:app --reload --log-level debug --port 8000
```

## Contributing

There are some hooks in `.pre-commit-config.yaml` to ensure:
- `pip-compile` is up-to-date with added dependencies
- code is well formatted and linted with `ruff` and `black`.

You can install these hooks with `pre-commit install` and run them on demand by `pre-commit run --all-files`.

## Contact

If you have any questions, feel free to contact me at
m0wer at autistici dot org.
```