n8n AI Content Creation Data Management
Contributed by Sabrina Ramonov 🍄. From the community directory at agents.sabrina.dev, republished with permission and full credit.
This n8n workflow demonstrates creating a recipe recommendation chatbot using the Qdrant vector store recommendation API. Use this example to build recommendation features in your AI Agents for your users.
How it works
For our recipes, we'll use HelloFresh's weekly course and recipes for data. We'll scrape the website for this data. Each recipe is split, vectorized, and inserted into a Qdrant Collection using Mistral Embeddings. Additionally, the whole recipe is stored in a SQLite database for later retrieval.
Our AI Agent is set up to recommend recipes from our Qdrant vector store. However, instead of the default similarity search, we'll use the Recommendation API instead. Qdrant's Recommendation API allows you to provide a negative prompt; in our case, the user can specify recipes or ingredients to avoid.
The AI Agent is now able to suggest a recipe recommendation better suited for the user and increase customer satisfaction.
Customizing the workflow
This workflow can work for a variety of different audiences. Try different sets of data such as clothes, sports shoes, vehicles, or even holidays.
Tools used: Qdrant, Mistral, SQLite
Download the workflow template (JSON)
Want this running in your business? KOBA42 builds and operates automations like this one.