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
When you run Mirror and Socket.IO in the same Unity project, you immediately hit a translation problem. Mirror identifies players by netId — a uint assigned at spawn time by the Mirror host. Socket.IO identifies players by playerId — a string assigned by your Node.js backend when they connect. These two IDs have nothing to do with each other. They're generated by different systems at different tim