LangChain Basics

A collection of simple, practical examples demonstrating basic LangChain methods, functions, and concepts. This repository serves as a learning resource for developers getting started with LangChain.

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Setting Up Your Environment

1. Clone the repository:

   git clone https://github.com/nicolasfmc/langchain_basics.git
   cd langchain_basics

2. Create a virtual environment:

   # Create virtual environment
   python -m venv venv
   
   # Activate virtual environment
   # On Windows:
   venv\Scripts\activate
   
   # On macOS/Linux:
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   Create a .env file in the root directory and add your API keys:

   OPENAI_API_KEY=your_openai_api_key_here
   # Add other API keys as needed

📚 Examples (Ordered by Complexity)

Beginner Level

1. Basic - Introduction to basics
2. Simple Sequential Chain - Basic sequential chain implementation

Intermediate Level

3. Conversation Buffer Memory - Basic memory management for conversations
4. Conversation Buffer Window Memory - Windowed memory for chat history
5. Conversation Summary Memory - Summarized conversation memory
6. LCEL Basic - The basic of LangChain Expression Language (LCEL)
7. LCEL Join Chains - LangChain Expression Language (LCEL) + Join chains
8. Conversation Buffer Custom Chain - Custom chain with buffer memory
9. JSON Output Parser - Parsing structured JSON outputs
10. Retrieval QA Tasks - Document retrieval and Q&A
11. Retrieval QA - Intermediate retrieval-based question answering

🛠️ What You'll Learn

• Basic LangChain Setup: Environment configuration and basic usage
• Memory Management: Different types of conversation memory (buffer, window, summary)
• Chain Operations: Sequential chains and custom chain implementations
• Output Parsing: Structured data extraction and JSON parsing
• Retrieval Systems: Document-based question answering and retrieval-augmented generation (RAG)
• Advanced Patterns: Custom chains and complex workflow implementations

📖 Usage

Each Python file in this repository is a standalone example. To run any example:

python filename.py

For example:

python simple_sequential_chain.py

🔧 Virtual Environment Management

Why Use Virtual Environments?

Virtual environments help you:

• Keep project dependencies isolated
• Avoid conflicts between different projects
• Maintain consistent development environments
• Easy dependency management

Managing Your Virtual Environment

Activate the environment (do this every time you work on the project):

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

Deactivate when done:

deactivate

Update requirements.txt when you add new packages:

pip freeze > requirements.txt

📦 Dependencies

The requirements.txt file contains all necessary dependencies. Key packages include:

• langchain - Core LangChain library
• openai - OpenAI API integration
• python-dotenv - Environment variable management
• Additional utilities for specific examples

🤝 Contributing

Feel free to contribute by:

1. Adding new examples
2. Improving existing code
3. Adding documentation
4. Reporting issues

📄 License

This project is open source and available under the MIT License.

🆘 Troubleshooting

Common Issues:

1. Import errors: Make sure your virtual environment is activated and dependencies are installed
2. API key errors: Verify your .env file is properly configured
3. Module not found: Check that you're running commands from the project root directory

Getting Help:

• Check the official LangChain documentation
• Review individual file comments for specific usage instructions
• Open an issue in this repository for bug reports or questions

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Happy coding! 🚀