Tutorial 2: Working with Language Models in LangChain

Welcome to the second tutorial in our LangChain and LangGraph series! In this tutorial, we'll dive deeper into working with language models in LangChain, focusing on the Groq LLM.

What you'll learn

1. How to connect to different language models in LangChain
2. Creating and using prompt templates
3. Building simple prompt chains
4. Handling model responses
5. Best practices for prompt engineering

Prerequisites

• Completion of Tutorial 1: Introduction to LangChain
• Basic understanding of Python and Jupyter Notebooks
• A Groq API key (sign up at https://console.groq.com)

Getting Started

1. Ensure Virtual Environment is Activated

For Linux/macOS:

cd langchain-langgraph-tutorial
source venv/bin/activate
cd Tutorial02

For Windows:

cd langchain-langgraph-tutorial
.\venv\Scripts\activate
cd Tutorial02

2. Launch Jupyter Notebook

jupyter notebook Tutorial_2_Working_with_Language_Models.ipynb

What's Included

Core Concepts

• Setting up different LLM providers
• Understanding prompt templates
• Chain composition basics
• Response parsing and handling
• Prompt engineering techniques

Files

• Tutorial_2_Working_with_Language_Models.ipynb: Main tutorial notebook
• examples/: Directory containing example prompts and chains
• utils/: Helper functions for working with LLMs
• README.md: This documentation file

Advanced Topics

Working with Different LLMs

• Configuring model parameters
• Handling rate limits and quotas
• Implementing fallback strategies
• Model response comparison

Prompt Engineering

• Template creation best practices
• Dynamic prompt generation
• Context window optimization
• Output formatting techniques

Error Handling

• Common API issues
• Token limit management
• Response validation
• Fallback strategies

Troubleshooting

Common Issues

1. Model Response Errors

   • Check API key validity
   • Verify prompt length
   • Monitor rate limits

2. Chain Execution Problems

   • Debug individual chain components
   • Check memory management
   • Verify input/output formats

Next Steps

After completing this tutorial:

1. Explore advanced chain architectures
2. Practice prompt engineering techniques
3. Experiment with different LLM providers
4. Prepare for Tutorial 3: Document Processing

Stay tuned for Tutorial 3 where we'll explore:

• Document loading and processing
• Text chunking strategies
• Vector stores and embeddings
• Retrieval-augmented generation

Additional Resources

• LangChain LLM Documentation
• Groq API Best Practices
• Prompt Engineering Guidelines
• Model Selection Guide

Happy learning!