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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Development Commands

Running the Application

# Quick start using the provided script
./run.sh

# Manual start (from backend directory)
cd backend && uv run uvicorn app:app --reload --port 8000

Package Management

# Install dependencies
uv sync

# Add new dependencies
uv add <package-name>

Environment Setup

Create .env file in root directory with:

ANTHROPIC_API_KEY=your_api_key_here

Architecture Overview

This is a Retrieval-Augmented Generation (RAG) chatbot system that answers questions about course materials using semantic search and AI generation.

Core Components

Backend Architecture (/backend/):

  • app.py - FastAPI server with CORS, serves frontend static files, provides /api/query and /api/courses endpoints
  • rag_system.py - Main orchestrator that coordinates all components
  • vector_store.py - ChromaDB wrapper for vector storage and semantic search
  • ai_generator.py - Anthropic Claude API wrapper with tool support
  • document_processor.py - Processes course documents into chunks
  • search_tools.py - Tool-based search system for Claude AI
  • session_manager.py - Manages conversation history
  • models.py - Data models for Course, Lesson, CourseChunk
  • config.py - Configuration settings loaded from environment

Frontend (/frontend/):

  • Static HTML/CSS/JS files served by FastAPI
  • Web interface for chatbot interactions

Data Flow

  1. Documents in /docs/ are processed into chunks and stored in ChromaDB
  2. User queries hit /api/query endpoint
  3. RAGSystem uses AI with search tools to find relevant content
  4. Claude generates responses using retrieved context
  5. Session manager maintains conversation history

Key Technical Details

  • Uses ChromaDB for vector storage with all-MiniLM-L6-v2 embeddings
  • Anthropic Claude Sonnet 4 model with function calling for search tools
  • Document chunking: 800 characters with 100 character overlap
  • Supports PDF, DOCX, and TXT documents
  • Session-based conversation history (max 2 exchanges)
  • Tool-based search approach rather than direct RAG retrieval

Configuration

Key settings in config.py:

  • CHUNK_SIZE: 800 (document chunk size)
  • CHUNK_OVERLAP: 100 (overlap between chunks)
  • MAX_RESULTS: 5 (search results returned)
  • MAX_HISTORY: 2 (conversation exchanges remembered)
  • CHROMA_PATH: "./chroma_db" (vector database location)