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This repository accompanies a three-day hands-on workshop on Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG). The workshop is designed to equip participants with practical skills and foundational knowledge to understand, deploy, and evaluate LLM-based applications across a wide range of fields. The program combines a lecture series covering theoretical foundations with a programming workshop in Jupyter Notebooks. Participants explore core technologies such as OpenAI APIs, LangChain, vector databases, document parsing, and AI toolchains—supported by real-world examples and scientific documents. The workshop is suitable for professionals, researchers, and students who seek a structured and practice-oriented introduction to state-of-the-art AI workflows based on LLMs.

The Prototype Document Extractor is a lightweight, containerized service designed to extract structured content from PDF files using the Unstructured IO library. It exposes a minimal HTTP API that allows users to submit PDFs and receive parsed content in JSON format. This project includes:

A backend service that handles PDF parsing using Unstructured IO. A Python client library for programmatically interacting with the API from within your code. Docker configurations to run the service in a portable, reproducible environment.