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
A short guide to organizing FastAPI apps beyond a single main.py file. FastAPI makes it easy to start with a single main.py file. That is great for demos, prototypes, and small APIs. But once your application grows, one file can quickly turn into a mix of routes, database logic, security helpers, settings, and business rules. A clear project structure helps keep the app easier to understand, test,
What is FastAPI? As the name suggests, FastAPI is a modern Python framework designed for building RESTful APIs with high performance and minimal boilerplate. In 2026, it has become the industry standard because it’s exceptionally fast, reliable, and includes powerful out of the box features — such as automatic interactive documentation and native support for asynchronous programming. These comma
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