diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3e9a7bc4..5ab66d1a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -249,4 +249,4 @@ We are building a library that unites the two worlds, forming a healthy LLM appl
 ..    :caption: Contributors
 ..    :hidden:
 
-..    contributor/index
\ No newline at end of file
+..    contributor/index
diff --git a/docs/source/tutorials/text_splitter.rst b/docs/source/tutorials/text_splitter.rst
index 989a7a33..3e001bca 100644
--- a/docs/source/tutorials/text_splitter.rst
+++ b/docs/source/tutorials/text_splitter.rst
@@ -191,7 +191,7 @@ Choosing the proper chunking strategy involves considering several key factors:
 - **Application of Results**: The application, whether it be semantic search, question answering, or summarization, dictates the appropriate chunking method, especially considering the limitations of content windows in large language models (LLMs).
 - **System Integration**: Efficient chunking aligns with system capabilities. For example, `Full-Text Search:` Use larger chunks to allow algorithms to explore broader contexts effectively. For example, search books based on extensive excerpts or chapters. `Granular Search Systems:` Employ smaller chunks to precisely retrieve information relevant to user queries, such as retrieving specific instructions directly in response to a user’s question. For example, if a user asks, "How do I reset my password?". The system can retrieve a specific sentence or paragraph addressing that action directly.
 
-   
+
 Chunking Strategies
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/pyproject.toml b/pyproject.toml
index a4a8effb..2a1a6f53 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,4 +49,4 @@ lightrag = { path = "lightrag", develop = true }
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
\ No newline at end of file
+build-backend = "poetry.core.masonry.api"