From b04fc663f069735322f6851477d1e83bb5bd875e Mon Sep 17 00:00:00 2001 From: Anthony Shaw Date: Wed, 6 Mar 2024 11:14:40 +1100 Subject: [PATCH] Improve text splitter for non-English documents (#1326) * Add tests to verify that tokens in each section can never be above 500 tokens * Improve the test so we parametrize it for each PDF instead of failing on the first one. * Add arabic book * Formatting fixes * Verbose assertion messages with the file name * Implement a recursive splitter when sections are too large. * 5 percent overlap with recursive splitter * Resolve some of the PR feedback * Make the tests set to the max tokens per section of the text splitter instead of hardcoded values * Find a better split position in the central third of the text * Correct the boundary check and fix the position to come after the full stops * Remove some silly line breaks * Update import * reformatting * Make the overlap percent and section size defaults module level constants * Reformatted PDFs using the online PDF parser for better accessibility * Add an RTL test * Add a korean test file about a mouse that goes to the big city. Add a test for table overlapping. * Add a snapshot of the content sections from the test data PDFs * Fix my formatting * Sort the keys first --------- Co-authored-by: Pamela Fox --- requirements-dev.txt | 1 + scripts/prepdocslib/textsplitter.py | 116 +++- .../text_splitter_sections.txt | 635 ++++++++++++++++++ .../test-data/ar_Tribute to Michael Hart.pdf | Bin 0 -> 138927 bytes .../en_An Occurrence at Owl Creek Bridge.pdf | Bin 0 -> 127176 bytes tests/test-data/ja_RTL_TopToBottom_Test.pdf | Bin 0 -> 114813 bytes .../ja_\346\203\241\351\255\224.pdf" | Bin 0 -> 692833 bytes ...353\241\234 \352\260\204 \354\245\220.pdf" | Bin 0 -> 105329 bytes ...\345\255\270\347\223\212\346\236\227).pdf" | Bin 0 -> 1430090 bytes tests/test_prepdocslib_textsplitter.py | 92 ++- 10 files changed, 834 insertions(+), 10 deletions(-) create mode 100644 tests/snapshots/test_prepdocslib_textsplitter/test_sentencetextsplitter_list_parse_and_split/text_splitter_sections.txt create mode 100644 tests/test-data/ar_Tribute to Michael Hart.pdf create mode 100644 tests/test-data/en_An Occurrence at Owl Creek Bridge.pdf create mode 100644 tests/test-data/ja_RTL_TopToBottom_Test.pdf create mode 100644 "tests/test-data/ja_\346\203\241\351\255\224.pdf" create mode 100644 "tests/test-data/ko_\353\217\204\354\213\234\353\241\234 \352\260\204 \354\245\220.pdf" create mode 100644 "tests/test-data/zh_You Xue Qiong Lin (\345\271\274\345\255\270\347\223\212\346\236\227).pdf" diff --git a/requirements-dev.txt b/requirements-dev.txt index f1de6bf8e9..0786cc68eb 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -9,6 +9,7 @@ coverage playwright pytest-cov pytest-playwright +pytest-snapshot pre-commit locust pip-tools diff --git a/scripts/prepdocslib/textsplitter.py b/scripts/prepdocslib/textsplitter.py index 928aedd7e6..8f06245a1d 100644 --- a/scripts/prepdocslib/textsplitter.py +++ b/scripts/prepdocslib/textsplitter.py @@ -1,6 +1,8 @@ from abc import ABC from typing import Generator, List +import tiktoken + from .page import Page, SplitPage @@ -16,20 +18,118 @@ def split_pages(self, pages: List[Page]) -> Generator[SplitPage, None, None]: yield # pragma: no cover - this is necessary for mypy to type check +ENCODING_MODEL = "text-embedding-ada-002" + +STANDARD_WORD_BREAKS = [",", ";", ":", " ", "(", ")", "[", "]", "{", "}", "\t", "\n"] + +# See W3C document https://www.w3.org/TR/jlreq/#cl-01 +CJK_WORD_BREAKS = [ + "、", + ",", + ";", + ":", + "(", + ")", + "【", + "】", + "「", + "」", + "『", + "』", + "〔", + "〕", + "〈", + "〉", + "《", + "》", + "〖", + "〗", + "〘", + "〙", + "〚", + "〛", + "〝", + "〞", + "〟", + "〰", + "–", + "—", + "‘", + "’", + "‚", + "‛", + "“", + "”", + "„", + "‟", + "‹", + "›", +] + +STANDARD_SENTENCE_ENDINGS = [".", "!", "?"] + +# See CL05 and CL06, based on JIS X 4051:2004 +# https://www.w3.org/TR/jlreq/#cl-04 +CJK_SENTENCE_ENDINGS = ["。", "!", "?", "‼", "⁇", "⁈", "⁉"] + +# NB: text-embedding-3-XX is the same BPE as text-embedding-ada-002 +bpe = tiktoken.encoding_for_model(ENCODING_MODEL) + +DEFAULT_OVERLAP_PERCENT = 10 # See semantic search article for 10% overlap performance +DEFAULT_SECTION_LENGTH = 1000 # Roughly 400-500 tokens for English + + class SentenceTextSplitter(TextSplitter): """ Class that splits pages into smaller chunks. This is required because embedding models may not be able to analyze an entire page at once """ - def __init__(self, has_image_embeddings: bool, verbose: bool = False): - self.sentence_endings = [".", "!", "?"] - self.word_breaks = [",", ";", ":", " ", "(", ")", "[", "]", "{", "}", "\t", "\n"] - self.max_section_length = 1000 + def __init__(self, has_image_embeddings: bool, verbose: bool = False, max_tokens_per_section: int = 500): + self.sentence_endings = STANDARD_SENTENCE_ENDINGS + CJK_SENTENCE_ENDINGS + self.word_breaks = STANDARD_WORD_BREAKS + CJK_WORD_BREAKS + self.max_section_length = DEFAULT_SECTION_LENGTH self.sentence_search_limit = 100 - self.section_overlap = 100 + self.max_tokens_per_section = max_tokens_per_section + self.section_overlap = self.max_section_length // DEFAULT_OVERLAP_PERCENT self.verbose = verbose self.has_image_embeddings = has_image_embeddings + def split_page_by_max_tokens(self, page_num: int, text: str) -> Generator[SplitPage, None, None]: + """ + Recursively splits page by maximum number of tokens to better handle languages with higher token/word ratios. + """ + tokens = bpe.encode(text) + if len(tokens) <= self.max_tokens_per_section: + # Section is already within max tokens, return + yield SplitPage(page_num=page_num, text=text) + else: + # Start from the center and try and find the closest sentence ending by spiralling outward. + # IF we get to the outer thirds, then just split in half with a 5% overlap + start = int(len(text) // 2) + pos = 0 + boundary = int(len(text) // 3) + split_position = -1 + while start - pos > boundary: + if text[start - pos] in self.sentence_endings: + split_position = start - pos + break + elif text[start + pos] in self.sentence_endings: + split_position = start + pos + break + else: + pos += 1 + + if split_position > 0: + first_half = text[: split_position + 1] + second_half = text[split_position + 1 :] + else: + # Split page in half and call function again + # Overlap first and second halves by DEFAULT_OVERLAP_PERCENT% + first_half = text[: int(len(text) // (2.0 + (DEFAULT_OVERLAP_PERCENT / 100)))] + second_half = text[int(len(text) // (1.0 - (DEFAULT_OVERLAP_PERCENT / 100))) :] + yield from self.split_page_by_max_tokens(page_num, first_half) + yield from self.split_page_by_max_tokens(page_num, second_half) + def split_pages(self, pages: List[Page]) -> Generator[SplitPage, None, None]: # Chunking is disabled when using GPT4V. To be updated in the future. if self.has_image_embeddings: @@ -49,7 +149,7 @@ def find_page(offset): length = len(all_text) if length <= self.max_section_length: - yield SplitPage(page_num=find_page(0), text=all_text) + yield from self.split_page_by_max_tokens(page_num=find_page(0), text=all_text) return start = 0 @@ -91,7 +191,7 @@ def find_page(offset): start += 1 section_text = all_text[start:end] - yield SplitPage(page_num=find_page(start), text=section_text) + yield from self.split_page_by_max_tokens(page_num=find_page(start), text=section_text) last_table_start = section_text.rfind(" 2 * self.sentence_search_limit and last_table_start > section_text.rfind("L$F}M-QC@SYjB4kFYfLV+}+*X-Q696yL)hGl705MXODZo?ms=g zF`6G#y|t#VSrj~zB9RjorD33DgNGrh|M-H3VIZI(I#ME{cVJRf&I^ck^Zm4pXdn~ z{}N&RON8++5vD&PS_J>;_qWCW=J%rzACcPss}c%Ec2*9yA8~)ADr#kE|NFo|!1CK2 zohTy#$M2JmyhWVt#T4!J?2SIw#T3~H{wkB0B0Iq!?;jF!wpIp;M)n$X9~(laWaMm5 zCjt1VxZqz0p}!6izvKHTqM@mtpp~=6hjDrW77i8yc4kH`f{%Jw+J8j$QNlm&#cZt{ 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scripts.prepdocslib.pdfparser import LocalPdfParser from scripts.prepdocslib.searchmanager import Section -from scripts.prepdocslib.textsplitter import SentenceTextSplitter, SimpleTextSplitter +from scripts.prepdocslib.textsplitter import ( + ENCODING_MODEL, + SentenceTextSplitter, + SimpleTextSplitter, +) def test_sentencetextsplitter_split_empty_pages(): @@ -26,7 +32,7 @@ def test_sentencetextsplitter_split_small_pages(): @pytest.mark.asyncio -async def test_sentencetextsplitter_list_parse_and_split(tmp_path): +async def test_sentencetextsplitter_list_parse_and_split(tmp_path, snapshot): text_splitter = SentenceTextSplitter(False, True) pdf_parser = LocalPdfParser(verbose=True) for pdf in Path("data").glob("*.pdf"): @@ -35,6 +41,7 @@ async def test_sentencetextsplitter_list_parse_and_split(tmp_path): list_file_strategy = LocalListFileStrategy(path_pattern=str(tmp_path / "*"), verbose=True) files = list_file_strategy.list() processed = 0 + results = {} async for file in files: pages = [page async for page in pdf_parser.parse(content=file.content)] assert pages @@ -43,8 +50,12 @@ async def test_sentencetextsplitter_list_parse_and_split(tmp_path): for split_page in text_splitter.split_pages(pages) ] assert sections + results[file.filename()] = [section.split_page.text for section in sections] processed += 1 assert processed > 1 + # Sort results by key + results = {k: results[k] for k in sorted(results)} + snapshot.assert_match(json.dumps(results, indent=2), "text_splitter_sections.txt") def test_simpletextsplitter_split_empty_pages(): @@ -77,3 +88,80 @@ def test_sentencetextsplitter_split_pages(): assert split_pages[2].page_num == 2 assert split_pages[2].text == 'e page"}' assert len(split_pages[2].text) <= max_object_length + + +def pytest_generate_tests(metafunc): + """Parametrize the test_doc fixture with all the pdf files in the test-data directory.""" + if "test_doc" in metafunc.fixturenames: + metafunc.parametrize("test_doc", [pdf for pdf in Path("tests", "test-data").glob("*.pdf")]) + + +@pytest.mark.asyncio +async def test_sentencetextsplitter_multilang(test_doc, tmp_path): + text_splitter = SentenceTextSplitter(False, True) + bpe = tiktoken.encoding_for_model(ENCODING_MODEL) + pdf_parser = LocalPdfParser() + + shutil.copy(str(test_doc.absolute()), tmp_path) + + list_file_strategy = LocalListFileStrategy(path_pattern=str(tmp_path / "*"), verbose=True) + files = list_file_strategy.list() + processed = 0 + async for file in files: + pages = [page async for page in pdf_parser.parse(content=file.content)] + assert pages + sections = [ + Section(split_page, content=file, category="test category") + for split_page in text_splitter.split_pages(pages) + ] + assert sections + processed += 1 + + # Verify the size of the sections + token_lengths = [] + for section in sections: + assert len(section.split_page.text) <= (text_splitter.max_section_length * 1.2) + # Verify the number of tokens is below 500 + token_lengths.append((len(bpe.encode(section.split_page.text)), len(section.split_page.text))) + # verify that none of the numbers in token_lengths are above 500 + assert all([tok_len <= text_splitter.max_tokens_per_section for tok_len, _ in token_lengths]), ( + test_doc.name, + token_lengths, + ) + assert processed == 1 + + +def test_split_tables(): + t = SentenceTextSplitter(has_image_embeddings=False, verbose=True) + + test_text_without_table = """Contoso Electronics is a leader in the aerospace industry, providing advanced electronic +components for both commercial and military aircraft. We specialize in creating cutting- +edge systems that are both reliable and efficient. Our mission is to provide the highest +quality aircraft components to our customers, while maintaining a commitment to safety +and excellence. We are proud to have built a strong reputation in the aerospace industry +and strive to continually improve our products and services. Our experienced team of +engineers and technicians are dedicated to providing the best products and services to our +customers. With our commitment to excellence, we are sure to remain a leader in the +aerospace industry for years to come. At Contoso Electronics, we strive to ensure our employees are getting the feedback they +need to continue growing and developing in their roles. We understand that performance +reviews are a key part of this process and it is important to us that they are conducted in an +effective and efficient manner Performance reviews are conducted annually and are an important part of your career +development. During the review, your supervisor will discuss your performance over the +past year and provide feedback on areas for improvement. They will also provide you with +an opportunity to discuss your goals and objectives for the upcoming year. + + """ + test_text_with_table = test_text_without_table.replace("", "") + + split_pages_with_table = list(t.split_pages(pages=[Page(page_num=0, offset=0, text=test_text_with_table)])) + split_pages_without_table = list(t.split_pages(pages=[Page(page_num=0, offset=0, text=test_text_without_table)])) + + assert len(split_pages_with_table) == 2 + + assert split_pages_with_table[0].text != split_pages_without_table[0].text + + # The table algorithm should move the start of the second section to include the table start + # but only in the test text that has a table tag.. + assert "