Add context engineering course with Redis University Class Agent

- Complete reference implementation of context-aware AI agent
- Educational notebooks covering context engineering concepts
- Fixed dependency compatibility issues (pydantic v2, redisvl 0.8+, redis 6+)
- Updated import paths for newer redisvl version
- Removed redis-om dependency to avoid pydantic conflicts
- All tests passing and imports working correctly

Features:
- LangGraph-based agent workflow
- Redis vector search for semantic course discovery
- Dual memory system (short-term + long-term)
- Personalized course recommendations
- CLI and Python API interfaces

Author: abrookins

python-recipes/context-engineering/README.md

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+# Context Engineering Recipes
+
+This section contains comprehensive recipes and tutorials for **Context Engineering** - the practice of designing, implementing, and optimizing context management systems for AI agents and applications.
+
+## What is Context Engineering?
+
+Context Engineering is the discipline of building systems that help AI agents understand, maintain, and utilize context effectively. This includes:
+
+- **System Context**: What the AI should know about its role, capabilities, and environment
+- **Memory Management**: How to store, retrieve, and manage both short-term and long-term memory
+- **Tool Integration**: How to define and manage available tools and their usage
+- **Context Optimization**: Techniques for managing context window limits and improving relevance
+
+## Repository Structure
+
+```
+context-engineering/
+├── README.md                    # This file
+├── reference-agent/             # Complete reference implementation
+│   ├── src/                     # Source code for the Redis University Class Agent
+│   ├── scripts/                 # Data generation and ingestion scripts
+│   ├── data/                    # Generated course catalogs and sample data
+│   └── tests/                   # Test suite
+├── notebooks/                   # Educational notebooks organized by section
+│   ├── section-1-introduction/  # What is Context Engineering?
+│   ├── section-2-system-context/# Setting up system context and tools
+│   └── section-3-memory/        # Memory management concepts
+└── resources/                   # Shared resources, diagrams, and assets
+```
+
+## Course Structure
+
+This repository supports a comprehensive web course on Context Engineering with the following sections:
+
+### Section 1: Introduction
+- **What is Context Engineering?** - Core concepts and principles
+- **The Role of a Context Engine** - How context engines work in AI systems
+- **Project Overview: Redis University Class Agent** - Hands-on project introduction
+
+### Section 2: Setting up System Context
+- **Prepping the System Context** - Defining what the AI should know
+- **Defining Available Tools** - Tool integration and management
+
+### Section 3: Memory
+- **Memory Overview** - Concepts and architecture
+- **Short-term/Working Memory** - Managing conversation context
+- **Summarizing Short-term Memory** - Context window optimization
+- **Long-term Memory** - Persistent knowledge storage and retrieval
+
+## Reference Agent: Redis University Class Agent
+
+The reference implementation is a complete **Redis University Class Agent** that demonstrates all context engineering concepts in practice. This agent can:
+
+- Help students find courses based on their interests and requirements
+- Maintain conversation context across sessions
+- Remember student preferences and academic history
+- Provide personalized course recommendations
+- Answer questions about course prerequisites, schedules, and content
+
+### Key Technologies
+
+- **LangGraph**: Agent workflow orchestration
+- **Redis Agent Memory Server**: Long-term memory management
+- **langgraph-redis-checkpointer**: Short-term memory and state persistence
+- **RedisVL**: Vector storage for course catalog and semantic search
+- **OpenAI GPT**: Language model for natural conversation
+
+## Getting Started
+
+1. **Set up the environment**: Install required dependencies
+2. **Run the reference agent**: Start with the complete implementation
+3. **Explore the notebooks**: Work through the educational content
+4. **Experiment**: Modify and extend the agent for your use cases
+
+## Prerequisites
+
+- Python 3.8+
+- Redis Stack (local or cloud)
+- OpenAI API key
+- Basic understanding of AI agents and vector databases
+
+## Quick Start
+
+```bash
+# Navigate to the reference agent directory
+cd python-recipes/context-engineering/reference-agent
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Generate sample course data
+python -m redis_context_course.scripts.generate_courses
+
+# Ingest data into Redis
+python -m redis_context_course.scripts.ingest_courses
+
+# Start the CLI agent
+python -m redis_context_course.cli
+```
+
+## Learning Path
+
+1. Start with **Section 1** notebooks to understand core concepts
+2. Explore the **reference agent** codebase to see concepts in practice
+3. Work through **Section 2** to learn system context setup
+4. Complete **Section 3** to master memory management
+5. Experiment with extending the agent for your own use cases
+
+## Contributing
+
+This is an educational resource. Contributions that improve clarity, add examples, or extend the reference implementation are welcome.