Python RAG Tutorial (with Local LLMs): AI For Your PDFs
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
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
