# Semantic Vector Search

This paper presents Universal Doc AI, a multimodal intelligent document management system that unifies Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), vector-based semantic search, and Large Language Models (LLMs) into a single platform that extracts text from user-uploaded PDFs, scans, images, and handwritten notes, chunks and embeds it for vector indexing, and answers natural-language questions grounded in the uploaded documents, reporting improvements in retrieval accuracy, response relevance, and interaction efficiency over traditional keyword-based search.

This paper presents Universal Doc AI, a multimodal intelligent document management system that unifies Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), vector-based semantic search, and Large Language Models (LLMs) into a single platform that extracts text from user-uploaded PDFs, scans, images, and handwritten notes, chunks and embeds it for vector indexing, and answers natural-language questions grounded in the uploaded documents, reporting improvements in retrieval accuracy, response relevance, and interaction efficiency over traditional keyword-based search.