How It Works

How It Works

Technical Architecture of the RAG Recommendation System

The Technical Implementation:

  1. Data Preparation: Upload the IMDB-top1000 dataset to GitHub for easy access

  2. Vector Database Setup: Create a Qdrant vector database cluster to store embedded movie descriptions

  3. Embedding Generation: Process movie descriptions through OpenAI to create semantic vector embeddings

  4. Chatbot Configuration: Set up an AI agent with a chat interface that calls the workflow

  5. Recommendation Engine: Create a workflow that processes user requests, separates positive and negative examples, and queries Qdrant's Recommendation API

  6. Results Delivery: Return only the top-3 most relevant recommendations based on the vector similarity search