Tutorial 10: Real-world Applications

Overview

This tutorial applies the concepts learned in previous sessions to build practical, real-world applications using LangChain and LangGraph. We'll implement four different applications to showcase the versatility and power of these libraries in solving complex problems.

Prerequisites

• Completion of Tutorials 1-9 in the series
• Strong understanding of LangChain components and LangGraph concepts
• Python 3.7+
• LangChain and LangGraph libraries installed
• Groq API key (for using Groq as the LLM)

Installation

   cd langchain-langgraph-tutorial/Tutorial10

Contents

1. Building a content moderation system
2. Implementing a language translation service
3. Creating an automated customer support chatbot
4. Developing a text-based game with AI-driven narrative

Getting Started

1. Clone this repository or download the tutorial_10_real_world_applications.ipynb file.
2. Open the notebook in Jupyter Lab or Jupyter Notebook.
3. Ensure you have set your Groq API key as an environment variable:

   export GROQ_API_KEY='your_api_key_here'

4. Follow the instructions in the notebook to build and test each application.

Additional Resources

• LangChain Documentation
• LangGraph Documentation
• Groq API Documentation

Next Steps

After completing this tutorial, you'll have hands-on experience building real-world applications with LangChain and LangGraph. The next tutorial in the series will cover working with structured data using Pydantic and JSON.