"""Experimental **text splitter** based on semantic similarity."""
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
[docs]def combine_sentences(sentences: List[dict], buffer_size: int = 1) -> List[dict]:
"""Combine sentences based on buffer size.
Args:
sentences: List of sentences to combine.
buffer_size: Number of sentences to combine. Defaults to 1.
Returns:
List of sentences with combined sentences.
"""
# Go through each sentence dict
for i in range(len(sentences)):
# Create a string that will hold the sentences which are joined
combined_sentence = ""
# Add sentences before the current one, based on the buffer size.
for j in range(i - buffer_size, i):
# Check if the index j is not negative
# (to avoid index out of range like on the first one)
if j >= 0:
# Add the sentence at index j to the combined_sentence string
combined_sentence += sentences[j]["sentence"] + " "
# Add the current sentence
combined_sentence += sentences[i]["sentence"]
# Add sentences after the current one, based on the buffer size
for j in range(i + 1, i + 1 + buffer_size):
# Check if the index j is within the range of the sentences list
if j < len(sentences):
# Add the sentence at index j to the combined_sentence string
combined_sentence += " " + sentences[j]["sentence"]
# Then add the whole thing to your dict
# Store the combined sentence in the current sentence dict
sentences[i]["combined_sentence"] = combined_sentence
return sentences
[docs]def calculate_cosine_distances(sentences: List[dict]) -> Tuple[List[float], List[dict]]:
"""Calculate cosine distances between sentences.
Args:
sentences: List of sentences to calculate distances for.
Returns:
Tuple of distances and sentences.
"""
distances = []
for i in range(len(sentences) - 1):
embedding_current = sentences[i]["combined_sentence_embedding"]
embedding_next = sentences[i + 1]["combined_sentence_embedding"]
# Calculate cosine similarity
similarity = cosine_similarity([embedding_current], [embedding_next])[0][0]
# Convert to cosine distance
distance = 1 - similarity
# Append cosine distance to the list
distances.append(distance)
# Store distance in the dictionary
sentences[i]["distance_to_next"] = distance
# Optionally handle the last sentence
# sentences[-1]['distance_to_next'] = None # or a default value
return distances, sentences
BreakpointThresholdType = Literal[
"percentile", "standard_deviation", "interquartile", "gradient"
]
BREAKPOINT_DEFAULTS: Dict[BreakpointThresholdType, float] = {
"percentile": 95,
"standard_deviation": 3,
"interquartile": 1.5,
"gradient": 95,
}
[docs]class SemanticChunker(BaseDocumentTransformer):
"""Split the text based on semantic similarity.
Taken from Greg Kamradt's wonderful notebook:
https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb
All credits to him.
At a high level, this splits into sentences, then groups into groups of 3
sentences, and then merges one that are similar in the embedding space.
"""
[docs] 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,
sentence_split_regex: str = r"(?<=[.?!])\s+",
):
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
self.sentence_split_regex = sentence_split_regex
if breakpoint_threshold_amount is None:
self.breakpoint_threshold_amount = BREAKPOINT_DEFAULTS[
breakpoint_threshold_type
]
else:
self.breakpoint_threshold_amount = breakpoint_threshold_amount
def _calculate_breakpoint_threshold(
self, distances: List[float]
) -> Tuple[float, List[float]]:
if self.breakpoint_threshold_type == "percentile":
return cast(
float,
np.percentile(distances, self.breakpoint_threshold_amount),
), distances
elif self.breakpoint_threshold_type == "standard_deviation":
return cast(
float,
np.mean(distances)
+ self.breakpoint_threshold_amount * np.std(distances),
), distances
elif self.breakpoint_threshold_type == "interquartile":
q1, q3 = np.percentile(distances, [25, 75])
iqr = q3 - q1
return np.mean(
distances
) + self.breakpoint_threshold_amount * iqr, distances
elif self.breakpoint_threshold_type == "gradient":
# Calculate the threshold based on the distribution of gradient of distance array. # noqa: E501
distance_gradient = np.gradient(distances, range(0, len(distances)))
return cast(
float,
np.percentile(distance_gradient, self.breakpoint_threshold_amount),
), distance_gradient
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
if x2 == x1:
y = y2
else:
y = y1 + ((y2 - y1) / (x2 - x1)) * (x - x1)
y = min(max(y, 0), 100)
return cast(float, np.percentile(distances, y))
def _calculate_sentence_distances(
self, single_sentences_list: List[str]
) -> Tuple[List[float], List[dict]]:
"""Split text into multiple components."""
_sentences = [
{"sentence": x, "index": i} for i, x in enumerate(single_sentences_list)
]
sentences = combine_sentences(_sentences, self.buffer_size)
embeddings = self.embeddings.embed_documents(
[x["combined_sentence"] for x in sentences]
)
for i, sentence in enumerate(sentences):
sentence["combined_sentence_embedding"] = embeddings[i]
return calculate_cosine_distances(sentences)
[docs] def split_text(
self,
text: str,
) -> List[str]:
# Splitting the essay (by default on '.', '?', and '!')
single_sentences_list = re.split(self.sentence_split_regex, text)
# having len(single_sentences_list) == 1 would cause the following
# np.percentile to fail.
if len(single_sentences_list) == 1:
return single_sentences_list
distances, sentences = self._calculate_sentence_distances(single_sentences_list)
if self.number_of_chunks is not None:
breakpoint_distance_threshold = self._threshold_from_clusters(distances)
breakpoint_array = distances
else:
(
breakpoint_distance_threshold,
breakpoint_array,
) = self._calculate_breakpoint_threshold(distances)
indices_above_thresh = [
i
for i, x in enumerate(breakpoint_array)
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([d["sentence"] for d in group])
chunks.append(combined_text)
# Update the start index for the next group
start_index = index + 1
# The last group, if any sentences remain
if start_index < len(sentences):
combined_text = " ".join([d["sentence"] for d in sentences[start_index:]])
chunks.append(combined_text)
return chunks
[docs] 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):
start_index = 0
for chunk in self.split_text(text):
metadata = copy.deepcopy(_metadatas[i])
if self._add_start_index:
metadata["start_index"] = start_index
new_doc = Document(page_content=chunk, metadata=metadata)
documents.append(new_doc)
start_index += len(chunk)
return documents
[docs] 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)
