BERT Embedding based chunking of texts

chunking_by_embedding.py

# --------------------------------------------------------------------- #
# Chunking Mechanism #

import numpy as np
from transformers import BertTokenizer, BertModel
from sklearn.metrics.pairwise import cosine_similarity

def calculate_embedding_difference(embeddings):
    return [1 - cosine_similarity(embeddings[i].reshape(1, -1), embeddings[i + 1].reshape(1, -1))[0][0] for i in range(len(embeddings) - 1)]

# Initialize the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

# Chunk the text
text = "your_text_here"
chunk_size = 10
chunks = chunk_text_by_words(text, chunk_size)

# Generate embeddings for each chunk
embeddings = []
for chunk in chunks:
    inputs = tokenizer(chunk, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    embeddings.append(outputs.pooler_output.squeeze().numpy())

# Calculate differences between consecutive embeddings
differences = calculate_embedding_difference(embeddings)

# Analyze the differences and find sharp changes
threshold = 0.5
sharp_changes = [i for i, diff in enumerate(differences) if diff >= threshold]

# Merge chunks based on sharp changes
context_chunks = []
start = 0
for idx in sharp_changes:
    context_chunks.append(" ".join(chunks[start:idx + 1]))
    start = idx + 1
context_chunks.append(" ".join(chunks[start:]))

# --------------------------------------------------------------------- #
# PDF Parser #

import os
import json
import PyPDF2

class PDFProcessor:
    def __init__(self, filepath, window_size=100, step_size=50, state_file='state.json'):
        self.filepath = filepath
        self.window_size = window_size
        self.step_size = step_size
        self.state_file = state_file
        self.state = {'position': 0}
        if os.path.exists(state_file):
            with open(state_file) as f:
                self.state = json.load(f)

    def extract_text(self):
        with open(self.filepath, "rb") as file:
            pdf_reader = PyPDF2.PdfFileReader(file)
            text = ""
            for page in range(pdf_reader.getNumPages()):
                text += pdf_reader.getPage(page).extractText()
        return text

    def clean_text(self, text):
        # Customize this method based on your specific cleaning requirements
        cleaned_text = text.replace('\n', ' ').replace('\r', '')
        return cleaned_text

    def process(self):
        text = self.extract_text()
        text = self.clean_text(text)
        words = text.split()
        position = self.state['position']
        while position + self.window_size < len(words):
            chunk = " ".join(words[position:position+self.window_size])
            # Process the chunk with BERT or another model here
            # ...
            position += self.step_size
            self.state['position'] = position
            with open(self.state_file, 'w') as f:
                json.dump(self.state, f)

The extract_text function has used some functions of PyPDF2 that are now depreciated and do not work in google colab any more.
I request you to update that function with this one I am providing:

def extract_text(self):
  with open(self.filepath, "rb") as file:
      pdf_reader = PyPDF2.PdfReader(file)
      text = ""
      for page in range(len(reader.pages)):
          text += pdf_reader.pages[page].extract_text()
  return text

where is chunk_text_by_words defeined?