Automated Resume Information Extractor
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Effortlessly extract crucial candidate information from resumes sent via email. This workflow automates the parsing of CVs, leveraging AI to identify and structure key data, and storing it for immediate use.
About This Workflow
This n8n workflow transforms manual resume review into an efficient, automated process. It begins by monitoring your Gmail inbox for incoming resumes. Upon detection, it downloads attachments, extracts text from PDF documents, and then utilizes an advanced Large Language Model (LLM) to meticulously identify and categorize essential candidate details such as Name, Email, Telephone, Experience, Skills, and Education. The extracted data is then structured and sent to your Supabase database for seamless integration with your HR systems or candidate tracking platforms. This solution drastically reduces processing time and ensures consistent data capture, allowing your HR team to focus on what matters most: talent acquisition.
Key Features
- Real-time Email Monitoring: Automatically detects and processes resumes sent to your designated Gmail account.
- AI-Powered Data Extraction: Employs LangChain and LLMs to accurately extract Name, Email, Phone, Experience, Skills, and Education.
- Structured Data Output: Organizes extracted information into a usable format for easy analysis and storage.
- Seamless Database Integration: Pushes candidate data directly into your Supabase database for efficient management.
- Attachment Handling: Configured to download and process resume attachments directly.
How To Use
- Gmail Trigger Setup: Configure the 'Gmail Trigger' node to connect to your Gmail account and set it to poll for new emails containing resume attachments.
- Attachment Extraction: Ensure the 'Extract from File' node is correctly configured to target the resume attachment (e.g., 'attachment_0') and extract its text content.
- LLM Configuration: Set up the 'Basic LLM Chain' and 'OpenRouter Chat Model' nodes. Define the prompt to instruct the AI on the specific fields to extract and choose your preferred LLM model.
- Structured Output: Configure the 'Structured Output Parser' with a JSON schema example that matches the desired output format for Name, Telephone, Email, Education, Skill, and Experience.
- Data Structuring: Utilize the 'Edit Fields' nodes ('Edit Fields', 'Edit Fields1', 'Edit Fields2', 'Edit Fields3') to prepare the extracted arrays for specific fields (Experience, Skills, Education) and prepare them for database insertion.
- API Integration: Set up the 'HTTP Request' node to connect to your Supabase API endpoint (replace
[supabase URL]and[key]) and send the extracted Name, Telephone, and Email data. - Database Insertion: Configure the 'Supabase' and 'Supabase1', 'Supabase2' nodes to insert the extracted experience, skills, and education data into their respective tables, linking them via the CV ID obtained from the HTTP request.
- Splitting Array Data: Use the 'Split Out' nodes ('Split Out', 'Split Out1') to process each item within the Experience and Skill arrays individually for insertion into the database.
Apps Used
Workflow JSON
{
"id": "c4fd1162-c40d-47be-8ea5-31c358045815",
"name": "Automated Resume Information Extractor",
"nodes": 22,
"category": "Operations",
"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: c4fd1162-c40d...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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