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