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