Building an AI-Powered Search Engine Using LangChain and Agents
The rise of Generative AI has transformed how we interact with information. Large Language Models (LLMs) like Llama3-8b-8192 can process complex queries, but they lack real-time search capabilities. To address this, I built an AI-powered search engine using LangChain Agents , integrating Wikipedia, Arxiv, and DuckDuckGo search tools to provide real-time, context-aware responses . This project not only improves search accuracy but also showcases the power of Retrieval-Augmented Generation (RAG) —a technique that enhances LLMs by fetching live information before generating responses. 💡 How This Search Engine Works 🔹 Core Components Used This project integrates multiple AI and search technologies: Component Purpose LangChain Framework to build AI applications with LLMs Streamlit Web interface for interactive AI search DuckDuckGo API Fetches real-time web results Wikipedia API Retrieves encyclopedic knowledge Arxiv API Fetches academic research pap...