Catalogue in. Product catalog out.
It knows when it can't.

One call. One clean product table.

Each product photo anchors a row: terbium extracts the image, names it from the label beneath it, and mines the nearby text for the SKU and the materials. Then a CSV, ready for a PIM, Shopify, or a marketplace feed.

import terbium

rows = terbium.build_catalog("vendor_catalogue.pdf", images_dir="images/")
terbium.to_catalog_csv(rows, "catalogue.csv")
# {"sku": "RG-1001", "name": "Anatolia Kilim",
#  "materials": "wool", "image": "Anatolia_Kilim.jpeg", "page": 12}

It asks for help by name.

Every run ends with a verdict, not a stack trace. When the layout carries the data, terbium finishes cold with zero tokens spent. When a catalogue buries its facts in photos or prose, it says exactly what is missing, where the data actually lives, and what to hand it next.

terbium: 8/121 products have a name, 0 a SKU, 9 materials/ingredients.
82 page(s) are image-only (1, 5, 6, 7, 8, ...) - the data lives in the photos, not the text.
-> set ANTHROPIC_API_KEY or pass ai=terbium.AI(...)   ·   recommended tier: Opus (vision)

Every file, reduced to columns, rows, and matrices.

PDF gets the full geometry engine because a PDF throws its structure away, terbium has to rebuild it. PPTX, XLSX, and CSV already carry native structure, so terbium leans on it and parses them cleanly and cheaply.

The detector is content-agnostic. Any column-aligned table, a pricelist, a spec sheet, a size x finish matrix, reconstructs the same way, whatever the category. The universal product schema then maps the columns to product fields by header meaning: a bag gets capacity, a lamp gets wattage, a rug gets pile, without a hand-written schema per category.

Not every PDF is a matrix. A lookbook, a grid of photos with a name under each, is reconstructed as a label grid: one record per product, grouped by collection. And when a page is image-only, terbium does not return nothing, it reports exactly which pages need the vision lane.

Pull out the product images, named by product.

terbium extracts every product photo losslessly and names each file after the product it sits beneath, dropping icons, thin banners, and logos that repeat across pages. One call, no AI key needed.

import terbium
manifest = terbium.export_images("lookbook.pdf", "out/")
# out/Kyoto_Bedside_Table.jpeg, out/Meadow_Bedside_Table.jpeg, ...

# or from the shell, with a manifest.csv alongside the photos
$ terbium lookbook.pdf --images out/

Per image: product, collection, page, format, pixel size, colorspace, effective dpi, dominant colour, and position, written to a manifest.csv.

Parse first. Add a key only when it asks.

Install, point it at a catalogue, and read the table. Wire up an AI key when, and only when, terbium tells you the data lives somewhere the algorithm cannot reach.

pip install terbium-parse
terbium catalogue.pdf --csv out.csv        # the product table + images/, no AI

import terbium

rows = terbium.build_catalog("catalogue.pdf", images_dir="images/")   # algorithmic only

rows = terbium.build_catalog("lookbook.pdf", images_dir="images/",
                             ai=terbium.AI(anthropic_key=...))       # vision fills the blanks

doc = terbium.parse("pricelist.xlsx", schema="product")              # raw records API

The hard page gets the strong model. Nothing else does.

terbium matches the difficulty of a page to the cheapest model that can actually solve it, so you never pay Opus prices for a page Haiku could clear.

Images, material icons and finish swatches, are read by a vision model and folded back into the record.