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import copy
import re
from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, cast
import numpy as np
from langchain_community.utils.math import (
cosine_similarity,
)
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.embeddings import Embeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
def calculate_cosine_distances(sentence_embeddings) -> np.array:
"""Calculate cosine distances between sentences.
Args:
sentence_embeddings: List of sentence embeddings to calculate distances for.
Returns:
Distance between each pair of adjacent sentences
"""
return (1 - cosine_similarity(sentence_embeddings, sentence_embeddings)).flatten()[1::len(sentence_embeddings) + 1]
BreakpointThresholdType = Literal["percentile", "standard_deviation", "interquartile"]
BREAKPOINT_DEFAULTS: Dict[BreakpointThresholdType, float] = {
"percentile": 95,
"standard_deviation": 3,
"interquartile": 1.5,
}
class BoundedSemanticChunker(BaseDocumentTransformer):
"""First splits the text using semantic chunking according to the specified
'breakpoint_threshold_amount', but then uses a RecursiveCharacterTextSplitter
to split all chunks that are larger than 'max_chunk_size'.
Adapted from langchain_experimental.text_splitter.SemanticChunker"""
def __init__(
self,
embeddings: Embeddings,
buffer_size: int = 1,
add_start_index: bool = False,
breakpoint_threshold_type: BreakpointThresholdType = "percentile",
breakpoint_threshold_amount: Optional[float] = None,
number_of_chunks: Optional[int] = None,
max_chunk_size: int = 500,
):
self._add_start_index = add_start_index
self.embeddings = embeddings
self.buffer_size = buffer_size
self.breakpoint_threshold_type = breakpoint_threshold_type
self.number_of_chunks = number_of_chunks
if breakpoint_threshold_amount is None:
self.breakpoint_threshold_amount = BREAKPOINT_DEFAULTS[
breakpoint_threshold_type
]
else:
self.breakpoint_threshold_amount = breakpoint_threshold_amount
self.max_chunk_size = max_chunk_size
# Splitting the text on '.', '?', and '!'
self.sentence_split_regex = re.compile(r"(?<=[.?!])\s+")
assert self.breakpoint_threshold_type == "percentile", "only breakpoint_threshold_type 'percentile' is currently supported"
assert self.buffer_size == 1, "combining sentences is not supported yet"
def _calculate_sentence_distances(
self, sentences: List[dict]
) -> Tuple[List[float], List[dict]]:
"""Split text into multiple components."""
embeddings = self.embeddings.embed_documents(sentences)
return calculate_cosine_distances(embeddings)
def _calculate_breakpoint_threshold(self, distances: np.array, alt_breakpoint_threshold_amount=None) -> float:
if alt_breakpoint_threshold_amount is None:
breakpoint_threshold_amount = self.breakpoint_threshold_amount
else:
breakpoint_threshold_amount = alt_breakpoint_threshold_amount
if self.breakpoint_threshold_type == "percentile":
return cast(
float,
np.percentile(distances, breakpoint_threshold_amount),
)
elif self.breakpoint_threshold_type == "standard_deviation":
return cast(
float,
np.mean(distances)
+ breakpoint_threshold_amount * np.std(distances),
)
elif self.breakpoint_threshold_type == "interquartile":
q1, q3 = np.percentile(distances, [25, 75])
iqr = q3 - q1
return np.mean(distances) + breakpoint_threshold_amount * iqr
else:
raise ValueError(
f"Got unexpected `breakpoint_threshold_type`: "
f"{self.breakpoint_threshold_type}"
)
def _threshold_from_clusters(self, distances: List[float]) -> float:
"""
Calculate the threshold based on the number of chunks.
Inverse of percentile method.
"""
if self.number_of_chunks is None:
raise ValueError(
"This should never be called if `number_of_chunks` is None."
)
x1, y1 = len(distances), 0.0
x2, y2 = 1.0, 100.0
x = max(min(self.number_of_chunks, x1), x2)
# Linear interpolation formula
y = y1 + ((y2 - y1) / (x2 - x1)) * (x - x1)
y = min(max(y, 0), 100)
return cast(float, np.percentile(distances, y))
def split_text(
self,
text: str,
) -> List[str]:
sentences = self.sentence_split_regex.split(text)
# having len(sentences) == 1 would cause the following
# np.percentile to fail.
if len(sentences) == 1:
return sentences
bad_sentences = []
num_good_sentences = 0
distances = self._calculate_sentence_distances(sentences)
if self.number_of_chunks is not None:
breakpoint_distance_threshold = self._threshold_from_clusters(distances)
else:
breakpoint_distance_threshold = self._calculate_breakpoint_threshold(
distances
)
indices_above_thresh = [
i for i, x in enumerate(distances) if x > breakpoint_distance_threshold
]
chunks = []
start_index = 0
# Iterate through the breakpoints to slice the sentences
for index in indices_above_thresh:
# The end index is the current breakpoint
end_index = index
# Slice the sentence_dicts from the current start index to the end index
group = sentences[start_index : end_index + 1]
combined_text = " ".join(group)
if len(combined_text) <= self.max_chunk_size:
chunks.append(combined_text)
else:
sent_lengths = np.array([len(sd) for sd in group])
good_indices = np.flatnonzero(np.cumsum(sent_lengths) <= self.max_chunk_size)
smaller_group = [group[i] for i in good_indices]
if smaller_group:
combined_text = " ".join(smaller_group)
chunks.append(combined_text)
group = group[good_indices[-1]:]
bad_sentences.extend(group)
# Update the start index for the next group
start_index = index + 1
# The last group, if any sentences remain
if start_index < len(sentences):
group = sentences[start_index:]
combined_text = " ".join(group)
if len(combined_text) <= self.max_chunk_size:
chunks.append(combined_text)
else:
sent_lengths = np.array([len(sd) for sd in group])
good_indices = np.flatnonzero(np.cumsum(sent_lengths) <= self.max_chunk_size)
smaller_group = [group[i] for i in good_indices]
if smaller_group:
combined_text = " ".join(smaller_group)
chunks.append(combined_text)
group = group[good_indices[-1]:]
bad_sentences.extend(group)
# If pure semantic chunking wasn't able to split all text for any breakpoint_threshold_amount,
# split the remaining problematic text using a recursive character splitter instead
if len(bad_sentences) > 0:
recursive_splitter = RecursiveCharacterTextSplitter(chunk_size=self.max_chunk_size, chunk_overlap=10,
separators=["\n\n", "\n", ".", ", ", " ", ""])
remaining_text = "".join(bad_sentences)
chunks.extend(recursive_splitter.split_text(remaining_text))
return chunks
def create_documents(
self, texts: List[str], metadatas: Optional[List[dict]] = None
) -> List[Document]:
"""Create documents from a list of texts."""
_metadatas = metadatas or [{}] * len(texts)
documents = []
for i, text in enumerate(texts):
index = -1
for chunk in self.split_text(text):
metadata = copy.deepcopy(_metadatas[i])
if self._add_start_index:
index = text.find(chunk, index + 1)
metadata["start_index"] = index
new_doc = Document(page_content=chunk, metadata=metadata)
documents.append(new_doc)
return documents
def split_documents(self, documents: Iterable[Document]) -> List[Document]:
"""Split documents."""
texts, metadatas = [], []
for doc in documents:
texts.append(doc.page_content)
metadatas.append(doc.metadata)
return self.create_documents(texts, metadatas=metadatas)
def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Transform sequence of documents by splitting them."""
return self.split_documents(list(documents))
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