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218 lines
8.3 KiB
Python
218 lines
8.3 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""Regression tests for ``naive_merge`` / ``naive_merge_with_images``.
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Guards against the regression introduced by commit db0f6840d (#11434) where the
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default (non-custom-delimiter) path stopped splitting oversized sections at
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sentence boundaries, and the overlap prefix was not counted toward a chunk's
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token budget.
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"""
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import re
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import pytest
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import rag.nlp as nlp
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from rag.nlp import naive_merge, naive_merge_with_images
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DEFAULT_DELIMITER = "\n!?。；！？"
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@pytest.fixture(autouse=True)
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def word_count_tokens(monkeypatch):
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"""Count tokens as whitespace-delimited words (ignoring ``@@..`` position tags).
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Deterministic and tokenizer-independent so chunk-size assertions are exact.
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"""
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def fake_num_tokens(s):
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s = re.sub(r"@@[0-9]+\t[^\t\n]*", "", s or "")
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return len(s.split())
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monkeypatch.setattr(nlp, "num_tokens_from_string", fake_num_tokens)
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return fake_num_tokens
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def _tok(s):
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return len(re.sub(r"@@[0-9]+\t[^\t\n]*", "", s or "").split())
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def _nonempty(chunks):
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return [c for c in chunks if c.strip()]
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# --------------------------------------------------------------------------- #
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# naive_merge — text path
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# --------------------------------------------------------------------------- #
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@pytest.mark.p2
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def test_oversized_section_is_split_at_sentence_boundaries():
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# One section far larger than chunk_token_num, sentences separated by '\n'.
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sentence = " ".join(["word"] * 10) # 10 tokens
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section = "\n".join([sentence] * 20) # 200 tokens, single section
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assert _tok(section) == 200
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chunks = _nonempty(naive_merge([section], chunk_token_num=50, delimiter=DEFAULT_DELIMITER))
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# Pre-regression behaviour: the section is broken into several chunks
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# instead of a single oversized one.
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assert len(chunks) > 1
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# No chunk should greatly exceed the budget (allow one trailing sentence of slack).
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assert all(_tok(c) <= 50 + 10 for c in chunks)
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# Content is preserved.
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assert "".join(chunks).count("word") == 200
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@pytest.mark.p2
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def test_small_sections_are_merged_not_oversplit():
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sentences = ["alpha beta gamma delta" for _ in range(8)] # 4 tokens each
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chunks = _nonempty(naive_merge(sentences, chunk_token_num=50, delimiter=DEFAULT_DELIMITER))
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# All 32 tokens comfortably fit one chunk.
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assert len(chunks) == 1
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assert _tok(chunks[0]) == 32
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@pytest.mark.p2
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def test_default_delimiters_are_honored_without_backticks():
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# Sentences delimited by '?' and '!' (part of the default set) must split.
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section = ("q " * 10).strip() + "?" + ("r " * 10).strip() + "!" + ("s " * 10).strip()
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chunks = _nonempty(naive_merge([section], chunk_token_num=12, delimiter=DEFAULT_DELIMITER))
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assert len(chunks) >= 2
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@pytest.mark.p2
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def test_empty_delimiter_falls_back_to_token_size_merge():
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# token_chunker.py calls naive_merge with delimiter="" as a size-only fallback.
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sections = [f"sentence number {i} here" for i in range(30)] # 4 tokens each
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chunks = _nonempty(naive_merge(sections, chunk_token_num=20, delimiter=""))
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assert len(chunks) >= 1
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# Must not crash and must not explode into per-character chunks.
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assert len(chunks) < len(sections)
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@pytest.mark.p2
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def test_overlap_prefix_is_counted_in_token_budget():
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# With overlap, each chunk = overlap-prefix + new content. The fix recomputes
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# the chunk's token count after prepending the prefix, so chunks stay bounded.
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# Pre-fix, the prefix tokens were not counted, so the per-chunk budget check
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# fired late and chunks systematically overshot chunk_token_num.
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sentences = [" ".join(["w"] * 10) for _ in range(30)]
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chunks = _nonempty(naive_merge(sentences, chunk_token_num=50, delimiter=DEFAULT_DELIMITER, overlapped_percent=20))
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assert len(chunks) > 1
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# Each 10-token sentence divides chunk_token_num evenly, so a correct
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# accounting yields chunks of exactly the budget. The buggy version
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# overshot (observed up to 63). A small tolerance guards tokenizer rounding.
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assert all(_tok(c) <= 50 + 2 for c in chunks)
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# --------------------------------------------------------------------------- #
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# Custom-delimiter path (intended #11434 behaviour must be preserved)
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# --------------------------------------------------------------------------- #
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@pytest.mark.p2
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def test_custom_delimiter_ignores_chunk_size():
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text = "partA##partB##partC"
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# Backtick-wrapped custom delimiter -> every segment is its own chunk,
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# regardless of chunk_token_num.
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chunks = [c.strip() for c in naive_merge([text], chunk_token_num=1000, delimiter="\n。`##`")]
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assert chunks == ["partA", "partB", "partC"]
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@pytest.mark.p2
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def test_custom_delimiter_does_not_size_merge():
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parts = [f"seg{i}" for i in range(5)]
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text = "##".join(parts)
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chunks = [c.strip() for c in naive_merge([text], chunk_token_num=1000, delimiter="`##`")]
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assert chunks == parts
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# --------------------------------------------------------------------------- #
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# naive_merge_with_images — image path
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# --------------------------------------------------------------------------- #
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@pytest.mark.p2
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def test_images_oversized_section_is_split():
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sentence = " ".join(["word"] * 10)
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section = "\n".join([sentence] * 20) # 200 tokens
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texts = [(section, "")]
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images = [None]
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chunks, imgs = naive_merge_with_images(texts, images, chunk_token_num=50, delimiter=DEFAULT_DELIMITER)
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nonempty = _nonempty(chunks)
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assert len(nonempty) > 1
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# Returned lists stay aligned.
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assert len(chunks) == len(imgs)
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assert all(_tok(c) <= 50 + 10 for c in nonempty)
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@pytest.mark.p2
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def test_images_custom_delimiter_preserved():
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chunks, imgs = naive_merge_with_images([("x##y##z", "")], [None], chunk_token_num=1000, delimiter="`##`")
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assert [c.strip() for c in chunks] == ["x", "y", "z"]
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assert len(chunks) == len(imgs)
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@pytest.mark.p2
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def test_images_plain_string_input():
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# texts may be plain strings (not tuples).
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sentence = " ".join(["word"] * 10)
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section = "\n".join([sentence] * 20)
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chunks, imgs = naive_merge_with_images([section], [None], chunk_token_num=50, delimiter=DEFAULT_DELIMITER)
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assert len(_nonempty(chunks)) > 1
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assert len(chunks) == len(imgs)
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@pytest.mark.p2
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def test_images_mismatched_lengths_returns_empty():
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assert naive_merge_with_images(["a"], [], chunk_token_num=50) == ([], [])
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@pytest.mark.p2
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def test_images_shared_lazyimage_not_stacked_across_split_sentences():
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# A single section carries one LazyImage. After splitting into sentences that
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# merge back into one chunk, the shared image must NOT be duplicated/stacked
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# (concat_img would otherwise concatenate the blob list with itself).
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from rag.utils.lazy_image import LazyImage
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image = LazyImage([b"FAKEBLOB"])
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section = "\n".join([" ".join(["word"] * 10)] * 20)
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_, imgs = naive_merge_with_images([(section, "")], [image], chunk_token_num=50, delimiter=DEFAULT_DELIMITER)
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for im in imgs:
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if isinstance(im, LazyImage):
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assert len(im._blobs) == 1 # never grows beyond the single source blob
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@pytest.mark.p2
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def test_images_distinct_lazyimages_are_concatenated():
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# Two different sections (small enough to land in one chunk) with distinct
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# images must still be merged together.
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from rag.utils.lazy_image import LazyImage
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a = LazyImage([b"BLOB_A"])
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b = LazyImage([b"BLOB_B"])
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texts = [("alpha beta gamma", ""), ("delta epsilon zeta", "")]
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_, imgs = naive_merge_with_images(texts, [a, b], chunk_token_num=100, delimiter=DEFAULT_DELIMITER)
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nonempty_imgs = [im for im in imgs if im is not None]
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assert len(nonempty_imgs) == 1
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merged = nonempty_imgs[0]
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assert isinstance(merged, LazyImage)
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assert merged._blobs == [b"BLOB_A", b"BLOB_B"]
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