This repository contains Question-Answering (QA) and Retrieval Augmented Generation (RAG) examples.
Each example/subproject is independent; specific sources, necessary packages, setup & Co. are explained inside each subproject folder.
qa-finetuning/: an LLM is fine-tuned to accomplish extractive QA tasks on a new dataset.qa-rag/: a basic system of Retrieval Augmented Generation (RAG) is implemented to showcase how to build a chatbot which dynamically answers questions of new datasets.advanced-rag-langchain/: notes and examples from the official LangChain course RAG from Scratch.local-rag/: a local Retrieval Augmented Generation (RAG) application, packaged and deployable.azure-rag/: a Retrieval Augmented Generation (RAG) application which uses Azure services and deployed on the same cloud provider.
Other related repositories of mine:
- My personal notes on the O'Reilly book Generative Deep Learning, 2nd Edition, by David Foster
- My personal notes on the O'Reilly book Natural Language Processing with Transformers, by Lewis Tunstall, Leandro von Werra and Thomas Wolf (O'Reilly)
- My personal notes on the Udacity Nanodegree Generative AI
- My personal notes on the University of Duke / Coursera course Operationalizing LLMs on Azure
- HuggingFace Guide:
mxagar/tool_guides/hugging_face - LangChain Guide:
mxagar/tool_guides/langchain - LLM Tools:
mxagar/tool_guides/llms - NLP Guide:
mxagar/nlp_guide - Deep Learning Methods for CV and NLP:
mxagar/computer_vision_udacity/CVND_Advanced_CV_and_DL.md - Deep Learning Methods for NLP:
mxagar/deep_learning_udacity/DLND_RNNs.md
Mikel Sagardia, 2025
No guaranties.