🚀 LangChain Learning Guide

Welcome to the LangChain Learning Guide – a comprehensive, beginner-friendly resource to help you master LangChain, one of the most powerful frameworks for building applications with LLMs (Large Language Models).

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📚 Table of Contents

• 🔍 Introduction
• ⚙️ Environment Setup
• 🧪 Tutorials
• 📎 Resources
• 🙌 Contributing
• 📄 License

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🔍 Introduction

LangChain is a powerful framework for developing applications powered by language models. It enables developers to connect LLMs with external data, tools, and APIs, allowing you to build things like:

• Chatbots
• RAG (Retrieval-Augmented Generation) systems
• Document QA systems
• Autonomous agents
• Text summarizers, and more

This repository is structured to help you understand and experiment with LangChain core concepts through hands-on tutorials.

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⚙️ Environment Setup

To get started:

1. Clone the Repository

git clone https://github.com/Arbazkhan-cs/GenAI.git
cd GenAI

2. Install Dependencies

Make sure Python 3.8 or higher is installed.

pip install -r requirements.txt

3. Configure Environment Variables

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

OPENAI_API_KEY=your_api_key_here
GROQ_API_KEY=your_api_key_here
HUGGINGFACE_API_ACCESS_KEY=your_api_key_here # optional

You may also configure model paths if running local models:

export LANGCHAIN_MODEL=<path/to/your/model>

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🧪 Tutorials

This guide includes modular tutorials categorized by LangChain components:

Directory	Description
Agents	Learn to build autonomous agents that make decisions using tools and memory.
ChatModels	Work with chat-optimized language models (like OpenAI's ChatGPT).
Chains	Compose sequences of calls (chains) to models and tools.
EmbeddingModels	Generate and use embeddings for vector search and retrieval.
OutputParsers	Parse and format outputs into structured formats (e.g., JSON).
Prompts	Craft and manage prompt templates for dynamic use cases.
RAG	Build Retrieval-Augmented Generation pipelines with vector stores.
Runnables	Combine components like chains and models into composable functions.
StructuredOutput	Enforce structured output from LLMs using schemas and validations.

📎 Resources

• 📘 LangChain Documentation
• 💬 LangChain Discord Community
• 🐛 GitHub Issues

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🙌 Contributing

Found a typo? Have suggestions for a new tutorial?

We welcome contributions! Feel free to open a pull request or raise an issue.

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📄 License

This project is licensed under the MIT License – see the LICENSE file for details.

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⭐ If you find this project helpful, consider giving it a star on GitHub!

Happy building! 🚀