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
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Full code: raflizocky/laravel11-sbadmin2. # Laravel 11 Requirements php -v # >= 8.2 composer -v node -v # >= v14.16 npm -v Start Apache & MySQL in your web server. # install laravel 11 composer create-project "laravel/laravel:^11.0" example-app # or you can use laravel installer composer global require laravel/installer laravel new example-app # .env DB_CONNECTION=mysql DB_HOST=127.0.0.1 DB_PO
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