Document Splitting with LangChain

LangChain provides a powerful tool for splitting large documents into smaller, more manageable chunks. This is particularly useful for vector databases, as it can help to improve search efficiency and reduce the computational cost of embedding generation. In this comprehensive guide, we will explore how to use LangChain’s document splitting capabilities.

Understanding Document Splitting

Document splitting involves breaking down a large document into smaller, more digestible chunks. This can be done based on various criteria, such as word count, sentence length, or semantic meaning. By splitting documents, you can create a more granular index, which can improve search accuracy and performance.

Using LangChain’s Document Splitters

LangChain offers several built-in document splitters that can be used to split documents based on different criteria. Here are some common examples:

• CharacterBasedSplitter: Splits documents based on a specified number of characters.
• SentenceSplitter: Splits documents based on sentence boundaries.
• RegexSplitter: Splits documents based on regular expressions.
• RecursiveCharacterSplitter: Recursively splits documents until the resulting chunks are below a specified length.

Example

Python

from langchain.text_splitter import CharacterBasedSplitter

text = "This is a long document that needs to be split."
splitter = CharacterBasedSplitter(chunk_size=100)
chunks = splitter.split_text(text)

for chunk in chunks:
    print(chunk)

Customizing Document Splitters

You can customize the document splitters to suit your specific needs. For example, you can adjust the chunk size, specify a minimum and maximum chunk length, or use different splitting criteria.

Considerations for Vector Databases

When splitting documents for vector databases, it’s important to consider the following:

• Chunk Size: The chunk size should be appropriate for your embedding model and the desired level of granularity.
• Overlap: You may want to overlap chunks to capture context and improve search accuracy.
• Semantic Coherence: Ensure that the split chunks maintain semantic coherence.

Document splitting is a crucial step in preparing documents for vector databases. By using LangChain’s document splitters, you can effectively break down large documents into smaller chunks, improving search efficiency and accuracy.