This workshop utilizes multiple tools that can have conflicting requirements. Recommend using pyenv to manage separate python versions, then virtualenv for separate environments for each component (e.g. data_prep, rag).
In this workshop, we show the two main workflows when working with LLMs: RAG (retrieval-augmented generation) and fine-tuning. We show how to take a PDF and generate a dataset for fine-tuning and evaluation.
We utilize marker to turn PDFs into markdown, which can then be easily parsed into a format suitable for LLM consumption. Please reference the marker docs for installation and setup. After it's successfully installed, convert the example PDF into markdown using marker.
Now that you have a suitable document, we'll turn it into a Q&A dataset using a local llm. First, install Ollama.