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.…