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 Recommendati

Help me set up the n8n workflow "Recipe Recommendations with Qdrant and Mistral" (https://n8n.io/workflows/2333). It uses: HTTP Request, Code, HTML, AI Agent, Recursive Character Text Splitter, Call n8n Workflow Tool, Default Data Loader, Embeddings Mistral Cloud, Mistral Cloud Chat Model, Qdrant Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.