Llama Index Splitter

The Llama Index Splitter converts large text sources into a sequenced set of chunks tailored for LlamaIndex workflows. It accepts raw_text and optional metadata, producing an ordered collection of chunks that preserve source context and sequence. It supports multiple splitting modes and configurable boundaries to align with embedding constraints and retrieval patterns. This tool is designed for data engineers, ML practitioners, and knowledge teams ingesting manuals, reports, codebases, and research papers into vector stores.

At its core, the splitter operates deterministically, allowing you to define chunk_size, overlap, and boundary separators. Optional strategies include recursive character-based splitting, token-based splitting with language-aware heuristics, and sentence-based chunking. Metadata retention and per-chunk annotations are available to preserve provenance and enable fine-grained indexing. The tool integrates with downstream index builders but does not perform embedding or storage itself.

The approach is language-agnostic and suited for multilingual content, though token estimates may vary by language, font, or encoding. It is suitable for preprocessing before vectorization, enabling predictable chunk counts and consistent boundaries across documents.

Typical use cases include large PDFs, source code archives, academic papers, and corporate reports where precise chunk boundaries improve retrieval quality and indexing throughput.