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# This script is adapted from the LangChain package, developed by LangChain AI.
# Original code can be found at: https://github.com/langchain-ai/langchain/blob/master/libs/text-splitters/langchain_text_splitters/character.py
# Original code can be found at: https://github.com/langchain-ai/langchain/blob/master/libs/text-splitters/langchain_text_splitters/base.py
# License: MIT License
from attr import dataclass
import numpy as np
import tiktoken
from chunking_evaluation.utils import Language
import re
from abc import ABC, abstractmethod
from enum import Enum
import logging
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Iterable,
List,
Literal,
Optional,
Sequence,
Type,
TypeVar,
Union,
)
class BaseChunker(ABC):
@abstractmethod
def split_text(self, text: str) -> List[str]:
pass
logger = logging.getLogger(__name__)
TS = TypeVar("TS", bound="TextSplitter")
class TextSplitter(BaseChunker, ABC):
"""Interface for splitting text into chunks."""
def __init__(
self,
chunk_size: int = 4000,
chunk_overlap: int = 200,
length_function: Callable[[str], int] = len,
keep_separator: bool = False,
add_start_index: bool = False,
strip_whitespace: bool = True,
) -> None:
"""Create a new TextSplitter.
Args:
chunk_size: Maximum size of chunks to return
chunk_overlap: Overlap in characters between chunks
length_function: Function that measures the length of given chunks
keep_separator: Whether to keep the separator in the chunks
add_start_index: If `True`, includes chunk's start index in metadata
strip_whitespace: If `True`, strips whitespace from the start and end of
every document
"""
if chunk_overlap > chunk_size:
raise ValueError(
f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
f"({chunk_size}), should be smaller."
)
self._chunk_size = chunk_size
self._chunk_overlap = chunk_overlap
self._length_function = length_function
self._keep_separator = keep_separator
self._add_start_index = add_start_index
self._strip_whitespace = strip_whitespace
@abstractmethod
def split_text(self, text: str) -> List[str]:
"""Split text into multiple components."""
def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
text = separator.join(docs)
if self._strip_whitespace:
text = text.strip()
if text == "":
return None
else:
return text
def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:
# We now want to combine these smaller pieces into medium size
# chunks to send to the LLM.
separator_len = self._length_function(separator)
docs = []
current_doc: List[str] = []
total = 0
for d in splits:
_len = self._length_function(d)
if (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size
):
if total > self._chunk_size:
logger.warning(
f"Created a chunk of size {total}, "
f"which is longer than the specified {self._chunk_size}"
)
if len(current_doc) > 0:
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size
and total > 0
):
total -= self._length_function(current_doc[0]) + (
separator_len if len(current_doc) > 1 else 0
)
current_doc = current_doc[1:]
current_doc.append(d)
total += _len + (separator_len if len(current_doc) > 1 else 0)
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
return docs
# @classmethod
# def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
# """Text splitter that uses HuggingFace tokenizer to count length."""
# try:
# from transformers import PreTrainedTokenizerBase
# if not isinstance(tokenizer, PreTrainedTokenizerBase):
# raise ValueError(
# "Tokenizer received was not an instance of PreTrainedTokenizerBase"
# )
# def _huggingface_tokenizer_length(text: str) -> int:
# return len(tokenizer.encode(text))
# except ImportError:
# raise ValueError(
# "Could not import transformers python package. "
# "Please install it with `pip install transformers`."
# )
# return cls(length_function=_huggingface_tokenizer_length, **kwargs)
@classmethod
def from_tiktoken_encoder(
cls: Type[TS],
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> TS:
"""Text splitter that uses tiktoken encoder to count length."""
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to calculate max_tokens_for_prompt. "
"Please install it with `pip install tiktoken`."
)
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
def _tiktoken_encoder(text: str) -> int:
return len(
enc.encode(
text,
allowed_special=allowed_special,
disallowed_special=disallowed_special,
)
)
if issubclass(cls, FixedTokenChunker):
extra_kwargs = {
"encoding_name": encoding_name,
"model_name": model_name,
"allowed_special": allowed_special,
"disallowed_special": disallowed_special,
}
kwargs = {**kwargs, **extra_kwargs}
return cls(length_function=_tiktoken_encoder, **kwargs)
class FixedTokenChunker(TextSplitter):
"""Splitting text to tokens using model tokenizer."""
def __init__(
self,
encoding_name: str = "cl100k_base",
model_name: Optional[str] = None,
chunk_size: int = 4000,
chunk_overlap: int = 200,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> None:
"""Create a new TextSplitter."""
super().__init__(chunk_size=chunk_size, chunk_overlap=chunk_overlap, **kwargs)
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for FixedTokenChunker. "
"Please install it with `pip install tiktoken`."
)
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
def split_text(self, text: str) -> List[str]:
def _encode(_text: str) -> List[int]:
return self._tokenizer.encode(
_text,
allowed_special=self._allowed_special,
disallowed_special=self._disallowed_special,
)
tokenizer = Tokenizer(
chunk_overlap=self._chunk_overlap,
tokens_per_chunk=self._chunk_size,
decode=self._tokenizer.decode,
encode=_encode,
)
return split_text_on_tokens(text=text, tokenizer=tokenizer)
@dataclass(frozen=True)
class Tokenizer:
"""Tokenizer data class."""
chunk_overlap: int
"""Overlap in tokens between chunks"""
tokens_per_chunk: int
"""Maximum number of tokens per chunk"""
decode: Callable[[List[int]], str]
""" Function to decode a list of token ids to a string"""
encode: Callable[[str], List[int]]
""" Function to encode a string to a list of token ids"""
def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]:
"""Split incoming text and return chunks using tokenizer."""
splits: List[str] = []
input_ids = tokenizer.encode(text)
start_idx = 0
cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
while start_idx < len(input_ids):
splits.append(tokenizer.decode(chunk_ids))
if cur_idx == len(input_ids):
break
start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
return splits
def _split_text_with_regex(
text: str, separator: str, keep_separator: bool
) -> List[str]:
# Now that we have the separator, split the text
if separator:
if keep_separator:
# The parentheses in the pattern keep the delimiters in the result.
_splits = re.split(f"({separator})", text)
splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)]
if len(_splits) % 2 == 0:
splits += _splits[-1:]
splits = [_splits[0]] + splits
else:
splits = re.split(separator, text)
else:
splits = list(text)
return [s for s in splits if s != ""]
class RecursiveTokenChunker(TextSplitter):
"""Splitting text by recursively look at characters.
Recursively tries to split by different characters to find one
that works.
"""
def __init__(
self,
chunk_size: int = 4000,
chunk_overlap: int = 200,
separators: Optional[List[str]] = None,
keep_separator: bool = True,
is_separator_regex: bool = False,
**kwargs: Any,
) -> None:
"""Create a new TextSplitter."""
super().__init__(chunk_size=chunk_size, chunk_overlap=chunk_overlap, keep_separator=keep_separator, **kwargs)
self._separators = separators or ["\n\n", "\n", ".", "?", "!", " ", ""]
self._is_separator_regex = is_separator_regex
def _split_text(self, text: str, separators: List[str]) -> List[str]:
"""Split incoming text and return chunks."""
final_chunks = []
# Get appropriate separator to use
separator = separators[-1]
new_separators = []
for i, _s in enumerate(separators):
_separator = _s if self._is_separator_regex else re.escape(_s)
if _s == "":
separator = _s
break
if re.search(_separator, text):
separator = _s
new_separators = separators[i + 1 :]
break
_separator = separator if self._is_separator_regex else re.escape(separator)
splits = _split_text_with_regex(text, _separator, self._keep_separator)
# Now go merging things, recursively splitting longer texts.
_good_splits = []
_separator = "" if self._keep_separator else separator
for s in splits:
if self._length_function(s) < self._chunk_size:
_good_splits.append(s)
else:
if _good_splits:
merged_text = self._merge_splits(_good_splits, _separator)
final_chunks.extend(merged_text)
_good_splits = []
if not new_separators:
final_chunks.append(s)
else:
other_info = self._split_text(s, new_separators)
final_chunks.extend(other_info)
if _good_splits:
merged_text = self._merge_splits(_good_splits, _separator)
final_chunks.extend(merged_text)
return final_chunks
def split_text(self, text: str) -> List[str]:
return self._split_text(text, self._separators)
# @classmethod
# def from_language(
# cls, language: Language, **kwargs: Any
# ) -> RecursiveCharacterTextSplitter:
# separators = cls.get_separators_for_language(language)
# return cls(separators=separators, is_separator_regex=True, **kwargs)
@staticmethod
def get_separators_for_language(language: Language) -> List[str]:
if language == Language.CPP:
return [
# Split along class definitions
"\nclass ",
# Split along function definitions
"\nvoid ",
"\nint ",
"\nfloat ",
"\ndouble ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.GO:
return [
# Split along function definitions
"\nfunc ",
"\nvar ",
"\nconst ",
"\ntype ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nswitch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.JAVA:
return [
# Split along class definitions
"\nclass ",
# Split along method definitions
"\npublic ",
"\nprotected ",
"\nprivate ",
"\nstatic ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.KOTLIN:
return [
# Split along class definitions
"\nclass ",
# Split along method definitions
"\npublic ",
"\nprotected ",
"\nprivate ",
"\ninternal ",
"\ncompanion ",
"\nfun ",
"\nval ",
"\nvar ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nwhen ",
"\ncase ",
"\nelse ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.JS:
return [
# Split along function definitions
"\nfunction ",
"\nconst ",
"\nlet ",
"\nvar ",
"\nclass ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
"\ndefault ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.TS:
return [
"\nenum ",
"\ninterface ",
"\nnamespace ",
"\ntype ",
# Split along class definitions
"\nclass ",
# Split along function definitions
"\nfunction ",
"\nconst ",
"\nlet ",
"\nvar ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
"\ndefault ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.PHP:
return [
# Split along function definitions
"\nfunction ",
# Split along class definitions
"\nclass ",
# Split along control flow statements
"\nif ",
"\nforeach ",
"\nwhile ",
"\ndo ",
"\nswitch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.PROTO:
return [
# Split along message definitions
"\nmessage ",
# Split along service definitions
"\nservice ",
# Split along enum definitions
"\nenum ",
# Split along option definitions
"\noption ",
# Split along import statements
"\nimport ",
# Split along syntax declarations
"\nsyntax ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.PYTHON:
return [
# First, try to split along class definitions
"\nclass ",
"\ndef ",
"\n\tdef ",
# Now split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.RST:
return [
# Split along section titles
"\n=+\n",
"\n-+\n",
"\n\\*+\n",
# Split along directive markers
"\n\n.. *\n\n",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.RUBY:
return [
# Split along method definitions
"\ndef ",
"\nclass ",
# Split along control flow statements
"\nif ",
"\nunless ",
"\nwhile ",
"\nfor ",
"\ndo ",
"\nbegin ",
"\nrescue ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.RUST:
return [
# Split along function definitions
"\nfn ",
"\nconst ",
"\nlet ",
# Split along control flow statements
"\nif ",
"\nwhile ",
"\nfor ",
"\nloop ",
"\nmatch ",
"\nconst ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.SCALA:
return [
# Split along class definitions
"\nclass ",
"\nobject ",
# Split along method definitions
"\ndef ",
"\nval ",
"\nvar ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nmatch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.SWIFT:
return [
# Split along function definitions
"\nfunc ",
# Split along class definitions
"\nclass ",
"\nstruct ",
"\nenum ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\ndo ",
"\nswitch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.MARKDOWN:
return [
# First, try to split along Markdown headings (starting with level 2)
"\n#{1,6} ",
# Note the alternative syntax for headings (below) is not handled here
# Heading level 2
# ---------------
# End of code block
"```\n",
# Horizontal lines
"\n\\*\\*\\*+\n",
"\n---+\n",
"\n___+\n",
# Note that this splitter doesn't handle horizontal lines defined
# by *three or more* of ***, ---, or ___, but this is not handled
"\n\n",
"\n",
" ",
"",
]
elif language == Language.LATEX:
return [
# First, try to split along Latex sections
"\n\\\\chapter{",
"\n\\\\section{",
"\n\\\\subsection{",
"\n\\\\subsubsection{",
# Now split by environments
"\n\\\\begin{enumerate}",
"\n\\\\begin{itemize}",
"\n\\\\begin{description}",
"\n\\\\begin{list}",
"\n\\\\begin{quote}",
"\n\\\\begin{quotation}",
"\n\\\\begin{verse}",
"\n\\\\begin{verbatim}",
# Now split by math environments
"\n\\\begin{align}",
"$$",
"$",
# Now split by the normal type of lines
" ",
"",
]
elif language == Language.HTML:
return [
# First, try to split along HTML tags
"<body",
"<div",
"<p",
"<br",
"<li",
"<h1",
"<h2",
"<h3",
"<h4",
"<h5",
"<h6",
"<span",
"<table",
"<tr",
"<td",
"<th",
"<ul",
"<ol",
"<header",
"<footer",
"<nav",
# Head
"<head",
"<style",
"<script",
"<meta",
"<title",
"",
]
elif language == Language.CSHARP:
return [
"\ninterface ",
"\nenum ",
"\nimplements ",
"\ndelegate ",
"\nevent ",
# Split along class definitions
"\nclass ",
"\nabstract ",
# Split along method definitions
"\npublic ",
"\nprotected ",
"\nprivate ",
"\nstatic ",
"\nreturn ",
# Split along control flow statements
"\nif ",
"\ncontinue ",
"\nfor ",
"\nforeach ",
"\nwhile ",
"\nswitch ",
"\nbreak ",
"\ncase ",
"\nelse ",
# Split by exceptions
"\ntry ",
"\nthrow ",
"\nfinally ",
"\ncatch ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.SOL:
return [
# Split along compiler information definitions
"\npragma ",
"\nusing ",
# Split along contract definitions
"\ncontract ",
"\ninterface ",
"\nlibrary ",
# Split along method definitions
"\nconstructor ",
"\ntype ",
"\nfunction ",
"\nevent ",
"\nmodifier ",
"\nerror ",
"\nstruct ",
"\nenum ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\ndo while ",
"\nassembly ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.COBOL:
return [
# Split along divisions
"\nIDENTIFICATION DIVISION.",
"\nENVIRONMENT DIVISION.",
"\nDATA DIVISION.",
"\nPROCEDURE DIVISION.",
# Split along sections within DATA DIVISION
"\nWORKING-STORAGE SECTION.",
"\nLINKAGE SECTION.",
"\nFILE SECTION.",
# Split along sections within PROCEDURE DIVISION
"\nINPUT-OUTPUT SECTION.",
# Split along paragraphs and common statements
"\nOPEN ",
"\nCLOSE ",
"\nREAD ",
"\nWRITE ",
"\nIF ",
"\nELSE ",
"\nMOVE ",
"\nPERFORM ",
"\nUNTIL ",
"\nVARYING ",
"\nACCEPT ",
"\nDISPLAY ",
"\nSTOP RUN.",
# Split by the normal type of lines
"\n",
" ",
"",
]
else:
raise ValueError(
f"Language {language} is not supported! "
f"Please choose from {list(Language)}"
)
class ClusterSemanticChunker(BaseChunker):
def __init__(self, embedding_function=None, max_chunk_size=400, min_chunk_size=50, length_function=openai_token_count):
self.splitter = RecursiveTokenChunker(
chunk_size=min_chunk_size,
chunk_overlap=0,
length_function=openai_token_count,
separators = ["\n\n", "\n", ".", "?", "!", " ", ""]
)
self._chunk_size = max_chunk_size
self.max_cluster = max_chunk_size//min_chunk_size
self.embedding_function = embedding_function
def _get_similarity_matrix(self, embedding_function, sentences):
BATCH_SIZE = 500
N = len(sentences)
embedding_matrix = None
for i in range(0, N, BATCH_SIZE):
batch_sentences = sentences[i:i+BATCH_SIZE]
embeddings = embedding_function(batch_sentences)
# Convert embeddings list of lists to numpy array
batch_embedding_matrix = np.array(embeddings)
# Append the batch embedding matrix to the main embedding matrix
if embedding_matrix is None:
embedding_matrix = batch_embedding_matrix
else:
embedding_matrix = np.concatenate((embedding_matrix, batch_embedding_matrix), axis=0)
similarity_matrix = np.dot(embedding_matrix, embedding_matrix.T)
return similarity_matrix
def _calculate_reward(self, matrix, start, end):
sub_matrix = matrix[start:end+1, start:end+1]
return np.sum(sub_matrix)
def _optimal_segmentation(self, matrix, max_cluster_size, window_size=3):
mean_value = np.mean(matrix[np.triu_indices(matrix.shape[0], k=1)])
matrix = matrix - mean_value # Normalize the matrix
np.fill_diagonal(matrix, 0) # Set diagonal to 1 to avoid trivial solutions
n = matrix.shape[0]
dp = np.zeros(n)
segmentation = np.zeros(n, dtype=int)
for i in range(n):
for size in range(1, max_cluster_size + 1):
if i - size + 1 >= 0:
# local_density = calculate_local_density(matrix, i, window_size)
reward = self._calculate_reward(matrix, i - size + 1, i)
# Adjust reward based on local density
adjusted_reward = reward
if i - size >= 0:
adjusted_reward += dp[i - size]
if adjusted_reward > dp[i]:
dp[i] = adjusted_reward
segmentation[i] = i - size + 1
clusters = []
i = n - 1
while i >= 0:
start = segmentation[i]
clusters.append((start, i))
i = start - 1
clusters.reverse()
return clusters
def split_text(self, text: str) -> List[str]:
sentences = self.splitter.split_text(text)
similarity_matrix = self._get_similarity_matrix(self.embedding_function, sentences)
clusters = self._optimal_segmentation(similarity_matrix, max_cluster_size=self.max_cluster)
docs = [' '.join(sentences[start:end+1]) for start, end in clusters]
return docs |