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HelloFresh Weekly Menu Intelligent Recommendation Workflow

This workflow automatically scrapes HelloFresh's weekly menu information, extracts recipe details, and builds a personalized recommendation engine that uses vector search technology to accurately match users' taste preferences. After integrating an AI chat agent, users can interactively receive intelligent recipe recommendations, enhancing the intelligence and precision of menu recommendations. This is applicable in various scenarios such as food e-commerce, healthy diet management, and catering businesses.

Workflow Diagram
HelloFresh Weekly Menu Intelligent Recommendation Workflow Workflow diagram

Workflow Name

HelloFresh Weekly Menu Intelligent Recommendation Workflow

Key Features and Highlights

This workflow automatically scrapes the weekly menu information from the official HelloFresh website, extracts detailed recipe data, and builds a personalized recipe recommendation engine based on vector search technology (Qdrant). It enables interactive recipe recommendations through an AI chat agent. By integrating web data scraping, natural language processing, vectorized storage and search, and intelligent recommendation technologies, it significantly enhances the intelligence and accuracy of menu recommendations.

Core Problems Addressed

  • Automatically acquiring and updating HelloFresh’s weekly menu data to eliminate manual maintenance.
  • Extracting key information from a vast amount of recipe data to construct structured and easily searchable recipe documents.
  • Utilizing vector search technology to precisely match user taste preferences, overcoming the limitations of traditional keyword search in meeting multidimensional personalized needs.
  • Providing a user-friendly recommendation consultation experience via an AI chat agent that caters to diverse dietary preferences and restrictions.

Application Scenarios

  • Menu recommendation systems for food e-commerce and meal subscription platforms.
  • Healthy diet management applications that intelligently recommend recipes based on user preferences.
  • Internal recipe management and intelligent recommendation tools for catering enterprises.
  • Any scenario requiring personalized recommendations based on regularly updated content.

Main Process Steps

  1. Scrape Weekly Menu: Retrieve the HelloFresh menu page corresponding to the current year and week number via HTTP requests.
  2. Extract Menu Data: Parse HTML and JSON data to extract the top 10 recipes along with their detailed metadata.
  3. Scrape Recipe Details: Access each recipe’s dedicated webpage to extract descriptions, ingredients, utensils, preparation steps, and nutritional information.
  4. Document Preparation and Merging: Organize and merge recipe information into structured text documents.
  5. Vectorization: Convert recipe texts into vectors using Mistral Cloud’s embedding model.
  6. Store Vectors and Data: Save vectors into the Qdrant vector database and store complete recipe data in an SQLite database.
  7. Build AI Recommendation Agent: Deploy a chat agent based on Mistral Cloud’s large language model, integrated with Qdrant’s recommendation API, to deliver intelligent recommendations based on users’ positive and negative preferences.
  8. User Interaction: Users input their likes and dietary restrictions through a chat interface; the AI agent queries the recommendation engine and returns personalized recipe suggestions.

Involved Systems and Services

  • HelloFresh Official Website Menu Data (scraped via HTTP requests)
  • Mistral Cloud (provides text vectorization and conversational language model services)
  • Qdrant (vector database for storing and retrieving recipe vectors)
  • SQLite Database (stores complete recipe data)
  • n8n Nodes (including HTTP requests, code execution, HTML parsing, text splitting and merging, wait nodes, etc.)
  • AI Agent (an intelligent recommendation chatbot built on the LangChain framework)

Target Users and Value

  • Technical teams in the food and beverage industry aiming to build automated menu recommendation systems.
  • Developers of smart kitchen assistants or healthy diet management tools.
  • Data scientists and AI engineers exploring recommendation solutions combining vector databases and language models.
  • Product managers and technical staff seeking to enhance user personalization through AI-driven intelligent recommendations of complex content.

This workflow automates the entire process from data acquisition and information extraction to intelligent vectorization and AI-driven personalized recommendation. It greatly improves the efficiency and user experience of HelloFresh menu recommendations and serves as a benchmark solution for digital transformation and intelligent service upgrades in the food and beverage industry.