RAG 2.0: Supercharging LLMs with Real-Time Web Data and LangGraph

Author(s): Samvardhan Singh

Originally published on Towards AI.

In a world of constant change, the AI that learns from the present will shape the future.

For those who don’t have the medium subscription, you can access this article for free hereIn today’s fast-moving world, artificial intelligence (AI) needs to keep up with the latest information to deliver accurate and relevant answers. Retrieval-Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by incorporating external data, and when paired with real-time web scraping, it becomes a powerhouse for applications requiring up-to-the-minute insights. This article dives into how LangGraph, a framework within the LangChain ecosystem, orchestrates real-time RAG workflows using web scraping, enabling modular, reactive, and scalable AI systems for tasks like financial market monitoring, breaking news summarisation, and emergency response.

RAG is a method that improves LLMs by allowing them to retrieve relevant information from external sources before generating responses. Traditional RAG relies on static datasets, which can quickly become outdated in dynamic environments. Real-time RAG addresses this by integrating live data, typically from the web, ensuring responses reflect the latest developments.In a world where information moves at lightning speed, outdated data can lead to missed opportunities or even critical errors. Real-time RAG ensures the AI stays… Read the full blog for free on Medium.

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Published via Towards AI