Large language models (LLMs) (opens new window)have transformed the natural language processing (NLP) (new window)domain by generating human-like text, answering complex questions, and analyzing large amounts of information with impressive accuracy. Their ability to process diverse queries and produce detailed responses makes them invaluable across many fields, from customer service to medical research. However, as LLMs scale to handle more data, they encounter challenges in managing long documents and retrieving only the most relevant information efficiently.
Although LLMs are good at processing and generating human-like text, they have a limited "context window." This means they can only keep a certain amount of information in memory at one time, which makes it hard to manage very long documents. It's also challenging for LLMs to quickly find the most relevant information from large datasets. On top of this, LLMs are trained on fixed data, so they can become outdated as new information appears. To stay accurate and useful, they need regular updates.
Retrieval-augmented generation (RAG) (opens new window)addresses these challenges. There are many components in the RAG workflow, such as query, embedding, indexing, and so on. Today, let's explore the chunking strategy.
By chunking documents into smaller, meaningful segments and embedding them in a vector database, RAG systems can search and retrieve only the most relevant chunks for each query. This approach allows LLMs to focus on specific information, improving response accuracy and efficiency.
In this blog, we'll explore chunking and its different strategies in more depth and their role in optimizing LLMs for real-world applications.
Chunking is about breaking big data sources into smaller, manageable pieces or "chunks." These chunks are stored in vector databases, allowing quick and efficient searches based on similarity. When a user submits a query, the vector database finds the most relevant chunks and sends them to the language model. This way, the model can focus only on the most relevant information, making its response faster and more accurate.
Chunking helps language models handle large datasets more smoothly and deliver precise answers by narrowing down the data it needs to look at.
For applications that need quick, precise answers -- like customer support or legal document searches -- chunking is an essential strategy that boosts both performance and reliability.
Here are some of the major chunking strategies that are used in RAG:
Now, let's dive deep into each chunking strategy in detail.
Fixed-size chunking involves dividing data into evenly-sized sections, making it easier to process large documents.
Sometimes, developers add a slight overlap between chunks, where a small part of one segment is repeated at the beginning of the next. This overlapping approach helps the model retain context across the boundaries of each chunk, ensuring that critical information isn't lost at the edges. This strategy is especially useful for tasks that require a continuous flow of information, as it enables the model to interpret text more accurately and understand the relationship between segments, leading to more coherent and contextually aware responses.
The above illustration is a perfect example of fixed-size chunking, where each chunk is represented by a unique color. The green section indicates the overlapping part between chunks, ensuring the model has access to the relevant context from one chunk when processing the next.
This overlap improves the model's ability to process and understand the full text, leading to better performance in tasks like summarization or translation, where maintaining the flow of information across chunk boundaries is critical.
Now, let's recreate this example using a coding example. We will use LangChain (opens a new window) to implement fixed-size chunking.
Recursive chunking is a method that systematically divides extensive text into smaller, manageable sections by repeatedly breaking it down into sub-chunks. This approach is particularly effective for complex or hierarchical documents, ensuring that each segment remains coherent and contextually intact. The process continues until the text reaches a size suitable for efficient processing.
For example, consider a lengthy document that needs to be processed by a language model with a limited context window. Recursive chunking would first split the document into major sections. If these sections are still too large, the method would further divide them into subsections and continue this process until each chunk fits within the model's processing capabilities. This hierarchical breakdown preserves the logical flow and context of the original document, enabling the model to handle long texts more effectively.
In practice, recursive chunking can be implemented using various strategies, such as splitting based on headings, paragraphs, or sentences, depending on the document's structure and the specific requirements of the task.
In the illustration, the text is divided into four chunks, each shown in a different color, using recursive chunking. The text is broken down into smaller, manageable parts, with each chunk containing up to 80 words. There is no overlap between chunks. The color coding helps show how the content is split into logical sections, making it easier for the model to process and understand long texts without losing important context.
Now, let's code an example of how we will implement recursive chunking.
Semantic chunking refers to dividing text into chunks based on the meaning or context of the content. This method typically uses machine learning (opens new window)or natural language processing (NLP) (opens new window)techniques, such as sentence embeddings, to identify sections of the text that share similar meaning or semantic structure.
In the illustration, each chunk is represented by a different color -- blue for AI and yellow for Prompt Engineering. These chunks are separated because they cover distinct ideas. This method ensures that the model can clearly understand each topic without mixing them.
Now, let's code an example of implementing semantic chunking.
Agentic chunking is a powerful strategy among these strategies. In this strategy, we utilize LLMs such as GPT to function as agents in the chunking procedure. Instead of manually determining how to segment content, the LLM proactively organizes or divides the information according to its comprehension input. The LLM determines the best method to break the content into manageable pieces, influenced by the task's context.
The illustration shows a chunking agent breaking down a large text into smaller, meaningful pieces. This agent is powered by AI, which helps him better understand the text and divide it into chunks that make sense. This is called agentic chunking, and it's a smarter way to process text compared to simply cutting it into equal parts.
Now, let's see how we can implement it in a coding example.
To make it easier to understand the different chunking methods, the table below compares fixed-size chunking, recursive chunking, semantic chunking, and agentic chunking. It highlights how each method works, when to use them, and their limitations.
Chunking strategies and RAG are essential for enhancing LLMs. Chunking aids in simplifying intricate data into smaller, manageable parts, facilitating more effective processing, whereas RAG improves LLMs by incorporating real-time data retrieval within the generation workflow. Collectively, these methods allow LLMs to deliver more precise, context-sensitive replies by merging organized data with lively, current information.
