AI-Powered Resume Screening with Weaviate and OpenAI
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Automate resume screening by using a Webhook Trigger to receive resumes, processing them with Text Splitter, Embeddings, and Weaviate for vector storage, and then utilizing a RAG Agent with an OpenAI Chat Model for intelligent analysis and response.
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About This Workflow
Overview
This n8n workflow automates the process of screening resumes using AI and vector databases. It leverages a combination of nodes to ingest, process, store, and analyze resume data.
Problem Solved: Manually screening resumes is time-consuming and prone to human error. This workflow significantly reduces the manual effort by using AI to process and analyze resumes, allowing for faster identification of suitable candidates.
Logic:
- Webhook Trigger: Receives new resume data via an HTTP POST request.
- Text Splitter: Breaks down the resume content into smaller, manageable chunks for AI processing.
- Embeddings (Cohere): Generates vector embeddings for each text chunk, enabling semantic understanding.
- Weaviate Insert: Stores these embeddings in a Weaviate vector database, creating a searchable index of resume data.
- Weaviate Query: Retrieves relevant resume data from Weaviate when needed for analysis.
- Vector Tool: Acts as an interface to interact with the Weaviate vector store.
- Window Memory: Maintains conversation history for the RAG Agent.
- Chat Model (OpenAI): Powers the AI's understanding and response generation.
- RAG Agent: Orchestrates the retrieval-augmented generation process, combining the chat model with retrieved context from Weaviate to provide informed responses and analysis.
- Append Sheet: Logs the screening status or results to a Google Sheet for tracking.
- Slack Alert: Notifies the team via Slack in case of any errors during the workflow execution.
Key Features
- Real-time resume ingestion via Webhook.
- Advanced text processing with chunking and embedding generation.
- Efficient vector storage and retrieval using Weaviate.
- Intelligent resume analysis and candidate matching powered by RAG and OpenAI.
- Automated logging to Google Sheets for easy tracking.
- Error alerting via Slack for immediate issue resolution.
How To Use
- Configure the Webhook Trigger node with your desired path (e.g.,
/resume-screening). - Set up the Embeddings node with your Cohere API credentials and choose the appropriate model.
- Configure the Weaviate Insert and Weaviate Query nodes with your Weaviate instance details and set the
indexNametoresume_screening. - Provide your OpenAI API credentials to the Chat Model node.
- Customize the system message in the RAG Agent to define the AI's role in resume screening.
- Set up the Append Sheet node with your Google Sheets credentials, specifying the spreadsheet ID and sheet name ('Log'). Ensure you have a 'Status' column.
- Configure the Slack Alert node with your Slack API credentials to receive error notifications.
- Connect the nodes according to the provided workflow structure.
- Activate the workflow and send resume data to the webhook URL.
Apps Used
Workflow JSON
{
"id": "78b868f7-b67e-4c1f-a634-55603178814a",
"name": "AI-Powered Resume Screening with Weaviate and OpenAI",
"nodes": 0,
"category": "AI & Machine Learning",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 78b868f7-b67e...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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