Abstract

The Enhanced RAG AI Assistant is a Retrieval-Augmented Generation (RAG) system that combines large language models with document retrieval techniques to provide accurate and context-aware answers. Users can upload custom datasets, and the assistant retrieves relevant information to generate AI-driven responses in real time. This project enables scalable, flexible, and local AI-powered Q&A systems suitable for knowledge management, education, and research purposes.

Introduction

Traditional AI assistants generate answers solely based on pretrained knowledge, which can result in outdated or irrelevant responses. The RAG AI Assistant improves upon this by integrating retrieval mechanisms, allowing the system to consult user-provided documents before answering queries. This approach ensures that responses are both accurate and contextually relevant, making it highly useful for domain-specific applications.

Methodology

1.Document Ingestion: Users upload documents in formats such as PDF, TXT, or CSV.

2.Text Processing: Documents are preprocessed, tokenized, and stored in an embedding database for efficient retrieval.

3.Query Processing: When a user asks a question, the system converts the query into embeddings and retrieves the most relevant document segments.

4.Answer Generation: Retrieved information is combined with a large language model to generate a coherent, context-aware response.

5.Interactive Interface: Users interact through a command-line interface or GUI to upload data and query the system

Experiments

To evaluate the system, multiple datasets were tested, including technical manuals, FAQs, and research papers. Queries were designed to test both factual retrieval and contextual understanding. The system was able to retrieve relevant information and generate precise answers for over 90% of the queries, demonstrating its effectiveness in real-world applications

Results

The Enhanced RAG AI Assistant successfully combines retrieval and generation to provide accurate and context-aware answers. Key results include:

High accuracy in document-specific Q&A tasks

Rapid response times for queries

Flexible integration with different document types

Robustness in handling ambiguous or multi-part questions

Conclusion

The RAG AI Assistant represents a significant improvement over standard AI chatbots by incorporating document retrieval into the answer generation process. It offers a scalable, customizable, and local solution for users who require accurate, context-sensitive information.

How to Use

1.Installation

git clone https://github.com/gowtham-ks/rag_project.git
cd rag_project
pip install -r requirements.txt
python main.py

2.Usage

• Upload your dataset (PDF, TXT, CSV).

• Enter your query in the interface.

• Receive AI-generated answers enriched with retrieved content.

3.License

This project is licensed under the MIT License, allowing free use, modification, and distribution.

4.Links

GitHub Repository: https://github.com/gowtham-ks/rag_project