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Getting started

Install iscc-sct, generate your first Semantic Text-Code, and watch two languages produce near-identical codes.

Prerequisites

  • Python 3.10 or later
  • uv (recommended) or pip

Install

iscc-sct needs an ONNX runtime, selected through an install extra. The cpu extra works everywhere:

uv add "iscc-sct[cpu]"

pip install "iscc-sct[cpu]"

Choose exactly one runtime

Install the cpu extra, or the gpu extra for NVIDIA CUDA acceleration — never both. The onnxruntime and onnxruntime-gpu packages unpack into the same directory and overwrite each other. A plain pip install iscc-sct installs no runtime, and the first code generation then fails with install instructions. If you are unsure which extra fits your machine, run iscc-sct doctor (see the command-line guide).

Verify the installation

import iscc_sct

print(iscc_sct.__version__)

Generate your first code

Pass any text to create(). The first call downloads the embedding model (about 450 MB) to your user data directory; later calls reuse it.

import iscc_sct as sct

text = "This is some sample text. It can be a longer document or even an entire book."
print(sct.create(text, bits=256).iscc)
# ISCC:CADV3GG6JH3XEVRNSVYGCLJ7AAV3BOT5J7EHEZKPFXEGRJ2CTWACGZI

The bits argument sets the code length. Longer codes carry more detail; 64 bits is the library default, and 256 bits is the most precise. See comparing texts for how length affects matching.

Match a translation

The point of a Semantic Text-Code is that meaning survives translation. Generate codes for the same passage in English and German, then measure their distance:

import iscc_sct as sct

english = (
    "An ISCC applies to a specific digital asset and is a data-descriptor deterministically "
    "constructed from multiple hash digests using the algorithms and rules in this document. "
    "This document does not provide information on registration of ISCCs."
)
german = (
    "Ein ISCC bezieht sich auf ein bestimmtes digitales Gut und ist ein Daten-Deskriptor, der "
    "deterministisch aus mehreren Hash-Digests unter Verwendung der Algorithmen und Regeln in "
    "diesem Dokument erstellt wird. Dieses Dokument enthält keine Informationen über die "
    "Registrierung von ISCCs."
)

a = sct.create(english)
b = sct.create(german)

print(sct.iscc_distance(a.iscc, b.iscc))
# 3

A distance of 3 bits out of 64 means the translation is a near-match. For contrast, an unrelated sentence lands far away:

import iscc_sct as sct

unrelated = (
    "The recipe calls for two cups of flour, a pinch of salt, and three ripe bananas mashed "
    "until smooth before folding the mixture into the buttered baking tin."
)

print(sct.iscc_distance(a.iscc, sct.create(unrelated).iscc))
# 33

Low distance means similar meaning; high distance means unrelated content. Translation matching is the behavior that lexical (word-based) codes cannot provide.

Look inside the text

Set granular=True to get per-chunk features alongside the document code. Each chunk carries its own offset, size, similarity-preserving fingerprint (simprint), and text:

import iscc_sct as sct

text = "This is some sample text. It can be a longer document or even an entire book."
meta = sct.create(text, bits=256, granular=True)

feature = meta.features[0].simprints[0]
print(feature.offset, feature.size, feature.simprint)
# 0 77 XZjeSfdyVi0

Granular features let you match individual passages across documents, even when the surrounding text differs. The granular features guide covers chunk-level matching and content reconstruction.

Next steps