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CLAUDE.md

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

Build and Run Commands

# Install dependencies
uv sync

# Run the application (from project root)
./run.sh

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

The application serves at http://localhost:8000 with API docs at /docs.

Architecture

This is a RAG (Retrieval-Augmented Generation) chatbot for course materials using FastAPI, ChromaDB, and Claude.

Core Flow

  1. Document Ingestion: Course documents (.txt, .pdf, .docx) from docs/ are processed on startup
  2. Chunking: Documents are split into sentence-based chunks with overlap for vector storage
  3. Query Processing: User queries trigger AI-driven tool use to search the vector store
  4. Response Generation: Claude generates responses using retrieved context

Key Components

Backend (backend/)

  • app.py - FastAPI server with /api/query and /api/courses endpoints
  • rag_system.py - Main orchestrator connecting all components
  • vector_store.py - ChromaDB wrapper with two collections: course_catalog (metadata) and course_content (chunks)
  • ai_generator.py - Claude API integration with tool-use support
  • document_processor.py - Parses course documents and chunks text
  • search_tools.py - Tool interface for AI to search course content
  • session_manager.py - Conversation history management
  • models.py - Pydantic models: Course, Lesson, CourseChunk

Frontend (frontend/) - Static HTML/CSS/JS served by FastAPI

Document Format

Course documents follow a specific format:

Course Title: [title]
Course Link: [url]
Course Instructor: [instructor]

Lesson 0: Introduction
Lesson Link: [url]
[content]

Lesson 1: [title]
...

Configuration

  • config.py - Settings loaded from environment (.env file required with ANTHROPIC_API_KEY)
  • Default embedding model: all-MiniLM-L6-v2
  • Default Claude model: claude-sonnet-4-20250514