Powerful Chatbot with Browser Use, Light RAG, and Local LLMs
A quick tutorial demonstrating how to create a powerful chatbot using Browser Use, Light RAG, and a local large language model (LLM) for web scraping and data querying.
Course Timeline
🎥 Introduction: Light RAG and Browser Use
Overview of the video and introduction to Light RAG and Browser Use, highlighting their advantages over existing RAG systems.
🤖 Demo: Live Chatbot in Action
Live demonstration of a chatbot scraping Amazon for the cheapest laptop and Google for information on supervised LLMs. Shows self-correction and vision model integration.
💡 Light RAG Explained: Graph-Based Retrieval
Detailed explanation of Light RAG's two-level retrieval system, graph-based data structure, and how it improves information retrieval accuracy and context awareness.
🌐 Browser Use: Web Automation Library
Explains the functionalities of Browser Use, including interaction with LLMs, handling interactive elements, and intelligent decision-making.
💻 Setting up the Development Environment
Guide on setting up the necessary Python libraries (pip install requirements), OpenAI API key setup, and importing required libraries.
👨💻 Code Walkthrough: Agent Initialization and Task Execution
Step-by-step walkthrough of the code, covering agent initialization, asynchronous function for concurrent tasks, setting max steps, and result handling.
🔎 Light RAG Search Modes: Naive, Local, Global, and Hybrid
Explanation and comparison of four different search modes within Light RAG: naive, local, global, and hybrid, demonstrating their respective strengths and use cases.
🚀 Conclusion: The Future of Information Retrieval
Summary of the video and discussion on the potential impact of Light RAG and Browser Use on information search and generation.