Locally Deployed NLP System for Secure Document Summarization and Context-Aware Question Answering Using LLMs and Vector Embeddings.

This paper presents a locally deployed Natural Language Processing (NLP) system designed to perform secure document summarization and context-aware question answering. The proposed framework addresses critical concerns related to data privacy by eliminating dependence on external APIs or cloud-based services. By leveraging advanced Large Language Models (LLMs), vector databases, and modern NLP tools such as LangChain, Hugging Face, Qdrant, and Streamlit, the system enables users to interact intelligently with unstructured documents in a fully offline environment. The architecture facilitates efficient document parsing, semantic embedding, and retrieval-augmented generation (RAG), empowering users to generate concise summaries and retrieve precise information through natural language queries. This solution is particularly well-suited for privacy-sensitive domains such as healthcare, law, finance, and research, where local data control and confidentiality are essential. The framework not only enhances document comprehension but also promotes efficient information retrieval and workflow automation