What this Workflow Does
This workflow detects when there is a new or updated PDF file in Airtable. It reads the PDF’s text with n8n and uses AI to find specific data like names or addresses. Then it fills those data fields back into Airtable automatically. This saves time and removes errors that happen with manual typing.
The workflow triggers on any change in Airtable rows or columns. It grabs the PDF file, converts it to text, and runs AI prompts tied to field descriptions to get exact values. Finally, it updates the Airtable record with those values without manual work.
Who Should Use this Workflow
This is for people who manage Airtable bases where many PDFs are added. They want quick and correct data from those PDFs entered into the table without typing.
If your team spends hours copying data from PDFs into Airtable, this workflow saves that effort. It works best when every field in your table has a clear description explaining what AI should find.
Tools and Services Used
- n8n: Automation platform that links all steps.
- Airtable: Where the PDFs and data fields live.
- OpenAI Large Language Models: Extracts info from PDF text.
- LangChain nodes in n8n: Run AI prompts per field.
These tools let the workflow watch Airtable, get PDFs, read texts, ask AI to find details, and write results back.
Inputs, Processing, and Outputs
Inputs
- Airtable records with attached PDF files.
- Webhook events notifying updates in rows or fields.
- Field descriptions in Airtable that act as AI prompts.
Processing Steps
- The webhook node listens for record or field changes.
- A Code node reads the event to find what changed.
- A Switch node separates events for row updates or field updates.
- On row updates, fetch only updated rows with PDFs. On field updates, fetch all rows with PDFs for column update.
- Download PDFs from Airtable record URLs.
- Extract text from PDFs using the Extract From File nodes.
- Use a Code node to pull field prompts from table schema.
- Send extracted text and prompts to OpenAI via LangChain AI nodes.
- AI returns field values based on prompts and PDF content.
- Set nodes format results as key-value pairs for Airtable fields.
- Update the corresponding Airtable records with new field values.
Output
Updated Airtable records with data fields automatically filled from PDF content.
Beginner Step-by-Step: How to Use This Workflow in n8n
Step 1: Import the Workflow
- Download the workflow file using the Download button on this page.
- Open the n8n editor and use “Import from File” to load the workflow.
Step 2: Add Credentials and API Keys
- Enter Airtable Personal Access Token with webhook and read/write rights.
- Add OpenAI API Key in the credentials for LangChain nodes.
Step 3: Configure IDs and Fields
- Check the “Set Airtable Vars” node and update the inputField variable if your PDF attachment column is named differently.
- Verify baseId, tableId fields match your Airtable base and table.
Step 4: Test the Workflow
- Run the manual trigger or update a record in Airtable with a PDF attached.
- Watch the workflow execution to ensure field data populates automatically.
Step 5: Activate the Workflow
- Set the workflow to active in n8n for automatic runs on Airtable changes.
If managing your own server, see self-host n8n for setup tips.
Handling Edge Cases and Failures
Empty or Missing PDF Attachments
If a record has no PDF in the configured attachment field, the workflow skips processing that record.
Make sure files are valid PDFs in the right column for data extraction.
OpenAI API Issues
Timeouts or errors may happen if the API key is wrong or requests exceed limits.
Check API keys and usage, and consider raising timeout durations in LangChain nodes.
Webhook Problems
If webhook triggers do not fire, re-register webhooks using the dedicated Airtable Webhook nodes.
Webhooks expire after 7+ inactive days, so renew them regularly.
Customization Ideas
- Change the inputField variable in the “Set Airtable Vars” node to match your attachment field.
- Edit the prompt text in the OpenAI LangChain nodes to improve extraction instructions or output format.
- Adjust batch sizes in Loop Over Items nodes for better API and speed control.
- Add caching by storing extracted PDF text to avoid repeated downloads or processing of the same file.
Summary of Results
✓ Automated extraction of data from PDFs in Airtable
✓ Saved time by removing manual typing
✓ Lowered errors from manual entry
✓ Kept Airtable records automatically up-to-date with exact extracted values
✓ Easy integration of AI to read PDFs per field prompts
✓ Supported row and field update events for flexibility
✓ Scalable batch processing for many records