⚒️ Building RAG chatbot for movie recommendations with Qdrant and Open AI
⚡ 35,862 views · ⚒️ Engineering
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.
🔗 Nodes Used
GitHub, HTTP Request, Execute Workflow Trigger, AI Agent, Embeddings OpenAI, OpenAI Chat Model
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File