Back to Catalog

Extract attendance from Google Drive images to sheets with VLM run AI & Gmail alerts

ShahrearShahrear
14 views
2/3/2026
Official Page

🧾 Attendance Extraction & Notification Pipeline (Google Drive + VLM Run + Google Sheets + Gmail)

⚙️ What This Workflow Does

This workflow automates daily attendance tracking by analyzing uploaded attendance images, extracting participant names via VLM Run’s Execute Agent, appending the structured data into Google Sheets, and emailing a formatted attendance summary through Gmail.

🧩 Requirements

  • A Google Drive account with a designated folder for attendance image uploads.

  • A VLM Run API account and your Execute Agent URL or API credentials.

  • A Gmail account connected to n8n for sending notification emails.

  • An n8n instance with the following credentials configured: Google Drive, Google Sheets, Gmail, VLM Run (HTTP API Credential)

⚡Quick Setup

  1. Install the verified VLM Run node by searching for VLM Run in the node list, then click Install. Once installed, you can start using it in your workflows.

  2. Add VLM Run API credentials for image parsing.

  3. Link your Google Drive, Google Sheets and Gmail accounts in the credentials section.

  4. In the “Google Drive Trigger” node, select the folder where attendance images will be uploaded.

  5. In the “Append Row” node, connect your Google Sheet and map columns manually (e.g., Date, Total, Names…).

  6. Add VLM Run execute agent endpoint.

  7. Upload an image (whiteboard attendance photo or scanned sheet) to your Drive folder.

  8. Wait for the automation to process and check your Google Sheet for results.

  9. After each extraction and logging step, the Gmail Node sends an automated summary email. Email includes:

    📅 Date of attendance 👥 Total participants detected 🧍 List of extracted names

⚙️ How It Works

  1. Monitor List Uploads – Watches a Google Drive folder for new attendance images (e.g., whiteboard snapshots, scanned sheets).

  2. Download List – Downloads each new image automatically for AI processing.

  3. VLM Run for Extraction sends the image to VLM Run Execute Agent, which uses an AI model to detect and extract attendee names from the image.

  4. Receive Attendance Data – The Webhook node (check-attendance) receives structured JSON data from VLM Run in the format:

    {
      "majorDimension": "ROWS",
      "values": [
        ["2025-10-03", "6", "Camila Torres Rivera", "Mellissa Richmond", "Captioner Javier", "Siobhan", "Catherine Soler", "Anisah Anif"]
      ]
    }
    
    
  5. The Google Sheets Node appends the structured attendance data to the selected sheet, maintaining a daily log for future reference.

  6. The Gmail Node sends an automatic email summarizing attendance.

💡Why Use This Workflow

🔄 Fully Automated: No manual data entry required.

🧠 AI-Powered Extraction: Uses VLM Run to read and parse images with handwritten or typed text.

📊 Centralized Logging: Attendance data neatly organized in Google Sheets for future analysis.

📬 Instant Notification: Keeps stakeholders informed automatically after each session.

Scalable: Works with multiple folders, daily batches, or parallel sessions.

🛠️ How to Customize

You can tailor this workflow to match your organization’s needs:

| Area | Customization Options | | ------------------------ | ---------------------------------------------------------------------------------------------------------- | | Drive Folder | Point to a different upload folder for each department or class. | | Google Sheet Mapping | Add more columns (e.g., “Class Name,” “Supervisor”) and map them in the Append Row node. | | Email Template | Modify the Gmail node’s subject and body to include custom formatting or logos. | | Trigger Schedule | Replace Google Drive Trigger with a Cron Node if you prefer scheduled checks instead of live watching. | | Data Validation | Add a Function Node to filter duplicates or incorrect entries before appending to Sheets. | | Notification Options | Send alerts via Slack, Telegram, or Microsoft Teams instead of Gmail if desired. |

⚠️ Community Node Disclaimer

This workflow uses community nodes (VLM Run) that may need additional permissions and custom setup.

Extract Attendance from Google Drive Images to Sheets with VLM (Run AI) and Gmail Alerts

This n8n workflow automates the process of extracting attendance data from images uploaded to Google Drive, processing them with a Visual Language Model (VLM) via the Run AI API, and sending email notifications with the results.

What it does

This workflow streamlines the attendance tracking process by:

  1. Monitoring Google Drive: It listens for new files (images) uploaded to a specified Google Drive folder.
  2. Downloading Images: Upon detecting a new image, it downloads the file from Google Drive.
  3. Sending to VLM (Run AI): It sends the downloaded image to a Visual Language Model (VLM) API (presumably Run AI, based on the directory name) for processing and attendance data extraction.
  4. Sending Email Alerts: It sends an email notification via Gmail, likely containing the extracted attendance data or a summary of the processing.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Drive Account: Configured with credentials in n8n to monitor a specific folder and download files.
  • Gmail Account: Configured with credentials in n8n to send email notifications.
  • Run AI API Key/Endpoint: Access to a Visual Language Model (VLM) service (e.g., Run AI) and its corresponding API endpoint and authentication details. This is inferred from the HTTP Request node and the directory name.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Google Drive Trigger: Set up your Google Drive credentials and specify the folder ID you want to monitor for new image uploads.
    • Google Drive (Download): Ensure your Google Drive credentials are set up for downloading files.
    • HTTP Request: Configure this node with the API endpoint, authentication (e.g., API key in headers or body), and any other required parameters for your VLM (Run AI) service.
    • Gmail: Set up your Gmail credentials and configure the recipient email address, subject, and body for the alert emails.
  3. Activate the Workflow: Once all credentials and settings are configured, activate the workflow.

The workflow will now automatically trigger whenever a new image file is uploaded to the specified Google Drive folder, process it with your VLM, and send an email alert.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942