AI-Powered Resume Screening with Weaviate and OpenAI

Name: AI-Powered Resume Screening with Weaviate and OpenAI
Rating: 5 (7 reviews)
Author: Free N8N

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AI & Machine LearningAIHR techLLMOpenAIRAGWeaviateautomationresume screening

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 indexName to resume_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

HR tech

LLM

OpenAI

RAG

Weaviate

automation

resume screening

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.

Statistics

Downloads0

Rating

7/5

Verification Info

Community Verified

This workflow has been verified by the community

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Source

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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.

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.

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 indexName to resume_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.

{ "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" }