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
By Q2 2026, engineering teams building local Retrieval-Augmented Generation (RAG) pipelines will waste $47M annually on managed vector databases they don't need – and Pinecone 2.0's 300% price hike over its 1.0 release is the biggest culprit. VS Code inserting 'Co-Authored-by Copilot' into commits regardless of usage (842 points) A Couple Million Lines of Haskell: Production Engineering at Mer
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