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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Querify: An AI-Powered Research Assistant Using Retrieval-Augmented Generation for Intelligent Document Analysis |
| Authors Name: | Swapnali Nilesh Jadhav , Mohan Bhoju Rathod , Ronit Sitaram Kaskar , Vedang Suhas Teli , Calvin John Dsouza |
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IJRTI_211260
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| Published Paper Id: | IJRTI2604106 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | Traditional research methods often involve analyzing large volumes of documents using manual and fragmented tools, leading to inefficiency and increased effort. This paper presents Querify, an AI-powered research assistant that leverages Retrieval-Augmented Generation (RAG) to enable intelligent document analysis. The proposed system integrates semantic retrieval with generative models to improve response accuracy and contextual relevance. Unlike standalone language models, Querify incorporates external document knowledge to reduce hallucinations and enhance reliability. The system utilizes LangChain for efficient pipeline orchestration and integrates advanced multimodal capabilities inspired by modern generative AI models. For scalable data management, MongoDB is used for structured storage, while ChromaDB enables efficient vector-based semantic search. Querify supports multi-modal document processing, allowing users to upload and analyze PDFs, images, and structured datasets through a conversational interface. The approach is grounded in established natural language processing principles and focuses on improving research productivity. Experimental results demonstrate that the proposed system improves accuracy, response relevance, and research efficiency compared to traditional search methods. Overall, Querify provides a unified platform for intelligent knowledge discovery, combining modern AI techniques with scalable system design to enhance the research workflow. |
| Keywords: | Retrieval-Augmented Generation, AI Research Assistant, Semantic Search, Natural Language Processing, Large Language Models, Document Analysis, Multi-Modal Processing, Vector Embeddings, Conversational AI. |
| Cite Article: | "Querify: An AI-Powered Research Assistant Using Retrieval-Augmented Generation for Intelligent Document Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a743-a747, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604106.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604106
Registration ID:211260
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a743-a747 Country: Kankavali, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604106 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604106 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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