International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Extracting meaningful information from large PDF documents remains a significant challenge for students, researchers, and professionals, as traditional document reading and keyword-based search methods are time-consuming and inefficient. Existing tools often lack semantic understanding, contextual awareness, and interactive capabilities, making it difficult for users to obtain precise answers from complex documents. This limitation results in reduced productivity and increased effort when analysing lengthy technical, academic, or legal PDFs.
To overcome these challenges, the AI Powered PDF Chat Application integrates Generative AI, Large Language Models (LLMs), and vector-based semantic search techniques to enable intelligent document interaction. The system processes uploaded PDF documents by extracting text, generating embeddings, and storing them in a vector database to support context-aware retrieval. When a user submits a query, the application identifies the most relevant document segments using similarity search and generates accurate, context-driven responses through an AI language model. This approach ensures that answers are grounded in the document content rather than relying on generic responses.
The application is implemented as a secure web-based platform featuring user authentication, PDF preview, interactive chat interface, conversation history management, and PDF-based chat export functionality. By combining retrieval-augmented generation with real-time user interaction, the system significantly improves information accessibility, reduces document analysis time, and enhances user comprehension. The proposed solution demonstrates how AI-driven document intelligence can transform traditional PDF reading into an efficient, interactive, and scalable knowledge retrieval experience.

Downloads: | DOI: 10.17148/IJARCCE.2026.15160