This is a movie app that uses 680 movies between 1990 and 2024 from TMDB stored in a vector database. The app uses a simple cosine similarity to find the most similar movies (recommendation) to a given movie.

Make sure you have docker installed on your machine!

1. Clone the repo

git clone https://github.com/Vicba/movies-recommendation.git

2. Run docker-compose up in the root directory

docker-compose up

3. Populate the database with the movies.

curl -X GET http://localhost:5000/populate

4. Open http://localhost:3000 in your browser
5. Browse around!

• Nextjs (typescript, Tailwindcss)
• Flask
• Weaviate
• Docker
• Huggingface API

The embedding model used is sentence-transformers/paraphrase-MiniLM-L6-v2 from huggingface. It has 384 dimensions.

If you want to use something else, you can change it in the /api/build_knowledge_base/embed.py file. Run the python script to generate the csv with embeddings csv in datasets folder.

cd api/build_knowledge_base
python embed.py

• Learned how to use Weaviate
• Refresh my knowledge in nextjs & docker
• Usign huggingface API
• Project went super smooth with the research and pre-defined scope