Offline Open-Source RAG System – Private AI Document Assistant

Developed an offline Retrieval-Augmented Generation (RAG) system that enables users to interact with their documents and retrieve relevant information using AI. The project focuses on local processing, data privacy, and open-source technologies, reducing reliance on cloud-based AI services.

Key Features

Offline AI Processing: Designed to support document-based AI workflows locally without requiring continuous internet access.

Document Question Answering: Ask questions about documents and receive context-based responses.

Retrieval-Augmented Generation (RAG): Retrieve relevant document content to provide more grounded AI-generated answers.

Semantic Search: Find relevant information based on meaning and context rather than exact keyword matches.

Privacy-Focused Architecture: Keep document processing local when using compatible local models and components.

Open-Source Technology: Built around open-source tools to support flexibility, customization, and local deployment.

Project Objective

The goal of this project is to make AI-powered document search and question answering more accessible, private, and cost-effective through an offline RAG architecture.

Tech Stack: Add the actual technologies used in your implementation, such as Python, LangChain, FAISS, ChromaDB, Ollama, or local LLMs.

Skills: RAG Development, Local AI, Semantic Search, LLM Integration, Document Processing, Python Development, Open-Source AI.