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
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
April is one of those months where the tool discovery inbox fills up faster than usual. Partly it's spring project energy, partly it's that the no-account tools space keeps expanding in genuinely interesting directions. This month's picks share a pattern: they're not stripped-down versions of paid products. They're tools where removing the account requirement made them better — faster to access, s