AI-Powered Movie Recommender Using Qdrant and OpenAI

This n8n workflow creates an intelligent movie recommendation system that leverages AI, vector search, and chat capabilities to suggest movies based on user preferences. The process begins with uploading a CSV file of top IMDB movies from GitHub, extracting movies’ descriptions, and creating embeddings with OpenAI. These embeddings are stored in a Qdrant vector database for efficient similarity search. When a user interacts via a chat trigger, the system uses GPT-4 to understand the request and retrieves top movie recommendations based on similarity scores from Qdrant. The workflow also supports querying the database for detailed metadata of recommended movies, making it a practical solution for building advanced, interactive movie recommendation bots or apps.