Madhan Kumar Selvaraj's blog

 Presenting by Madhan Kumar S    Introduction The Agentic AI MCP Tour Planner is an end-to-end, AI-powered travel planning application. It leverages advanced agentic workflows, Retrieval Augmented Generation (RAG), and multi-tool orchestration to deliver highly personalized travel recommendations and accommodation options. The project is designed with modularity and scalability in mind, using modern Python frameworks and containerization for ease of deployment.  Technologies Used LangGraph: Agentic workflow orchestration LangChain: LLM and tool integration Streamlit: Frontend UI FastAPI: Backend API FAISS: Vector search for RAG Tavily: Internet search tool MCP Tool: Accommodation search Ollama/Groq: LLM inference Docker: Containerization  Flow Chart  LangGraph state flow  Key Features Agentic AI Flow: Utilizes  LangGraph  to manage complex, multi-step reason...

 Presenting by Madhan Kumar Selvaraj  After a ver y long time, I am blogging about new project named Review-driven recommendation. In this Artificial Intelligence (AI) era, data is a called as new gold. Now it's very common to buy any products and choosing a restaurant to eat we are checking the other user reviews. Once we are satisfied with their review we'll choose that particular product. Real-world issue Let's assume we are planning to buy an ABC brand model mobile . But the product review is available at many platforms like Google reviews, MouthShut, yelp and lot more.  We need to check all the reviews from the users and from different platforms to get most of it. It'll take more time and effort.   To fix this issue, we are extracting user reviews from all the platforms and using some NLP models to get the top best products list with some visualization. Project roadmap The workflow of the project Scraping user reviews from the multiple platforms  Extracte...