liteparse-documents

name

liteparse-documents

description

Use this skill to parse documents that live OUTSIDE the pod's file system — a PDF an agent fetched from the web, a local one-off, or any file you won't upload — or as a fallback when a pod file lacks its auto-produced markdown/page-images. Extracts text, layout, bounding boxes, OCR, page screenshots, or structured output from PDF, DOCX, PPTX, XLSX, CSV, TSV, images, and other LiteParse formats. For documents that live in the pod, prefer the pod's built-in conversion/search instead.

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.)
• 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 should provide:

• lit from @llamaindex/liteparse
• LibreOffice for Office document conversion
• ImageMagick for image conversion
• English Tesseract trained data at $TESSDATA_PREFIX

Check availability with:

lit --help

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, verify LibreOffice is installed with libreoffice --version.
• If image inputs fail, verify ImageMagick is installed with magick --version or convert --version.
• If OCR needs another language, pass --ocr-language <lang> and ensure the matching .traineddata file exists in $TESSDATA_PREFIX.
• 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