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