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
Purpose of Variables in Terraform Variables prevent repetitive hardcoding of values in Terraform configuration files. They reduce errors due to inconsistent value entries across multiple resources. Simplify updating environment-specific configurations (e.g., changing from dev to stage). Types of Variables Based on Purpose Input Variables: Accept values from users or other sources. Output Variables
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
Go tem duas formas de declarar variáveis: var e :=. Elas existem por motivos diferentes e têm regras diferentes. Saber quando cada uma se aplica evitamos erros bobos e código que não compila. var (forma longa) var x int // tipo explícito, recebe o zero value var x int = 5 // tipo e valor var x = 5 // valor com tipo inferido var x, y = 1, 2 // múltiplas variáveis