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

ragretrieval augmented generationgenerative aipython ragadvanced rag tutoriallangchain ragpython rag projectadvanced rag projectadvanced ragai chatbotdocument interactionresponse testingpython programmingvector databasetext embeddingsnatural language processingpdf document retrievalpython chat interfacerag techniquesrag projectrag locallocal rag tutoriallocal llmsrag ollamalangchain projectlocal ai project