cognee/cognee/tasks/chunks/chunk_by_paragraph.py
2024-11-13 15:35:03 +01:00

79 lines
No EOL
3 KiB
Python

from uuid import uuid5, NAMESPACE_OID
from typing import Dict, Any, Iterator
from .chunk_by_sentence import chunk_by_sentence
def chunk_by_paragraph(data: str, paragraph_length: int = 1024, batch_paragraphs: bool = True) -> Iterator[Dict[str, Any]]:
"""
Chunks text by paragraph while preserving exact text reconstruction capability.
When chunks are joined with empty string "", they reproduce the original text exactly.
"""
current_chunk = ""
current_word_count = 0
chunk_index = 0
last_paragraph_id = None
last_cut_type = None
for paragraph_id, _, sentence, word_count, end_type in chunk_by_sentence(data, maximum_length=paragraph_length):
assert word_count <= paragraph_length, f"{paragraph_length = } is smaller than {word_count = }"
# Check if this sentence would exceed length limit
if current_word_count > 0 and current_word_count + word_count > paragraph_length:
# Yield current chunk
chunk_dict = {
"text": current_chunk,
"word_count": current_word_count,
"chunk_id": uuid5(NAMESPACE_OID, current_chunk),
"chunk_index": chunk_index,
"cut_type": last_cut_type
}
if batch_paragraphs:
chunk_dict["id"] = chunk_dict["chunk_id"]
else:
chunk_dict["id"] = last_paragraph_id
yield chunk_dict
# Start new chunk with current sentence
current_chunk = sentence
current_word_count = word_count
chunk_index += 1
else:
# Just concatenate directly - no space handling
current_chunk += sentence
current_word_count += word_count
# Handle end of paragraph
if end_type in ("paragraph_end", "sentence_cut") and not batch_paragraphs:
# For non-batch mode, yield each paragraph separately
chunk_dict = {
"text": current_chunk,
"word_count": current_word_count,
"id": paragraph_id,
"chunk_id": uuid5(NAMESPACE_OID, current_chunk),
"chunk_index": chunk_index,
"cut_type": end_type
}
yield chunk_dict
current_chunk = ""
current_word_count = 0
chunk_index = 0
last_cut_type = end_type
last_paragraph_id = paragraph_id
# Yield any remaining text
if current_chunk:
chunk_dict = {
"text": current_chunk,
"word_count": current_word_count,
"chunk_id": uuid5(NAMESPACE_OID, current_chunk),
"chunk_index": chunk_index,
"cut_type": last_cut_type
}
if batch_paragraphs:
chunk_dict["id"] = chunk_dict["chunk_id"]
else:
chunk_dict["id"] = last_paragraph_id
yield chunk_dict