Add CLAUDE.md with project instructions

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+CLAUDE.local.md

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a Course Materials RAG (Retrieval-Augmented Generation) chatbot - a full-stack web application that answers questions about course materials using semantic search and Claude AI.
+
+## Commands
+
+**Always use UV to run the server and manage dependencies, never pip.**
+
+```bash
+# Install dependencies
+uv sync
+
+# Run the application (starts FastAPI server on port 8000)
+./run.sh
+# Or manually:
+cd backend && uv run uvicorn app:app --reload --port 8000
+
+# Web interface: http://localhost:8000
+# API docs: http://localhost:8000/docs
+```
+
+## Environment Setup
+
+Copy `.env.example` to `.env` and set `ANTHROPIC_API_KEY`.
+
+## Architecture
+
+### Query Flow
+
+```
+Frontend (script.js)
+    → POST /api/query
+    → app.py endpoint
+    → rag_system.query()
+    → ai_generator.generate_response() [calls Claude API with tools]
+    → Claude decides whether to use search_course_content tool
+    → If tool used: search_tools.py → vector_store.py → ChromaDB
+    → Response + sources returned to frontend
+```
+
+### Key Components
+
+- **rag_system.py**: Central orchestrator that coordinates all subsystems
+- **ai_generator.py**: Claude API integration with tool-use support. Claude decides when to search (tool_choice: auto)
+- **search_tools.py**: Tool definitions for Claude's function calling. `CourseSearchTool` wraps vector store searches
+- **vector_store.py**: ChromaDB wrapper with two collections:
+  - `course_catalog`: Course metadata for semantic course name resolution
+  - `course_content`: Document chunks for content search
+- **document_processor.py**: Parses course documents, identifies lessons, chunks text with sentence-aware splitting
+- **session_manager.py**: Conversation history management per session
+
+### Configuration (backend/config.py)
+
+Key settings: `CHUNK_SIZE=800`, `CHUNK_OVERLAP=100`, `MAX_RESULTS=5`, `MAX_HISTORY=2`
+
+### Data Models (backend/models.py)
+
+- `Course`: Title, link, instructor, lessons
+- `Lesson`: Number, title, link
+- `CourseChunk`: Text chunk with course/lesson metadata
+
+### Frontend
+
+Vanilla HTML/CSS/JS in `frontend/`. Uses marked.js for markdown rendering. No build step required.
+
+### Course Documents
+
+Sample documents in `docs/` are auto-loaded on server startup by `app.py` lifespan handler.