This AI System Finds Amazon's Top Products on AUTOPILOT (No-Code n8n Tutorial)
By AI Workshop
TechnologyAIBusiness
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Key Concepts:
- No-Code Automation: Building automated workflows without writing code.
- Web Scraping: Extracting data from websites.
- Bright Data: A web data platform used for proxy and web scraping services.
- NADN: A no-code automation platform.
- OpenAI Node: Using OpenAI's models (like GPT-4) for text summarization and data extraction.
- Google Sheets Integration: Outputting scraped data to a Google Sheet.
- Data Extraction Schema: Defining the structure of data to be extracted by AI.
- HTTP Request Node: Sending requests to external APIs (like Bright Data).
- Split Item Node: Splitting a list of items into individual data rows.
1. Introduction
- The video demonstrates how to automate the process of identifying top-selling products on Amazon using AI and no-code tools.
- The goal is to create a system that automatically sends daily updates on top-selling products in specific niches (e.g., home improvement, electronics) to the user's email or Google Sheet.
- The solution is built using NADN, a no-code platform, making it accessible to users without programming experience.
2. Overview of the Automated Workflow
- The workflow involves scraping Amazon product pages, extracting relevant data, summarizing it using AI, and outputting the results to a Google Sheet and/or a Slack channel.
- The process is designed to be fully automated, running daily without manual intervention once set up.
3. Setting Up the NADN Workflow
- Creating a NADN Account: Users are directed to sign up for a free 14-day trial of NADN via a link in the video description.
- Importing a Blueprint (JSON File): The video demonstrates how to import a pre-built workflow blueprint (JSON file) into NADN to quickly set up the automation. This blueprint contains all the necessary nodes and configurations.
- The blueprint is available to members of the creator's community.
- Manual vs. Scheduled Trigger: The video uses a manual trigger ("When clicking a test workflow") for demonstration purposes. For production use, a scheduled trigger is recommended to automate the process daily.
4. Web Scraping with Bright Data
- Bright Data Account Setup: Users are instructed to create a Bright Data account to access web scraping tools. A free trial is available.
- Creating a Web Unlocker Zone: Within Bright Data, a "Web Unlocker API" zone is created. This zone allows the user to scrape websites without being blocked.
- The zone requires a name (lowercase) and generates an API token.
- Set Node Configuration: A "Set" node in NADN is used to define variables:
zone
: Stores the name of the Bright Data Web Unlocker zone.URL
: Stores the Amazon URL to be scraped (e.g., the URL for "Bestsellers in Home & Kitchen").
- HTTP Request Node Configuration: An "HTTP Request" node is configured to send a POST request to the Bright Data API:
- Method: POST
- URL:
api.brightdata.com/request
- Authentication: Header with "Authorization" set to "Bearer [API Token]"
- Body:
zone
: The Bright Data zone name.URL
: The Amazon URL to scrape.format
: Set to "raw".
5. Data Extraction and Summarization with OpenAI
- Code Node for HTML Sampling: A "Code" node is used to extract a sample of HTML from the scraped data. This is done to reduce the amount of data sent to the OpenAI node, improving efficiency.
- The code (provided in the video description or community) uses JavaScript to grab a partial HTML structure.
- Information Extractor Node (OpenAI): An "Information Extractor" node is used to extract structured data from the HTML sample using OpenAI's models.
- Model: GPT-4 (or similar) is used for its ability to understand and extract data from unstructured text.
- Schema Definition: A schema is defined to specify the data fields to extract (e.g., title, stars, total ratings, offer, product URL, image URL). The schema is provided in the video description or community.
- Split Item Node: A "Split Item" node is used to split the output from the OpenAI node into individual items (rows), making it easier to process and output to a Google Sheet.
6. Outputting Data to Google Sheets
- Google Sheets Node Configuration: A "Google Sheets" node is configured to append or update rows in a Google Sheet.
- Credentials: Google Sheets credentials are set up to allow NADN to access the sheet.
- Resource: "Sheet within document"
- Operation: "Append or update row from list"
- Document ID: The ID of the Google Sheet.
- Sheet Name: The name of the sheet within the document (e.g., "Sheet1").
- Column to Match On: A column to use for matching rows (e.g., "ranking").
- Column Mapping: Data fields from the "Split Item" node are mapped to the corresponding columns in the Google Sheet (e.g., title to title, stars to stars).
- Google Sheet Template: A Google Sheet template is provided to ensure the correct column names and structure for the data.
7. Optional: Slack Integration for Summarized Updates
- Message a Model Node (OpenAI): A "Message a Model" node is used to generate a summarized update of the top-selling products using OpenAI.
- Model: A smaller model like "nano" can be used for faster processing.
- Prompt: A prompt is defined to instruct the AI to summarize the top 10 products.
- Slack Node: A "Slack" node is used to send the summarized update to a Slack channel or user.
8. Example and Customization
- The video demonstrates how to change the Amazon URL to scrape different product categories (e.g., clothing).
- The process is the same: update the URL in the "Set" node, and the workflow will automatically scrape and extract data for the new category.
9. Conclusion
- The video provides a step-by-step guide to automating the process of identifying top-selling products on Amazon using no-code tools and AI.
- The solution can be customized to track different product categories and output data to Google Sheets and/or Slack.
- The automated workflow saves time and effort by eliminating the need to manually monitor Amazon product pages.
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