remarx ¶

remarx: Marx quote identification for the CDH Citing Marx project.

MODULE	DESCRIPTION
`app`	This module contains the UI and utilities for the remarx app
`quotation`	This module contains libraries for embedding generation and quotation detection.
`sentence`	This module contains libraries for sentence segmentation and sentence corpus construction.
`utils`	Utility functions for the remarx package

utils ¶

Utility functions for the remarx package

CLASS	DESCRIPTION
`CorpusPath`	Paths for the default corpus directory structure.

FUNCTION	DESCRIPTION
`get_default_corpus_path`	Return default corpus directories and optionally create them if missing.
`get_default_quote_output_path`	Return the default quote finder output directory path and optionally create it if missing.
`configure_logging`	Configure logging for the remarx application.

CorpusPath `dataclass` ¶

CorpusPath(
    root: Path | None = None, original: Path | None = None, reuse: Path | None = None
)

Paths for the default corpus directory structure.

Populates unspecified directories based on the default data folder. Supports expansion of "~" or "~user" paths.

METHOD	DESCRIPTION
`__post_init__`	Populate unset directories using the default data root, expanding "~"
`ready`	Return True if both default corpus directories already exist.
`ensure_directories`	Create the corpus directories if they do not exist.

__post_init__ ¶

__post_init__() -> None

Populate unset directories using the default data root, expanding "~" or "~user" values with pathlib.Path.expanduser() so shell-style root paths are accepted. Callers can override any directory; otherwise the root defaults to DEFAULT_CORPUS_ROOT under remarx-data and the original and reuse directories live as its subfolders.

ready ¶

ready() -> bool

Return True if both default corpus directories already exist.

ensure_directories ¶

ensure_directories() -> None

Create the corpus directories if they do not exist.

get_default_corpus_path ¶

get_default_corpus_path(create: bool = False) -> tuple[bool, CorpusPath]

Return default corpus directories and optionally create them if missing.

get_default_quote_output_path ¶

get_default_quote_output_path(create: bool = False) -> tuple[bool, Path]

Return the default quote finder output directory path and optionally create it if missing.

PARAMETER	DESCRIPTION
`create`	If True, create the directory if it doesn't exist TYPE: `bool` DEFAULT: `False`

RETURNS	DESCRIPTION
`tuple[bool, Path]`	Tuple of (ready flag, path to quote output directory)

configure_logging ¶

configure_logging(
    log_destination: Path | TextIO | None = None, log_level: int = INFO
) -> Path | None

Configure logging for the remarx application. Supports logging to any text stream, a specified file, or auto-generated timestamped file.

PARAMETER	DESCRIPTION
`log_destination`	Where to write logs. Can be: - None (default): Creates a timestamped log file in ./logs/ directory - pathlib.Path: Write to the specified file path - Any io.TextIOBase (e.g., sys.stdout, sys.stderr, or any io.TextIOBase): Write to the given stream TYPE: `Path \| TextIO \| None` DEFAULT: `None`
`log_level`	Logging level for remarx logger (default to logging.INFO) TYPE: `int` DEFAULT: `INFO`

RETURNS	DESCRIPTION
`Path \| None`	Path to the created log file if file logging is used, None if stream logging

app ¶

This module contains the UI and utilities for the remarx app

MODULE	DESCRIPTION
`corpus_builder`	The marimo notebook corresponding to the `remarx` application. The application
`log_viewer`	Utilities for rendering remarx logs inside marimo notebooks.
`quote_finder`	The marimo notebook corresponding to the `remarx` application. The application
`utils`	Utility methods associated with the remarx app

utils ¶

Utility methods associated with the remarx app

FUNCTION	DESCRIPTION
`lifespan`	Lifespan context manager to open browser when server starts
`redirect_root`	Redirect root path to corpus-builder, since app currently has no home page
`launch_app`	Launch the remarx app in the default web browser
`get_current_log_file`	Get the path to the current log file from the root logger's file handler.
`create_header`	Create the header for the remarx notebooks
`create_temp_input`	Context manager to create a temporary file with the file contents and name of a file uploaded
`summarize_corpus_selection`	Summarize a sentence corpus CSV selected in the UI.
`handle_default_corpus_creation`	Update default corpus directory state based on the create button.

lifespan `async` ¶

lifespan(app: FastAPI) -> AsyncGenerator[None, None]

Lifespan context manager to open browser when server starts

redirect_root `async` ¶

redirect_root() -> RedirectResponse

Redirect root path to corpus-builder, since app currently has no home page

launch_app ¶

launch_app() -> None

Launch the remarx app in the default web browser

get_current_log_file ¶

get_current_log_file() -> Path | None

Get the path to the current log file from the root logger's file handler. Returns None if logging is not configured to a file.

create_header ¶

create_header() -> None

Create the header for the remarx notebooks

create_temp_input ¶

create_temp_input(file_upload: FileUploadResults) -> Generator[Path, None, None]

Context manager to create a temporary file with the file contents and name of a file uploaded to a web browser as returned by marimo.ui.file. This should be used in with statements.

RETURNS	DESCRIPTION
`Generator[Path, None, None]`	Yields the path to the temporary file

summarize_corpus_selection ¶

summarize_corpus_selection(
    selection: _HasPath | str | Path | None,
) -> dict[str, int | str] | None

Summarize a sentence corpus CSV selected in the UI.

Accepts either a file-browser selection (with a path attribute) or a filesystem path and returns corpus statistics that can be displayed in the Marimo table.

handle_default_corpus_creation ¶

handle_default_corpus_creation(
    button: run_button,
    default_dirs_initial: CorpusPath,
    *,
    ready_message: str = ":white_check_mark: Default corpus folders are ready.",
    missing_message: str = ":x: Default corpus folders were not found."
) -> tuple[bool, CorpusPath, str, str]

Update default corpus directory state based on the create button.

RETURNS	DESCRIPTION
`tuple[bool, CorpusPath, str, str]`	Tuple of (ready flag, directories object, status message, callout kind)

sentence ¶

This module contains libraries for sentence segmentation and sentence corpus construction.

MODULE	DESCRIPTION
`corpus`	Functionality for loading and chunking input files for sentence corpus creation.
`segment`	Provides functionality to break down input text into individual

segment ¶

Provides functionality to break down input text into individual sentences and return them as tuples containing the character index where each sentence begins and the sentence text itself.

FUNCTION	DESCRIPTION
`segment_text`	Segment a string of text into sentences with character indices.

segment_text ¶

segment_text(text: str, model: str = 'de_core_news_sm') -> list[tuple[int, str]]

Segment a string of text into sentences with character indices.

Automatically downloads the spaCy model on first use if it is not installed.

PARAMETER	DESCRIPTION
`text`	Input text to be segmented into sentences TYPE: `str`
`model`	spaCy model name, defaulted to "de_core_news_sm" TYPE: `str` DEFAULT: `'de_core_news_sm'`

RETURNS	DESCRIPTION
`list[tuple[int, str]]`	List of tuples where each tuple contains (start_char_index, sentence_text)

corpus ¶

Functionality for loading and chunking input files for sentence corpus creation.

MODULE	DESCRIPTION
`alto_input`	Functionality related to parsing ALTO XML content packaged within a zipfile,
`base_input`	Base file input class with common functionality. Provides a factory
`create`	Preliminary script and method to create sentence corpora from input
`tei_input`	Functionality related to parsing MEGA TEI/XML content with the
`text_input`	Input class for handling basic text file as input for corpus creation.

CLASS	DESCRIPTION
`ALTOInput`	FileInput implementation for ALTO XML delivered as a zipfile.
`FileInput`	Base class for file input for sentence corpus creation
`TEIDocument`	Custom :class:`neuxml.xmlmap.XmlObject` instance for TEI XML document.
`TEIinput`	Input class for TEI/XML content. Takes a single input file,
`TextInput`	Basic text file input handling for sentence corpus creation. Takes

ATTRIBUTE	DESCRIPTION
`TEI_TAG`	Convenience access to namespaced TEI tag names

ALTOInput `dataclass` ¶

ALTOInput(
    input_file: Path, filename_override: str = None, filter_sections: bool = True
)

Bases: FileInput

FileInput implementation for ALTO XML delivered as a zipfile. Iterates through ALTO XML members and yields text blocks with ALTO metadata.

METHOD	DESCRIPTION
`get_text`	Iterate over ALTO XML files contained in the zipfile and return
`update_current_metadata`	Update current article metadata.
`check_zipfile_path`	Check an individual file included in the zip archive to determine if

ATTRIBUTE	DESCRIPTION
`field_names`	List of field names for sentences originating from ALTO XML content. TYPE: `tuple[str, ...]`
`file_type`	Supported file extension for ALTO zipfiles (.zip) TYPE: `str`
`default_include`	Default content sections to include TYPE: `set[str]`
`filter_sections`	Whether to filter text sections by block type TYPE: `bool`

field_names `class-attribute` ¶

field_names: tuple[str, ...] = (
    *(field_names),
    "section_type",
    "title",
    "author",
    "page_number",
    "page_file",
)

List of field names for sentences originating from ALTO XML content.

file_type `class-attribute` ¶

file_type: str = '.zip'

Supported file extension for ALTO zipfiles (.zip)

default_include `class-attribute` ¶

default_include: set[str] = {'text', 'footnote', 'Title'}

Default content sections to include

filter_sections `class-attribute` `instance-attribute` ¶

filter_sections: bool = True

Whether to filter text sections by block type

get_text ¶

get_text() -> Generator[dict[str, str], None, None]

Iterate over ALTO XML files contained in the zipfile and return a generator of text content.

update_current_metadata ¶

update_current_metadata(blocks: list[TextBlock]) -> None

Update current article metadata.

check_zipfile_path ¶

check_zipfile_path(zip_filepath: ZipInfo, zip_archive: ZipFile) -> None | AltoDocument

Check an individual file included in the zip archive to determine if parsing should be attempted and if it is a valid ALTO XML file. Returns AltoDocument if valid, otherwise None.

FileInput `dataclass` ¶

FileInput(input_file: Path, filename_override: str = None)

Base class for file input for sentence corpus creation

METHOD	DESCRIPTION
`include_sentence`	Return True if a sentence should be included in the corpus.
`get_text`	Get plain-text contents for this input file with any desired chunking
`get_extra_metadata`	Hook method for subclasses to override to provide extra metadata for a sentence (e.g. line number).
`get_sentences`	Get sentences for this file, with associated metadata.
`subclasses`	List of available file input classes.
`subclass_by_type`	Dictionary of subclass by supported file extension for available
`supported_types`	Unique list of supported file extensions for available input classes.
`create`	Instantiate and return the appropriate input class for the specified

ATTRIBUTE	DESCRIPTION
`input_file`	Reference to input file. Source of content for sentences. TYPE: `Path`
`filename_override`	Optional filename override, e.g. when using temporary files as input TYPE: `str`
`field_names`	List of field names for sentences from text input files. TYPE: `tuple[str, ...]`
`file_type`	Supported file extension; subclasses must define TYPE: `str`
`file_name`	Input file name. Associated with sentences in generated corpus. TYPE: `str`

input_file `instance-attribute` ¶

input_file: Path

Reference to input file. Source of content for sentences.

filename_override `class-attribute` `instance-attribute` ¶

filename_override: str = None

Optional filename override, e.g. when using temporary files as input

field_names `class-attribute` ¶

field_names: tuple[str, ...] = ('sent_id', 'file', 'sent_index', 'text')

List of field names for sentences from text input files.

file_type `class-attribute` ¶

file_type: str

Supported file extension; subclasses must define

file_name `cached` `property` ¶

file_name: str

Input file name. Associated with sentences in generated corpus.

include_sentence ¶

include_sentence(sentence: str) -> bool

Return True if a sentence should be included in the corpus.

Drops sentences that are: - Punctuation/digits-only (or the letter 'p' alone, e.g. p.) - Fewer than min_words tokens (whitespace split)

get_text ¶

get_text() -> Generator[dict[str, str]]

Get plain-text contents for this input file with any desired chunking (e.g. pages or other semantic unit). Subclasses must implement; no default implementation.

RETURNS	DESCRIPTION
`Generator[dict[str, str]]`	Generator with a dictionary of text and any other metadata that applies to this unit of text.

get_extra_metadata ¶

get_extra_metadata(
    chunk_info: dict[str, Any], _char_idx: int, sentence: str
) -> dict[str, Any]

Hook method for subclasses to override to provide extra metadata for a sentence (e.g. line number).

RETURNS	DESCRIPTION
`dict[str, Any]`	Dictionary of additional metadata fields to include, or empty dict

get_sentences ¶

get_sentences() -> Generator[dict[str, Any]]

Get sentences for this file, with associated metadata.

RETURNS	DESCRIPTION
`Generator[dict[str, Any]]`	Generator of one dictionary per sentence; dictionary always includes: `text` (text content), `file` (filename), `sent_index` (sentence index within the document), and `sent_id` (sentence id). It may include other metadata, depending on the input file type.

subclasses `classmethod` ¶

subclasses() -> list[type[Self]]

List of available file input classes.

subclass_by_type `classmethod` ¶

subclass_by_type() -> dict[str, type[Self]]

Dictionary of subclass by supported file extension for available input classes.

supported_types `classmethod` ¶

supported_types() -> list[str]

Unique list of supported file extensions for available input classes.

create `classmethod` ¶

create(input_file: Path, filename_override: str | None = None) -> Self

Instantiate and return the appropriate input class for the specified input file. Takes an optional filename override parameter, which is passed through to the input class.

RAISES	DESCRIPTION
`ValueError`	if input_file is not a supported type

TEI_TAG `module-attribute` ¶

TEI_TAG = TagNames(**{tag: f'{{TEI_NAMESPACE}}{tag}'for tag in (_fields)})

Convenience access to namespaced TEI tag names

TEIDocument ¶

Bases: BaseTEIXmlObject

Custom :class:neuxml.xmlmap.XmlObject instance for TEI XML document. Customized for MEGA TEI XML.

METHOD	DESCRIPTION
`init_from_file`	Class method to initialize a new :class:`TEIDocument` from a file.

init_from_file `classmethod` ¶

init_from_file(path: Path) -> Self

Class method to initialize a new :class:TEIDocument from a file.

TEIinput `dataclass` ¶

TEIinput(input_file: Path, filename_override: str = None)

Bases: FileInput

Input class for TEI/XML content. Takes a single input file, and yields text content by page, with page number. Customized for MEGA TEI/XML: follows standard edition page numbering and ignores pages marked as manuscript edition.

METHOD	DESCRIPTION
`__post_init__`	After default initialization, parse the input file as a
`get_text`	Get document content as plain text. The document's content is yielded in segments
`get_line_number`	Return the TEI line number for the specified text index and
`get_extra_metadata`	Calculate extra metadata including line number for a sentence in TEI documents

ATTRIBUTE	DESCRIPTION
`xml_doc`	Parsed XML document; initialized from inherited input_file TYPE: `TEIDocument`
`field_names`	List of field names for sentences from TEI XML input files TYPE: `tuple[str, ...]`
`file_type`	Supported file extension for TEI/XML input

xml_doc `class-attribute` `instance-attribute` ¶

xml_doc: TEIDocument = field(init=False)

Parsed XML document; initialized from inherited input_file

field_names `class-attribute` ¶

field_names: tuple[str, ...] = (
    *(field_names),
    "page_number",
    "section_type",
    "line_number",
)

List of field names for sentences from TEI XML input files

file_type `class-attribute` `instance-attribute` ¶

file_type = '.xml'

Supported file extension for TEI/XML input

__post_init__ ¶

__post_init__() -> None

After default initialization, parse the input file as a TEIDocument and store it as xml_doc.

get_text ¶

get_text() -> Generator[dict[str, str]]

Get document content as plain text. The document's content is yielded in segments with each segment corresponding to a dictionary of containing its text content, page number and section type ("text" or "footnote"). Body text is yielded once per page, while each footnote is yielded individually.

RETURNS	DESCRIPTION
`Generator[dict[str, str]]`	Generator with dictionaries of text content with page number and section_type ("text" or "footnote").

get_line_number ¶

get_line_number(text_index: int, char_index: int) -> int | None

Return the TEI line number for the specified text index and character index. Returns the line number at or before char_index; line number offsets must be populated by get_text(). Returns None if line number cannot be determined.

get_extra_metadata ¶

get_extra_metadata(
    chunk_info: dict[str, Any], char_idx: int, sentence: str
) -> dict[str, Any]

Calculate extra metadata including line number for a sentence in TEI documents based on the character position within the text chunk (page body or footnote).

RETURNS	DESCRIPTION
`dict[str, Any]`	Dictionary with line_number for the sentence or empty dict

TextInput `dataclass` ¶

TextInput(input_file: Path, filename_override: str = None)

Bases: FileInput

Basic text file input handling for sentence corpus creation. Takes a single text input file and returns text without chunking.

METHOD	DESCRIPTION
`get_text`	Get plain-text contents for this file with any desired chunking (e.g.

ATTRIBUTE	DESCRIPTION
`file_type`	Supported file extension for text input

file_type `class-attribute` `instance-attribute` ¶

file_type = '.txt'

Supported file extension for text input

get_text ¶

get_text() -> Generator[dict[str, str]]

Get plain-text contents for this file with any desired chunking (e.g. pages or other semantic unit). Default implementation does no chunking, no additional metadata.

RETURNS	DESCRIPTION
`Generator[dict[str, str]]`	Generator with a dictionary of text and any other metadata that applies to this unit of text.

base_input ¶

Base file input class with common functionality. Provides a factory method for initialization of known input classes based on supported file types.

To initialize the appropriate subclass for a supported file type, use FileInput.create().

For a list of supported file types across all registered input classes, use FileInput.supported_types().

Subclasses must define a supported file_type extension and implement the get_text method. For discovery, input classes must be imported in remarx.sentence.corpus.__init__ and included in __all__ to ensure they are found as available input classes.

tei_input ¶

Functionality related to parsing MEGA TEI/XML content with the goal of creating a sentence corpora with associated metadata from the TEI.

text_input ¶

Input class for handling basic text file as input for corpus creation.

quotation ¶

This module contains libraries for embedding generation and quotation detection.

MODULE	DESCRIPTION
`consolidate`	functionality for consolidating sequential quotes into passages
`embeddings`	Library for generating sentence embeddings from pretrained Sentence Transformer models.
`find_quotes`	Command-line script to identify sentence-level quotation pairs between corpora.
`pairs`	Library for finding sentence-level quote pairs.

embeddings ¶

Library for generating sentence embeddings from pretrained Sentence Transformer models.

FUNCTION	DESCRIPTION
`get_cached_embeddings`	Get sentence embeddings, with file caching based on source file.
`get_sentence_embeddings`	Extract embeddings for each sentence using the specified pretrained Sentence

get_cached_embeddings ¶

get_cached_embeddings(
    source_file: Path,
    sentences: list[str],
    model_name: str = DEFAULT_MODEL,
    show_progress_bar: bool = False,
) -> tuple[NDArray, bool]

Get sentence embeddings, with file caching based on source file.

Returns a tuple of embeddings array and a boolean indicating whether the data was loaded from cache.

get_sentence_embeddings ¶

get_sentence_embeddings(
    sentences: list[str],
    model_name: str = DEFAULT_MODEL,
    show_progress_bar: bool = False,
) -> NDArray

Extract embeddings for each sentence using the specified pretrained Sentence Transformers model (default is paraphrase-multilingual-mpnet-base-v2). Returns a numpy array of the embeddings with shape [# sents, # dims].

PARAMETER	DESCRIPTION
`sentences`	List of sentences to generate embeddings for TYPE: `list[str]`
`model_name`	Name of the pretrained sentence transformer model to use (default: paraphrase-multilingual-mpnet-base-v2) TYPE: `str` DEFAULT: `DEFAULT_MODEL`

RETURNS	DESCRIPTION
`NDArray`	2-dimensional numpy array of normalized sentence embeddings with shape [# sents, # dims]

consolidate ¶

functionality for consolidating sequential quotes into passages

FUNCTION	DESCRIPTION
`identify_sequences`	Given a polars dataframe, identify and label rows that are sequential
`consolidate_quotes`	Consolidate quotes that are sequential in both original and reuse texts.

identify_sequences ¶

identify_sequences(df: DataFrame, field: str, group_field: str) -> DataFrame

Given a polars dataframe, identify and label rows that are sequential for the specified field, within the specified group field. Returns a modified dataframe with the following columns, prefixed by field name:

_sequential : boolean indicating whether a row is in a sequence,
_group : group identifier; uses field value for first in sequence

consolidate_quotes ¶

consolidate_quotes(df: DataFrame) -> DataFrame

Consolidate quotes that are sequential in both original and reuse texts. Required fields:

reuse_sent_index and original_sent_index must be present for aggregation, and must be numeric
reuse_file and original_file must be present to ensure aggregation only happens for sequences within specific input files

If required fields are not present, raises polars.exceptions.ColumnNotFoundError. Raises ValueError when called on an empty dataframe.

Consolidation only occurs when:

Sentences are sequential in both reuse and original corpora
All sentences within a sequence belong to a single reuse corpus and original corpus (seemingly sequential sentences that span multiple files are not consolidated)

DataFrame is expected to include standard quote pair fields; for consolidated quotes, fields are aggregated as follows:

match_score average across the group
id and sent_index (both reuse and original): first value in group
reuse_text and original_text: combined with whitespace delimiter
For all other fields, unique values are combined, delimited by semicolon and space

The returned DataFrame includes a new column num_sentences which documents the number of sentences in a group (1 for unconsolidated quotes).

pairs ¶

Library for finding sentence-level quote pairs.

Note: Currently this script only supports one original and reuse corpus.

FUNCTION	DESCRIPTION
`build_vector_index`	Builds an index for a given set of embeddings with the specified
`get_sentence_pairs`	Given an array of original and reuse sentence embeddings, identify pairs
`load_sent_corpus`	Takes a sentence corpus file and loads it into a polars DataFrame,
`compile_quote_pairs`	Combine sentence metadata from original and reuse corpora with detected
`find_quote_pairs`	For a set of original sentence corpora and one reuse sentence corpus, finds

build_vector_index ¶

build_vector_index(embeddings: NDArray) -> Index

Builds an index for a given set of embeddings with the specified number of trees.

get_sentence_pairs ¶

get_sentence_pairs(
    original_vecs: NDArray,
    reuse_vecs: NDArray,
    score_cutoff: float,
    show_progress_bar: bool = False,
) -> DataFrame

Given an array of original and reuse sentence embeddings, identify pairs of original-reuse pairs with high similarity (i.e., likely quotation). Returns a polars DataFrame of sentence pairs with the following information:

original_index: the index of the original sentence
reuse_index: the index of the reuse sentence
match_score: the quality of the match

Uses embeddings and a vector index to find the nearest original sentence for each reuse sentence. Sentence pairs are filtered to those pairs with a match score (cosine similarity) above the specified cutoff.

load_sent_corpus ¶

load_sent_corpus(
    sentence_corpus: Path, col_pfx: str | None = None, show_progress_bar: bool = False
) -> tuple[DataFrame, NDArray]

Takes a sentence corpus file and loads it into a polars DataFrame, and generates sentence embeddings for the text of each sentence in the corpus. Optionally supports adding a prefix to all column names in the DataFrame.

The resulting dataframe has the same fields as the input corpus, with the following adjustments:

a new field index corresponding to the row index
the sentence id field sent_id is renamed to id
all field names are prefixed if a column prefix is specified

Returns a tuple of DataFrame and numpy array with embeddings vectors

compile_quote_pairs ¶

compile_quote_pairs(
    original_corpus: DataFrame, reuse_corpus: DataFrame, detected_pairs: DataFrame
) -> DataFrame

Combine sentence metadata from original and reuse corpora with detected sentence pair identifiers to form quote pairs. The original and reuse corpus dataframes must contain a row index column named original_index and reuse_index respectively. Ideally, these dataframes should be built using load_sent_corpus.

Returns a dataframe with the following fields:

match_score: Estimated quality of the match
All other fields in order from the reuse corpus except row index
All other fields in order from the original corpus except row index

find_quote_pairs ¶

find_quote_pairs(
    original_corpus: list[Path],
    reuse_corpus: Path,
    output_path: Path,
    score_cutoff: float = 0.225,
    consolidate: bool = True,
    show_progress_bar: bool = False,
    benchmark: bool = False,
) -> None

For a set of original sentence corpora and one reuse sentence corpus, finds the likely sentence-level quote pairs, which are saved as a CSV file.

Optional parameters allow configuring the score_cutoff for threshold to include quote pairs, and consolidation of consecutive sentences (on by default). When benchmark is enabled, summary information is logged to report on corpus size and timings to generate embeddings and search for pairs.

remarx ¶

utils ¶

CorpusPath dataclass ¶

__post_init__ ¶

ready ¶

ensure_directories ¶

get_default_corpus_path ¶

get_default_quote_output_path ¶

configure_logging ¶

app ¶

utils ¶

lifespan async ¶

redirect_root async ¶

launch_app ¶

get_current_log_file ¶

create_header ¶

create_temp_input ¶

summarize_corpus_selection ¶

handle_default_corpus_creation ¶

sentence ¶

segment ¶

segment_text ¶

corpus ¶

ALTOInput dataclass ¶

field_names class-attribute ¶

file_type class-attribute ¶

default_include class-attribute ¶

filter_sections class-attribute instance-attribute ¶

get_text ¶

update_current_metadata ¶

check_zipfile_path ¶

FileInput dataclass ¶

input_file instance-attribute ¶

filename_override class-attribute instance-attribute ¶

field_names class-attribute ¶

file_type class-attribute ¶

file_name cached property ¶

include_sentence ¶

get_text ¶

get_extra_metadata ¶

get_sentences ¶

subclasses classmethod ¶

subclass_by_type classmethod ¶

supported_types classmethod ¶

create classmethod ¶

TEI_TAG module-attribute ¶

TEIDocument ¶

init_from_file classmethod ¶

TEIinput dataclass ¶

xml_doc class-attribute instance-attribute ¶

field_names class-attribute ¶

file_type class-attribute instance-attribute ¶

__post_init__ ¶

get_text ¶

get_line_number ¶

get_extra_metadata ¶

TextInput dataclass ¶

file_type class-attribute instance-attribute ¶

get_text ¶

base_input ¶

tei_input ¶

text_input ¶

quotation ¶

embeddings ¶

get_cached_embeddings ¶

get_sentence_embeddings ¶

consolidate ¶

identify_sequences ¶

consolidate_quotes ¶

pairs ¶

build_vector_index ¶

get_sentence_pairs ¶

load_sent_corpus ¶

compile_quote_pairs ¶

find_quote_pairs ¶

CorpusPath `dataclass` ¶

lifespan `async` ¶

redirect_root `async` ¶

ALTOInput `dataclass` ¶

field_names `class-attribute` ¶

file_type `class-attribute` ¶

default_include `class-attribute` ¶

filter_sections `class-attribute` `instance-attribute` ¶

FileInput `dataclass` ¶

input_file `instance-attribute` ¶

filename_override `class-attribute` `instance-attribute` ¶

field_names `class-attribute` ¶

file_type `class-attribute` ¶

file_name `cached` `property` ¶

subclasses `classmethod` ¶

subclass_by_type `classmethod` ¶

supported_types `classmethod` ¶

create `classmethod` ¶

TEI_TAG `module-attribute` ¶

init_from_file `classmethod` ¶

TEIinput `dataclass` ¶

xml_doc `class-attribute` `instance-attribute` ¶

field_names `class-attribute` ¶

file_type `class-attribute` `instance-attribute` ¶

TextInput `dataclass` ¶

file_type `class-attribute` `instance-attribute` ¶