Course Materials RAG System

A Retrieval-Augmented Generation (RAG) system designed to answer questions about course materials using semantic search and AI-powered responses.

Overview

This application is a full-stack web application that enables users to query course materials and receive intelligent, context-aware responses. It uses ChromaDB for vector storage, Anthropic's Claude for AI generation, and provides a web interface for interaction.

Prerequisites

• Python 3.13 or higher
• uv (Python package manager)
• An Anthropic API key (for Claude AI)
• For Windows: Use Git Bash to run the application commands - Download Git for Windows

Installation

1. Install uv (if not already installed)

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install Python dependencies

   uv sync

3. Set up environment variables

   Create a .env file in the root directory:

   ANTHROPIC_API_KEY=your_anthropic_api_key_here

Running the Application

Quick Start

Use the provided shell script:

chmod +x run.sh
./run.sh

Manual Start

cd backend
uv run uvicorn app:app --reload --port 8000

The application will be available at:

• Web Interface: http://localhost:8000
• API Documentation: http://localhost:8000/docs

Architecture & Data Flow

graph TD
    A["👤 User Frontend<br/>HTML/CSS/JavaScript"] -->|Enters Query| B["🌐 FastAPI Backend<br/>app.py"]
    
    B -->|1. Route Query| C["RAGSystem<br/>Main Orchestrator"]
    
    D["📚 Course Documents<br/>docs/ folder"] -->|Load & Process| E["DocumentProcessor<br/>Chunking & Preprocessing"]
    E -->|Store Chunks| F["VectorStore<br/>ChromaDB"]
    
    C -->|2. Search| F
    F -->|3. Return Relevant Context| C
    
    C -->|4. Add History| G["SessionManager<br/>Conversation Memory"]
    G -->|5. Retrieve Chat History| C
    
    C -->|6. Generate Response| H["AIGenerator<br/>Anthropic Claude API"]
    H -->|7. Stream Response| C
    
    C -->|8. Return JSON| B
    B -->|Display Answer| A
    
    style A fill:#e1f5ff
    style B fill:#fff3e0
    style C fill:#f3e5f5
    style D fill:#e8f5e9
    style E fill:#e8f5e9
    style F fill:#fce4ec
    style G fill:#f1f8e9
    style H fill:#ede7f6

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Component Details

Component	Purpose
RAGSystem	Orchestrates the entire query pipeline
DocumentProcessor	Chunks course materials into searchable segments
VectorStore	Stores embeddings in ChromaDB for semantic search
AIGenerator	Interfaces with Claude API for answer generation
SessionManager	Maintains conversation history for context
ToolManager	Abstracts search tools for flexible integration

Data Flow Steps

1. User Query: User submits a question via the web interface
2. Backend Processing: FastAPI routes the request to RAGSystem
3. Semantic Search: VectorStore performs semantic search across stored chunks
4. Context Retrieval: Relevant course materials are retrieved (max 5 results)
5. Conversation History: SessionManager provides prior conversation context
6. AI Generation: Claude processes query + context + history to generate answer
7. Response: Answer is streamed back to frontend and displayed