A Primer on Text Chunking and Its Types

Text chunking is a natural language processing technique that divides text into smaller, manageable segments based on parts of speech and grammatical meanings, aiding in the extraction of meaningful information like noun and verb phrases. This process is crucial for building large language model (LLM)-based systems, as it enhances the precision and detail of the results by addressing issues like context window limitations and embedding precision. Various text chunking strategies, such as sentence splitting using tools like NLTK and spaCy, recursive splitting, and structured splitting for formats like HTML, Markdown, and LaTex, are discussed in the blog, each offering unique strengths and weaknesses depending on the use case. The blog also introduces the use of LanceDB, an open-source vector database for storing text chunks and their embeddings, highlighting its integration with Python data tools. The post emphasizes that while text chunking is straightforward, it requires careful consideration of strategy and chunk size, as different types of data and solutions demand tailored approaches.