LatinCy Readers

Corpus readers for Latin and Ancient Greek texts with LatinCy NLP integration.

Version 1.4.1; Python 3.10+; LatinCy 3.9.0+

Installation

# Install with Latin model (recommended)
pip install latincy-readers[la]

# Install with Greek model
pip install latincy-readers[grc]

# Install with all models and vector support
pip install latincy-readers[all]

# Install without models (bring your own)
pip install latincy-readers

# For development (editable install)
git clone https://github.com/diyclassics/latincy-readers.git
cd latincy-readers
pip install -e ".[dev]"

Quick Start

from latincyreaders import TesseraeReader, AnnotationLevel

# Auto-download corpus on first use
reader = TesseraeReader()

# Or specify a custom path
reader = TesseraeReader("/path/to/tesserae/corpus")

# Iterate over documents as spaCy Docs
for doc in reader.docs():
    print(f"{doc._.fileid}: {len(list(doc.sents))} sentences")

# Search for sentences containing specific forms
for result in reader.find_sents(forms=["Caesar", "Caesarem"]):
    print(f"{result['citation']}: {result['sentence']}")

# Get raw text (no NLP processing)
for text in reader.texts():
    print(text[:100])

Readers

Reader	Format	Auto-Download	Description
TesseraeReader	.tess	Yes	CLTK Latin Tesserae corpus
GreekTesseraeReader	.tess	Yes	CLTK Greek Tesserae corpus
PlaintextReader	.txt	No	Plain text files
LatinLibraryReader	.txt	Yes	Latin Library corpus
TEIReader	.xml	No	TEI-XML documents
PerseusReader	.xml	No	Perseus Digital Library TEI
CamenaReader	.xml	Yes	CAMENA Neo-Latin corpus
TxtdownReader	.txtd	No	Txtdown format with citations
UDReader	.conllu	No	Universal Dependencies CoNLL-U
LatinUDReader	.conllu	Yes	All 6 Latin UD treebanks

Auto-Download

Readers with auto-download support will automatically fetch the corpus on first use:

# Downloads to ~/latincy_data/lat_text_tesserae/texts if not found
reader = TesseraeReader()

# Disable auto-download
reader = TesseraeReader(auto_download=False)

# Use environment variable for custom location
# export TESSERAE_PATH=/custom/path
reader = TesseraeReader()

# Manual download to specific location
TesseraeReader.download("/path/to/destination")

Ancient Greek (GreekTesseraeReader)

Read Ancient Greek texts from the CLTK Greek Tesserae corpus using LatinCy Greek NLP models:

from latincyreaders import GreekTesseraeReader, AnnotationLevel

# Auto-download Greek Tesserae corpus on first use
reader = GreekTesseraeReader()

# Use TOKENIZE level (no Greek model needed)
reader = GreekTesseraeReader(annotation_level=AnnotationLevel.TOKENIZE)

# Iterate over citation lines
for citation, text in reader.texts_by_line():
    print(f"{citation}: {text[:60]}...")

# Search for Greek words
for fid, cit, text, matches in reader.search(r"Ἀχιλ"):
    print(f"{cit}: found {matches}")

# Environment variable for custom location
# export GRC_TESSERAE_PATH=/custom/path
reader = GreekTesseraeReader()

Universal Dependencies Treebanks

Access gold-standard linguistic annotations from Latin UD treebanks:

from latincyreaders import LatinUDReader, PROIELReader

# See available treebanks
LatinUDReader.available_treebanks()
# {'proiel': 'Vulgate, Caesar, Cicero, Palladius',
#  'perseus': 'Classical texts from Perseus Digital Library',
#  'ittb': 'Index Thomisticus (Thomas Aquinas)',
#  'llct': 'Late Latin Charter Treebank',
#  'udante': "Dante's Latin works",
#  'circse': 'CIRCSE Latin treebank'}

# Use a specific treebank
reader = PROIELReader()

# Iterate sentences with UD annotations
for sent in reader.ud_sents():
    print(f"{sent._.citation}: {sent.text}")

# Access full UD token data
for token in doc:
    ud = token._.ud  # dict with all 10 CoNLL-U columns
    print(f"{token.text}: {ud['upos']} {ud['feats']}")

# Read from all treebanks at once
reader = LatinUDReader()
LatinUDReader.download_all()  # Download all 6 treebanks

Note: Unlike other readers, UDReader constructs spaCy Docs directly from gold UD annotations rather than running the spaCy NLP pipeline.

Core API

All readers provide a consistent interface:

reader.fileids()              # List available files
reader.texts(fileids=...)     # Raw text strings (generator)
reader.docs(fileids=...)      # spaCy Doc objects (generator)
reader.sents(fileids=...)     # Sentence spans (generator)
reader.tokens(fileids=...)    # Token objects (generator)
reader.metadata(fileids=...)  # File metadata (generator)

FileSelector: Fluent File Filtering

Use the select() method for complex file queries combining filename patterns and metadata:

# Filter by filename pattern (regex)
vergil_docs = reader.select().match(r"vergil\..*")

# Filter by metadata
epics = reader.select().where(genre="epic")

# Multiple conditions (AND)
vergil_epics = reader.select().where(author="Vergil", genre="epic")

# Match any of multiple values
major_authors = reader.select().where(author__in=["Vergil", "Ovid", "Horace"])

# Date ranges
augustan = reader.select().date_range(-50, 50)

# Chain multiple filters
selection = (reader.select()
    .match(r".*aen.*")
    .where(genre="epic")
    .date_range(-50, 50))

# Use with docs(), sents(), etc.
for doc in reader.docs(selection):
    print(doc._.fileid)

# Preview results
print(selection.preview(5))
print(f"Found {len(selection)} files")

Search API

# Fast regex search (no NLP)
reader.search(pattern=r"\bbell\w+")

# Form-based sentence search
reader.find_sents(forms=["amor", "amoris"])

# Lemma-based search (requires NLP)
reader.find_sents(lemma="amo")

# spaCy Matcher patterns
reader.find_sents(matcher_pattern=[{"POS": "ADJ"}, {"POS": "NOUN"}])

Text Analysis

# Build a concordance (word -> citations mapping)
conc = reader.concordance(basis="lemma")
print(conc["amor"])  # ['<catull. 1.1>', '<verg. aen. 4.1>', ...]

# Keyword in Context
for hit in reader.kwic("amor", window=5, by_lemma=True):
    print(f"{hit['left']} [{hit['match']}] {hit['right']}")
    print(f"  -- {hit['citation']}")

# N-grams
for ngram in reader.ngrams(n=2, basis="lemma"):
    print(ngram)  # "qui do", "do lepidus", ...

# Skip-grams (n-grams with gaps)
for sg in reader.skipgrams(n=2, k=1):
    print(sg)

Sentence Vector Search

Find semantically similar sentences across the corpus using sentence-level embeddings. Requires the vectors extra (pip install latincyreaders[vectors]).

from latincyreaders import TesseraeReader
from latincyreaders.cache.vectors import SentenceVectorConfig, SentenceVectorStore

reader = TesseraeReader()

# Build a vector index (saved to ~/latincy_data/vectors/<collection>/)
cfg = SentenceVectorConfig(collection="tesserae")
store = SentenceVectorStore(cfg)
store.build(reader)

# Semantic search
results = store.similar_to_sent("arma virumque cano", reader.nlp, top_k=5)
for r in results:
    print(f"[{r['score']:.3f}] {r['citation']}: {r['text'][:80]}")

# Or use the reader shortcut
results = reader.find_similar("amor", top_k=5, config=cfg)

# Auto-build on first query (builds index if none exists)
results = reader.find_similar("amor", auto_build=True)

# Find sentences similar to one already in the index
results = store.similar_to_doc_sent("vergil.aeneid.part.1.tess", 0, top_k=5)

# Index statistics
print(store.stats())
# {'collection': 'tesserae', 'sentences': 15800, 'vector_dim': 300, ...}

Vectors are stored as memory-mapped NumPy arrays for efficient search without external dependencies. See notebooks/vector-search-demo.ipynb for a full walkthrough.

Document Caching

Documents are cached by default for better performance when accessing the same file multiple times:

# Caching enabled by default
reader = TesseraeReader()

# Disable caching
reader = TesseraeReader(cache=False)

# Configure cache size
reader = TesseraeReader(cache_maxsize=256)

# Check cache statistics
print(reader.cache_stats())  # {'hits': 5, 'misses': 3, 'size': 3, 'maxsize': 128}

# Clear the cache
reader.clear_cache()

Persistent Disk Cache

For large corpora, enable persistent caching to avoid re-running the NLP pipeline across sessions. Cached documents are stored as .spacy DocBin files in ~/.latincy_cache/<collection>/ by default:

from latincyreaders import TesseraeReader
from latincyreaders.cache.disk import CacheConfig

# Enable disk caching for the Tesserae corpus
config = CacheConfig(persist=True, collection="tesserae")
reader = TesseraeReader(model_name="la_core_web_lg", cache_config=config)

# First call runs NLP and caches to disk
doc = next(reader.docs(fileids="vergil.aeneid.part.1.tess"))

# Subsequent calls load from cache (~100x faster)
doc = next(reader.docs(fileids="vergil.aeneid.part.1.tess"))

# Custom cache location
config = CacheConfig(
    persist=True,
    collection="tesserae",
    cache_dir="/path/to/cache",
)

# Time-to-live (auto-expire after N days)
config = CacheConfig(persist=True, collection="tesserae", ttl_days=30)

Annotation Levels

All linguistic annotations are provided by LatinCy spaCy-based pipelines. The full pipeline provides POS tagging, lemmatization, morphological analysis, and named entity recognition—but this can be slow for large corpora. If you don't need all annotations, you can get significant performance gains by selecting a lighter annotation level:

from latincyreaders import AnnotationLevel

# Full pipeline: POS, lemma, morphology, NER (default)
reader = TesseraeReader(annotation_level=AnnotationLevel.FULL)

# Basic: tokenization + sentence boundaries only
reader = TesseraeReader(annotation_level=AnnotationLevel.BASIC)

# Tokenization only (no sentence boundaries)
reader = TesseraeReader(annotation_level=AnnotationLevel.TOKENIZE)

# No NLP at all - use texts() for raw strings
for text in reader.texts():
    print(text)

Metadata Management

from latincyreaders import MetadataManager, MetadataSchema

# Load and merge metadata from JSON files
manager = MetadataManager("/path/to/corpus")

# Access metadata
meta = manager.get("vergil.aen.tess")
print(meta["author"], meta["date"])

# Filter files by metadata
for fileid in manager.filter_by(author="Vergil", genre="epic"):
    print(fileid)

# Date range filtering
for fileid in manager.filter_by_range("date", -50, 50):
    print(fileid)

# Validate metadata against a schema
schema = MetadataSchema(
    required={"author": str, "title": str},
    optional={"date": int, "genre": str}
)
manager = MetadataManager("/path/to/corpus", schema=schema)
result = manager.validate()
if not result.is_valid:
    print(result.errors)

Corpora Supported

• Tesserae Latin Corpus
• Tesserae Greek Corpus
• Perseus Digital Library TEI
• Latin Library
• CAMENA Neo-Latin
• Universal Dependencies Latin Treebanks (PROIEL, Perseus, ITTB, LLCT, UDante, CIRCSE)
• Any plaintext, TEI-XML, or CoNLL-U collection

CLI Tools

Tools in cli/:

# Sentence search
python cli/reader_search.py --lemmas Caesar --limit 100
python cli/reader_search.py --forms Caesar Caesarem --limit 100
python cli/reader_search.py --pattern "\\bTheb\\w+" --output thebes.tsv

# Vector search — build and query sentence vector indices
python cli/vector_search.py build
python cli/vector_search.py build --collection vergil --fileids "vergil.*"
python cli/vector_search.py query "arma virumque cano" --top-k 10
python cli/vector_search.py stats

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Bibliography

• Bird, S., E. Loper, and E. Klein. 2009. Natural Language Processing with Python. O'Reilly: Sebastopol, CA.
• Bengfort, Benjamin, Rebecca Bilbro, and Tony Ojeda. 2018. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. O'Reilly: Sebastopol, CA.

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Developed by Patrick J. Burns with Claude Code in 2026.