[This article was first published on pharmaverse blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Disclaimer: This blog contains opinions that are of the authors alone and do not necessarily reflect the strategy of their respective organizations.Why this matters for pharmaverse programmersClinical reporting is built on repeatable patterns for ADaM derivations, TLGs, and QC, but finding these patterns is a significant challenge. Relevant code is often scattered, and traditional keyword searches fail for non-standard logic because the underlying variable names and structural conventions differ from study to study. CSA directly addresses this root cause by using semantic retrieval to find code based on its conceptual intent, not just matching keywords (e.g: plain English query such as “derive ABLFL in ADLB using a pre-dose baseline rule and analysis-visit windows”). This means programmers can reliably discover, review, and reuse relevant patterns from across all repositories, saving substantial time and ensuring greater consistency in their work.What we built?CSA is a focused agent inside our Clinical Analysis Assistant. A user asks a question; we create an embedding of that query, apply optional metadata filters, search the vector database, and return the most relevant code chunks alongside their origins. CSA supports both SAS and R programs.Code Search Agent – High level overviewBehind the scenes, we index code from our repositories, split it into coherent chunks, and generate short summaries that describe what each piece of code does. We then convert these summaries into numerical representations called embeddings (or vectors), which capture their semantic meaning. This allows the system to find code based on the idea of what it does, not just keyword matching. We store these vectors in a specialized database (vector database) and retain rich metadata such as programming language, file name, repository, and study description. The interface displays both the results and the parameters used so retrieval stays transparent and audit-friendly.Here is an example of chunk and its metadata stored in the vector database.FieldContentMetadata: Program Namet_vs_orth.RMetadata: TLG TitleSummary of Orthostatic ChangeMetadata: TLG FootnoteResult (Orthostatic Change) = Standing Vital Sign – Supine Vital Sign | The All Placebo column includes participants administered placebo from all cohorts.Code (truncated)library(magrittr)library(citril)library(chevron)…adam_db