Customizing Document Splitting
Implement advanced text splitting techniques, including semantic chunking and handling code or specific data structures.
Why Customize Text Splitting?
When preparing documents for Retrieval Augmented Generation (RAG), how you split them into chunks is crucial. Basic text splitters are a good start, but they often fall short for complex or highly structured content.
Customizing your text splitting strategy allows you to maintain better contextual integrity, leading to more accurate retrievals and better LLM responses.
Tailoring Character Splitters
LangChain's CharacterTextSplitter is simple but powerful. You can customize it by providing specific separator characters. This is useful when your documents have unique delimiters you want to respect, like a specific tag or a unique line break pattern.
By defining your own separators, you can ensure logical breaks rather than arbitrary character counts.
from langchain.text_splitter import CharacterTextSplitter
class Main:
def run(self):
text = "Chapter 1: Intro.Section 1.1: Basics.Section 1.2: Advanced."
# Custom separator is "."
splitter = CharacterTextSplitter(
separator=".",
chunk_size=20,
chunk_overlap=0
)
chunks = splitter.split_text(text)
for i, chunk in enumerate(chunks):
print(f"Chunk {i+1}: {chunk}")
if __name__ == "__main__":
Main().run()All lessons in this course
- Loading Diverse Document Types
- Understanding Text Splitting Strategies
- Customizing Document Splitting
- Handling Document Metadata and Filtering