Explore a 16-minute video tutorial demonstrating the creation of a RAG (Retrieval-Augmented Generation) pipeline for automating article discovery and summarization based on Google Trends. Learn how to build the "Keep Up With The Trends" web app using Streamlit, which scrapes relevant articles, generates concise summaries and titles using the LLaMA 3.1 model, and offers an option to create tweets. Gain insights into implementing RAG techniques, integrating Google Trends data, and leveraging the LLaMA 3.1 model for natural language processing tasks. Access the project repository on GitHub for hands-on experience and further exploration of the code.
RAG with Llama 3.1 for Google Trends Data Scraping and Summarization - Streamlit Web App
Machine Learning With Hamza via YouTube
Overview
Syllabus
How I Used RAG with Llama 3.1 to Scrape & Summarize Google Trends Data | Streamlit Web App
Taught by
Machine Learning With Hamza
Related Courses
-
RAG from Scratch with Llama 3.1 - Building a Custom Data Chatbot
-
End-to-End Multi AI Agents RAG with LangGraph, AstraDB, and Llama 3.1
-
Better Llama with Retrieval Augmented Generation - RAG
4.0 -
Local RAG with Llama 3.1 for PDFs - Private Chat with Documents using LangChain and Streamlit
-
Build Web Scraper with Llama 3.1 - Get Structured Data by Scraping Web Content with AI
