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This project is a Retrieval-Augmented Generation (RAG) system that processes multiple PDFs to generate context-aware responses to user queries. It leverages LangChain, ChromaDB, HuggingFace Embeddings, and Google Gemini for embedding, retrieval, and answer generation, with Streamlit providing a simple front-end interface.
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This project is a Retrieval-Augmented Generation (RAG) system that processes multiple PDFs to generate context-aware responses to user queries. It leverages LangChain, ChromaDB, HuggingFace Embeddings, and Google Gemini for embedding, retrieval, and answer generation, with Streamlit providing a simple front-end interface.
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This project is a Retrieval-Augmented Generation (RAG) system that processes multiple PDFs to generate context-aware responses to user queries. It leverages LangChain, ChromaDB, HuggingFace Embeddings, and Google Gemini for embedding, retrieval, and answer generation, with Streamlit providing a simple front-end interface.
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Welcome to Ramadan Labs! This is where Khaouiti Abdlhakim and Bnegmah Anass bring you an exciting journey into the world of web and mobile development. We believe in making learning fun and practical, covering a wide range of topics from foundational concepts to advanced frameworks.
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Pour réaliser cette étude comparative, nous avons simulé des réseaux sans fil en utilisant NS2, un simulateur de réseau largement utilisé dans la recherche en informatique réseau. L'outil de visualisation NAM a également été employé pour analyser les résultats des simulations de manière graphique.
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