Document AI: OCR, Markdown & Data Extraction for RAG

Document AI: OCR, Markdown & Data Extraction for RAG

Document AI is an MCP tool designed to bridge the gap between physical documents and structured digital data, making them readily usable for Retrieval Augmented Generation (RAG) systems. It leverages Optical Character Recognition (OCR) to convert scanned documents into machine-readable text, then processes this text to generate Markdown and extract specific data fields. This ensures that the information contained within your documents is not only accessible but also organized and ready for integration into AI workflows.

What it Does

This tool acts as a robust data pipeline for your physical documents. It takes image-based documents (like PDFs or scanned images) and applies OCR to extract raw text. Subsequently, it transforms this raw text into a structured Markdown format, preserving formatting where possible. Crucially, Document AI can be configured to identify and extract specific data fields, such as names, dates, invoice numbers, or any other predefined information. This extracted data, along with the Markdown representation, is then made available for consumption by RAG models and other AI applications.

Key Features

• OCR Integration: Converts image-based documents into editable text.
• Markdown Generation: Creates structured Markdown output from extracted text.
• Field Extraction: Configurable extraction of specific data points from documents.
• RAG-Ready Output: Provides data in formats suitable for AI and RAG systems.
• Source Code Available: Open-source implementation hosted on GitHub for transparency and customization.

Who it's For

Document AI is an essential tool for AI developers and data engineers working with document-heavy datasets. It is particularly beneficial for:

• Developers building RAG systems that need to ingest information from physical documents.
• Teams requiring automated data extraction from invoices, forms, contracts, or other business documents.
• Researchers and analysts who need to process and structure information from scanned archives.
• Anyone looking to transform unstructured document content into usable, machine-readable data for AI applications.