Chunk Text with Overlap
Split text into overlapping chunks for RAG.
Given a text (list of words), split it into chunks of size chunk_size with overlap_size words overlapping between consecutive chunks.
Input:
- Line 1: chunk_size overlap_size
- Line 2: text (space-separated words)
Output: One chunk per line (space-separated words).
Example:
4 2 a b c d e f g h
a b c d c d e f e f g h
- The input values are
chunk_size = 4andoverlap_size = 2, which means each chunk will have 4 words and overlap with the next chunk by 2 words. - The text is split into chunks of size 4, starting from the beginning:
a b c dis the first chunk. - The next chunk starts
overlap_size = 2words after the beginning of the previous chunk, so it starts withc dand includes the next 2 wordse f, resulting inc d e f. - This process continues, with the next chunk starting with
e f(the overlap) and including the next 2 wordsg h, resulting ine f g h.
Constraints:
- chunk_size > overlap_size >= 0
- Last chunk may be smaller than chunk_size
- Don't create empty chunks
More from LLM 3: Applications & Evaluation
Background Knowledge
The problem of splitting text into overlapping chunks is a fundamental concept in natural language processing (NLP) and information retrieval. This technique is often used in applications such as text summarization, question answering, and text classification. The goal is to divide a large piece of text into smaller, manageable chunks, called windows or segments, while maintaining some overlap between consecutive chunks to preserve context.
In the context of Retrieval & Similarity, splitting text into overlapping chunks is essential for tasks like passage retrieval and question answering. By creating overlapping chunks, we can ensure that important information is not lost at the boundaries between chunks. The overlap size determines how much context is preserved between consecutive chunks, while the chunk size controls the amount of text in each chunk.
The concept of overlapping chunks is closely related to the sliding window technique, which is commonly used in string processing and signal processing. The sliding window approach involves moving a fixed-size window over a sequence of data, processing each window independently. In the case of text chunking, the sliding window is used to extract overlapping chunks of text, which can then be processed further using various NLP techniques.
Algorithm/Approach
The general approach to solving this problem involves using a sliding window algorithm to extract overlapping chunks of text. The algorithm will iterate over the input text, maintaining a window of size chunk_size and moving it by chunk_size - overlap_size positions at each step. This will ensure that each chunk overlaps with the previous one by overlap_size words.
Step-by-Step Strategy
To implement the solution, follow these steps:
- Read the input chunk_size and overlap_size from the first line.
- Read the input text from the second line and split it into a list of words.
- Initialize an empty list to store the resulting chunks.
- Iterate over the input text using a sliding window of size chunk_size, moving the window by chunk_size - overlap_size positions at each step.
- At each step, extract the current chunk of text and add it to the list of resulting chunks.
- Repeat the process until the entire input text has been processed.
Common Pitfalls
When implementing the solution, watch out for the following common pitfalls:
- Incorrectly calculating the step size for the sliding window, which can result in incorrect overlap between chunks.
- Failing to handle edge cases, such as when the input text is shorter than the chunk_size.
- Not properly splitting the input text into a list of words, which can affect the accuracy of the chunking process.
Time & Space Complexity
The expected time complexity for this problem is O(n), where n is the length of the input text, since we are iterating over the text once using a sliding window. The space complexity is also O(n), as we need to store the resulting chunks in memory. However, the actual space complexity may be lower if the input text is highly repetitive, resulting in fewer unique chunks.