AI Product Metadata Generator for Inventory
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Automate the generation of structured product metadata in multiple languages by analyzing images and enriching data using AI.
About This Workflow
This workflow automates the process of enriching product inventory data. It starts by fetching product entries from Airtable that have images and are not yet AI-processed. An AI vision model then analyzes the product image to extract initial details. Following this, an AI agent uses this information along with internet search tools (reverse image search via SerpApi and web scraping via Firecrawl) to gather more comprehensive product attributes like title, model, material, color, and condition. Finally, the enriched data is updated back into Airtable, marking the entry as AI-processed.
Key Features
- Airtable Integration: Reads product data and images from Airtable.
- AI Image Analysis: Utilizes OpenAI's vision capabilities to describe product images.
- AI Agent for Research: Employs an AI agent to intelligently search the internet for missing product details.
- Tool Utilization: Integrates with
serpApifor reverse image searches andFirecrawlfor web scraping. - Structured Output: Parses AI output into a structured format for consistency.
- Data Enrichment: Updates Airtable with generated metadata, including title, description, model, material, color, and condition.
- Workflow Automation: Automates a time-consuming data entry and enrichment process.
How To Use
- Setup Airtable: Ensure you have an Airtable base and table with a field for product images (e.g.,
Image) and an AI status field (e.g.,AI_status). TheGet Applicable Rowsnode is configured to fetch rows whereImageis not empty andAI_statusisFALSE(). - Configure Credentials: Set up and provide credentials for OpenAI (
OpenAi account), Airtable (Airtable Personal Access Token account), and SerpAPI (SerpAPI account). Ensure the Firecrawl API key is also set in theFirecrawl APIcredential. - Airtable Configuration: In the
Get Applicable Rowsnode, verify that your Airtable Base and Table IDs are correctly set. - OpenAI Vision: The
Analyse Imagenode uses the image URL from Airtable to get an initial description and identify attributes. You can customize the prompt to focus on specific details. - AI Agent Configuration: The
Object Identifier Agentnode is the core of the intelligence. It receives the image URL and initial description, then uses thereverse_image_searchandwebpage_url_scraper_toolto find missing information.- Tool Setup: Ensure the
reverse_image_searchtool (Reverse Image Search APInode) andwebpage_url_scraper_tool(Firecrawl Scrape APInode) are correctly configured with their respective API endpoints and parameters. - Tool Routing: The
Actions Routernode directs the agent's requests to the appropriate tool based on therouteparameter.
- Tool Setup: Ensure the
- Data Parsing: The
Structured Output Parsernode is intended to help structure the AI's output, though the currentObject Identifier Agentdirectly outputs structured data. - Airtable Update: The
Enrich Product Rowsnode takes the output from the agent and updates the corresponding row in Airtable with the generatedTitle,Model,Material,Color,Condition, andDescription. It also setsAI_statustoTRUE(). - Manual Trigger: The workflow is currently triggered manually by the
When clicking "Test workflow"node. This can be replaced with other triggers like webhooks or scheduled intervals.
Apps Used
Workflow JSON
{
"id": "8d34e601-9a9c-41f9-8c43-4ad5ec1d828a",
"name": "AI Product Metadata Generator for Inventory",
"nodes": 9,
"category": "Data Enrichment",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 8d34e601-9a9c...
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