Building a Python RAG App for Interacting with PDFs Using Local LLMs

Learn how to build a RAG (Retrieval Augmented Generation) app in Python that can let you query/chat with your PDFs using generative AI. This project contains some more advanced topics, like how to run RAG apps locally (with Ollama), how to update a vector DB with new items, how to use RAG with PDFs (or any other files), and how to test the quality of AI generated responses. 👉 Links 🔗 GitHub: https://github.com/pixegami/rag-tutorial-v2 🔗 Basic RAG Tutorial: https://youtu.be/tcqEUSNCn8I 🔗 PyTest Video: https://youtu.be/YbpKMIUjvK8 👉 Resources 🔗 Document loaders: https://python.langchain.com/docs/modules/data_connection/document_loaders 🔗 PDF Loader: https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf 🔗 Ollama: https://ollama.com 📚 Chapters 00:00 Introduction 01:06 RAG Recap 03:22 Loading PDF Data 05:08 Generate Embeddings 07:16 How To Store and Update Data 10:46 Updating Database 11:45 Running RAG Locally 15:12 Unit Testing AI Output 20:29 Wrapping Up