AI Markdown Engine: Universal Document Prep for LLMs & RAG
An open-source ingestion pipeline that converts, cleans, and chunks heterogeneous documents into LLM-optimized Markdown. Attainable and auditable results designed for developers building RAG applications.
Interactive Pipeline Sandbox
Simulate document conversion steps right from the landing page without uploading real files.
Click "Simulate Pipeline" to run the pipeline nodes sequentially.
Ingestion pipeline stage flows
AI Markdown Engine coordinates parsing and chunking deterministically.
Upload
Heterogeneous files selection
Convert
Format-specific Markdown parsing
Quality
100-point structure scorecard
Optimize
12 safe token cleanup passes
RAG
Heading-anchored chunk split
Export
JSON vector manifest delivery
Ingestion features overview
Deterministic, subsecond steps designed for vector indexing payloads.
Multi-Engine Parsers
Dedicated parsing adaptors extract structure, worksheet cells, tables, slides, text runs, page coordinates, and link boundaries natively.
Deterministic Scores
Applies structural checks to evaluate headings hierarchies, list integrity, links formatting, code fences, and image annotations in a 100-point grade.
Token Footprint Optimization
Executes empty table removal, html comments stripping, duplicated blank line collapses, and redundant line markers removal to save payload context budget.
AI Markdown Engine System Architecture
A decoupled client-server architecture built for high-performance document ingestion. Integrates a Next.js frontend console, a fast FastAPI REST gateway, and a modular Python document pipeline.