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
Hey everyone, I shared this earlier as a CLI to analyse npm packages before installing. Since then, I’ve added something I think is even more useful: 👉 You can now scan GitHub repos before cloning or running them npx guard-install --repo https://github.com/user/repo There’s a growing pattern (especially in crypto interviews / side projects): “Clone this repo and run it locally” Some of these rep
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