AI-Powered Resume Screening with Weaviate and OpenAI

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:

1. Webhook Trigger: Receives new resume data via an HTTP POST request.
2. Text Splitter: Breaks down the resume content into smaller, manageable chunks for AI processing.
3. Embeddings (Cohere): Generates vector embeddings for each text chunk, enabling semantic understanding.
4. Weaviate Insert: Stores these embeddings in a Weaviate vector database, creating a searchable index of resume data.
5. Weaviate Query: Retrieves relevant resume data from Weaviate when needed for analysis.
6. Vector Tool: Acts as an interface to interact with the Weaviate vector store.
7. Window Memory: Maintains conversation history for the RAG Agent.
8. Chat Model (OpenAI): Powers the AI's understanding and response generation.
9. RAG Agent: Orchestrates the retrieval-augmented generation process, combining the chat model with retrieved context from Weaviate to provide informed responses and analysis.
10. Append Sheet: Logs the screening status or results to a Google Sheet for tracking.
11. Slack Alert: Notifies the team via Slack in case of any errors during the workflow execution.