gutenberg-sdk 0.1.0

Python SDK for the Gutenberg SAE Activation API

gutenberg-sdk

Python SDK for the Gutenberg SAE activation API — interpretability-based observability for language models. Upload text, read it through a sparse autoencoder (SAE) feature dictionary, and find the features that separate any two classes of documents.

• Docs: https://console.gutenberg.ai/d/docs
• Console: https://console.gutenberg.ai
• Get an API key: https://console.gutenberg.ai/d/keys

Install

uv add gutenberg-sdk          # or: pip install gutenberg-sdk

The distribution is gutenberg-sdk; the import is gutenberg:

from gutenberg import gutenberg

Optional pandas extra (for load_activations_df and parquet helpers):

uv add "gutenberg-sdk[pandas]"

Quickstart

from gutenberg import gutenberg

client = gutenberg(api_key="gtn_...")   # or set GUTENBERG_API_KEY

# 1. upload a parquet dataset (text + a binary target column)
dataset = client.datasets.upload("examples/simple_binary_features_extraction_100.parquet")

# 2. launch hosted SAE feature extraction
job = client.jobs.create(
    dataset_id=dataset.dataset_id,
    model_id="google/gemma-3-27b-it",
    sae_id="layer_31_width_262k_l0_medium",
)
job = client.jobs.wait(job.job_id)

# 3. score every feature against the target with AUROC
exp = client.experiments.create(
    job_id=job.job_id,
    target_column="is_ai",
    target_column_type="binary",
    positive_value="1",
    scoring_method="auroc",
)
exp = client.experiments.wait(exp.experiment_id)

# 4. read back ranked features and token-level examples
for feature in client.experiments.features(exp.experiment_id)[:10]:
    print(feature.rank, feature.feature_id, feature.score)

The full runnable script lives in examples/simple_binary_features_extraction.py, with a companion 100-row parquet. On production the whole flow runs in a couple of minutes. See docs/getting-started.md for the walkthrough, SAE selection guidance, and how token examples are served.

The bundled example is a curated showcase, not a benchmark — its 50 AI passages were picked to exhibit a handful of recognizable AI-writing features, so those features separate the two classes near-perfectly there. Run it on your own data to see a realistic ranking.

API surface

A single gutenberg(...) client with namespaced resources: datasets, jobs, experiments, aggregations, autointerp, meta_autointerp, subsets, plus the sync helpers activations(), interpret(), models(), and saes().

License

MIT