Resume data extraction and storage in Supabase from email attachments

Description

What Problem Does This Solve? 🛠️

This workflow automates the process of extracting key information from resumes received as email attachments and storing that data in a structured format within a Supabase database. It eliminates the manual effort of reviewing each resume, identifying relevant details, and entering them into a database. This streamlines the hiring process, making it faster and more efficient for recruiters and HR professionals.

Target audience: Recruiters, HR departments, and talent acquisition teams.

What Does It Do? 🌟

• Monitors a designated email inbox for new messages with resume attachments.
• Extracts key information such as name, contact details, education, work experience, and skills from the attached resumes.
• Cleans and formats the extracted data.
• Stores the processed data securely in a Supabase database.

Key Features 📋

• Automatic email monitoring for resume attachments.
• Intelligent data extraction from various resume formats (e.g., PDF, DOC, DOCX).
• Customizable data fields to capture specific information.
• Seamless integration with Supabase for data storage.
• Uses OpenRouter to streamline API key management for services such as AI-powered parsing.

Setup Instructions

Prerequisites ⚙️

• n8n Instance: Self-hosted or cloud instance of n8n.
• Email Account: Gmail account with Gmail API access for receiving resumes.
• Supabase Account: A Supabase project with a database/table ready to store extracted resume data. You'll need the Supabase URL and API key.
• OpenRouter Account: For managing AI model API keys centrally when using LLM-based resume parsing.

Installation Steps 📦

1. Import the Workflow:

• Copy the exported workflow JSON.
• Import it into your n8n instance via “Import from File” or “Import from URL”.

2. Configure Credentials:

• In n8n > Credentials, add credentials for:

  • Email account (Gmail API): Provide Client ID and Client Secret from the Google Cloud Platform.
  • Supabase: Provide the Supabase URL and the anon public API key.
  • OpenRouter (Optional): Add your OpenRouter API key for use with any AI-powered resume parsing nodes.

• Assign these credentials to their respective nodes:

  • Gmail Trigger → Email credentials.
  • Supabase Insert → Supabase credentials.
  • AI Parsing Node → OpenRouter credentials.

3. Set Up Supabase Table:

Create a table in Supabase with columns such as: name, email, phone, education, experience, skills, received_date, etc.

Make sure the field names align with the structure used in your workflow.

4. Customize Nodes:

• Parsing Node(s): Modify the workflow to use an OpenAI model directly for field extraction, replacing the Basic LLM Chain node that utilizes OpenRouter.

5. Test the Workflow:

• Send a test email with a resume attachment.
• Check n8n's execution log to confirm the workflow triggered, parsed the data, and inserted it into Supabase.
• Verify data integrity in your Supabase table.

How It Works

High-Level Workflow 🔍

1. Email Monitoring: Triggered when a new email with an attachment is received (via Gmail API).
2. Attachment Check: Verifies the email contains at least one attachment.
3. Prepare Data: Extracts the attachment and prepares it for analysis.
4. Data Extraction: Uses OpenRouter-powered LLM (if configured) to extract structured information from the resume.
5. Data Storage: The structured information is saved into the Supabase database.

Node Names and Actions (Example)

• Gmail Trigger: Triggers when a new email is received.
• IF: Checks whether the received email includes any attachments.
• Get Attachments: Retrieves attachments from the triggering email.
• Prepare Data: Prepares the attachment content for processing.
• Basic LLM Chain: Uses an AI model via OpenRouter to extract relevant resume data and returns it as structured fields.
• Supabase-Insert: Inserts the structured resume data into your Supabase database.