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 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 o

Help me set up the n8n workflow "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" (https://n8n.io/workflows/2440). It uses: GitHub, HTTP Request, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Simple Memory, Token Splitter, Call n8n Workflow Tool, Default Data Loader, Qdrant Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.