The document twin of meetus. Replicates a whole local drive tree into a single index.json, preserving the folder hierarchy (no flattening). Every file is a node; the original file is the artifact (what a QA/PM opens, what a package links to). doocus only extracts text for the heavy formats (docx/pdf/pptx/xlsx) — and that text is a search index only, never a replacement for the document.

Extraction is deterministic and offline — no cloud AI touches the raw source. The raw source stays on disk; permitted services (Gemini web, NotebookLM) get the original (as a link once we have Drive URLs, or re-uploaded).

Quick start

# index a whole downloaded drive tree  → docs-output/index.json
uv run make docs IN="/mnt/win/drive"
uv run make docs IN="..." DOC_EXTRA="--ocr"      # OCR scanned pdfs
make docs-dry IN="..."                           # counts only, no writes

# or directly
uv run process_tree.py /mnt/win/drive --output docs-output

# one-off single file (older per-file model, still handy)
uv run process_doc.py notes.docx

Output contract

docs-output/
  index.json                          whole tree; every file a node:
                                        { path, name, ext, family, mode, bytes,
                                          modified, url:null }   ◀ url = Drive link (later)
  <mirrored path>/<file>.doocus/      sidecar — ONLY for extracted formats:
     ├── content.md                     extracted text — SEARCH INDEX ONLY
     └── meta.json                      source/sha/dates/author/pages/... metadata

Node modes: extracted (docx/pdf/pptx/xlsx → text sidecar) · meeting (mp4/mkv/... → belongs to meetus, not a doc) · link (everything else → original is the artifact, nothing duplicated).

Supported types

Family	Extensions	Notes
text	md, txt, yaml/yml, json	passthrough; json/yaml add a structure summary
tabular	csv	markdown table + row/col metadata (stdlib)
office	docx, pptx, xlsx	text + core properties (author, dates, title)
pdf	pdf	text per page + info dict; `--render` for page-1 thumb
web	html/htm	visible text + title/meta tags
image	jpg/jpeg, png	EXIF (incl. capture date) + thumbnail; `--ocr` for text
media	mp4	ffprobe metadata + thumbnail; delegates transcription to meetus

Each extractor is isolated: a missing optional dependency or a corrupt file is recorded as a warning in meta.json, never aborting a batch (same resilience as ctrl/batch.sh).

Dependencies

Core text/tabular use only the stdlib. Everything else is a uv group in the repo's pyproject.toml:

uv sync --group doocus                 # Pillow, PyYAML, python-docx, python-pptx,
                                       # openpyxl, pypdf, beautifulsoup4, lxml
uv sync --group doocus --group ocr     # + pytesseract (needs system `tesseract`)

mp4 uses system ffmpeg/ffprobe (shared with meetus). PDF page rendering (--render) additionally needs the pdf-render group (pdf2image) + system poppler.

Browser UI

ui/doocus-app/ (sibling of ui/meetus-app/, shares ui/framework/) reads index.json and shows the full tree (folders preserved). Select a file → detail view: for extracted docs, a split of the markdown-rendered extracted text (clearly labelled "search index, not the document") beside the native view of the original (pdf inline; docx/xlsx → download); for linked files the original renders directly (image / html / text / markdown). A package builder zips selected originals (+ the content.md for extracted ones) for a target (Gemini web / NotebookLM), and will emit Drive links once nodes carry a url.

cd ui/doocus-app && npm install
DOOCUS_OUTPUT=/abs/path/to/docs-output npm run dev