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, receiveddate, 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.