Tutorial 11: Working with Structured Data

What you'll learn

1. Pydantic Integration:

   • Data validation and modeling
   • Type annotations
   • Schema creation
   • Custom validators

2. Structured I/O:

   • Input validation
   • Output parsing
   • Type safety
   • Error handling

3. JSON Processing:

   • Complex data manipulation
   • Schema validation
   • Data transformation
   • Query operations

4. Application Integration:

   • LangChain components
   • LangGraph workflows
   • Data persistence
   • API development

Prerequisites

• Completion of Tutorials 1-10
• Python 3.7+
• Groq API key

Getting Started

1. Ensure Virtual Environment is Activated

Linux/macOS:

cd langchain-langgraph-tutorial
source venv/bin/activate
cd Tutorial11

Windows:

cd langchain-langgraph-tutorial
.\venv\Scripts\activate
cd Tutorial11

2. Launch Jupyter Notebook

jupyter notebook Tutorial_11_structured_data.ipynb

Components

Core Files

• Tutorial_11_structured_data.ipynb: Main tutorial notebook
• utils/: Helper functions
• examples/: Sample implementations

Key Features

Data Modeling

• Type validation
• Schema definition
• Model inheritance
• Custom validators

Data Processing

• Input parsing
• Output formatting
• Error handling
• Data transformation

Integration Tools

• API connectivity
• Database operations
• State management
• Workflow automation

Next Steps

After completing this tutorial:

1. Build type-safe applications
2. Implement data validation
3. Create structured workflows
4. Develop API integrations

Additional Resources

• Pydantic Documentation
• LangChain Documentation
• LangGraph Documentation
• Groq API Documentation