semantic-chunker: add min_chunk_size (#4)

Moving code changes here from
langchain-ai/langchain#26398

libs/experimental/langchain_experimental/text_splitter.py

@@ -117,6 +117,7 @@ def __init__(
         breakpoint_threshold_amount: Optional[float] = None,
         number_of_chunks: Optional[int] = None,
         sentence_split_regex: str = r"(?<=[.?!])\s+",
+        min_chunk_size: Optional[int] = None,
     ):
         self._add_start_index = add_start_index
         self.embeddings = embeddings
@@ -130,6 +131,7 @@ def __init__(
             ]
         else:
             self.breakpoint_threshold_amount = breakpoint_threshold_amount
+        self.min_chunk_size = min_chunk_size
 
     def _calculate_breakpoint_threshold(
         self, distances: List[float]
@@ -250,6 +252,12 @@ def split_text(
             # Slice the sentence_dicts from the current start index to the end index
             group = sentences[start_index : end_index + 1]
             combined_text = " ".join([d["sentence"] for d in group])
+            # If specified, merge together small chunks.
+            if (
+                self.min_chunk_size is not None
+                and len(combined_text) < self.min_chunk_size
+            ):
+                continue
             chunks.append(combined_text)
 
             # Update the start index for the next group

libs/experimental/tests/unit_tests/test_text_splitter.py

@@ -1,5 +1,5 @@
 import re
-from typing import List
+from typing import List, Optional
 
 import pytest
 from langchain_core.embeddings import Embeddings
@@ -52,3 +52,27 @@ def test_split_text_gradient(input_length: int, expected_length: int) -> None:
     chunks = chunker.split_text(" ".join(list_of_sentences))
 
     assert len(chunks) == expected_length
+
+
+@pytest.mark.parametrize(
+    "min_chunk_size, expected_chunks",
+    [
+        (None, 4),
+        (30, 4),
+        (60, 3),
+        (120, 3),
+        (240, 2),
+    ],
+)
+def test_min_chunk_size(min_chunk_size: Optional[int], expected_chunks: int) -> None:
+    embeddings = MockEmbeddings()
+    chunker = SemanticChunker(
+        embeddings,
+        breakpoint_threshold_type="percentile",
+        breakpoint_threshold_amount=50,
+        min_chunk_size=min_chunk_size,
+    )
+
+    chunks = chunker.split_text(SAMPLE_TEXT)
+
+    assert len(chunks) == expected_chunks