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
While setting up Apache Airflow using Docker on Windows 11 WSL, I needed to extend the image to install some python packages. I created a dockerfile and requirements.txt, but every time I ran "docker-compose up --build", I received the error: ERROR: Invalid requirement: '<package-name': Expected semicolon (after name with no version specifier) or end To fix the error, I needed to change the encod
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