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https://github.com/hwchase17/langchain.git
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text-splitters[minor], langchain[minor], community[patch], templates, docs: langchain-text-splitters 0.0.1 (#18346)
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Bagatur
GitHub
324
libs/text-splitters/langchain_text_splitters/base.py
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324
libs/text-splitters/langchain_text_splitters/base.py
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from __future__ import annotations
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import copy
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import logging
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from enum import Enum
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from typing import (
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AbstractSet,
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Any,
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Callable,
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Collection,
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Iterable,
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List,
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Literal,
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Optional,
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Sequence,
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Type,
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TypeVar,
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Union,
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)
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from langchain_core.documents import BaseDocumentTransformer, Document
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logger = logging.getLogger(__name__)
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TS = TypeVar("TS", bound="TextSplitter")
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class TextSplitter(BaseDocumentTransformer, ABC):
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"""Interface for splitting text into chunks."""
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def __init__(
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self,
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chunk_size: int = 4000,
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chunk_overlap: int = 200,
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length_function: Callable[[str], int] = len,
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keep_separator: bool = False,
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add_start_index: bool = False,
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strip_whitespace: bool = True,
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) -> None:
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"""Create a new TextSplitter.
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Args:
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chunk_size: Maximum size of chunks to return
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chunk_overlap: Overlap in characters between chunks
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length_function: Function that measures the length of given chunks
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keep_separator: Whether to keep the separator in the chunks
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add_start_index: If `True`, includes chunk's start index in metadata
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strip_whitespace: If `True`, strips whitespace from the start and end of
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every document
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"""
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if chunk_overlap > chunk_size:
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raise ValueError(
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f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
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f"({chunk_size}), should be smaller."
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)
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self._chunk_size = chunk_size
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self._chunk_overlap = chunk_overlap
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self._length_function = length_function
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self._keep_separator = keep_separator
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self._add_start_index = add_start_index
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self._strip_whitespace = strip_whitespace
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@abstractmethod
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def split_text(self, text: str) -> List[str]:
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"""Split text into multiple components."""
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def create_documents(
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self, texts: List[str], metadatas: Optional[List[dict]] = None
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) -> List[Document]:
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"""Create documents from a list of texts."""
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_metadatas = metadatas or [{}] * len(texts)
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documents = []
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for i, text in enumerate(texts):
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index = 0
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previous_chunk_len = 0
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for chunk in self.split_text(text):
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metadata = copy.deepcopy(_metadatas[i])
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if self._add_start_index:
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offset = index + previous_chunk_len - self._chunk_overlap
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index = text.find(chunk, max(0, offset))
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metadata["start_index"] = index
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previous_chunk_len = len(chunk)
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new_doc = Document(page_content=chunk, metadata=metadata)
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documents.append(new_doc)
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return documents
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def split_documents(self, documents: Iterable[Document]) -> List[Document]:
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"""Split documents."""
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texts, metadatas = [], []
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for doc in documents:
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texts.append(doc.page_content)
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metadatas.append(doc.metadata)
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return self.create_documents(texts, metadatas=metadatas)
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def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
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text = separator.join(docs)
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if self._strip_whitespace:
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text = text.strip()
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if text == "":
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return None
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else:
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return text
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def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:
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# We now want to combine these smaller pieces into medium size
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# chunks to send to the LLM.
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separator_len = self._length_function(separator)
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docs = []
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current_doc: List[str] = []
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total = 0
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for d in splits:
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_len = self._length_function(d)
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if (
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total + _len + (separator_len if len(current_doc) > 0 else 0)
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> self._chunk_size
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):
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if total > self._chunk_size:
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logger.warning(
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f"Created a chunk of size {total}, "
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f"which is longer than the specified {self._chunk_size}"
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)
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if len(current_doc) > 0:
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doc = self._join_docs(current_doc, separator)
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if doc is not None:
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docs.append(doc)
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# Keep on popping if:
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# - we have a larger chunk than in the chunk overlap
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# - or if we still have any chunks and the length is long
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while total > self._chunk_overlap or (
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total + _len + (separator_len if len(current_doc) > 0 else 0)
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> self._chunk_size
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and total > 0
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):
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total -= self._length_function(current_doc[0]) + (
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separator_len if len(current_doc) > 1 else 0
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)
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current_doc = current_doc[1:]
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current_doc.append(d)
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total += _len + (separator_len if len(current_doc) > 1 else 0)
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doc = self._join_docs(current_doc, separator)
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if doc is not None:
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docs.append(doc)
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return docs
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@classmethod
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def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
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"""Text splitter that uses HuggingFace tokenizer to count length."""
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try:
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from transformers import PreTrainedTokenizerBase
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if not isinstance(tokenizer, PreTrainedTokenizerBase):
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raise ValueError(
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"Tokenizer received was not an instance of PreTrainedTokenizerBase"
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)
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def _huggingface_tokenizer_length(text: str) -> int:
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return len(tokenizer.encode(text))
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except ImportError:
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raise ValueError(
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"Could not import transformers python package. "
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"Please install it with `pip install transformers`."
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)
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return cls(length_function=_huggingface_tokenizer_length, **kwargs)
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@classmethod
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def from_tiktoken_encoder(
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cls: Type[TS],
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encoding_name: str = "gpt2",
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model_name: Optional[str] = None,
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allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
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disallowed_special: Union[Literal["all"], Collection[str]] = "all",
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**kwargs: Any,
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) -> TS:
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"""Text splitter that uses tiktoken encoder to count length."""
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try:
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import tiktoken
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except ImportError:
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raise ImportError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate max_tokens_for_prompt. "
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"Please install it with `pip install tiktoken`."
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)
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if model_name is not None:
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enc = tiktoken.encoding_for_model(model_name)
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else:
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enc = tiktoken.get_encoding(encoding_name)
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def _tiktoken_encoder(text: str) -> int:
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return len(
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enc.encode(
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text,
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allowed_special=allowed_special,
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disallowed_special=disallowed_special,
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)
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)
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if issubclass(cls, TokenTextSplitter):
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extra_kwargs = {
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"encoding_name": encoding_name,
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"model_name": model_name,
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"allowed_special": allowed_special,
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"disallowed_special": disallowed_special,
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}
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kwargs = {**kwargs, **extra_kwargs}
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return cls(length_function=_tiktoken_encoder, **kwargs)
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def transform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Transform sequence of documents by splitting them."""
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return self.split_documents(list(documents))
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class TokenTextSplitter(TextSplitter):
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"""Splitting text to tokens using model tokenizer."""
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def __init__(
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self,
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encoding_name: str = "gpt2",
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model_name: Optional[str] = None,
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allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
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disallowed_special: Union[Literal["all"], Collection[str]] = "all",
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**kwargs: Any,
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) -> None:
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"""Create a new TextSplitter."""
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super().__init__(**kwargs)
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try:
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import tiktoken
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except ImportError:
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raise ImportError(
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"Could not import tiktoken python package. "
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"This is needed in order to for TokenTextSplitter. "
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"Please install it with `pip install tiktoken`."
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)
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if model_name is not None:
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enc = tiktoken.encoding_for_model(model_name)
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else:
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enc = tiktoken.get_encoding(encoding_name)
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self._tokenizer = enc
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self._allowed_special = allowed_special
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self._disallowed_special = disallowed_special
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def split_text(self, text: str) -> List[str]:
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def _encode(_text: str) -> List[int]:
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return self._tokenizer.encode(
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_text,
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allowed_special=self._allowed_special,
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disallowed_special=self._disallowed_special,
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)
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tokenizer = Tokenizer(
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chunk_overlap=self._chunk_overlap,
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tokens_per_chunk=self._chunk_size,
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decode=self._tokenizer.decode,
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encode=_encode,
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)
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return split_text_on_tokens(text=text, tokenizer=tokenizer)
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class Language(str, Enum):
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"""Enum of the programming languages."""
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CPP = "cpp"
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GO = "go"
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JAVA = "java"
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KOTLIN = "kotlin"
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JS = "js"
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TS = "ts"
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PHP = "php"
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PROTO = "proto"
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PYTHON = "python"
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RST = "rst"
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RUBY = "ruby"
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RUST = "rust"
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SCALA = "scala"
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SWIFT = "swift"
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MARKDOWN = "markdown"
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LATEX = "latex"
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HTML = "html"
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SOL = "sol"
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CSHARP = "csharp"
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COBOL = "cobol"
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C = "c"
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LUA = "lua"
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PERL = "perl"
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@dataclass(frozen=True)
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class Tokenizer:
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"""Tokenizer data class."""
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chunk_overlap: int
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"""Overlap in tokens between chunks"""
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tokens_per_chunk: int
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"""Maximum number of tokens per chunk"""
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decode: Callable[[List[int]], str]
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""" Function to decode a list of token ids to a string"""
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encode: Callable[[str], List[int]]
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""" Function to encode a string to a list of token ids"""
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def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]:
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"""Split incoming text and return chunks using tokenizer."""
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splits: List[str] = []
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input_ids = tokenizer.encode(text)
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start_idx = 0
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cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
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chunk_ids = input_ids[start_idx:cur_idx]
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while start_idx < len(input_ids):
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splits.append(tokenizer.decode(chunk_ids))
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if cur_idx == len(input_ids):
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break
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start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
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cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
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chunk_ids = input_ids[start_idx:cur_idx]
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return splits
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