What is Chunk Size and Chunk Overlap

Chunk size refers to the maximum number of characters or tokens allowed in a single chunk, making it easier to process and analyze large pieces of text. For example, if the chunk size is 40 characters, the text will be divided into chunks of up to 40 characters each. Chunk overlap refers to the number of characters or tokens shared between consecutive chunks, ensuring that important context is not lost. Overlapping chunks prevent sentences from being split in a way that loses meaning and preserve context for tasks like embedding or searching. To chunk text programmatically, you can use a library like Langchain, which allows you to set the chunk size and overlap. The chunk size should be set to fit within the model's token limit, typically ranging from 200 to 500 tokens per chunk for embedding tasks. The chunk overlap should be set to 10%-20% of the chunk size to ensure continuity. Chunking is important for embedding models, as it ensures the text fits within the model's token limit, and improves retrieval by capturing text more accurately. Chunking also allows for scalability by breaking down large texts into smaller pieces for efficient processing. By setting the chunk size and overlap correctly, you can ensure that your text is processed efficiently and accurately.