7 Chunking Strategies in RAG You Need To Know

Sharmila Ananthasayanam

Oct 2, 2024 • 7 min read

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.

What is Chunking?

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.

Improved Accuracy: Chunks allow for more precise matching between queries and relevant text, reducing noise and irrelevant information.
Enhanced Efficiency: Smaller chunks are processed faster and use memory more efficiently, enabling RAG to handle large datasets effectively.
Preserved Context: Well-designed chunks maintain logical coherence, balancing specificity with necessary context.
Information Access: Chunking supports a range of query types, from specific questions to broader topics, enabling RAG to provide tailored responses.

Why is Chunking Necessary in RAG?

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.

Want to see chunking in action? Our AI POC demonstrates how we implemented different chunking strategies in real-world applications. For example, our document chat systems use optimized chunking to efficiently process and retrieve information from various types of documents, ensuring accurate and context-aware responses.

7 Types of Chunking Strategies in RAG

Several chunking strategies are employed in RAG chunking, each with its own advantages and use cases:

1. Fixed Size Chunking

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.

Advantages

Simplicity: Easy to implement and understand.
Efficiency: Fast processing, especially for large datasets.
Consistency: Uniform chunk sizes across documents.
Low computational requirements: Doesn't need complex algorithms or models.

Disadvantages

Context fragmentation: May split sentences or logical units of information.
Inflexibility: Doesn't account for varying content density or structure.
Potential information loss: Important context might be split across chunks.
Sub-optimal for heterogeneous content: Less effective for documents with varying structures or lengths.

2. Recursive-Based Chunking

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.

Advantages

Meaningful Chunks: Preserves semantic and structural integrity by splitting at logical points like paragraphs or sections.
Flexibility: Adapts to various types of content by using multiple separators, making it useful for both text and code.
Fine-Grained Control: Allows precise control over chunk size and overlap to fit specific needs without losing important context.
Handles Complex Content: Particularly useful for structured or hierarchical content like technical documents or programming code.

Disadvantages

Increased Complexity: Requires a more complex setup compared to fixed-size chunking.
Higher Computational Overhead: Recursive calls and multiple separator checks can slow down processing for large texts.
Dependence on Separators: If the text lacks clear separators, it may not split effectively, leading to larger or uneven chunks.
Slower Performance: This can be slower for very large datasets compared to simpler chunking methods.

3. Document-Based Chunking

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.

Advantages

Full Context Preservation: Retains the entire document’s flow and meaning without cutting through critical sections.
Ideal for Structured Texts: Works well for highly structured documents like legal contracts or medical reports.
Simplicity: Straightforward to implement without complex chunking rules.

Disadvantages

Scalability Issues: Not ideal for large documents that exceed token or memory limits, as they may be too big to process in a single chunk.
Reduced Efficiency: Handling large documents as a whole can lead to inefficiencies and higher resource consumption.
Limited Specificity: Lacks granularity, making it difficult to extract or retrieve specific sections or details within the document.

4. Semantic Chunking

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.

Advantages

Preserves meaning: Chunks are created around logical content breaks, ensuring coherent units of information.
Adaptable to diverse content: Works well with documents of varying topics or sections, such as research papers or technical documents.
Improves retrieval accuracy: Chunks maintain their semantic integrity, making responses to queries more relevant.

Disadvantages

Complex setup: Requires advanced techniques to measure semantic shifts between sentences.
Higher computational cost: Analyzing meaning differences can be resource-intensive, especially for longer documents.
Threshold tuning: The quality of chunking depends on setting the right threshold, which may vary for different types of content or domains.

5. Token-Based Chunking

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.

Advantages

Optimized for language models: Ensures chunks stay within the token limits of models like GPT, improving processing efficiency.
Precise control over chunk size: Allows for fine-tuning the number of tokens per chunk to match model requirements.
Consistency across content: Divides text into consistent token counts, making processing easier for large datasets.

Disadvantages

Context fragmentation: May break sentences or paragraphs mid-way, leading to incomplete information in chunks.
Ignores semantic structure: Focuses only on token count, potentially losing important context or meaning.
Limited flexibility: This doesn't account for varying content density or the natural breaks in text, which may reduce chunk coherence.

6. Sentence-Based Chunking

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.

Advantages

Preserves context: Each chunk contains full sentences, ensuring no loss of meaning or disruption in the flow of information.
Better readability: Chunks are more coherent and readable, making it easier for models or users to process them.
Natural division: The text is split at logical points, preventing chunks from cutting through ideas mid-sentence.

Disadvantages

Variable chunk sizes: Depending on sentence length, chunks can vary significantly in size, making it harder to control chunk consistency.
Inefficient for long sentences: In texts with very long sentences, chunks may exceed token limits or contain too much information in a single chunk.
Less control over chunk size: Chunking based on sentences may result in uneven chunks, which could affect performance in models with strict size limits.

7. Agentic Chunking

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.

Advantages

Task-Oriented Efficiency: Helps optimize the AI’s performance by tailoring chunks to specific tasks or actions, reducing confusion and improving decision-making.
Better Focus on Relevant Data: The AI can hone in on exactly what it needs for each task, leading to more accurate responses or analysis.
Flexibility: Offers adaptability to a wide range of use cases, such as answering questions, summarizing, or performing tasks based on predefined roles.

Disadvantages

Complex Setup: Requires clear definitions of agent roles and task-specific chunking rules, which may involve significant setup time and effort.
Over-Specialization: Risk of oversimplifying or fragmenting complex documents into overly specific tasks, potentially missing broader patterns or nuances.
May Lose Global Context: Since each chunk is optimized for a specific task, there’s a risk of losing the overall context of the document, which could affect the quality of holistic tasks like summarization.

Conclusion

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.