Recipe Recommendation Engine with Bright Data MCP & OpenAI GPT-4 Mini

### Notice Community nodes can only be installed on self-hosted instances of n8n. ### Who this is for Recipe Recommendation Engine with Bright Data MCP & OpenAI is a powerful automated workflow that combines Bright Data's MCP for scraping trending or regional recipe data with OpenAI GPT-4 mini to generate personalized recipe recommendations. This automated workflow is designed for: **Food Bloggers & Culinary Creators**: Who want to automate the extraction and curation of recipes from across the web to generate content, compile cookbooks, or publish newsletters. **Nutritionists & Health Coaches**: Who need structured recipe data to analyze ingredients, calories, and nutrition for personalized meal planning or dietary tracking. **AI/ML Engineers & Data Scientists**: Building models that classify cuisines, predict recipes from ingredients, or generate dynamic meal suggestions using clean, structured datasets. **Grocery & Meal Kit Platforms**: Who aim to extract recipes to power recommendation engines, ingredient lists, or personalized meal plans. **Recipe Aggregator Startups**: Looking to scale recipe data collection, filtering, and standardization across diverse cooking websites with minimal human intervention. **Developers Integrating Cooking Features**: Into apps or digital assistants that offer recipe recommendations, step-by-step cooking instructions, or nutritional insights. ### What problem is this workflow solving? This workflow solves: - Automated recipe data extraction from any public URL - AI-driven structured data extraction - Scalable looped crawling and processing - Real-time notifications and data persistence ### What this workflow does **1. Set Recipe Extract URL** - Configure the recipe website URL in the input node - Set your Bright Data zone name and authentication **2. Paginated Data Extract** - Triggers a paginated extraction across multiple pages (recipe listing, index, or search pages) - Returns a list of recipe links for processing **3. Loop Over Items** - Loops through the array of recipe links - Each link is passed individually to the scraping engine **4. Bright Data MCP Client (Per Recipe)** - Scrapes each individual recipe page using scrape_as_html - Smartly bypasses common anti-bot protections via Bright Data Web Unlocker **5. Structured Recipe Data Extract (via OpenAI GPT-4 mini)** - Converts raw HTML to clean text using an LLM preprocessing node - Uses OpenAI GPT-4 mini to extract structured data **6. Webhook Notification** - Pushes the structured recipe data to your configured webhook endpoint - Format: JSON payload, ideal for Slack, internal APIs, or dashboards **7. Save Response to Disk** - Saves the structured recipe JSON information to the local file system ### Pre-conditions 1. You need to have a [Bright Data](https://brightdata.com/) account and do the necessary setup as mentioned in the Setup section below. 2. You need to have an OpenAI Account. ### Setup - Sign up at [Bright Data](https://brightdata.com/). - Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. - In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication).  The Value field should be set with the **Bearer XXXXXXXXXXXXXX**. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker token. - In n8n, configure the OpenAI account credentials. - Make sure to set the fields as part of **Set the Recipe Extract URL**. Remember to set the webhook_url to send a webhook notification of recipe response. - Set the desired local path in the **Write the structured content to disk** node to save the recipe response. ### How to customize this workflow to your needs You can tailor the Recipe Recommendation Engine workflow to better fit your specific use case by modifying the following key components: **1. Input Fields Node** - Update the Recipe URL to target specific cuisine sites or recipe types (e.g., vegan, keto, regional dishes). **2. LLM Configuration** - Swap out the OpenAI GPT-4 mini model with another provider (like Google Gemini) if you prefer. - Modify the structured data prompt to extract custom fields that you wish. **3. Webhook Notification** - Configure the Webhook Notification node to point to your preferred integration (e.g., Slack, Discord, internal APIs). **4. Storage Destination** Change the **Save to Disk** node to store the structured recipe data in: - A cloud bucket (S3, GCS, Azure Blob, etc.) - A database (MongoDB, PostgreSQL, Firestore) - Google Sheets or Airtable for spreadsheet-style access.