What is chunk overlap and why is it important?

In Retrieval-Augmented Generation (RAG) systems, large documents are often broken down into smaller, manageable pieces called 'chunks' to facilitate efficient retrieval. Chunking is a crucial step, and how these chunks are created can significantly impact the quality of the retrieved information and the final generated response.

What is Chunk Overlap?

Chunk overlap refers to the practice of including a small portion of text from the end of one chunk at the beginning of the subsequent chunk when splitting a document. For example, if a document is split into Chunk A, Chunk B, and Chunk C, Chunk B would contain some text from the end of Chunk A, and Chunk C would contain some text from the end of Chunk B. This overlapping section acts as a bridge between adjacent chunks.

The size of this overlap is typically a configurable parameter, often specified in characters or tokens, and it's a critical decision in the chunking strategy.

Why is Chunk Overlap Important?

Chunk overlap is important primarily because it helps maintain context across chunk boundaries. When a query's most relevant information spans across two or more chunks, overlap ensures that the full context required to answer the query is available within one of the retrieved chunks, or across a set of retrieved chunks that include the overlapping sections.

Key Benefits of Chunk Overlap:

• Mitigates Information Loss: Prevents the loss of crucial context that might occur if a semantic boundary or key piece of information falls exactly at a chunk split point.
• Improves Retrieval Recall: By having shared content, the embeddings for overlapping chunks become more similar, increasing the chances that a relevant query will retrieve all necessary parts of the context, even if the primary relevant sentence is near a boundary.
• Ensures Complete Context for LLM: When a large language model (LLM) receives retrieved chunks, the overlap helps ensure that sentences or ideas that started in one chunk and continued into the next are fully represented, preventing incomplete or confusing responses.
• Reduces Semantic Ambiguity: Overlap can provide additional surrounding context, helping to clarify the meaning of terms or concepts that might otherwise be ambiguous when viewed in isolation within a single, smaller chunk.

Considerations for Setting Overlap:

While beneficial, the amount of overlap needs careful consideration. Too little overlap can lead to context loss, while too much overlap can lead to redundant information, increased processing time, higher storage costs for embeddings, and potentially pushing retrieved context beyond the LLM's token limit without adding significant value. The optimal overlap often depends on the nature of the text, the chosen chunk size, and the specific use case.

from langchain.text_splitter import RecursiveCharacterTextSplitter

text = "Your very long document content goes here. It contains multiple paragraphs and sections. We need to split it effectively to retrieve relevant information."

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200, # This defines the overlap
    length_function=len,
    is_separator_regex=False,
)

chunks = text_splitter.create_documents([text])

for i, chunk in enumerate(chunks):
    print(f"Chunk {i}:\n{chunk.page_content[:100]}...")