Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? 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. Dimension Fixed-Length / Recur
Try this. Find a photo on your phone that you love. Now squint, or zoom out until it's the size of a stamp. It's still the same photo. You can still tell what's in it. But something about it has gone a little flat — the part that made you take it in the first place has quietly walked out of the room. Most of us would describe what just happened with a shrug: "it's just smaller." But the truth is m
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E