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
Postmortem: How a LangGraph 0.1 Multi-Agent Bug Broke Our 2026 Customer Support Bot Executive Summary On October 12, 2026, our production customer support bot experienced a 4-hour partial outage caused by an unpatched edge case in LangGraph 0.1’s multi-agent orchestration layer. The bug triggered infinite agent handoff loops for 18% of inbound customer queries, leading to SLA breaches