🤖 AI Assistant – Local RAG-based Document Intelligence System

🚀 Built as a hands-on exploration of AI engineering using Spring AI and Retrieval-Augmented Generation (RAG) patterns.

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🚧 Project Status

This is an actively evolving project focused on learning and implementing real-world AI engineering patterns.

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📌 Overview

AI Assistant is a backend application that enables querying and extracting insights from documents using retrieval-based techniques.

The system is designed to simulate real-world LLM-powered applications while running fully locally, without requiring paid APIs.

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🎯 Purpose

This project was built to:

• Move beyond theoretical AI learning into hands-on implementation
• Understand how real-world AI systems are structured (chat, retrieval, services)
• Explore Retrieval-Augmented Generation (RAG) patterns in a practical setup
• Build a cost-efficient AI system that runs without external dependencies

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✨ Key Features

• 📄 Document ingestion and processing
• 🔍 Context-aware retrieval
• 💬 Chat-based query interface
• 🧩 Modular architecture (Chat + RAG layers)
• 💻 Fully local execution (no paid LLM APIs)

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🧱 Tech Stack

• Backend: Java, Spring Boot
• AI Framework: Spring AI
• Architecture: Layered (Controller → Service → AI components)
• Configuration: YAML

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🧠 Architecture Overview

The application is structured into clear, modular components:

🔹 Chat Layer

Handles user interaction and query processing.

🔹 RAG Layer

Responsible for:

• Document ingestion
• Context retrieval

🔹 Service Layer

Encapsulates business logic and orchestration.

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⚙️ How It Works

1. User submits a query via the chat endpoint
2. System retrieves relevant document context
3. Context is used to generate a response
4. Response is returned to the user

This follows a simplified Retrieval-Augmented Generation (RAG) workflow.

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