Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI

Description

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
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 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 (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 to your GitHub.