This n8n workflow is designed to automate the process of extracting data from PDFs and updating records in Airtable based on real-time webhook events. It enables dynamic, user-defined prompts for data extraction and automates the update process, making it highly useful for scenarios where PDF data needs to be regularly processed and kept in sync with a database.

The process begins with a webhook trigger, which captures updates or changes in Airtable such as new fields or rows. The workflow fetches the schema of the relevant Airtable base and listens for specific events, like row updates or new fields being created, through Airtable webhooks.

When a relevant event occurs, the workflow retrieves the affected row, downloads the associated PDF file, and extracts its content using the ‘Extract from File’ node. Dynamic prompts are generated for the language model (such as OpenAI’s GPT) based on the schema descriptions, guiding the AI to extract specific data points from the PDF.

The extracted information is then used to update the respective Airtable record, ensuring the database remains current with the latest data from the PDFs. The workflow also filters for valid data, loops through multiple records for batch updates, and handles both single and multiple field updates in Airtable.

This automation is ideal for use cases like processing uploaded PDFs, extracting specific details, and maintaining synchronized records in Airtable without manual intervention, thus saving time and reducing errors.

Node Count	>20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatOpenAi, airtable, code, extractFromFile, filter, httpRequest, manualTrigger, noOp, set, splitInBatches, stickyNote, switch, webhook