DocQuery AI: Intelligent Answer Generation from PDFs

Abstract

This research paper presents the development and evaluation of a system designed to generate intelligent, context-aware responses from PDF documents using advanced AI models, such as Google Gemini and OpenAI Codex. The DocQuery AI project introduces an innovative platform for extracting and analyzing information from PDF documents using advanced AI models such as OpenAI Codex and Google Gemini. Designed for users needing precise and efficient querying in complex document structures, the system employs cutting-edge Natural Language Processing (NLP) techniques, including document embeddings and similarity search, to deliver accurate and contextually relevant responses. The project's main objective is to create a user-friendly tool that interprets large volumes of text from PDFs, providing insightful information with minimal manual input. By utilizing advanced algorithms such as vector similarity search and transformer-based models like GPT-4, DocQuery AI ensures accurate processing across diverse document types. Transfer learning and fine-tuning enhance its performance in handling various document structures. The architecture is optimized for memory efficiency, fast response times, and scalability, allowing it to manage extensive document sets seamlessly. During testing, DocQuery AI achieved a 90% similarity score with OpenAI GPT-4, reflecting its high accuracy in generating relevant answers. Additionally, the platform improved response accuracy by 25% and reduced processing time by 40%, making it a highly efficient solution for automated document analysis, outperforming traditional methods.