TARS AI: Transforming Static Documents into Intelligent Systems for Enhanced Knowledge Accessibility and Productivity
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Abstract
The rapid growth of heterogeneous data sources such as PDF, DOCX, images, and audio recordings has created significant challenges in efficient information retrieval and knowledge accessibility. Traditional search systems operate in isolated modalities and fail to provide context-aware, cross-format understanding, resulting in reduced productivity and increased effort in data exploration. To address this limitation, this paper presents TARS AI, a multimodal Retrieval-Augmented Generation system designed to operate in offline mode for unified semantic retrieval across diverse data formats. The proposed system integrates advanced preprocessing techniques including optical character recognition for images, speech-to-text conversion for audio, and text extraction from structured documents, followed by embedding generation using transformer-based models. All modalities are indexed within a shared vector space using FAISS to enable efficient similarity search. A hybrid retrieval mechanism combines semantic and keyword-based approaches to fetch relevant context, which is then processed by a Large Language Model for generating accurate, context-aware responses with source citations. The system supports natural language queries and enables cross-modal linking, allowing users to retrieve related information across text, images, and audio seamlessly. The architecture is implemented using React and TypeScript for the frontend, FastAPI for backend services, and integrates scalable databases including PostgreSQL, MongoDB, and Redis. Experimental evaluation demonstrates improved retrieval accuracy, reduced response time, and enhanced user interaction compared to traditional document search systems. TARS AI provides a scalable and efficient solution for intelligent document processing, with applications in government, education, and enterprise domains, significantly improving knowledge accessibility and decision-making efficiency
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References
[1] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, 2021.
[2] V. Karpukhin et al., “Dense passage retrieval for open-domain question answering,” arXiv preprint arXiv:2004.04906, 2021.
[3] G. Izacard and E. Grave, “Leveraging passage retrieval with generative models for open domain question answering,” arXiv preprint arXiv:2007.01282, 2021.
[4] A. Radford et al., “Learning transferable visual models from natural language supervision,” in Proceedings of the International Conference on Machine Learning (ICML), 2021, pp. 8748–8763.
[5] Ameta, Upasana, Mayank Patel, and Ajay Kumar Sharma. "Scaled Agile Framework Implementation in Organizations', its Shortcomings and an AI Based Solution to Track Team's Performance." 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022
[6] T. Baltrušaitis, C. Ahuja, and L.-P. Morency, “Multimodal machine learning: A survey and taxonomy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 1–20, 2022.
[7] Ameta, Upasana and Patel, Mayank and Rathore, Narendra Singh, Fusing Artificial Intelligence with Scrum Framework (April 25, 2023). Available at SSRN: https://ssrn.com/abstract=4428286 or http://dx.doi.org/10.2139/ssrn.4428286
[8] K. Guu et al., “REALM: Retrieval-augmented language model pre-training,” in Proceedings of the International Conference on Machine Learning (ICML), 2021.