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127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
"""Sentence transformers text splitter."""
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from __future__ import annotations
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from typing import Any, cast
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from langchain_text_splitters.base import TextSplitter, Tokenizer, split_text_on_tokens
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try:
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# Type ignores needed as long as sentence-transformers doesn't support Python 3.14.
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from sentence_transformers import ( # type: ignore[import-not-found, unused-ignore]
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SentenceTransformer,
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)
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_HAS_SENTENCE_TRANSFORMERS = True
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except ImportError:
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_HAS_SENTENCE_TRANSFORMERS = False
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class SentenceTransformersTokenTextSplitter(TextSplitter):
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"""Splitting text to tokens using sentence model tokenizer."""
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def __init__(
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self,
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chunk_overlap: int = 50,
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model_name: str = "sentence-transformers/all-mpnet-base-v2",
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tokens_per_chunk: int | None = None,
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model_kwargs: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> None:
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"""Create a new `TextSplitter`.
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Args:
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chunk_overlap: The number of tokens to overlap between chunks.
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model_name: The name of the sentence transformer model to use.
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tokens_per_chunk: The number of tokens per chunk.
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If `None`, uses the maximum tokens allowed by the model.
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model_kwargs: Additional parameters for model initialization.
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Parameters of sentence_transformers.SentenceTransformer can be used.
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Raises:
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ImportError: If the `sentence_transformers` package is not installed.
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"""
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super().__init__(**kwargs, chunk_overlap=chunk_overlap)
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if not _HAS_SENTENCE_TRANSFORMERS:
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msg = (
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"Could not import sentence_transformers python package. "
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"This is needed in order to use SentenceTransformersTokenTextSplitter. "
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"Please install it with `pip install sentence-transformers`."
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)
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raise ImportError(msg)
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self.model_name = model_name
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self._model = SentenceTransformer(self.model_name, **(model_kwargs or {}))
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self.tokenizer = self._model.tokenizer
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self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk)
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def _initialize_chunk_configuration(self, *, tokens_per_chunk: int | None) -> None:
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self.maximum_tokens_per_chunk = self._model.max_seq_length
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if tokens_per_chunk is None:
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self.tokens_per_chunk = self.maximum_tokens_per_chunk
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else:
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self.tokens_per_chunk = tokens_per_chunk
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if self.tokens_per_chunk > self.maximum_tokens_per_chunk:
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msg = (
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f"The token limit of the models '{self.model_name}'"
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f" is: {self.maximum_tokens_per_chunk}."
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f" Argument tokens_per_chunk={self.tokens_per_chunk}"
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f" > maximum token limit."
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)
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raise ValueError(msg)
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def split_text(self, text: str) -> list[str]:
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"""Splits the input text into smaller components by splitting text on tokens.
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This method encodes the input text using a private `_encode` method, then
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strips the start and stop token IDs from the encoded result. It returns the
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processed segments as a list of strings.
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Args:
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text: The input text to be split.
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Returns:
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A list of string components derived from the input text after encoding and
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processing.
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"""
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def encode_strip_start_and_stop_token_ids(text: str) -> list[int]:
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return self._encode(text)[1:-1]
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tokenizer = Tokenizer(
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chunk_overlap=self._chunk_overlap,
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tokens_per_chunk=self.tokens_per_chunk,
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decode=self.tokenizer.decode,
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encode=encode_strip_start_and_stop_token_ids,
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)
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return split_text_on_tokens(text=text, tokenizer=tokenizer)
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def count_tokens(self, *, text: str) -> int:
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"""Counts the number of tokens in the given text.
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This method encodes the input text using a private `_encode` method and
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calculates the total number of tokens in the encoded result.
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Args:
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text: The input text for which the token count is calculated.
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Returns:
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The number of tokens in the encoded text.
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"""
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return len(self._encode(text))
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_max_length_equal_32_bit_integer: int = 2**32
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def _encode(self, text: str) -> list[int]:
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token_ids_with_start_and_end_token_ids = self.tokenizer.encode(
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text,
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max_length=self._max_length_equal_32_bit_integer,
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truncation="do_not_truncate",
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)
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return cast("list[int]", token_ids_with_start_and_end_token_ids)
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