39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
import pytest
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from datasets import Dataset
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from haiku.rag.chunker import Chunker
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from haiku.rag.utils import text_to_docling_document
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@pytest.mark.asyncio
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async def test_chunker(qa_corpus: Dataset):
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chunker = Chunker()
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doc_text = qa_corpus[0]["document_extracted"]
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# Convert text to DoclingDocument
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doc = text_to_docling_document(doc_text, name="test.md")
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chunks = await chunker.chunk(doc)
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# Ensure that the text is split into multiple chunks
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assert len(chunks) > 1
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# Ensure that chunks are reasonably sized (allowing more flexibility for structure-aware chunking)
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total_tokens = 0
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for chunk in chunks:
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encoded_tokens = Chunker.encoder.encode(chunk, disallowed_special=())
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token_count = len(encoded_tokens)
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total_tokens += token_count
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# Each chunk should be reasonably sized (allowing more flexibility than the old strict limits)
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assert (
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token_count <= chunker.chunk_size * 1.2
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) # Allow some flexibility for semantic boundaries
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assert token_count > 5 # Ensure chunks aren't too small
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# Ensure that all chunks together contain roughly the same content as original
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original_tokens = len(Chunker.encoder.encode(doc_text, disallowed_special=()))
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# Due to structure-aware chunking, we might have some variation in token count
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# but it should be reasonable
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assert abs(total_tokens - original_tokens) <= original_tokens * 0.1
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