Students and researchers often struggle with efficiently extracting information from large volumes of PDF documents. Traditional search methods are ineffective for understanding context and providing comprehensive answers from multiple sources. Users needed an intelligent system that could understand their questions in natural language, search across multiple documents simultaneously, provide accurate answers based solely on the content of those documents, and maintain conversation history for follow-up questions. The challenge was to build a RAG-based system that could handle PDF processing, create searchable vector embeddings, integrate with advanced language models, implement user-based session management, and ensure responses remained grounded in the source material without hallucination.

Developed a sophisticated RAG-based AI agent using modern AI/ML technologies and vector databases. Implemented a robust PDF processing pipeline that extracts, chunks, and vectorizes content from multiple documents using advanced embedding models. Created a vector database for efficient semantic search and retrieval of relevant document sections. Integrated ChatGPT's latest language model with a carefully designed prompt engineering strategy to ensure responses are grounded in the retrieved context. Built a user authentication and session management system that tracks individual chat histories, allowing users to maintain continuous learning conversations. Designed an intuitive interface where users can upload PDFs, ask questions in natural language, and receive accurate, contextual answers with source references. Implemented conversation memory to enable follow-up questions and contextual understanding across multiple interactions.

Developed a sophisticated RAG-based AI agent using modern AI/ML technologies and vector databases. Implemented a robust PDF processing pipeline that extracts, chunks, and vectorizes content from multiple documents using advanced embedding models. Created a vector database for efficient semantic search and retrieval of relevant document sections. Integrated ChatGPT's latest language model with a carefully designed prompt engineering strategy to ensure responses are grounded in the retrieved context. Built a user authentication and session management system that tracks individual chat histories, allowing users to maintain continuous learning conversations. Designed an intuitive interface where users can upload PDFs, ask questions in natural language, and receive accurate, contextual answers with source references. Implemented conversation memory to enable follow-up questions and contextual understanding across multiple interactions.

Students and researchers often struggle with efficiently extracting information from large volumes of PDF documents. Traditional search methods are ineffective for understanding context and providing comprehensive answers from multiple sources. Users needed an intelligent system that could understand their questions in natural language, search across multiple documents simultaneously, provide accurate answers based solely on the content of those documents, and maintain conversation history for follow-up questions. The challenge was to build a RAG-based system that could handle PDF processing, create searchable vector embeddings, integrate with advanced language models, implement user-based session management, and ensure responses remained grounded in the source material without hallucination.