\subsection{Applications of LLMs}
LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.
\end{document}
"""
latex_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
latex_docs = latex_splitter.create_documents([latex_text])
latex_docs
[Document(page_content='\\documentclass{article}\n\n\x08egin{document}\n\n\\maketitle', metadata={}),
 Document(page_content='\\section{Introduction}', metadata={}),
 Document(page_content='Large language models (LLMs) are a type of machine learning', metadata={}),
 Document(page_content='model that can be trained on vast amounts of text data to', metadata={}),
 Document(page_content='generate human-like language. In recent years, LLMs have', metadata={}),
 Document(page_content='made significant advances in a variety of natural language', metadata={}),
 Document(page_content='processing tasks, including language translation, text', metadata={}),
 Document(page_content='generation, and sentiment analysis.', metadata={}),
 Document(page_content='\\subsection{History of LLMs}', metadata={}),
 Document(page_content='The earliest LLMs were developed in the 1980s and 1990s,', metadata={}),
 Document(page_content='but they were limited by the amount of data that could be', metadata={}),
 Document(page_content='processed and the computational power available at the', metadata={}),
 Document(page_content='time. In the past decade, however, advances in hardware and', metadata={}),
 Document(page_content='software have made it possible to train LLMs on massive', metadata={}),
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/text_splitters/examples/code_splitter.html