LiteParse Documents

Use LiteParse (lit) to parse documents that aren't in the pod — a PDF an agent pulled from the web, a local scratch file, anything you won't upload — when you need text, spatial layout, OCR, bounding boxes, or page screenshots. It's also the fallback for a pod file whose auto-produced markdown/page-images are missing or insufficient (scanned/OCR, bounding boxes). For a document that lives in (or is going into) the pod, prefer the pod's built-in conversion and search — see below.

When lit vs. the pod's built-in processing

Decide by where the document lives — that's the first question, not an afterthought:

• It's a pod file (or going into the pod) → use the pod, not lit. lemma files upload <file> /knowledge/<name> and the pod auto-converts and indexes it: semantic+keyword search (lemma files search), page-marked markdown (lemma files cat --pages), rendered page images and figures (lemma files child …/pages/page_0001.jpg) — no local parsing. The pod is the RAG system; don't re-implement extraction for documents you're putting there. (See lemma-builder/references/files.md and the lemma-user skill.) The auto-conversion is an allow-list — PDF, DOC/DOCX, ODT, RTF, Markdown, plain text, HTML, EPUB — so uploading a spreadsheet, presentation, image or email stores the bytes and produces nothing to read (lemma files stat reports NOT_REQUIRED). Those are lit's lane even when they live in the pod: download and parse locally.
• It's outside the pod, or the pod artifact is missing/insufficient → use lit. A PDF fetched from the web, a local file you won't upload, a scanned PDF needing OCR, bounding-box/layout extraction, or screenshotting pages to decide what's worth keeping — and the fallback when a pod file lacks its derived markdown/images. That's LiteParse's lane.

Common flow: lit screenshot or lit parse an outside file to inspect it, then lemma files upload the ones worth keeping so the pod converts and indexes them.

Tooling

The workspace image installs lit (and the liteparse alias) from @llamaindex/liteparse, alongside Node and a headed Chromium. That is what you can count on:

lit --help

Everything else is a probe, not a promise. LibreOffice, ImageMagick and Tesseract language data are not installed by the workspace image, and nothing sets $TESSDATA_PREFIX. Some runtimes inherit them from their base image and some do not, so check before you plan around them rather than after a parse fails:

command -v libreoffice soffice magick convert tesseract
echo "${TESSDATA_PREFIX:-<unset>}"

When one is missing, say so and take the other route rather than trying to install it: an Office file can go through lemma files upload and be read back as the pod's converted markdown, and a scanned PDF can be rendered with lit screenshot and read with the image-viewing capability instead of OCR.

Workflow

1. Identify the file type and the desired output: plain text, JSON with bounding boxes, or page screenshots.
2. For searchable text or layout extraction, run lit parse.
3. For visual inspection, charts, scans, handwriting, dense tables, or agent vision workflows, run lit screenshot.
4. Save generated outputs beside the source file or in a clearly named working directory, then inspect the result before relying on it.
5. When a file is scanned or image-heavy, keep OCR enabled. Use --no-ocr only when the user wants embedded text only or speed matters more than recall.

Common Commands

Parse to text:

lit parse input.pdf -o output.txt

Parse to JSON with bounding boxes:

lit parse input.pdf --format json -o output.json

Parse selected pages:

lit parse input.pdf --target-pages "1-5,10" --format json -o output.json

Parse Office documents or images:

lit parse input.docx --format json -o output.json
lit parse input.png --format json -o output.json

Generate screenshots:

lit screenshot input.pdf --target-pages "1-3" --dpi 200 -o screenshots

Batch parse a directory:

lit batch-parse input-directory output-directory --recursive --format json

Output Guidance

• Use text output for quick reading, summarization, search, or simple extraction.
• Use JSON when downstream code needs bounding boxes, page numbers, or structured blocks.
• Use screenshots when text extraction may miss visual relationships, tables, signatures, charts, diagrams, or scanned content.
• For large documents, start with --target-pages or --max-pages to avoid unnecessary processing.
• Do not claim perfect table, handwriting, or chart extraction from LiteParse alone. For complex visual documents, combine screenshots with model vision or tell the user that a heavier parser may be needed.

Troubleshooting

• If Office files fail to parse, check libreoffice --version (or soffice). The workspace image does not install it; when it is absent, upload the file and read the pod's converted markdown instead.
• If image inputs fail, check magick --version or convert --version. Same story — not installed by the workspace image.
• OCR needs Tesseract and its language data, neither of which the workspace image installs. If tesseract --version works, pass --ocr-language <lang> and make sure the matching .traineddata file exists in $TESSDATA_PREFIX. If it does not, render pages with lit screenshot and read them with the image-viewing capability rather than reaching for OCR.
• If the document is password protected, use --password <password> only when the user has provided the password.

See also

• Upload + the pod's auto-index/search/markdown/page-images → lemma-builder/references/files.md
• Operate pod files from the CLI (search, cat, child, view-image) → the lemma-user skill