RAG -CLONE A Generic Framework

This paper presents a RAG system (Retrieval Augmented Generation)that aims to improve how AI processes, accesses ,and generates information. Our approach uses Vector embedding to improve data absorption and provide more accurate and contextual answers using FAISS-based on the similarity and the mistral searches. The system is created to process unstructured, unstructured data from a variety of sources, including PDFs and Excel files. Users can interact with text-based queries as well as voice commands. To make this simple, we integrate Whisper AI for speech recognition, allowing users to ask questions verbally, but Google's text speech (GTTS) gives the answer generated by AI to speak the spoken language. Convert to feedback. An important feature of our system is the ability to store and show information at a granular level. Instead of dealing with the entire document, organize and retrieve relevant sections to ensure more detailed answers. FAISS-based similarity search helps you efficiently find the most relevant information in large data records, but Mistral AI produces documents to improve the quality and consistency o f answers will be improved. User can perform a profound search process, extract meaningful knowledge, and interact in a seamless, intuitive way using AI-controlled knowledge .Ultimately,ourrag system bridges the gap between data calls andAI- controlled content generation, making information accessible and easy implementable in a variety of applications. Keywords: Large Language Models (LLMs), Data Pipelines, Data Retrieval