mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
2c91f0d750
Adds a new parameter `relative_chunk_overlap` for the `SentenceTransformersTokenTextSplitter` constructor. The parameter sets the chunk overlap using a relative factor, e.g. for a model where the token limit is 100, a `relative_chunk_overlap=0.5` implies that `chunk_overlap=50` Tag maintainers/contributors who might be interested: @hwchase17, @dev2049
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
"""Test text splitters that require an integration."""
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import pytest
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from langchain.text_splitter import (
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CharacterTextSplitter,
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SentenceTransformersTokenTextSplitter,
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TokenTextSplitter,
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)
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def test_huggingface_type_check() -> None:
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"""Test that type checks are done properly on input."""
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with pytest.raises(ValueError):
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CharacterTextSplitter.from_huggingface_tokenizer("foo")
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def test_huggingface_tokenizer() -> None:
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"""Test text splitter that uses a HuggingFace tokenizer."""
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from transformers import GPT2TokenizerFast
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
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text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(
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tokenizer, separator=" ", chunk_size=1, chunk_overlap=0
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)
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output = text_splitter.split_text("foo bar")
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assert output == ["foo", "bar"]
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def test_token_text_splitter() -> None:
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"""Test no overlap."""
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splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=0)
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output = splitter.split_text("abcdef" * 5) # 10 token string
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expected_output = ["abcdefabcdefabc", "defabcdefabcdef"]
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assert output == expected_output
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def test_token_text_splitter_overlap() -> None:
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"""Test with overlap."""
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splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=1)
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output = splitter.split_text("abcdef" * 5) # 10 token string
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expected_output = ["abcdefabcdefabc", "abcdefabcdefabc", "abcdef"]
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assert output == expected_output
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def test_token_text_splitter_from_tiktoken() -> None:
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splitter = TokenTextSplitter.from_tiktoken_encoder(model_name="gpt-3.5-turbo")
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expected_tokenizer = "cl100k_base"
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actual_tokenizer = splitter._tokenizer.name
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assert expected_tokenizer == actual_tokenizer
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def test_sentence_transformers_count_tokens() -> None:
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splitter = SentenceTransformersTokenTextSplitter(
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model_name="sentence-transformers/paraphrase-albert-small-v2"
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)
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text = "Lorem ipsum"
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token_count = splitter.count_tokens(text=text)
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expected_start_stop_token_count = 2
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expected_text_token_count = 5
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expected_token_count = expected_start_stop_token_count + expected_text_token_count
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assert expected_token_count == token_count
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def test_sentence_transformers_split_text() -> None:
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splitter = SentenceTransformersTokenTextSplitter(
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model_name="sentence-transformers/paraphrase-albert-small-v2"
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)
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text = "lorem ipsum"
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text_chunks = splitter.split_text(text=text)
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expected_text_chunks = [text]
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assert expected_text_chunks == text_chunks
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def test_sentence_transformers_multiple_tokens() -> None:
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splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0)
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text = "Lorem "
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text_token_count_including_start_and_stop_tokens = splitter.count_tokens(text=text)
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count_start_and_end_tokens = 2
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token_multiplier = (
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count_start_and_end_tokens
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+ (splitter.maximum_tokens_per_chunk - count_start_and_end_tokens)
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// (
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text_token_count_including_start_and_stop_tokens
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- count_start_and_end_tokens
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)
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+ 1
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)
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# `text_to_split` does not fit in a single chunk
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text_to_embed = text * token_multiplier
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text_chunks = splitter.split_text(text=text_to_embed)
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expected_number_of_chunks = 2
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assert expected_number_of_chunks == len(text_chunks)
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actual = splitter.count_tokens(text=text_chunks[1]) - count_start_and_end_tokens
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expected = (
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token_multiplier * (text_token_count_including_start_and_stop_tokens - 2)
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- splitter.maximum_tokens_per_chunk
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)
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assert expected == actual
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