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langchain/libs/experimental/langchain_experimental/text_splitter.py

166 lines
6.3 KiB
Python

import copy
import re
from typing import Any, Iterable, List, Optional, Sequence, Tuple
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
def combine_sentences(sentences: List[dict], buffer_size: int = 1) -> List[dict]:
# 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
def calculate_cosine_distances(sentences: List[dict]) -> Tuple[List[float], List[dict]]:
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
class SemanticChunker(BaseDocumentTransformer):
"""Splits the text based on semantic similarity.
Taken from Greg Kamradt's wonderful notebook:
https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb
All credit 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.
"""
def __init__(self, embeddings: Embeddings, add_start_index: bool = False):
self._add_start_index = add_start_index
self.embeddings = embeddings
def split_text(self, text: str) -> List[str]:
"""Split text into multiple components."""
# Splitting the essay on '.', '?', and '!'
single_sentences_list = re.split(r"(?<=[.?!])\s+", 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
sentences = [
{"sentence": x, "index": i} for i, x in enumerate(single_sentences_list)
]
sentences = combine_sentences(sentences)
embeddings = self.embeddings.embed_documents(
[x["combined_sentence"] for x in sentences]
)
for i, sentence in enumerate(sentences):
sentence["combined_sentence_embedding"] = embeddings[i]
distances, sentences = calculate_cosine_distances(sentences)
start_index = 0
# Create a list to hold the grouped sentences
chunks = []
breakpoint_percentile_threshold = 95
breakpoint_distance_threshold = np.percentile(
distances, breakpoint_percentile_threshold
) # If you want more chunks, lower the percentile cutoff
indices_above_thresh = [
i for i, x in enumerate(distances) if x > breakpoint_distance_threshold
] # The indices of those breakpoints on your list
# 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
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))