Browser-use + LightRAG Agent That Can Scrape 99% websites with LLM
LightRAG
AI Summary
Video Summary: Creating a Powerful Chatbot with Light RAG and Browser Use
Overview
- Tutorial on creating a chatbot using Light RAG and Browser Use.
- Light RAG addresses limitations of existing RAG systems by incorporating graph structures for better context and interdependency understanding.
- Browser Use is an open-source web automation library that works with large language models (LLMs) for tasks like data scraping.
Light RAG
- Integrates graph structures into text indexing and retrieval.
- Uses a two-level retrieval system for comprehensive information retrieval.
- Extracts entities and relationships to build a knowledge graph.
- Performs low-level and high-level searches for concrete entities and abstract topics.
- Outperforms traditional systems with efficiency and handling complex queries.
Browser Use
- Enables LLMs to interact with websites for various tasks.
- Features include detecting clickable elements, handling prompts, tab switching, form filling, and taking screenshots.
- Supports intelligent decision-making and has memory capabilities.
- Compatible with models like GPT-4 and Codex 3.5.
Implementation Steps
- Install necessary Python libraries.
- Set the OpenAI API key for using the OpenAI large model.
- Initialize agents and a controller to manage browser state.
- Define an asynchronous function for concurrent task execution.
- Save extracted content to a file upon task completion.
- Configure Light RAG with a working directory and a default language model.
- Perform different search modes (naive, local, global, hybrid) to retrieve information based on the query.
Potential Impact
- Light RAG and Browser Use could revolutionize how we interact with information.
- They aim to provide accurate, comprehensive search capabilities and up-to-date responses.
Note
- No detailed instructions such as CLI commands, website URLs, or tips were provided in the text for extraction.