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.

Key Features

• Semantic Search: Vector-based search through course materials using ChromaDB
• AI-Powered Responses: Context-aware answers using Anthropic's Claude AI
• Tool Integration: Extensible search tools for dynamic content retrieval
• Session Management: Conversation history tracking across interactions
• Dual Vector Storage: Separate collections for course metadata and content
• Smart Document Processing: Structured course document parsing with metadata

Architecture

Technology Stack

• Backend: Python 3.13+, FastAPI, ChromaDB, Anthropic Claude, Sentence Transformers
• Frontend: Vanilla HTML/CSS/JavaScript with Marked.js for markdown rendering
• Package Management: uv (modern Python package manager)
• Vector Database: ChromaDB with dual collections

Core Components

• RAG System (backend/rag_system.py): Central orchestrator
• Document Processor (backend/document_processor.py): Handles structured course documents
• Vector Store (backend/vector_store.py): Dual ChromaDB collections management
• Search Tools (backend/search_tools.py): Tool-based semantic search
• AI Generator (backend/ai_generator.py): Anthropic Claude integration
• Session Manager (backend/session_manager.py): Conversation context tracking

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

API Endpoints

• POST /api/query - Process user questions with session context
• GET /api/courses - Retrieve course statistics
• GET / - Frontend interface
• GET /docs - FastAPI interactive documentation

Adding Course Documents

Place structured course files in the docs/ folder. The system expects this format:

Course Title: [Course Name]
Course Link: [URL]
Course Instructor: [Name]

Lesson 0: [Title]
Lesson Link: [URL]
[Content...]

Lesson 1: [Title]
Lesson Link: [URL]
[Content...]

Documents are automatically processed on startup with:

• Smart text chunking (800 characters, 100 overlap)
• Metadata extraction from headers
• Contextual prefixes for better retrieval

Development

Package Management

IMPORTANT: Always use uv for dependency operations:

# Install dependencies
uv sync

# Add new dependency
uv add package_name

# Add development dependency
uv add --dev package_name

# Run commands with project dependencies
uv run python script.py
uv run command_name

Testing

Run the test suite:

uv run pytest

Test configuration is in pyproject.toml with markers for:

• unit: Individual component tests
• integration: Component interaction tests
• api: API endpoint tests

Configuration

Key settings in backend/config.py:

• Chunk size: 800 characters
• Chunk overlap: 100 characters
• Max search results: 5
• ChromaDB path: ./chroma_db
• Session history limit: 2 exchanges

Usage Examples

Query Interface

The web interface provides a chat-like experience where you can:

• Ask questions about course materials
• Get context-aware responses with source references
• Maintain conversation history across sessions

Example Queries

"What is the main topic of Course 1?"
"Explain the concept covered in Lesson 3 of the Python course"
"Compare the approaches mentioned in different courses"

Project Structure

├── backend/
│   ├── app.py              # FastAPI application
│   ├── rag_system.py       # Main RAG orchestrator
│   ├── document_processor.py # Course document parsing
│   ├── vector_store.py     # ChromaDB operations
│   ├── search_tools.py     # Semantic search tools
│   ├── ai_generator.py     # Claude AI integration
│   ├── session_manager.py  # Conversation tracking
│   ├── config.py           # Configuration settings
│   ├── models.py           # Pydantic data models
│   └── tests/              # Test suite
├── frontend/
│   ├── index.html          # Web interface
│   ├── script.js           # Frontend logic
│   └── style.css           # Styling
├── docs/                   # Course material files
├── run.sh                  # Quick start script
├── pyproject.toml          # Project dependencies
└── CLAUDE.md               # Development guidelines

Contributing

1. Follow the development guidelines in CLAUDE.md
2. Use uv for all dependency management
3. Run tests before submitting changes: uv run pytest
4. Ensure type safety with Pydantic models
5. Follow the existing code conventions and patterns

License

This project is licensed under the MIT License.