AI Agent for Dynamic PDF Data Extraction into Airtable

Unlock Automated Data Entry with this AI Agent for Airtable & PDFs

This n8n workflow acts as a powerful AI Agent that connects to your Airtable base, monitors for changes (new PDFs, updated rows/fields), and uses OpenAI's language models (via Langchain) to extract specific information from PDF documents. The clever part? You define what data to extract by simply writing prompts in your Airtable field descriptions. It then automatically populates your Airtable with the extracted data, saving you countless hours of manual data entry.

This agent listens for Airtable events such as row.updated, field.created, and field.updated. When triggered, it fetches the relevant PDF, extracts its text, and then uses an LLM with dynamically constructed prompts (from your Airtable field descriptions) to identify and retrieve the required information. The extracted data is then written back to the appropriate Airtable cells.

Key Features & Benefits

• Dynamic Prompting via Airtable: Define extraction tasks directly in Airtable field descriptions – no need to modify the workflow for different data points.
• AI-Powered PDF Extraction: Leverages OpenAI (or compatible LLMs configured via Langchain) to intelligently understand and pull data from PDF text content.
• Reactive Automation: Triggers automatically when PDFs are added/updated in Airtable records or when relevant fields (and their descriptive prompts) are created or updated.
• Handles Various Event Types: Differentiates between row updates (targeted extraction for a single record) and field updates/creations (batch extraction across multiple records if a prompt/field is new or changed).
• Efficient Processing: Includes logic to only process valid inputs (e.g., rows with PDFs) and optimize updates by checking for already populated fields.
• Flexible & Extensible: Built on n8n, allowing for easy customization and integration with other tools in your stack.
• Automates Tedious Data Entry: Frees up your team from manual PDF data transcription, allowing focus on higher-value tasks.

• An n8n instance (Cloud or self-hosted).
• Airtable account and a Personal Access Token with permissions: data.records:read, data.records:write, schema.bases:read, webhook:manage.
• OpenAI API Key (or credentials for another LLM compatible with the Langchain nodes) with access to a suitable model (e.g., gpt-3.5-turbo, gpt-4).
• The n8n 'Airtable Webhook' trigger URL must be publicly accessible for Airtable to send notifications.

1. Download the n8n workflow JSON file.
2. Import the workflow into your n8n instance.
3. Configure Airtable Credentials: In all Airtable nodes (e.g., 'Get Table Schema', 'Fetch Records', 'Update Row'), select or create your Airtable Personal Access Token credentials.
4. Configure OpenAI Credentials: In the 'OpenAI Chat Model' nodes, select or create your OpenAI API credentials. You can adapt these nodes if using a different LLM provider compatible with Langchain.
5. **Initial Airtable Webhook Setup (One-Time Mini-Flow): a. Locate the 'When clicking ‘Test workflow’' manual trigger and the 'Set Airtable Vars' node. b. In the 'Set Airtable Vars' node, replace placeholder values: * appId: Your Airtable Base ID. * tableId: Your Airtable Table ID. * notificationUrl: The production URL of the 'Airtable Webhook' trigger node in this workflow (copy this after you activate the main workflow or get it from the webhook node settings). * inputField: The name of your Airtable file attachment field (default is 'File'). c. Temporarily activate the main workflow to ensure the 'Airtable Webhook' URL is live, or use its test URL if appropriate for initial setup. d. Manually execute the 'When clicking ‘Test workflow’' trigger once. This runs a sub-flow that calls the Airtable API to create the necessary webhooks for your base and table, listening for record and field changes. Note: Ensure these webhooks remain active in your Airtable base settings.
6. Airtable Table Configuration for Dynamic Prompts: a. In your target Airtable table, ensure you have an Attachment field named 'File' (or as specified in inputField in step 5b). This is where you'll upload PDFs for processing. b. For each field you want the AI to populate with extracted data: Go to that field's configuration in Airtable and write a clear instruction (this is your prompt) in its 'Description' box. For example, for an Airtable field named 'Invoice Total', its description might be 'Extract the final total amount from the invoice.'. The AI will use this description to find the data in the PDF.
7. Review & Customize (Optional): a. Inspect the 'Parse Event' Code node to understand how it interprets different Airtable events. b. Review the prompts within the 'Generate Field Value' and 'Generate Field Value1' (Langchain LLM Chain) nodes. You can adjust the main text prompt or the system message for more specific behavior or output formatting.
8. Ensure the main workflow (starting with 'Airtable Webhook') is activated to process live Airtable events.