Our infrastructure includes a robust PDF processing pipeline tailored for Retrieval-Augmented Generation (RAG) applications. Python services handle the complex task of splitting PDFs into individual pages, ensuring each page is processed efficiently. The system generates messages for each page, enabling parallelized OCR text extraction across multiple instances. Once extracted, another service analyzes the text using spatial grouping and semantic data extraction, leveraging the open-source stevenic/agentm-py library.

The pipeline integrates seamlessly with OpenAI models via API and supports alternatives like Llama3, ensuring flexibility and performance.

Regardless of the PDF’s length, the system consistently produces a structured data format, handling documents with hundreds of pages with precision and speed.

This is not meant for quick POCs.. This is for scaling the real applications with disaster recovery plans and encryption.