This is a semantic search service for movies based on a query. For example, "great fictional beautiful animals." should return a list of movies such as "Madagascar 3: Europe's Most Wanted", "Zoology" and so on.

This is based on the Vector Search provided by DataStax on its Cassandra database.

The plot of a movie was converted into a vector (embedding vector) and stored in the database which was customized with an index to calculate the nearest distance between vectors.

The converting service is provided by CO.HERE.

When a user inputs a query, the string is converted into a vector and used to find the closest search in the database.

The details of the conversion and lookup functions are provided here https://github.com/linhhlp/Machine-Learning-Applications/tree/main/Text-2-Vect-Vector-Search

The database used in this application is uploaded to Kaggle: https://www.kaggle.com/datasets/linhhlp/35k-movies-with-embedded-plots-to-vectors

To provide credentials to the services of CO.HERE AI and Cassandra by DataStax, the credentials must be stored in the cred.py file. There is a template file called cred-template.py that contains the examples of credentials. Rename this template file to cred.py after that.

With the provided Dockerfile, the container can be built with Docker.

$ docker image build -t "find_movies" .

$ docker run -d -p 80:80 find_movies

To investigate the app and the usage of users, I made a simple logging tool to save a basic data into Cassandra. If you do not want it, just remove it from app.py

The service is running on: https://find-movies.com/

The service was deployed on AWS Lambda using zappa. The process of deployment is very simple and straightforward with zappa. However, there are some issues with Zappa including do not upload zip files.

Follow this guide for more information: https://github.com/zappa/Zappa#deploying-to-a-domain-with-aws-certificate-manager