Digital Stackroom

How to Build a Production RAG App With ChromaDB and FastAPI?

In our previous RAG tutorial, we built everything ourselves. PDFs were converted into text. Text became chunks. Chunks became embeddings. And those embeddings were stored in a local JSON file and searched using NumPy. That architecture is excellent for learning because every stage is visible. But eventually a RAG application needs more.

How to Build a RAG App in Python: Chat With Your PDFs Step by Step?

Large language models can answer an enormous range of questions. But there is one obvious problem. The model does not automatically know what is inside: You could paste an entire document into a prompt. That works for small experiments. It becomes inefficient once you have dozens, hundreds, or thousands of documents.

How to Build an AI Model Router in Python: Route Tasks by Cost and Complexity?

Most AI applications begin with a very simple architecture: Every request goes to the same model.