DPR Analyser System: Automating Detailed Project Report Analysis using RAG and Large Language Models

The DPR Analyser System demonstrates the transformative potential of artificial intelligence in the realm of urban planning and infrastructure management. By leveraging Ruby on Rails, Vue.js, and the OpenAI Assistants API, the project achieved its goal of building a smart, secure platform that automates the tedious process of reviewing Detailed Project Reports. Features such as citation-backed responses and intelligent web-search fallbacks effectively address the limitations of standard generative models, providing a reliable tool that improves efficiency, accuracy, and decision-making speed. Future enhancements will focus on integrating multi-modal AI capabilities, enabling the system to interpret visual data, architectural blueprints, and engineering diagrams embedded within the reports. Expanding the architecture to support cross-document querying—allowing users to compare multiple DPRs simultaneously—will add significant value. Additionally, direct integration with internal government databases could automate the cross-referencing of proposed project data against existing municipal infrastructure records.

[1] Sommerville, I. (2016). Software Engineering, 10th Edition, Pearson Education. [2] Pressman, R. S. (2014). Software Engineering: A Practitioner's Approach, McGraw-Hill. [3] Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems. [4] Vue.js Team. (2024). Vue.js Documentation. Available: https://vuejs.org/guide/introduction.html [5] Ruby on Rails Core Team. (2024). Ruby on Rails Guides. Available: https://guides.rubyonrails.org/ [6] OpenAI. (2024). OpenAI Assistants API Documentation. Available: https://platform.openai.com/docs/assistants/overview [7] Internet Engineering Task Force. (2015). JSON Web Token (JWT) Specification. Available: https://jwt.io/