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
TL;DR: Mistral Medium 3.5 is a 128B open-weight model released April 29, 2026, with a 256K context window, configurable reasoning, and native multimodal input. It scores 77.6% on SWE-Bench Verified — close but not ahead of Claude Sonnet 4.6 — and ships alongside Vibe, a cloud coding agent that submits pull requests directly to GitHub without you babysitting it. API pricing is $1.50 per million inp
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