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
I spent the last few months building BlazOrbit, a component library for Blazor. It's not the first of its kind —MudBlazor, Radzen and Blazorise already exist— so I had to answer a hard question from the start: why does this need to exist? The answer turned out to be a set of architectural decisions I want to share, because each one taught me something about building UI frameworks that I didn't kno
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