Generative AI is here to stay. Tools to generate text, images, or data are now common goods. Large Language models (LLMs) only have the knowledge they acquired through learning, and even that knowledge does not include all the details. To overcome the knowledge problem, the Retrieval Augmented Generation (RAG) pattern arose. An essential part of RAG is the retrieval part. Retrieval is not new. The search or retrieval domain is rich with tools, metrics and research. The new kid on the block is semantic search using vectors. Vector search got a jump start with the rise of LLMs and RAG.
This workshop aims to build a high-quality retriever, integrate the retriever into your LLM solution and measure the overall quality of your RAG system.
The workshop uses our Rag4j/Rag4p framework, which we created especially for workshops. It is easy to learn, so you can focus on understanding and building the details of the components during the workshop. You experiment with different chunking mechanisms (sentence, max tokens, semantic). After that, you use various strategies to construct the context for the LLM (TopN, Window, Document, Hierarchical). To find the optimum combination, you'll use quality metrics for the retriever as well as the other components of the RAG system.
You can do the workshop using Python or Java. We provide access to a remote LLM. You can also run an open-source LLM on Ollama on your local machine.