Semantic Chunking — Split Documents by Meaning

I will implement semantic chunking for your document corpus — splitting at points of low semantic similarity between adjacent sentences (using embedding distance to detect topic shifts) rather than at arbitrary character count boundaries, producing chunks that contain coherent, topically unified content. Semantic chunking produces meaningfully different chunks from fixed-size chunking — fixed-size splits that cut mid-topic produce chunks where the first half discusses one concept and the second half discusses another, reducing retrieval precision for both; semantic splits that detect topic boundaries produce chunks covering one unified concept, improving retrieval precision for queries about that concept.

The implementation covers sentence embedding generation for boundary detection, cosine similarity threshold calibration for your content, split point detection algorithm, chunk size validation with merge for very small chunks, and retrieval quality comparison against fixed-size chunking on your query set.