Chatbot Integrated with Gen AI

Abstract

Conversational AI systems powered by Large Language Models (LLMs) have improved natural human–computer interaction but remain limited by static training data and frequent inaccuracies. To address these challenges, this project implements a Retrieval-Augmented Generation (RAG) chatbot that grounds responses in user-uploaded documents. The system uses a modular full-stack architecture with a React frontend, Node.js/Express backend, and a Python microservice for document processing, embedding generation, and semantic retrieval through FAISS. A commercial LLM (Cohere) generates responses only after relevant context is retrieved, ensuring privacy since raw documents are never exposed for training. Testing confirmed that the chatbot delivers domain-specific, reliable answers while minimizing hallucinations and safeguarding sensitive data. The prototype establishes a scalable and secure framework for enterprise and educational use. Future work includes expanding to multimodal data, federated learning, and integration with knowledge graphs for greater adaptability and transparency

Introduction

Overview

Modern chatbots powered by Large Language Models (LLMs) excel in human-like conversations but face key issues:

• Hallucinations: Generating incorrect but plausible answers.

• Outdated knowledge: LLMs can't dynamically adapt to new or domain-specific content.

• Privacy risks: Sending data to third-party servers raises concerns.

To overcome these, the proposed system integrates Retrieval-Augmented Generation (RAG) with Generative AI, allowing the chatbot to:

• Ground responses in user-uploaded documents.

• Preserve privacy by limiting data exposure.

• Adapt dynamically to domain-specific knowledge.

???? Objectives

The system aims to:

• Build a document-grounded chatbot.

• Maintain modular architecture (frontend, backend, AI microservices).

• Ensure privacy by keeping raw documents internal.

• Deliver scalable, testable, and enterprise-ready conversational AI.

???? Literature Survey Insights

• Chatbot evolution: From rule-based (ELIZA, PARRY) to LLM-based (GPT-3).

• RAG bridges LLMs with external knowledge sources to reduce hallucinations.

• Techniques like Sentence-BERT, FAISS, and Fusion-in-Decoder improve retrieval and response quality.

• Privacy-preserving RAG systems are becoming crucial in enterprise and academic use.

?? Proposed System Highlights

A modular, three-tiered architecture combining RAG and generative AI:

???? Key Features:

• Hybrid system: Uses retrieval for accuracy and generative models for fluency.

• User-driven knowledge base: Documents uploaded by users form the chatbot’s dynamic dataset.

• Privacy-by-design: Only selected snippets, not entire documents, are passed to the LLM.

???? Dataset:

• No fixed dataset.

• User-uploaded documents are:

  • Preprocessed and chunked.

  • Embedded using Sentence-BERT.

  • Stored in FAISS for fast semantic search.

????? System Architecture

• Presentation Layer (Frontend): Built with ReactJS; handles file uploads and user queries.

• Application Layer (Backend): Uses Node.js and Python microservices for preprocessing, embedding, and query management.

• Data & Intelligence Layer:

  • FAISS for fast similarity search.

  • Cohere LLM for response generation.

  • Privacy Module to protect raw documents.

???? Methodology Workflow

1. Upload: User adds PDF/TXT files.

2. Preprocessing: Documents are cleaned, chunked, and embedded.

3. Query: User submits a question.

4. Semantic Retrieval: FAISS returns relevant text chunks.

5. Privacy Enforcement: Only retrieved snippets go to LLM.

6. Generation: Cohere LLM uses query + context to generate a response.

7. Response Delivery: Output is returned to the chat interface.

???? Experimental Analysis & Results

? Key Features:

• Semantic Retrieval for relevance.

• Context-aware generation to reduce hallucinations.

• Privacy enforcement to ensure document confidentiality.

• Robust error handling for unsupported file formats or failed queries.

???? Results Summary:

• Accurate Responses: Answers were fact-based and grounded in uploaded documents.

• Fast Performance: Responses generated in 3–5 seconds; retrieval under 150 ms.

• Privacy Validation: Only document snippets passed to LLM.

• Error Handling: Invalid uploads (e.g., .exe files) safely rejected.

?? Limitations

• Relies on external APIs (e.g., Cohere) → needs internet access.

• Retrieval accuracy may decline with very large/noisy datasets.

• Supports text-only documents (no multimedia processing).

???? Future Scope

• Multimodal support: Integrate images, audio, and videos.

• Knowledge graphs/ontologies: Improve contextual accuracy.

• Reinforcement learning: Allow chatbot to learn from user feedback.

• Offline/on-premise deployment: For full data control in enterprises.

Conclusion

This paper presented a Retrieval-Augmented Generation (RAG)-based chatbot integrated with Generative AI, designed to address the limitations of conventional LLM-driven conversational systems. By grounding responses in uploaded documents, the system ensures accuracy, adaptability, and privacy preservation. The modular three-layered architecture enables scalability, while the privacy enforcement mechanism guarantees the security of sensitive documents. Experimental results validated the system’s ability to provide factually correct, domain-specific, and efficient responses with reduced hallucinations compared to baseline LLMs. With its adaptable design, the chatbot has strong potential for deployment in academic, enterprise, and research domains, offering a reliable and privacy-conscious conversational interface.

References

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Copyright

Copyright © 2025 Battu Nagasahithi , Dr. V. Uma Rani, Dr. Sunitha Vanamala . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.