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ac4560f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | """
Document chunking with hierarchical approach.
Creates overlapping chunks while preserving context.
"""
import json
from pathlib import Path
from typing import List, Dict, Tuple
import PyPDF2
import tiktoken
import logging
logger = logging.getLogger(__name__)
class ChunkProcessor:
"""Process PDFs into hierarchical chunks for embedding."""
def __init__(self, chunk_size: int = 1000, overlap: int = 200):
"""Initialize chunk processor.
Args:
chunk_size: Target size for chunks in tokens
overlap: Number of tokens to overlap between chunks
"""
self.chunk_size = chunk_size
self.overlap = overlap
self.encoding = tiktoken.encoding_for_model("gpt-4")
def count_tokens(self, text: str) -> int:
"""Count tokens in text."""
return len(self.encoding.encode(text))
def extract_text_from_pdf(self, pdf_path: Path) -> List[Tuple[str, int]]:
"""Extract text from PDF with page numbers."""
text_pages = []
try:
with open(pdf_path, 'rb') as file:
pdf_reader = PyPDF2.PdfReader(file)
for page_num, page in enumerate(pdf_reader.pages, 1):
text = page.extract_text()
if text.strip():
text_pages.append((text, page_num))
except Exception as e:
logger.error(f"Error reading PDF {pdf_path}: {e}")
return text_pages
def chunk_text(self, text: str, source: str, page: int) -> List[Dict]:
"""Split text into overlapping chunks."""
chunks = []
# Split into sentences (simple approach)
sentences = text.replace('\n', ' ').split('. ')
sentences = [s.strip() + '.' for s in sentences if s.strip()]
current_chunk = []
current_tokens = 0
chunk_index = 0
for sentence in sentences:
sentence_tokens = self.count_tokens(sentence)
# If adding this sentence would exceed chunk size
if current_tokens + sentence_tokens > self.chunk_size and current_chunk:
# Create chunk
chunk_text = ' '.join(current_chunk)
chunks.append({
"text": chunk_text,
"metadata": {
"source": source,
"page": page,
"chunk_index": chunk_index,
"token_count": current_tokens
}
})
# Start new chunk with overlap
overlap_tokens = 0
overlap_sentences = []
# Add sentences from end of current chunk for overlap
for sent in reversed(current_chunk):
sent_tokens = self.count_tokens(sent)
if overlap_tokens + sent_tokens <= self.overlap:
overlap_sentences.insert(0, sent)
overlap_tokens += sent_tokens
else:
break
current_chunk = overlap_sentences
current_tokens = overlap_tokens
chunk_index += 1
current_chunk.append(sentence)
current_tokens += sentence_tokens
# Add final chunk
if current_chunk:
chunk_text = ' '.join(current_chunk)
chunks.append({
"text": chunk_text,
"metadata": {
"source": source,
"page": page,
"chunk_index": chunk_index,
"token_count": current_tokens
}
})
return chunks
def chunk_pdf(self, pdf_path: Path) -> List[Dict]:
"""Process entire PDF into chunks."""
all_chunks = []
text_pages = self.extract_text_from_pdf(pdf_path)
for text, page_num in text_pages:
chunks = self.chunk_text(text, pdf_path.name, page_num)
all_chunks.extend(chunks)
logger.info(f"Created {len(all_chunks)} chunks from {pdf_path.name}")
return all_chunks
def process_directory(self, pdf_dir: Path) -> Dict[str, List[Dict]]:
"""Process all PDFs in directory, organized by version."""
version_chunks = {}
for pdf_path in pdf_dir.glob("*.pdf"):
# Extract version from filename (e.g., harmony_1_8_guide.pdf)
filename = pdf_path.stem.lower()
# Determine version
if "harmony" in filename:
if "1_8" in filename or "1.8" in filename:
version = "harmony_1_8"
elif "1_6" in filename or "1.6" in filename:
version = "harmony_1_6"
elif "1_5" in filename or "1.5" in filename:
version = "harmony_1_5"
elif "1_2" in filename or "1.2" in filename:
version = "harmony_1_2"
else:
version = "harmony_general"
elif "chorus" in filename:
version = "chorus_1_1"
else:
version = "general_faq"
# Process PDF
chunks = self.chunk_pdf(pdf_path)
# Add to version collection
if version not in version_chunks:
version_chunks[version] = []
version_chunks[version].extend(chunks)
return version_chunks
def save_chunks(self, chunks: List[Dict], output_path: Path):
"""Save chunks to JSON file."""
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, 'w') as f:
json.dump({
"chunks": chunks,
"chunk_size": self.chunk_size,
"overlap": self.overlap
}, f, indent=2)
logger.info(f"Saved {len(chunks)} chunks to {output_path}") |