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
Adding a third person to an encrypted conversation seems like it should be simple. It isn't. The cryptographic properties that make 1:1 messaging secure — forward secrecy, post-compromise security, deniability — become significantly harder to preserve as group size grows. When Signal introduced group chats, they faced a problem that doesn't exist in 1:1 messaging: how do you efficiently encrypt a
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