🔧 Recipe recommendations with Qdrant and Mistral
⚡ 7,054 views · 🔧 Miscellaneous
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Description
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.
🔗 Nodes Used
HTTP Request, Execute Workflow Trigger, AI Agent, Recursive Character Text Splitter, Call n8n Workflow Tool, Default Data Loader
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File