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chore: import upstream snapshot with attribution
2026-07-13 13:33:56 +08:00

200 lines
9.2 KiB
Python

"""Implementation of chunking by title.
Main entry point is the `@add_chunking_strategy()` decorator.
"""
from __future__ import annotations
from functools import cached_property
from typing import Iterable, Iterator, Optional
from unstructured.chunking.base import (
CHUNK_MULTI_PAGE_DEFAULT,
BoundaryPredicate,
ChunkingOptions,
PreChunkCombiner,
PreChunker,
is_on_next_page,
is_title,
)
from unstructured.documents.elements import Element
def chunk_by_title(
elements: Iterable[Element],
*,
combine_text_under_n_chars: Optional[int] = None,
include_orig_elements: Optional[bool] = None,
max_characters: Optional[int] = None,
max_tokens: Optional[int] = None,
multipage_sections: Optional[bool] = None,
new_after_n_chars: Optional[int] = None,
new_after_n_tokens: Optional[int] = None,
overlap: Optional[int] = None,
overlap_all: Optional[bool] = None,
tokenizer: Optional[str] = None,
repeat_table_headers: Optional[bool] = None,
skip_table_chunking: Optional[bool] = None,
isolate_table: Optional[bool] = None,
) -> list[Element]:
"""Uses title elements to identify sections within the document for chunking.
Splits off into a new CompositeElement when a title is detected or if metadata changes, which
happens when page numbers or sections change. Cuts off sections once they have exceeded a
character length of max_characters (or token count of max_tokens).
Parameters
----------
elements
A list of unstructured elements. Usually the output of a partition function.
combine_text_under_n_chars
Combines elements (for example a series of titles) until a section reaches a length of
n characters. Defaults to `max_characters` which combines chunks whenever space allows.
Specifying 0 for this argument suppresses combining of small chunks. Note this value is
"capped" at the `new_after_n_chars` value since a value higher than that would not change
this parameter's effect.
include_orig_elements
When `True` (default), add elements from pre-chunk to the `.metadata.orig_elements` field
of the chunk(s) formed from that pre-chunk. Among other things, this allows access to
original-element metadata that cannot be consolidated and is dropped in the course of
chunking.
max_characters
Chunks elements text and text_as_html (if present) into chunks of length
n characters (hard max). Mutually exclusive with `max_tokens`.
max_tokens
Chunks elements into chunks of n tokens (hard max). Requires `tokenizer` to be specified.
Mutually exclusive with `max_characters`.
multipage_sections
If True, sections can span multiple pages. Defaults to True.
new_after_n_chars
Cuts off new sections once they reach a length of n characters (soft max). Defaults to
`max_characters` when not specified, which effectively disables any soft window.
Specifying 0 for this argument causes each element to appear in a chunk by itself (although
an element with text longer than `max_characters` will be still be split into two or more
chunks).
new_after_n_tokens
Token-based equivalent of `new_after_n_chars`. Cuts off new sections once they reach
n tokens (soft max). Requires `max_tokens` and `tokenizer` to be specified.
overlap
Specifies the length of a string ("tail") to be drawn from each chunk and prefixed to the
next chunk as a context-preserving mechanism. By default, this only applies to split-chunks
where an oversized element is divided into multiple chunks by text-splitting.
overlap_all
Default: `False`. When `True`, apply overlap between "normal" chunks formed from whole
elements and not subject to text-splitting. Use this with caution as it entails a certain
level of "pollution" of otherwise clean semantic chunk boundaries.
tokenizer
The tokenizer to use for token-based chunking. Can be either an encoding name (e.g.,
"cl100k_base") or a model name (e.g., "gpt-4"). Required when using `max_tokens`.
repeat_table_headers
Default: `True`. When `True`, repeated table-header behavior is enabled for chunked table
continuations. Specify `False` to opt out and preserve legacy table-chunk behavior.
skip_table_chunking
Default: `False`. When `True`, `Table` elements are passed through unchanged without
being split into `TableChunk` elements, regardless of their size.
isolate_table
Default: `True`. When `True`, `Table` and `TableChunk` elements are always staged in
their own pre-chunk and never combined with adjacent non-table elements. Specify
`False` to allow tables to share pre-chunks with adjacent elements (the pre-#4307
behavior).
"""
opts = _ByTitleChunkingOptions.new(
combine_text_under_n_chars=combine_text_under_n_chars,
include_orig_elements=include_orig_elements,
max_characters=max_characters,
max_tokens=max_tokens,
multipage_sections=multipage_sections,
new_after_n_chars=new_after_n_chars,
new_after_n_tokens=new_after_n_tokens,
overlap=overlap,
overlap_all=overlap_all,
tokenizer=tokenizer,
repeat_table_headers=repeat_table_headers,
skip_table_chunking=skip_table_chunking,
isolate_table=isolate_table,
)
return _chunk_by_title(elements, opts)
def _chunk_by_title(elements: Iterable[Element], opts: _ByTitleChunkingOptions) -> list[Element]:
"""Implementation of actual "by-title" chunking."""
# -- Note(scanny): it might seem like over-abstraction for this to be a separate function but
# -- it eases overriding or adding individual chunking options when customizing a stock chunker.
pre_chunks = PreChunkCombiner(
PreChunker.iter_pre_chunks(elements, opts), opts=opts
).iter_combined_pre_chunks()
return [chunk for pre_chunk in pre_chunks for chunk in pre_chunk.iter_chunks()]
class _ByTitleChunkingOptions(ChunkingOptions):
"""Adds the by-title-specific chunking options to the base case.
`by_title`-specific options:
combine_text_under_n_chars
A remedy to over-chunking caused by elements mis-identified as Title elements.
Every Title element would start a new chunk and this setting mitigates that, at the
expense of sometimes violating legitimate semantic boundaries.
multipage_sections
Indicates that page-boundaries should not be respected while chunking, i.e. elements
appearing on two different pages can appear in the same chunk.
"""
@cached_property
def boundary_predicates(self) -> tuple[BoundaryPredicate, ...]:
"""The semantic-boundary detectors to be applied to break pre-chunks.
For the `by_title` strategy these are sections indicated by a title (section-heading), an
explicit section metadata item (only present for certain document types), and optionally
page boundaries.
"""
def iter_boundary_predicates() -> Iterator[BoundaryPredicate]:
yield is_title
if not self.multipage_sections:
yield is_on_next_page()
return tuple(iter_boundary_predicates())
@cached_property
def combine_text_under_n_chars(self) -> int:
"""Combine consecutive text pre-chunks if former is smaller than this and both will fit.
- Does not combine text chunks if together they would exceed the chunking window.
- Defaults to `max_characters` when not specified.
- Is reduced to `new_after_n_chars` when it exceeds that value.
"""
# -- `combine_text_under_n_chars` defaults to `max_characters` when not specified --
arg_value = self._kwargs.get("combine_text_under_n_chars")
return self.hard_max if arg_value is None else arg_value
@cached_property
def multipage_sections(self) -> bool:
"""When False, break pre-chunks on page-boundaries."""
arg_value = self._kwargs.get("multipage_sections")
return CHUNK_MULTI_PAGE_DEFAULT if arg_value is None else bool(arg_value)
def _validate(self) -> None:
"""Raise ValueError if request option-set is invalid."""
# -- start with base-class validations --
super()._validate()
# -- `combine_text_under_n_chars == 0` is valid (suppresses chunk combination)
# -- but a negative value is not
if self.combine_text_under_n_chars < 0:
raise ValueError(
f"'combine_text_under_n_chars' argument must be >= 0,"
f" got {self.combine_text_under_n_chars}"
)
# -- `combine_text_under_n_chars` > `max_characters` can produce behavior confusing to
# -- users. The chunking behavior would be no different than when
# -- `combine_text_under_n_chars == max_characters`, but if `max_characters` is left to
# -- default (500) then it can look like chunk-combining isn't working.
if self.combine_text_under_n_chars > self.hard_max:
raise ValueError(
f"'combine_text_under_n_chars' argument must not exceed `max_characters`"
f" value, got {self.combine_text_under_n_chars} > {self.hard_max}"
)