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MarkItDown

A linear, one-concept-per-lesson path through MarkItDown, Microsoft's open-source library for converting a wide range of file formats into Markdown an LLM can read, from your first .convert() call to a Gemini-backed searchable index over a full mixed document folder. 12 lessons · 3 tiers.

Prerequisites: No hard prerequisite, can be started cold. Lessons 10 and 11 assume basic familiarity with the LlamaIndex course, Lessons 1-4 (Document, VectorStoreIndex, query engines), and Lesson 11 compares directly against the LiteParse course, the same way LiteParse's own README notes its soft prerequisite on LlamaIndex.

No new API key needed, markitdown[all] ships in this project's existing setup. Lessons 6, 10, and 12 use the same GOOGLE_API_KEY every other course uses, for LLM image captioning (Lesson 6) and LlamaIndex embeddings/generation (Lessons 10 and 12).

Built on MarkItDown, the open-source library this course teaches.

Course source

Every lesson's README and lesson.py for this course live in the ai-agent-engineering repo.

View on GitHub →
MarkItDown

Beginner

The core .convert() call, office documents, streams and URLs, and a first batch-conversion checkpoint.

  1. 01What Is MarkItDown?GitHub
  2. 02First Convert CallGitHub
  3. 03Converting Office DocumentsGitHub
  4. 04Converting From Streams and URLsGitHub
  5. 05Checkpoint: Batch Folder ConverterGitHub

Intermediate

LLM image captioning, custom converters and plugins, and format hints that can silently go wrong.

  1. 06Images and LLM CaptioningGitHub
  2. 07Plugins and Custom ConvertersGitHub
  3. 08Stream Info and Format HintsGitHub
  4. 09Checkpoint: Drop-Folder ConverterGitHub

Advanced

Feeding MarkItDown into LlamaIndex, comparing it against LiteParse, and a full multi-format capstone index.

  1. 10Feeding MarkItDown Into LlamaIndexGitHub
  2. 11Comparing MarkItDown and LiteParseGitHub
  3. 12Capstone: One Index, Every FormatGitHub
Start at Lesson 1

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