SEARCHABLE KNOWLEDGE FOR ENTERPRISE PDFS USING VECTOR DATABASE

Abstract

Enterprises today accumulate enormous volumes of unstructured data from reports, proposals, technical documentation, and policy manuals. Over time, these documents become fragmented across storage systems, making it difficult for employees to efficiently locate relevant information. Conventional keyword-based search engines often fail to capture the semantic meaning of user queries, leading to poor retrieval accuracy and wasted time in manual searches. This project addresses this challenge by developing a Semantic Knowledge Base (SKB)—an enterprise-grade system that enables context-aware document search and intelligent interaction. Leveraging vector databases for semantic similarity mapping, the system transforms documents into high-dimensional embeddings, allowing searches based on meaning rather than exact word matches. A chatbot interface provides users with a natural language medium to query and explore organizational knowledge. The implementation adopts an offline-first architecture, ensuring all data processing and retrieval occur within the organization's secure infrastructure—crucial for enterprises prioritizing data confidentiality. The backend is built using Python, FastAPI, and FAISS.