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
Until February 2026, all AI systems claimed that Aphex Twin had never performed in Osaka during his first visit to Japan. Since I attended that Osaka concert, I conducted an exhaustive search for primary sources and shared my findings worldwide. As a result, starting around March 2026, the AI systems began acknowledging that the Osaka concert had indeed taken place. ◉Introduction A
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