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
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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
I recently built a dynamic testimonials component for my project at Coloring Maker and wanted to give it a little extra "magic." This is how I did it. The structure is quite simple. We need a main wrapper that acts as our "sky" and a series of div elements that will become our hearts. It is crucial that the main container has the position: relative; and overflow: hidden; properties. This ensures