Retrieval Augmented Generation (RAG) enhances AI responses by retrieving relevant external information in real time. To make this process efficient, RAG relies on chunking strategies - breaking large documents into smaller, manageable pieces for faster retrieval and processing.
A great way to understand RAG is to think of it as an open-book exam. In contrast to traditional models that retrieve answers from their internal memory (similar to closed-book exams), RAG looks up external knowledge sources to "consult" the right information before answering. This retrieval mechanism, combined with a generation model, enables RAG to produce more accurate and contextually grounded responses.
To make this process efficient and scalable, RAG relies on chunking—breaking large documents into smaller, manageable pieces for faster retrieval. In this blog, we will dive into the chunking strategies and their role in making RAG more effective.
Chunking involves dividing large documents into smaller segments called chunks. These can be paragraphs, sentences, or token-limited segments, making it easier for the model to search and retrieve only what's needed. This chunking technique is crucial for optimizing RAG performance.
In RAG, retrieving the right information is key, but what happens when the knowledge base is vast—potentially containing millions of words or documents? Retrieving relevant information efficiently from such a large dataset can be difficult. This is where chunking becomes essential.
Several chunking strategies are employed in RAG chunking, each with its own advantages and use cases:
Fixed-size chunking is a straightforward approach where text is divided into uniform chunks based on a predefined character count.
For example, you might split a document into chunks of 500 tokens each, regardless of whether the chunk ends mid-sentence or across paragraphs.
To mitigate this, an overlap feature can be introduced, where a certain number of tokens or characters from the end of one chunk is repeated at the start of the next. This helps preserve context across chunks and prevents loss of meaning at the boundaries.
Recursive Character Text Splitting is a more adaptive approach that breaks text into chunks by using multiple separators in a specified order. It tries each separator (like paragraphs, sentences, or specific markers) in a descending order of importance to find the most meaningful boundaries in the text.
The method recursively splits text until the chunks meet a specified size, preserving logical structure.
For example, in a Python code document, it may first try splitting by class definitions, then function definitions, and finally by line breaks. This ensures that chunks are as meaningful as possible.
Document-based chunking treats the entire document as a single chunk or divides it as little as possible. This method aims to preserve the full structure and context of the document, making it ideal for content where splitting may disrupt the flow or meaning.
Best suited for tasks that require processing large, detailed texts, such as legal, medical, or scientific document analysis. It ensures that key information and context remain intact across the document.
For instance, a legal document might be chunked by individual charges, with each charge treated as a chunk. This method maintains the document's structural integrity and ensures that no important legal context is lost.
Semantic chunking breaks text into chunks based on meaning rather than fixed sizes. It ensures that each chunk contains coherent and relevant information by analyzing shifts in the text’s semantic structure. This is typically done by measuring differences in sentence embeddings, which represent the meaning of sentences mathematically.
For example, the chunker splits the text when it detects a significant change in meaning between two sentences based on their embeddings. Thresholds can be set to control when this break happens, ensuring each chunk is logically connected.
Token-based chunking splits text based on a predefined number of tokens (words or subwords) rather than characters or sentences. Tokens are the smallest meaningful units of text, and the chunk size is controlled by a set token limit.
For example, a document might be divided into chunks of 300 tokens each, ensuring that each chunk is within a model’s token limit for processing, even if it cuts across sentences or paragraphs.
Sentence-based chunking divides text into full sentences, ensuring that each chunk contains complete thoughts. This method helps preserve the logical flow of information by splitting natural sentence boundaries.
For example, a document might be broken into chunks where each contains 5 to 10 sentences, maintaining the semantic integrity of each chunk while keeping the size manageable.
Agentic chunking breaks down a text into smaller, semantically meaningful sections based on the roles or tasks an AI agent needs to perform. Instead of treating a document or passage as a uniform whole, agentic chunking organizes content into "actionable" chunks—each optimized for a specific purpose, such as answering a question, summarizing, or making decisions. These chunks are structured to give the AI clear cues about the task, making it more efficient and goal-oriented when processing information.
For example, if a document describes a process, agentic chunking would split the text into task-relevant parts like "step 1: preparation," "step 2: execution," and "step 3: conclusion," with each part mapped to a specific agent action or goal.
Incorporating chunking into Retrieval-Augmented Generation (RAG) is essential for optimizing the retrieval and generation process. By intelligently breaking down information into manageable chunks, we enhance the relevance and accuracy of the data fed into the model, improve context preservation, and ensure efficient processing. Each chunking method—whether fixed-size, semantic, token-based, or agentic—offers unique advantages, allowing RAG to be more adaptable to various tasks and document structures.
Choosing the right chunking strategy is crucial for maximizing the potential of RAG systems. By understanding and implementing appropriate document chunking techniques, we can significantly enhance the accuracy, efficiency, and contextual awareness of AI-powered information retrieval and generation systems.
AIML Intern @F22 Labs
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