Merge remote-tracking branch 'origin/release' into release

SylphAI-Inc · Jul 12, 2024 · cb191eb · cb191eb
2 parents dc28ef6 + fdcf692
commit cb191eb
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diff --git 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
+..    contributor/index
diff --git 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.
 
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 Chunking Strategies
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

diff --git 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"
+build-backend = "poetry.core.masonry.api"