BUILDING A RAG PIPELINE FOR SEMANTIC CODE SEARCH: A DEVELOPER DIARY AND FIELD NOTES

Summary from the TLDR Dev newsletter

This field report breaks down the first stages of a production semantic code search pipeline, covering structure-aware chunking, chunk evaluation, and vectorization for large repositories. It explains why the system keeps all 4,096 embedding dimensions at one-bit precision for a 32-fold storage reduction, when 16-bit vectors are still needed, and how the design avoids storing source code on the server.