Developer Builds InsightFetch, a RAG App That Answers Questions From Any Webpage

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A developer has shared their first Retrieval-Augmented Generation (RAG) project, called InsightFetch, built using LangChain and deployed via Streamlit. The web application allows users to input one or more URLs and ask natural language questions about the content found on those pages. The project leverages tools including Groq's Llama 3.1 model, Hugging Face Embeddings, and FAISS for vector search and retrieval-based question answering. Built as a learning exercise, InsightFetch covers key RAG pipeline concepts such as document loading, text chunking, and embedding generation. The developer has made the app publicly accessible and is seeking community feedback to improve the project further.

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