Recipe recommendations with Qdrant and Mistral

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, vectorised 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 setup 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.

Requirements

• Qdrant vector store instance to save the recipes
• Mistral.ai account for embeddings and LLM agent

Customising 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.