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
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
Introduction It’s a wonderful time to be a developer with rich tools, documentation, and artificial intelligence. Still, at least for now and the foreseeable future, developers must learn to write code, as artificial intelligence tools are not perfect and may produce code that is difficult to integrate into an existing code base. For developers just starting out, they need to learn the basics, t