Disable unproven paragraph flattening in split_text()

autogpt/processing/text.py

@@ -1,7 +1,7 @@
 """Text processing functions"""
 import logging
 from math import ceil
-from typing import Iterator, Optional, Sequence
+from typing import Iterator, Optional, Sequence, TypeVar
 
 import spacy
 import tiktoken
@@ -13,14 +13,18 @@
 
 logger = logging.getLogger(__name__)
 
+T = TypeVar("T")
 
-def batch(iterable: Sequence, max_batch_length: int, overlap: int = 0):
+
+def batch(
+    sequence: Sequence[T], max_batch_length: int, overlap: int = 0
+) -> Iterator[Sequence[T]]:
     """Batch data from iterable into slices of length N. The last batch may be shorter."""
     # batched('ABCDEFG', 3) --> ABC DEF G
     if max_batch_length < 1:
         raise ValueError("n must be at least one")
-    for i in range(0, len(iterable), max_batch_length - overlap):
-        yield iterable[i : i + max_batch_length]
+    for i in range(0, len(sequence), max_batch_length - overlap):
+        yield sequence[i : i + max_batch_length]
 
 
 def _max_chunk_length(model: str, max: Optional[int] = None) -> int:
@@ -42,7 +46,7 @@ def chunk_content(
     content: str,
     for_model: str,
     max_chunk_length: Optional[int] = None,
-    with_overlap=True,
+    with_overlap: bool = True,
 ) -> Iterator[tuple[str, int]]:
     """Split content into chunks of approximately equal token length."""
 
@@ -155,7 +159,7 @@ def split_text(
     text: str,
     for_model: str,
     config: Config,
-    with_overlap=True,
+    with_overlap: bool = True,
     max_chunk_length: Optional[int] = None,
 ) -> Iterator[tuple[str, int]]:
     """Split text into chunks of sentences, with each chunk not exceeding the maximum length
@@ -176,8 +180,6 @@ def split_text(
 
     max_length = _max_chunk_length(for_model, max_chunk_length)
 
-    # flatten paragraphs to improve performance
-    text = text.replace("\n", " ")
     text_length = count_string_tokens(text, for_model)
 
     if text_length < max_length: