Author(s): Aditya Baser Originally published on Towards AI. 1. Introduction 1.1. What is chunking, and why do we need it? The intuition behind chunking and how it helps in the retrieval of information Imagine you are searching for a specific piece of information in a vast library. If the books are arranged haphazardly — some with irrelevant sections bound together and others with critical pages scattered across volumes — you’d spend a frustrating amount of time flipping through unrelated content. Now, consider a library where each book is carefully organized by topic, with coherent sections that neatly encapsulate a single idea or concept. This is the intuition behind chunking in the context of retrieval-augmented generation (RAG): it’s about organizing information so it can be easily retrieved and understood. RAG Workflow — Our emphasis would be on understanding chunking Chunking refers to the process of dividing large bodies of text into smaller, self-contained segments called chunks. Each chunk is designed to encapsulate a coherent unit of information that can be efficiently stored, retrieved, and used for downstream tasks like search, indexing, or contextual input for an LLM. 1.2. What are the different types of chunking methods? Extending the library analogy, imagine you walk into the library to find information about “The Effects of Climate Change on Marine Life.” The way the books are organized will determine how easily you can find the specific information you’re looking for: 1.2.1. Fixed-Length Chunking Every book in the library is arbitrarily divided into fixed-sized sections, say, 100 pages each. No matter what the content is, each section stops at the 100-page mark. As a result, a chapter about coral bleaching might be split across two sections, leaving you scrambling to piece together the full information. Fixed-Length Chunking splits the text into chunks based on a fixed token, word, or character count. While this method is simple to implement, it often causes relevant information to be split among different chunks or for the same chunk to have information on different topics, making retrieval less accurate. 1.2.2. Recursive Chunking (Hierarchical) The books are structured into sections, chapters, and paragraphs following their natural hierarchy. For instance, a book on climate change might have sections on global warming, rising sea levels, and marine ecosystems. However, if a section about marine life is too large, it may remain unwieldy and difficult to search through quickly. Recursive chunking breaks text hierarchically, following natural structures such as chapters, sections, or paragraphs. While it preserves the natural structure of the document it would lead to chunks that are too large for cases when sections are lengthy and poorly organized. 1.2.3. Semantic Chunking In this case, the books are reorganized based on meaning and topic coherence. Instead of rigidly splitting sections by length or following a strict hierarchy, every section focuses on a specific topic or concept. For example, a section might cover “The Impact of Rising Temperatures on Coral Reefs” in its entirety, regardless of length, ensuring all related content stays together. As a result, you can retrieve exactly what you need without having to sift through unrelated material. Semantic chunking uses meaning or context to define chunk boundaries, often leveraging embeddings or similarity measures to detect where one topic ends and another begins. 2. Semantic Chunking: 101 Semantic chunking involves breaking text into smaller, meaningful units (chunks) that retain context and meaning. 2.1. Why Semantic Chunking is Superior Semantic chunking stands out among chunking methods because it optimizes the retrieval process for contextual relevance, precision, and user satisfaction. In retrieval-augmented generation (RAG), where the goal is to feed highly relevant and coherent information into a large language model (LLM), semantic chunking eliminates many pitfalls associated with fixed-length and hierarchical approaches. Let’s explore the unique advantages of semantic chunking and why it is crucial for building high-performance RAG systems. 2.1.1. Context Preservation Semantic chunking ensures that each chunk contains complete, self-contained information related to a single topic. This contrasts with fixed-length chunking, were arbitrary boundaries often split context, leading to incomplete or fragmented information retrieval. When feeding an LLM, context completeness is critical. Missing context forces the LLM to “hallucinate” or generate suboptimal answers, while semantic chunking minimizes this risk by delivering coherent inputs. 2.1.2. Improved Retrieval Precision Semantic chunking generates chunks that are tightly focused on specific topics. This makes it easier for retrieval systems to match queries to the most relevant chunks, improving the precision of retrieval. Precise retrieval reduces the number of irrelevant chunks passed to the LLM. This saves tokens, minimizes noise, and ensures the LLM focuses only on information that directly answers the query. 2.1.3. Minimized Redundancy Semantic chunking reduces overlap and redundancy across chunks. While some overlap is necessary for preserving context, semantic chunking ensures this is deliberate and optimized, unlike fixed-length chunking, where overlaps are arbitrary and often wasteful. RAG pipelines often must deal with token constraints. Redundancy wastes valuable token space, while semantic chunking maximizes the information density of each chunk. 3. Implementing Semantic Chunking 3.1. Loading the dataset and setting up the API key We will use the dataset “jamescalam/ai-arxiv2”, which contains research papers on artificial intelligence. These papers are often long and contain distinct sections like abstracts, methodologies, experiments, and conclusions. Chunking this dataset using semantic methods ensures we preserve context within sections and facilitate efficient retrieval for downstream tasks like summarization or question answering. Snippet of the dataset "jamescalam/ai-arxiv2” Semantic chunking stands out by splitting text based on meaning and context rather than arbitrary rules, ensuring each chunk is coherent and self-contained. One of the key tools for implementing semantic chunking is the semantic_router package. Among its core features, the semantic_router.splitters module is specifically designed for splitting text into meaningful chunks using cutting-edge semantic methods. The semantic_router.splitters module is central to the chunking functionality. It offers three key chunking methods—consecutive_sim, cumulative_sim, and rolling_window—each catering to different document structures and use cases. To use OpenAI’s tools, you need an API key for authentication, which we securely load from a .env file using […]
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