Extract and log data to Airtable with Google Gemini, ILovePDF, and Google Drive
Who is this workflow template for?
This workflow template is perfect for freelancers, small business owners, accounting teams, or anyone responsible for managing and recording invoices regularly. If you deal with multiple invoices and spend considerable time manually entering invoice data into a database, this automation will significantly simplify your daily operations and reduce potential errors.
What this workflow does
The workflow automates the entire invoice logging process. It continuously monitors a designated Google Drive folder every minute for new PDF invoice uploads. Once a new invoice is detected, it is automatically converted from PDF to an image format using the ILovePDF API. After conversion, Google's Gemini AI analyzes the image, intelligently extracting essential details such as vendor name, item description, invoice amount, invoice date, payment date, and bank reference numbers. Finally, this structured data is automatically recorded in an Airtable database (or optionally in a Google Sheet), ensuring organized, accessible records.
Detailed Workflow Explanation
-
Step 1: Invoice Detection
- Monitors Google Drive for newly uploaded PDF invoices.
-
Step 2: PDF to Image Conversion
- Converts PDFs into images using ILovePDF.
-
Step 3: Data Extraction via Gemini AI
-
Uses Gemini AI to analyze the invoice image.
-
Extracts data such as Vendor, Description, Amount, Invoice Date, Paid Date, and Bank Reference.
-
Provides clear descriptions even when original invoice descriptions are vague or missing by analyzing vendor context.
-
-
Step 4: Structured Data Storage
- Automatically sends extracted data to Airtable or Google Sheets.
-
Step 5: File Management
- Moves processed PDF files into a separate "Done" folder to clearly differentiate between processed and unprocessed invoices.
Step-by-Step Setup Instructions
-
Set Up Google Drive:
-
Log in to Google Drive and create two folders:
-
One named Invoices (for incoming PDF files)
-
One named Processed (for processed files)
-
-
-
Obtain API Credentials:
-
ILovePDF API:
-
Sign up at ILovePDF Developers.
-
Retrieve your API key from your account dashboard.
-
-
Google Gemini AI API:
- Register at Google AI and generate an API key.
-
-
Airtable Database Preparation:
-
Create an Airtable base with the following columns:
-
Vendor (Text)
-
Description (Text)
-
Amount (Number or Text)
-
Invoice Date (Date)
-
Paid Date (Date)
-
Bank Reference (Text)
-
-
-
Import and Configure Workflow in n8n:
-
Import the provided workflow JSON file into your n8n instance.
-
Connect your Google Drive, ILovePDF, Google Gemini AI, and Airtable accounts by entering your credentials in their respective nodes.
-
-
Adjust Workflow Settings:
-
In the Google Drive nodes, ensure your newly created Invoices and Processed folders are correctly selected.
-
Update the ILovePDF public key in the appropriate HTTP Request node.
-
Customize the Gemini AI prompt to refine or expand data extraction according to your specific needs.
-
-
Testing Your Setup:
-
Upload a sample PDF invoice into the Invoices folder.
-
Execute the workflow by clicking Test Workflow in n8n and verify if data extraction and Airtable logging operate correctly.
-
Airtable Column Specifications
Ensure your Airtable includes the following structure:
-
Vendor: Single Line Text
-
Description: Single Line Text
-
Amount: Currency or Single Line Text
-
Invoice Date: Date (formatted as YYYY-MM-DD)
-
Paid Date: Date (formatted as YYYY-MM-DD)
-
Bank Reference: Single Line Text
How to Customize the Workflow
-
System Prompt: Adjust the AI instructions by modifying the prompt text to focus on additional or fewer invoice details.
-
Structured Output Parser: Modify the JSON schema in the parser node to match the structure and data points your project specifically requires:
By following these instructions, you’ll have a fully automated, reliable system for handling and logging invoice data, significantly enhancing your productivity.
Extract and Log Data to Airtable with Google Gemini, iLovePDF, and Google Drive
This n8n workflow automates the process of extracting information from PDF documents uploaded to Google Drive, summarizing the content using Google Gemini, and logging the extracted data into Airtable. It also handles PDF compression and OCR if needed.
What it does
This workflow performs the following steps:
- Triggers on new Google Drive files: It listens for new files uploaded to a specified Google Drive folder.
- Filters for PDF files: It checks if the uploaded file is a PDF document.
- Compresses PDF (Optional): If the PDF is larger than 1MB, it compresses it using iLovePDF to optimize for further processing.
- Performs OCR (Optional): If the PDF is not text-searchable, it performs OCR using iLovePDF to convert it into a searchable PDF.
- Extracts content from PDF: It extracts the text content from the (potentially compressed and OCR'd) PDF.
- Summarizes with Google Gemini: It uses a Google Gemini Chat Model via an AI Agent to summarize the extracted text and structure it into a JSON object with specific fields (e.g.,
title,summary,tags). - Parses structured output: A Structured Output Parser extracts the
title,summary, andtagsfrom the AI Agent's response. - Logs to Airtable: It creates a new record in a specified Airtable base and table, populating it with the extracted
title,summary, andtags.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Account: A running instance of n8n.
- Google Drive Account: Configured with a specific folder for new PDF uploads.
- Google Drive Credential: Connected to your n8n instance.
- Airtable Account: With a base and table set up to receive the extracted data (e.g., columns for "Title", "Summary", "Tags").
- Airtable Credential: Connected to your n8n instance.
- iLovePDF Account: For PDF compression and OCR (if used).
- iLovePDF Credential: Connected to your n8n instance.
- Google Gemini API Key: For the Google Gemini Chat Model.
- Google Gemini Credential: Connected to your n8n instance.
Setup/Usage
- Import the workflow: Download the JSON provided and import it into your n8n instance.
- Configure Credentials:
- Set up your Google Drive Credential for the "Google Drive Trigger" node.
- Set up your iLovePDF Credential for the "iLovePDF Compress" and "iLovePDF OCR" nodes.
- Set up your Google Gemini Credential for the "Google Gemini Chat Model" node.
- Set up your Airtable Credential for the "Airtable" node.
- Configure Google Drive Trigger:
- Specify the Folder ID in your Google Drive where new PDF files will be uploaded.
- Choose the "File Uploaded" trigger event.
- Configure iLovePDF Nodes (if used):
- Adjust the "Compress" node's settings if you want to change the compression threshold or quality.
- Adjust the "OCR" node's settings if you want to change OCR language or other options.
- Configure AI Agent and Structured Output Parser:
- Review the prompt in the "AI Agent" node to ensure it extracts the desired information from your PDFs. The current prompt is designed to extract
title,summary, andtags. - Ensure the "Structured Output Parser" node is configured to correctly parse the JSON output from the AI Agent based on your prompt.
- Review the prompt in the "AI Agent" node to ensure it extracts the desired information from your PDFs. The current prompt is designed to extract
- Configure Airtable Node:
- Select your Base and Table in Airtable.
- Map the output fields from the "Structured Output Parser" (e.g.,
title,summary,tags) to the corresponding columns in your Airtable table.
- Activate the workflow: Once all configurations are complete, activate the workflow.
Now, whenever a new PDF file is uploaded to your specified Google Drive folder, the workflow will automatically process it, summarize its content, and log the details into Airtable.
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.
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
Automate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte