This n8n workflow automates the process of resume screening by capturing incoming resumes via a webhook, processing and embedding the resume content for intelligent analysis, and storing the data in a vector database for future retrieval. The workflow also incorporates a Retrieval-Augmented Generation (RAG) approach with OpenAI’s language model to evaluate resumes, generate insights, and log outcomes into Google Sheets. In case of errors, Slack alerts notify users promptly.
The process begins with a webhook triggering upon resume submission, followed by splitting the resume text into manageable chunks. These chunks are then embedded using Cohere’s embedding model and stored in a Weaviate vector database for efficient semantic search. When a new resume arrives, related previous resumes are retrieved through vector similarity, enabling the AI to compare and assess the new applicant effectively. The chat model, powered by OpenAI, interacts with the RAG agent to analyze the resume, generate screening results, and update a Google Sheet with the screening status. If any errors occur during processing, a Slack notification is dispatched.
This workflow is ideal for HR teams or recruitment systems aiming to automate resume sorting, ranking, and initial assessment with AI-driven insights, saving time and increasing accuracy.
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