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"""
Semantic chunking for intelligent context segmentation.
"""
import logging
import uuid
from typing import List, Dict, Any, Optional, Tuple
from efficient_context.chunking.base import BaseChunker, Chunk
from efficient_context.utils.text import split_into_sentences, calculate_text_overlap
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class SemanticChunker(BaseChunker):
"""
Chunker that creates chunks based on semantic boundaries.
This chunker aims to keep semantically related content together, unlike
simple token-based chunking that might split content mid-thought.
"""
def __init__(
self,
chunk_size: int = 512,
chunk_overlap: int = 50,
respect_paragraphs: bool = True,
min_chunk_size: int = 100,
max_chunk_size: int = 1024
):
"""
Initialize the SemanticChunker.
Args:
chunk_size: Target size for chunks in tokens (words)
chunk_overlap: Number of tokens to overlap between chunks
respect_paragraphs: Whether to avoid breaking paragraphs across chunks
min_chunk_size: Minimum chunk size in tokens
max_chunk_size: Maximum chunk size in tokens
"""
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
self.respect_paragraphs = respect_paragraphs
self.min_chunk_size = min_chunk_size
self.max_chunk_size = max_chunk_size
logger.info(
"SemanticChunker initialized with target size: %d tokens, overlap: %d tokens",
chunk_size, chunk_overlap
)
def _estimate_tokens(self, text: str) -> int:
"""
Estimate the number of tokens in text.
Args:
text: Text to estimate tokens for
Returns:
token_count: Estimated number of tokens
"""
# Simple whitespace-based token estimation
# This is much faster than using a tokenizer and good enough for chunking
return len(text.split())
def _identify_paragraphs(self, content: str) -> List[str]:
"""
Split content into paragraphs.
Args:
content: Content to split
Returns:
paragraphs: List of paragraphs
"""
# Split on empty lines (common paragraph separator)
paragraphs = [p.strip() for p in content.split("\n\n")]
# Handle other kinds of paragraph breaks and clean up
result = []
current = ""
for p in paragraphs:
# Skip empty paragraphs
if not p:
continue
# Handle single newlines that might indicate paragraphs
lines = p.split("\n")
for line in lines:
if not line.strip():
if current:
result.append(current)
current = ""
else:
if current:
current += " " + line.strip()
else:
current = line.strip()
if current:
result.append(current)
current = ""
# Add any remaining content
if current:
result.append(current)
return result if result else [content]
def _create_semantic_chunks(
self,
paragraphs: List[str],
document_id: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None
) -> List[Chunk]:
"""
Create chunks from paragraphs respecting semantic boundaries.
Args:
paragraphs: List of paragraphs to chunk
document_id: Optional ID of the source document
metadata: Optional metadata for the chunks
Returns:
chunks: List of Chunk objects
"""
chunks = []
current_chunk_text = ""
current_token_count = 0
for paragraph in paragraphs:
paragraph_tokens = self._estimate_tokens(paragraph)
# Check if adding this paragraph would exceed the max chunk size
if (current_token_count + paragraph_tokens > self.max_chunk_size and
current_token_count >= self.min_chunk_size):
# Create a new chunk with the current content
chunk_id = str(uuid.uuid4())
chunk = Chunk(
content=current_chunk_text.strip(),
chunk_id=chunk_id,
document_id=document_id,
metadata=metadata
)
chunks.append(chunk)
# Start a new chunk with overlap
if self.chunk_overlap > 0 and current_chunk_text:
# Get the last N tokens for overlap
words = current_chunk_text.split()
overlap_text = " ".join(words[-min(self.chunk_overlap, len(words)):])
current_chunk_text = overlap_text + " " + paragraph
current_token_count = self._estimate_tokens(current_chunk_text)
else:
# No overlap
current_chunk_text = paragraph
current_token_count = paragraph_tokens
# Handle very large paragraphs that exceed max_chunk_size on their own
elif paragraph_tokens > self.max_chunk_size:
# If we have existing content, create a chunk first
if current_chunk_text:
chunk_id = str(uuid.uuid4())
chunk = Chunk(
content=current_chunk_text.strip(),
chunk_id=chunk_id,
document_id=document_id,
metadata=metadata
)
chunks.append(chunk)
current_chunk_text = ""
current_token_count = 0
# Split the large paragraph into sentences
sentences = split_into_sentences(paragraph)
sentence_chunk = ""
sentence_token_count = 0
for sentence in sentences:
sentence_tokens = self._estimate_tokens(sentence)
# Check if adding this sentence would exceed the max chunk size
if (sentence_token_count + sentence_tokens > self.max_chunk_size and
sentence_token_count >= self.min_chunk_size):
# Create a new chunk with the current sentences
chunk_id = str(uuid.uuid4())
chunk = Chunk(
content=sentence_chunk.strip(),
chunk_id=chunk_id,
document_id=document_id,
metadata=metadata
)
chunks.append(chunk)
# Start a new chunk with overlap
if self.chunk_overlap > 0 and sentence_chunk:
words = sentence_chunk.split()
overlap_text = " ".join(words[-min(self.chunk_overlap, len(words)):])
sentence_chunk = overlap_text + " " + sentence
sentence_token_count = self._estimate_tokens(sentence_chunk)
else:
sentence_chunk = sentence
sentence_token_count = sentence_tokens
else:
# Add the sentence to the current chunk
if sentence_chunk:
sentence_chunk += " " + sentence
else:
sentence_chunk = sentence
sentence_token_count += sentence_tokens
# Add any remaining sentence content as a chunk
if sentence_chunk:
chunk_id = str(uuid.uuid4())
chunk = Chunk(
content=sentence_chunk.strip(),
chunk_id=chunk_id,
document_id=document_id,
metadata=metadata
)
chunks.append(chunk)
else:
# Add the paragraph to the current chunk
if current_chunk_text:
current_chunk_text += " " + paragraph
else:
current_chunk_text = paragraph
current_token_count += paragraph_tokens
# Check if we've reached the target chunk size
if current_token_count >= self.chunk_size:
chunk_id = str(uuid.uuid4())
chunk = Chunk(
content=current_chunk_text.strip(),
chunk_id=chunk_id,
document_id=document_id,
metadata=metadata
)
chunks.append(chunk)
# Start a new chunk with overlap
if self.chunk_overlap > 0:
words = current_chunk_text.split()
current_chunk_text = " ".join(words[-min(self.chunk_overlap, len(words)):])
current_token_count = self._estimate_tokens(current_chunk_text)
else:
current_chunk_text = ""
current_token_count = 0
# Add any remaining content as a final chunk
if current_chunk_text and current_token_count >= self.min_chunk_size:
chunk_id = str(uuid.uuid4())
chunk = Chunk(
content=current_chunk_text.strip(),
chunk_id=chunk_id,
document_id=document_id,
metadata=metadata
)
chunks.append(chunk)
return chunks
def chunk(
self,
content: str,
metadata: Optional[Dict[str, Any]] = None,
document_id: Optional[str] = None
) -> List[Chunk]:
"""
Split content into semantic chunks.
Args:
content: Content to be chunked
metadata: Optional metadata to associate with chunks
document_id: Optional document ID to associate with chunks
Returns:
chunks: List of Chunk objects
"""
if not content.strip():
return []
# Identify paragraphs
if self.respect_paragraphs:
paragraphs = self._identify_paragraphs(content)
else:
# Treat the whole content as one paragraph
paragraphs = [content]
# Create chunks from paragraphs
chunks = self._create_semantic_chunks(paragraphs, document_id, metadata)
logger.info("Created %d chunks from content", len(chunks))
return chunks
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