Skip to content

chunklet.document_chunker.document_chunker

Classes:

  • DocumentChunker –

    A comprehensive document chunker that handles various file formats.

DocumentChunker 

DocumentChunker(
    sentence_splitter: Any | None = None,
    verbose: bool = False,
    continuation_marker: str = "...",
    token_counter: Callable[[str], int] | None = None,
)

Bases: BaseChunker

A comprehensive document chunker that handles various file formats.

This class provides a high-level interface to chunk text from different document types. It automatically detects the file format and uses the appropriate method to extract content before passing it to an underlying PlainTextChunker instance.

Key Features: - Multi-Format Support: Chunks text from PDF, TXT, MD, and RST files. - Metadata Enrichment: Automatically adds source file path and other document-level metadata (e.g., PDF page numbers) to each chunk. - Bulk Processing: Efficiently chunks multiple documents in a single call. - Pluggable Document processors: Integrate custom processors allowing definition of specific logic for extracting text from various file types.

Initializes the DocumentChunker.

Parameters:

  • sentence_splitter

    (BaseSplitter | None, default: None ) –

    An optional BaseSplitter instance. If None, a default SentenceSplitter will be initialized.

  • verbose

    (bool, default: False ) –

    Enable verbose logging.

  • continuation_marker

    (str, default: '...' ) –

    The marker to prepend to unfitted clauses. Defaults to '...'.

  • token_counter

    (Callable[[str], int] | None, default: None ) –

    Function that counts tokens in text. If None, must be provided when calling chunk() methods.

Raises:

  • InvalidInputError –

    If any of the input arguments are invalid or if the provided sentence_splitter is not an instance of BaseSplitter.

Methods:

  • batch_chunk –

    Chunks multiple documents from a list of file paths.

  • chunk –

    Chunks a single document from a given path.

Attributes:

Source code in src/chunklet/document_chunker/document_chunker.py 
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
def __init__(
    self,
    sentence_splitter: Any | None = None,
    verbose: bool = False,
    continuation_marker: str = "...",
    token_counter: Callable[[str], int] | None = None,
):
    """
    Initializes the DocumentChunker.

    Args:
        sentence_splitter (BaseSplitter | None): An optional BaseSplitter instance.
            If None, a default SentenceSplitter will be initialized.
        verbose (bool): Enable verbose logging.
        continuation_marker (str): The marker to prepend to unfitted clauses. Defaults to '...'.
        token_counter (Callable[[str], int] | None): Function that counts tokens in text.
            If None, must be provided when calling chunk() methods.

    Raises:
        InvalidInputError: If any of the input arguments are invalid or if the provided `sentence_splitter` is not an instance of `BaseSplitter`.
    """
    self._verbose = verbose
    self.token_counter = token_counter
    self.continuation_marker = continuation_marker

    # Explicit type validation for sentence_splitter
    if sentence_splitter is not None and not isinstance(
        sentence_splitter, BaseSplitter
    ):
        raise InvalidInputError(
            f"The provided sentence_splitter must be an instance of BaseSplitter, "
            f"but got {type(sentence_splitter).__name__}."
        )

    self.plain_text_chunker = PlainTextChunker(
        sentence_splitter=sentence_splitter,
        verbose=self._verbose,
        continuation_marker=self.continuation_marker,
        token_counter=self.token_counter,
    )

    self.processors = {
        ".pdf": pdf_processor.PDFProcessor,
        ".epub": epub_processor.EPUBProcessor,
        ".docx": docx_processor.DOCXProcessor,
        ".odt": odt_processor.ODTProcessor,
    }
    self.converters = {
        ".html": html_2_md.html_to_md,
        ".hml": html_2_md.html_to_md,
        ".rst": rst_2_md.rst_to_md,
        ".tex": latex_2_md.latex_to_md,
        ".csv": table_2_md.table_to_md,
        ".xlsx": table_2_md.table_to_md,
    }
    self.processor_registry = CustomProcessorRegistry()

supported_extensions property 

supported_extensions

Get the supported extensions, including the custom ones.

verbose property writable 

verbose: bool

Get the verbosity status.

batch_chunk 

batch_chunk(
    paths: restricted_iterable(str | Path),
    *,
    lang: str = "auto",
    max_tokens: Annotated[int | None, Field(ge=12)] = None,
    max_sentences: Annotated[
        int | None, Field(ge=1)
    ] = None,
    max_section_breaks: Annotated[
        int | None, Field(ge=1)
    ] = None,
    overlap_percent: Annotated[
        int, Field(ge=0, le=75)
    ] = 20,
    offset: Annotated[int, Field(ge=0)] = 0,
    token_counter: Callable[[str], int] | None = None,
    separator: Any = None,
    n_jobs: Annotated[int, Field(ge=1)] | None = None,
    show_progress: bool = True,
    on_errors: Literal["raise", "skip", "break"] = "raise"
) -> Generator[Box, None, None]

Chunks multiple documents from a list of file paths.

This method is a memory-efficient generator that yields chunks as they are processed, without loading all documents into memory at once. It handles various file types.

Parameters:

  • paths

    (restricted_iterable[str | Path]) –

    A restricted iterable of paths to the document files.

  • lang

    (str, default: 'auto' ) –

    The language of the text (e.g., 'en', 'fr', 'auto'). Defaults to "auto".

  • max_tokens

    (int, default: None ) –

    Maximum number of tokens per chunk. Must be >= 12.

  • max_sentences

    (int, default: None ) –

    Maximum number of sentences per chunk. Must be >= 1.

  • max_section_breaks

    (int, default: None ) –

    Maximum number of section breaks per chunk. Must be >= 1.

  • overlap_percent

    (int | float, default: 20 ) –

    Percentage of overlap between chunks (0-85).

  • offset

    (int, default: 0 ) –

    Starting sentence offset for chunking. Defaults to 0.

  • token_counter

    (callable | None, default: None ) –

    Optional token counting function. Required if max_tokens is provided.

  • separator

    (Any, default: None ) –

    A value to be yielded after the chunks of each text are processed. Note: None cannot be used as a separator.

  • n_jobs

    (int | None, default: None ) –

    Number of parallel workers to use. If None, uses all available CPUs. Must be >= 1 if specified.

  • show_progress

    (bool, default: True ) –

    Flag to show or disable the loading bar.

  • on_errors

    (Literal['raise', 'skip', 'break'], default: 'raise' ) –

    How to handle errors during processing. Can be 'raise', 'ignore', or 'break'.

Yields:

  • Box ( Box ) –

    Box object, representing a chunk with its content and metadata.

Raises:

Source code in src/chunklet/document_chunker/document_chunker.py 
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
@validate_input
def batch_chunk(
    self,
    paths: restricted_iterable(str | Path),
    *,
    lang: str = "auto",
    max_tokens: Annotated[int | None, Field(ge=12)] = None,
    max_sentences: Annotated[int | None, Field(ge=1)] = None,
    max_section_breaks: Annotated[int | None, Field(ge=1)] = None,
    overlap_percent: Annotated[int, Field(ge=0, le=75)] = 20,
    offset: Annotated[int, Field(ge=0)] = 0,
    token_counter: Callable[[str], int] | None = None,
    separator: Any = None,
    n_jobs: Annotated[int, Field(ge=1)] | None = None,
    show_progress: bool = True,
    on_errors: Literal["raise", "skip", "break"] = "raise",
) -> Generator[Box, None, None]:
    """
    Chunks multiple documents from a list of file paths.

    This method is a memory-efficient generator that yields chunks as they
    are processed, without loading all documents into memory at once. It
    handles various file types.

    Args:
        paths (restricted_iterable[str | Path]): A restricted iterable of paths to the document files.
        lang (str): The language of the text (e.g., 'en', 'fr', 'auto'). Defaults to "auto".
        max_tokens (int, optional): Maximum number of tokens per chunk. Must be >= 12.
        max_sentences (int, optional): Maximum number of sentences per chunk. Must be >= 1.
        max_section_breaks (int, optional): Maximum number of section breaks per chunk. Must be >= 1.
        overlap_percent (int | float): Percentage of overlap between chunks (0-85).
        offset (int): Starting sentence offset for chunking. Defaults to 0.
        token_counter (callable | None): Optional token counting function.
            Required if `max_tokens` is provided.
        separator (Any): A value to be yielded after the chunks of each text are processed.
            Note: None cannot be used as a separator.

        n_jobs (int | None): Number of parallel workers to use. If None, uses all available CPUs.
               Must be >= 1 if specified.
        show_progress (bool): Flag to show or disable the loading bar.
        on_errors: How to handle errors during processing. Can be 'raise', 'ignore', or 'break'.

    yields:
        Box: `Box` object, representing a chunk with its content and metadata.

    Raises:
        InvalidInputError: If the input arguments aren't valid.
        FileNotFoundError: If provided file path not found.
        UnsupportedFileTypeError: If the file extension is not supported or is missing.
        MissingTokenCounterError: If `max_tokens` is provided but no `token_counter` is provided.
        CallbackError: If a callback function (e.g., custom processors callbacks) fails during execution.
    """
    sentinel = object()

    gathered_data = self._gather_all_data(paths, on_errors)

    all_chunks_gen = self.plain_text_chunker.batch_chunk(
        texts=gathered_data["all_texts_gen"],
        lang=lang,
        max_tokens=max_tokens,
        max_sentences=max_sentences,
        max_section_breaks=max_section_breaks,
        overlap_percent=overlap_percent,
        offset=offset,
        token_counter=token_counter or self.token_counter,
        separator=sentinel,
        n_jobs=n_jobs,
        show_progress=show_progress,
        on_errors=on_errors,
    )

    all_chunk_groups = split_at(all_chunks_gen, lambda x: x is sentinel)
    path_section_counts = gathered_data["path_section_counts"]
    all_metadata = gathered_data["all_metadata"]

    # HACK: Since a sentinel is always at the end of the gen,
    # the last list of the groups will be an empty one.
    # The only work-around to add a sentinel at paths
    paths = list(path_section_counts.keys()) + [None]

    # If no files were successfully processed, return empty
    if not path_section_counts:
        return

    doc_count = 0
    curr_path = paths[0]
    for chunks in all_chunk_groups:
        if path_section_counts.get(curr_path, 0) == 0:
            if separator is not None:
                yield separator

            doc_count += 1
            curr_path = paths[doc_count]
            if curr_path is None:
                return

        for i, ch in enumerate(chunks, start=1):
            doc_metadata = all_metadata[doc_count]
            doc_metadata["section_count"] = path_section_counts[curr_path]
            doc_metadata["curr_section"] = i

            ch["metadata"].update(doc_metadata)
            yield ch

        path_section_counts[curr_path] -= 1

chunk 

chunk(
    path: str | Path,
    *,
    lang: str = "auto",
    max_tokens: Annotated[int | None, Field(ge=12)] = None,
    max_sentences: Annotated[
        int | None, Field(ge=1)
    ] = None,
    max_section_breaks: Annotated[
        int | None, Field(ge=1)
    ] = None,
    overlap_percent: Annotated[
        int, Field(ge=0, le=75)
    ] = 20,
    offset: Annotated[int, Field(ge=0)] = 0,
    token_counter: Callable[[str], int] | None = None
) -> list[Box]

Chunks a single document from a given path.

This method automatically detects the file type and uses the appropriate processor to extract text before chunking. It then adds document-level metadata to each resulting chunk.

Parameters:

  • path

    (str | Path) –

    The path to the document file.

  • lang

    (str, default: 'auto' ) –

    The language of the text (e.g., 'en', 'fr', 'auto'). Defaults to "auto".

  • max_tokens

    (int, default: None ) –

    Maximum number of tokens per chunk. Must be >= 12.

  • max_sentences

    (int, default: None ) –

    Maximum number of sentences per chunk. Must be >= 1.

  • max_section_breaks

    (int, default: None ) –

    Maximum number of section breaks per chunk. Must be >= 1.

  • overlap_percent

    (int | float, default: 20 ) –

    Percentage of overlap between chunks (0-85).

  • offset

    (int, default: 0 ) –

    Starting sentence offset for chunking. Defaults to 0.

  • token_counter

    (callable | None, default: None ) –

    Optional token counting function. Required if max_tokens is provided.

Returns:

  • list[Box] –

    list[Box]: A list of Box objects, each representing

  • list[Box] –

    a chunk with its content and metadata.

Raises:

Source code in src/chunklet/document_chunker/document_chunker.py 
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
@validate_input
def chunk(
    self,
    path: str | Path,
    *,
    lang: str = "auto",
    max_tokens: Annotated[int | None, Field(ge=12)] = None,
    max_sentences: Annotated[int | None, Field(ge=1)] = None,
    max_section_breaks: Annotated[int | None, Field(ge=1)] = None,
    overlap_percent: Annotated[int, Field(ge=0, le=75)] = 20,
    offset: Annotated[int, Field(ge=0)] = 0,
    token_counter: Callable[[str], int] | None = None,
) -> list[Box]:
    """
    Chunks a single document from a given path.

    This method automatically detects the file type and uses the appropriate
    processor to extract text before chunking. It then adds document-level
    metadata to each resulting chunk.

    Args:
        path (str | Path): The path to the document file.
        lang (str): The language of the text (e.g., 'en', 'fr', 'auto'). Defaults to "auto".
        max_tokens (int, optional): Maximum number of tokens per chunk. Must be >= 12.
        max_sentences (int, optional): Maximum number of sentences per chunk. Must be >= 1.
        max_section_breaks (int, optional): Maximum number of section breaks per chunk. Must be >= 1.
        overlap_percent (int | float): Percentage of overlap between chunks (0-85).
        offset (int): Starting sentence offset for chunking. Defaults to 0.
        token_counter (callable | None): Optional token counting function.
            Required if `max_tokens` is provided.

    Returns:
        list[Box]: A list of `Box` objects, each representing
        a chunk with its content and metadata.

    Raises:
        InvalidInputError: If the input arguments aren't valid.
        FileNotFoundError: If provided file path not found.
        UnsupportedFileTypeError: If the file extension is not supported or is missing.
        MissingTokenCounterError: If `max_tokens` is provided but no `token_counter` is provided.
        CallbackError: If a callback function (e.g., custom processors callbacks) fails during execution.
    """
    path = Path(path)
    ext = self._validate_and_get_extension(path)

    text_content_or_generator, document_metadata = self._extract_data(path, ext)

    if not isinstance(text_content_or_generator, str):
        raise UnsupportedFileTypeError(
            f"File type '{ext}' is not supported by the general chunk method.\n"
            "Reason: The processor for this file returns iterable, "
            "so it must be processed in parallel for efficiency.\n"
            "💡 Hint: use `chunker.batch_chunk([file.ext])` for this file type."
        )

    self.log_info("Starting chunk processing for path: {}.", path)

    text_content = text_content_or_generator

    # Process as a single block of text
    chunk_boxes = self.plain_text_chunker.chunk(
        text=text_content,
        lang=lang,
        max_tokens=max_tokens,
        max_sentences=max_sentences,
        max_section_breaks=max_section_breaks,
        overlap_percent=overlap_percent,
        offset=offset,
        token_counter=token_counter or self.token_counter,
        base_metadata=document_metadata,
    )

    self.log_info("Generated {} chunks for {}.", len(chunk_boxes), path)

    return chunk_boxes