Tomasz Kalinowski
Tomasz Kalinowski is a scientist turned software engineer at Posit, building open-source tools for data and scientific computing across Python and R. His work focuses on cross-language interoperability, machine learning workflows, and performance. He maintains reticulate and the tensorflow/keras R interfaces, coauthored Deep Learning with R, and helps lead Posit’s open-source PyData team (Great Tables, Plotnine, pointblank).
Session
LLMs are powerful, but their knowledge is frozen — they can't access your private documents or recent information. Retrieval-Augmented Generation (RAG) solves this by searching relevant documents and including them in the prompt, grounding responses in real information. But building a good retrieval system involves many steps: reading diverse file formats, chunking text at sensible boundaries, computing embeddings, and combining search strategies. This talk introduces raghilda, a Python framework that handles the full retrieval pipeline. We'll cover how RAG works, how to build a retrieval system with raghilda, and how to connect it to an LLM with a practical example.