Building RAG chatbot for movie recommendations with Qdrant and Open AI

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations.

How it works - [a video with the full design process](https://www.youtube.com/watch?v=O5mT8M7rqQQ) - Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; - Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; - Create a workflow which calls [Qdrant's Recommendation API](https://qdrant.tech/articles/new-recommendation-api/) to retrieve top-3 recommendations of movies based on your positive and negative examples.

Set Up Steps - You'll need to create a free tier [Qdrant Cluster](https://cloud.qdrant.io/) (Qdrant can also be used locally; it's open-sourced) and set up API credentials - You'll OpenAI credentials - You'll need GitHub credentials & to upload the [IMDB Kaggle dataset](https://www.kaggle.com/datasets/omarhanyy/imdb-top-1000) to your GitHub.