Browse all 9 articles in this open-source series. Nine source-backed guides to document conversion: first file, deployment, dispatch internals, security and a quality-focused ingestion lab.
Featured starting point
Understand what Microsoft MarkItDown preserves, where conversion loses information, and how to choose a first document for an LLM ingestion pipeline.
Suggested learning path
01 → 09
Understand what Microsoft MarkItDown preserves, where conversion loses information, and how to choose a first document for an LLM ingestion pipeline.
Install a focused MarkItDown environment, use the CLI and Python API, and diagnose missing dependencies separately from extraction-quality problems.
Design a conversion worker around local inputs, selected extras, resource limits and durable outputs; distinguish this deployment design from the upstream library.
Follow the inspected MarkItDown source from local input through StreamInfo guesses, prioritized converters, Markdown normalization and explicit failure classes.
Inspect the core and PlainTextConverter at a fixed commit to understand acceptance, charset handling, stream restoration and a focused plugin regression test.
Build a per-format conversion benchmark, account for buffering and optional services, and report accepted documents instead of raw conversion throughput.
Apply the project’s documented I/O boundary using narrow conversion APIs, isolated workers and explicit plugin/cloud settings for document ingestion.
Compare document conversion approaches by text extraction, visual fidelity, optional services and corpus-level acceptance rather than an unsupported feature ranking.
Design a small ingestion project that stores original files, conversion options, expected facts and review decisions, with a clear separation between current code and proposed extensions.
