Document segmentation in LlamaIndex involves breaking down large documents into smaller, manageable chunks (nodes) to improve processing and retrieval efficiency. LlamaIndex provides built-in tools like NodeParser classes (e.g., SentenceSplitter, TokenTextSplitter) to split text based on sentence boundaries, token limits, or custom rules. For example, SentenceSplitter divides text into sentences with optional overlap between chunks, while TokenTextSplitter ensures chunks fit within token limits for LLM compatibility. Developers configure parameters like chunk_size (e.g., 512 tokens) and chunk_overlap (e.g., 20 tokens) to balance context retention and chunk size. Segmentation is often applied after loading raw data (e.g., using SimpleDirectoryReader), transforming unstructured text into structured nodes for indexing.
For complex documents (e.g., PDFs with mixed text and tables), segmentation requires combining multiple strategies. LlamaIndex supports hierarchical parsing, where documents are first split into logical sections (e.g., using UnstructuredElementNodeParser for HTML/PDF elements) before applying sentence-level splitting. For instance, a research paper might be split into abstract, methods, and results sections, with each section further divided into sentences. Metadata (e.g., section titles) is preserved in nodes to maintain context. Developers can also use custom regex patterns or third-party libraries (e.g., PyMuPDF for PDF tables) to extract structured data, then inject results into nodes. This approach ensures semantic coherence while handling diverse content types.
Advanced use cases involve dynamic segmentation based on content type. For code repositories, CodeSplitter can split code files into functions or classes. For markdown docs, MarkdownNodeParser splits text by headers. Developers can chain multiple splitters (e.g., split a PDF into pages, then into paragraphs) or implement custom node creation logic. LlamaIndex also supports parent-child node relationships, where a parent node represents a section (e.g., “Chapter 1”) and child nodes contain its subsections. This hierarchy improves retrieval accuracy by allowing the framework to traverse relationships during queries. For example, a query about “neural networks” might first match a parent node about machine learning chapters, then drill into specific child nodes for detailed answers.