Installation

Installation

pyaegean requires Python ≥ 3.10. The core has zero hard third-party dependencies — the wheel ships code and JSON only, so import aegean is instant. Everything heavier is an optional extra: pandas (DataFrame interop), the provider AI SDKs, and the Greek NLP backends are all imported lazily inside their adapters and pulled in only when you ask for them.

From PyPI

pip install pyaegean            # core: Linear A + Greek, zero hard deps

Optional extras

Extra	Pulls in	For
pyaegean[data]	pandas	DataFrame interop (to_dataframe)
pyaegean[neural]	onnxruntime, tokenizers, numpy	the neural Greek pipeline (use_neural_pipeline()) and lemmatizer (use_neural_lemmatizer())
pyaegean[anthropic]	anthropic	Anthropic (default) AI provider
pyaegean[openai]	openai	OpenAI provider
pyaegean[grok]	openai	xAI Grok (OpenAI-API-compatible)
pyaegean[gemini]	google-genai	Google Gemini provider
pyaegean[ai]	all of the above providers	the full AI layer
pyaegean[epidoc]	lxml	EpiDoc I/O
pyaegean[geo]	geopandas, shapely	geographic analysis
pyaegean[viz]	matplotlib	one-line plots (aegean.viz, aegean plot)
pyaegean[parquet]	pyarrow	Parquet export (io.to_parquet)
pyaegean[cli]	typer, rich	the aegean command line
pyaegean[all]	ai, epidoc, geo, data, cli, viz	everything except neural and parquet

pip install "pyaegean[ai]"
pip install "pyaegean[neural]"
pip install "pyaegean[all]"

Verify

import aegean
print(aegean.__version__)
print(aegean.registered_scripts())       # ['cypriot', 'cyprominoan', 'greek', 'lineara', 'linearb']
print(len(aegean.load("lineara")))        # 1721
print(len(aegean.load("greek")))          # 5  (bundled offline sample; real works
                                          #     via greek.load_work("tlg0012.tlg001"))

Offline & data

The compact text corpora (Linear A inscriptions/signs, Greek seeds) ship inside the wheel and work fully offline. Large or NC-licensed assets are not bundled — they are fetched on demand into a user cache on first use: the full DAMOS Linear B corpus (aegean.load("damos"), ~2 MB) and the SigLA Linear A dataset (aegean.load("sigla"), ~1 MB; both CC BY-NC-SA), the ~116 MB Linear A facsimile imagery, plus the opt-in Greek backends' data. For speed, the Greek backends now prefer small prebuilt artifacts hosted by the project: greek.use_lsj() fetches a ~15 MB prebuilt index (instead of downloading ~270 MB of Perseus TEI and building locally), and greek.use_treebank() / use_tagger() / use_lemmatizer() / use_parser() share one ~15 MB AGDT-derived bundle (instead of a 75 MB download and minutes of training). If a prebuilt asset is ever unreachable, each falls back to building from the upstream source. The neural lemmatizer (greek.use_neural_lemmatizer(), the [neural] extra) fetches a ~232 MB int8 ONNX GreTa model; the neural joint pipeline (greek.use_neural_pipeline(), same extra) fetches a ~518 MB fp32 ONNX model bundle. All remain offline after the first fetch. See Data & Provenance.

From source

See Development.