AI-powered CV filtering with Llama3.1 LLM, Google Drive & Sheets
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
Nodes used (13)
- Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Merge
n8n-nodes-base.merge - Aggregate
n8n-nodes-base.aggregate - Ollama Model
@n8n/n8n-nodes-langchain.lmOllama - Auto-fixing Output Parser
@n8n/n8n-nodes-langchain.outputParserAutofixing - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Code
n8n-nodes-base.code - Google Drive
n8n-nodes-base.googleDrive - Google Sheets
n8n-nodes-base.googleSheets - Extract from File
n8n-nodes-base.extractFromFile - Webhook
n8n-nodes-base.webhook - Respond to Webhook
n8n-nodes-base.respondToWebhook - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm
What it does
This visual workflow represents an AI-powered automated CV filtering system created using tools like n8n, Google Drive, Google Sheets, and Ollama (LLM) ⚙️ Key Features - 📂 Google Drive Integration – Automatically searches and downloads CVs (PDF/DOCX/PPTX) from a shared folder. - 📋 Criteria Matching – Reads and applies filtering rules from a Google Sheet. - 🧠 LLM-Based Analysis – Uses a Large Language Model (Ollama) to assess and interpret CV content. - 🧪 Smart Parsing – Includes structured and auto-fixing output parsers to ensure data accuracy. - 📊 Automated Results Output – Writes matching candidates and analysis to a Google Sheet. - 🔁 Loop and Aggregate Logic – Handles multiple CVs with iterative processing and aggregation. - 🚀 No-Code Automation with n8n – Fully visual, modifiable without programming. 🛠️ How It Works Trigger: Workflow is initiated via a Webhook (from a UI “Start Workflow” button). CV Search: Searches for CV files in a designated Google Drive folder. Loop Over Files: Each file is downloaded. Text is extracted (from PDFs or other formats). Criteria Input: Matching rules are fetched from a predefined Google Sheet. Merge & Aggregate: Combines file text and criteria for unified processing. LLM Processing: Text + criteria are sent to the Basic LLM Chain. Utilizes Ollama Model for advanced language understanding. Structured or auto-fixing output parsers enhance reliability. Custom Code Execution: Optionally enriches or reformats the data. Output: Results are appended to a shared Google Sheet (the output sheet).