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Baserow Dynamic PDF Data Extraction and Auto-Fill Workflow

This workflow automatically extracts and fills in the content of uploaded PDF files by listening for update events in the table. Utilizing AI technology, it generates dynamic extraction prompts based on field descriptions to ensure that data is accurately and efficiently entered into the table. It can automatically process PDF files, dynamically respond to field changes, and support both batch and single record processing, greatly simplifying the information entry process for unstructured documents and enhancing the efficiency of data management in enterprises.

Workflow Diagram
Baserow Dynamic PDF Data Extraction and Auto-Fill Workflow Workflow diagram

Workflow Name

Baserow Dynamic PDF Data Extraction and Auto-Fill Workflow

Key Features and Highlights

This workflow listens to row updates and field change events within Baserow tables to automatically extract content from uploaded PDF files. Leveraging field descriptions as dynamic prompts, it utilizes the OpenAI language model (LLM) to intelligently extract required data and updates the Baserow table in real time. Highlights include:

  • Supports highly customizable data extraction through dynamic prompts based on field descriptions.
  • Automatically recognizes and processes PDF files, combining AI to extract precise data.
  • Employs an event routing pattern to separately handle row updates and field creation/update events, optimizing processing efficiency.
  • Supports both batch and single-record iterative processing to ensure timely data updates.
  • Integrates flexibly with Baserow’s official API via n8n, compatible with both cloud and self-hosted deployments.

Core Problems Addressed

Manual data entry from unstructured documents such as PDFs is cumbersome and error-prone. This workflow solves:

  • Automated extraction of field-specified content from PDF files, eliminating manual input.
  • Dynamic adaptation to table structure changes by automatically applying new field extraction rules.
  • Precise control to update only necessary data, reducing redundant operations and improving efficiency.

Application Scenarios

  • Automated key information entry from PDFs such as financial reports, contracts, and invoices into databases.
  • Dynamic tables requiring frequent changes in data collection rules.
  • Enterprise-level office automation to reduce manual data organization.
  • Preparation of structured data prior to analysis.

Main Process Steps

  1. Listen to Baserow Webhook Events: Capture row updates, field creation, or field update events.
  2. Retrieve Table Structure and Field Descriptions: Pull current table field information and descriptions via API to serve as dynamic extraction prompts.
  3. Filter Valid Data Rows and Fields: Identify rows containing uploaded PDF files and fields with descriptions.
  4. Download and Parse PDF Files: Access file URLs and use the ExtractFromFile node to parse PDF content.
  5. Dynamic Data Extraction via OpenAI LLM: Generate prompts based on field descriptions; AI automatically extracts corresponding data from PDF text.
  6. Update Baserow Table Data: Write extracted results back to relevant fields of the corresponding rows using PATCH requests.
  7. Iteratively Process All Affected Rows or Fields: Ensure complete and consistent data updates.

Involved Systems and Services

  • Baserow: Serves as both the data source and update target, providing database tables and APIs.
  • n8n: Automation workflow engine handling event listening, data processing, and API calls.
  • OpenAI Chat Model (LLM): Natural language processing to parse PDF content and generate structured data.
  • Webhook: Receives event pushes from Baserow.
  • HTTP Request: Calls Baserow API and downloads files.
  • Extract From File: Node for extracting content from PDF files.

Target Users and Value

  • Enterprises and teams needing automated ingestion of unstructured document information into databases.
  • Data managers handling complex data collection workflows with dynamically changing fields.
  • Automation solution designers and developers aiming to improve data processing efficiency.
  • Users of Baserow as a database platform seeking to integrate intelligent data extraction capabilities.

This workflow significantly simplifies the process of populating table data from PDF documents. By combining dynamic prompts with AI technology, it empowers users to achieve intelligent, flexible, and efficient data management.