AI-Powered Weekly Meal Recommendation Workflow

This n8n workflow automates the process of fetching, processing, and recommending weekly meal options from HelloFresh, enhancing meal planning through AI integration. It begins with the manual trigger to start the workflow, then fetches this week’s menu data from HelloFresh’s website using HTTP requests. The workflow extracts relevant recipe details and metadata, including ingredients, categories, and tags, and prepares these documents for storage. It leverages advanced AI and vector search technologies, such as Qdrant and Mistral Cloud, to create a recommendation engine that analyzes user preferences. The data is stored both in a Qdrant vector store for similarity-based recommendations and in an SQLite database for full document retrieval. This allows for intelligent AI chat interactions, where users can specify preferences and receive personalized recipe suggestions based on AI-powered recommendations. The process includes handling API rate limits, fetching embeddings, and executing complex queries to produce tailored meal suggestions. Practical use cases include meal planning services, personalized diet recommendations, or culinary AI assistants able to suggest recipes based on available ingredients, dietary restrictions, or flavor preferences.