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.
You can self-host this project by running the following command:
cp .env.example .env
docker-compose -f docker-compose-cpu.yml upThere's also the normal docker-compose.yaml file, that uses NVIDIA drivers
to run the backend with GPU support.
Test it with:
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.
To install the required libraries, run the following command in the backend or frontend:
pip install -r requirements.txtcd backend
python -m uvicorn app.main:app --reload --log-level debug --port 8000There are some hooks in .pre-commit-config.yaml to ensure:
pip-compileis up-to-date with added dependencies- code is well formatted and linted with
ruffandblack.
You can install these hooks with pre-commit install and run them on demand by pre-commit run --all-files.
If you have any questions, feel free to contact me at m0wer at autistici dot org.