AI Document Assistant is an internal intelligent document assistant designed to help employees instantly find answers from company documents without having to manually search through files. By leveraging Retrieval-Augmented Generation (RAG) technology, the system combines semantic document search with AI-powered response generation to deliver accurate, context-grounded answers in natural language. The application enables HR teams, managers, and employees to upload internal documents such as policies, SOPs, and guidelines, which are then indexed and made queryable through a simple chat interface. Instead of browsing through lengthy PDF files or waiting for HR to respond, employees can simply type their question and receive a structured, relevant answer within seconds. This project addresses a common organizational challenge — knowledge accessibility — by centralizing institutional knowledge and making it conversational. It is particularly useful for onboarding new employees, clarifying company procedures, and reducing repetitive inquiries directed at HR or management. Built with a modern web stack, the system is designed to be scalable, easy to maintain, and adaptable to various business domains beyond HR, including legal, finance, and operations.

• Developed an AI-powered web-based RAG Knowledge Base that enables employees to query internal company documents in natural language and receive accurate, context-aware answers in real-time, improving organizational knowledge accessibility and reducing repetitive HR inquiries.
• Implemented a Retrieval-Augmented Generation (RAG) architecture integrating Large Language Models (LLMs) — including Ollama (LLaMA 3.2) and OpenAI (GPT-3.5-turbo) — with a ChromaDB vector database for semantic document retrieval, significantly increasing response accuracy and contextual relevance compared to standalone LLM responses.
• Engineered a multilingual semantic embedding pipeline using the Paraphrase-multilingual-MiniLM-L12-v2 model, enabling accurate document retrieval across multiple languages and expanding the system's usability for diverse, multilingual work environments.
• Built a RESTful backend API using FastAPI and Python, designing efficient document ingestion, chunking, and vector indexing endpoints to support scalable knowledge base management and low-latency query processing.
• Developed a responsive and intuitive chat interface using HTML and Vanilla JavaScript, implementing real-time question submission, dynamic response rendering, and a character-limited input system to ensure a seamless and user-friendly experience across devices.