chore: lint

semantic_chunkers/chunkers/base.py

@@ -2,8 +2,8 @@
 
 from colorama import Fore, Style
 from pydantic.v1 import BaseModel, Extra
-
 from semantic_router.encoders.base import BaseEncoder
+
 from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 

semantic_chunkers/chunkers/consecutive.py

@@ -1,11 +1,11 @@
 from typing import Any, List
-from tqdm.auto import tqdm
 
 import numpy as np
-
 from semantic_router.encoders.base import BaseEncoder
-from semantic_chunkers.schema import Chunk
+from tqdm.auto import tqdm
+
 from semantic_chunkers.chunkers.base import BaseChunker
+from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
 
@@ -58,7 +58,9 @@ def _chunk(self, splits: List[Any], batch_size: int = 64) -> List[Chunk]:
         self.chunks = chunks
         return chunks
 
-    async def _async_chunk(self, splits: List[Any], batch_size: int = 64) -> List[Chunk]:
+    async def _async_chunk(
+        self, splits: List[Any], batch_size: int = 64
+    ) -> List[Chunk]:
         """Merge splits into chunks using semantic similarity.
 
         :param splits: splits to be merged into chunks.
@@ -90,7 +92,6 @@ async def _async_chunk(self, splits: List[Any], batch_size: int = 64) -> List[Ch
         self.chunks = chunks
         return chunks
 
-
     def __call__(self, docs: List[Any]) -> List[List[Chunk]]:
         """Split documents into smaller chunks based on semantic similarity.
 
@@ -120,4 +121,4 @@ async def acall(self, docs: List[Any]) -> List[List[Chunk]]:
                 splits = doc
             doc_chunks = await self._async_chunk(splits)
             all_chunks.append(doc_chunks)
-        return all_chunks
+        return all_chunks

semantic_chunkers/chunkers/cumulative.py

@@ -1,11 +1,11 @@
 from typing import Any, List
-from tqdm.auto import tqdm
 
 import numpy as np
-
 from semantic_router.encoders import BaseEncoder
-from semantic_chunkers.schema import Chunk
+from tqdm.auto import tqdm
+
 from semantic_chunkers.chunkers.base import BaseChunker
+from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
 
@@ -76,7 +76,9 @@ def _chunk(self, splits: List[Any], batch_size: int = 64) -> List[Chunk]:
 
         return chunks
 
-    async def _async_chunk(self, splits: List[Any], batch_size: int = 64) -> List[Chunk]:
+    async def _async_chunk(
+        self, splits: List[Any], batch_size: int = 64
+    ) -> List[Chunk]:
         """Merge splits into chunks using semantic similarity.
 
         :param splits: splits to be merged into chunks.
@@ -166,4 +168,4 @@ async def acall(self, docs: List[str]) -> List[List[Chunk]]:
                 splits = doc
             doc_chunks = await self._async_chunk(splits)
             all_chunks.append(doc_chunks)
-        return all_chunks
+        return all_chunks

semantic_chunkers/chunkers/statistical.py

@@ -2,16 +2,15 @@
 from typing import Any, List
 
 import numpy as np
-
 from semantic_router.encoders.base import BaseEncoder
-from semantic_chunkers.schema import Chunk
+from tqdm.auto import tqdm
+
 from semantic_chunkers.chunkers.base import BaseChunker
+from semantic_chunkers.schema import Chunk
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
-from semantic_chunkers.utils.text import tiktoken_length
 from semantic_chunkers.utils.logger import logger
-
-from tqdm.auto import tqdm
+from semantic_chunkers.utils.text import tiktoken_length
 
 
 @dataclass
@@ -236,7 +235,6 @@ def __call__(self, docs: List[str], batch_size: int = 64) -> List[List[Chunk]]:
                 raise ValueError("The document must be a string.")
         return all_chunks
 
-
     async def acall(self, docs: List[str], batch_size: int = 64) -> List[List[Chunk]]:
         """Split documents into smaller chunks based on semantic similarity.
 
@@ -265,7 +263,6 @@ async def acall(self, docs: List[str], batch_size: int = 64) -> List[List[Chunk]
                 raise ValueError("The document must be a string.")
         return all_chunks
 
-
     def _encode_documents(self, docs: List[str]) -> np.ndarray:
         """
         Encodes a list of documents into embeddings. If the number of documents

semantic_chunkers/splitters/__init__.py

@@ -1,7 +1,6 @@
 from semantic_chunkers.splitters.base import BaseSplitter
 from semantic_chunkers.splitters.sentence import RegexSplitter
 
-
 __all__ = [
     "BaseSplitter",
     "RegexSplitter",

semantic_chunkers/splitters/sentence.py

@@ -1,6 +1,7 @@
-import regex
 from typing import List
 
+import regex
+
 from semantic_chunkers.splitters.base import BaseSplitter
 
 

tests/unit/test_splitters.py

@@ -2,15 +2,16 @@
 
 import numpy as np
 import pytest
-
 from semantic_router.encoders.base import BaseEncoder
 from semantic_router.encoders.cohere import CohereEncoder
-from semantic_chunkers import BaseChunker
-from semantic_chunkers import BaseSplitter
-from semantic_chunkers import ConsecutiveChunker
-from semantic_chunkers import CumulativeChunker
-from semantic_chunkers import StatisticalChunker
 
+from semantic_chunkers import (
+    BaseChunker,
+    BaseSplitter,
+    ConsecutiveChunker,
+    CumulativeChunker,
+    StatisticalChunker,
+)
 
 
 def test_consecutive_sim_splitter():
@@ -112,6 +113,7 @@ def test_cumulative_sim_splitter():
     # The expected outcome needs to match the logic defined in your mock_encoder's side_effect
     assert len(splits) == 5, f"{len(splits)}"
 
+
 @pytest.mark.asyncio
 async def test_async_cumulative_sim_splitter():
     # Mock the BaseEncoder