Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The Short Answer The core idea of semantic chunking is to split text at semantic boundaries . Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Traditional Chunking vs Semantic Chunking Dimension Fixed-Length / Recursive Semantic Chunking Split criteria Character count, token count, delimiters Semantic similarity between adjacent sentences Requires Embedding ❌ No ✅ Yes Split quality May break in the middle of a topic Splits at topic transitions, preserving semantic coherence Fixed-length chunking is like measuring paper with a ruler — regardless of content, it cuts every 500 characters. Semantic chunking is like a reader who, after finishing a paragraph, asks "is the next part still about the same thing?" If not, that's where the cut goes.…