Automated PDF Segmentation via Table of Contents Using Gemini AI & Chunkrai

Overview

This workflow automates the segmentation of PDF documents based on their table of contents, leveraging Google Gemini AI and Chunkrai for intelligent parsing and extraction. It is designed to streamline document processing, making large PDFs more accessible and actionable.

Key Features

• AI-Powered Parsing: Utilizes Gemini AI to interpret and extract section headers and content structure from PDFs.
• Fallback Logic: Employs custom code nodes to handle cases where the table of contents is missing or unclear.
• Structured Output: Uses output parsers for clean, structured data ready for downstream processing.
• Manual & Automated Triggers: Can be executed manually or triggered by other workflows for flexible integration.

Benefits

• Efficiency: Reduces manual effort in document segmentation, saving significant time.
• Accuracy: AI-driven extraction ensures precise identification of document sections.
• Scalability: Handles large and complex PDFs, making it suitable for high-volume operations.

Use Cases

• Content Management: Streamline ingestion of reports, manuals, or research papers.
• Data Processing: Prepare segmented data for analytics or further automation.
• Integration: Easily connect with other workflows for end-to-end document automation.

Integrations & Automation

Key integrations include Google Gemini AI for language understanding and Chunkrai for document chunking, with robust error handling and fallback mechanisms to ensure reliability.