Fixed-Size Chunking — Configurable Text Splitting Pipeline

I will implement a fixed-size chunking pipeline for your document corpus — covering recursive character-based splitting with configurable chunk size and overlap, separator hierarchy for intelligent split point selection, chunk validation ensuring minimum quality standards, and a metadata attachment scheme carrying document provenance into each chunk. Fixed-size chunking with recursive splitting is the most reliable general-purpose approach — LangChain's RecursiveCharacterTextSplitter splits on paragraph boundaries first, then sentence boundaries, then word boundaries, choosing the largest natural boundary within the chunk size limit; this produces more semantically coherent chunks than character-count splitting that cuts mid-sentence.

The pipeline covers recursive character splitter configuration, separator hierarchy setup, chunk size and overlap calibration for your embedding model, chunk validation (minimum length, language detection), metadata attachment, and a chunk quality report.