How to Build Your Own (DIY) Document Parsing Agent from Scratch

2025-12-05 · Source: LlamaIndex · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

Llama Index presented a webinar on building custom AI document parsing agents, addressing the limitations of traditional OCR and raw LLM feeding for unstructured enterprise documents. Pierre, an AI engineering lead at Llama Index, demonstrated three approaches: a heuristic-based traditional parser, a GenAI-based parser using screenshot-to-markdown conversion, and an agentic document parsing system. The traditional method involves libraries like PDFPlumber and PyMuPDF to handle text blocks, reading order, multicolumn content, heading detection, and table extraction, requiring approximately 200 lines of code. The GenAI approach converts PDF pages to base64-encoded images and prompts a Vision Language Model (VLM) like Anthropic's Claude for markdown conversion, offering simplicity but facing issues like token limits and repetition. The agentic system, built with Llama Index Workflow, addresses these VLM limitations by orchestrating events and steps to ensure complete and accurate transcription, managing failure modes like repetition and incomplete output.

Key takeaway

For AI Engineers building robust document processing solutions, consider adopting an agentic parsing framework. While direct VLM-based parsing is simpler, it often suffers from token limits and output inconsistencies. Implementing an agentic system allows you to systematically detect and correct these common failure modes, ensuring higher accuracy and completeness, especially for diverse and complex document types, despite the increased initial development effort and potential cost per page.

Key insights

Agentic systems enhance GenAI document parsing by addressing VLM limitations like token limits and repetition.

Principles

• PDF parsing is complex due to its instruction-based nature.
• GenAI models excel at screenshot-to-markdown conversion.
• Agentic orchestration improves VLM parsing reliability.

Method

Build an agentic parser by defining event-driven steps to handle VLM failures like max token limits, repetition loops, and incomplete outputs, using a framework like Llama Index Workflow.

In practice

• Use PDFPlumber for table detection in heuristic parsing.
• Convert PDFs to base64 images for VLM input.
• Implement retry loops for VLM parsing errors.

Topics

• Document Parsing Agents
• Heuristic Parsing
• GenAI Document Processing
• Agentic AI Workflows
• Vision-Language Models

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LlamaIndex.