This repository contains practice implementations of different LangChain chains. Each file demonstrates how to structure and execute various types of chains in LangChain for building flexible AI workflows.

1. simple_chain.py

   • A basic chain connecting prompt templates with an LLM.
   • Demonstrates the core concept of passing inputs → processing → outputs.

2. sequential_chain.py

   • Combines multiple chains in sequence.
   • The output of one chain is passed as input to the next.
   • Useful for building step-by-step pipelines.

3. parallel_chain.py

   • Runs multiple chains in parallel.
   • Collects and aggregates results.
   • Helpful when different models or prompts should run independently.

4. conditional_chain.py

   • Uses conditions to decide which chain to run.
   • Demonstrates branching logic based on input.
   • Useful for dynamic workflows.

Python 3.9+

LangChain

OpenAI or Hugging Face API key (depending on LLM used)

This repo is for learning and practicing LangChain chains. Each script highlights a different chaining mechanism to help in building more complex AI agents and applications.

Special thanks to the CampusX YouTube channel for providing valuable tutorials and guidance that inspired this practice.

Muqadas Ejaz

BS Computer Science (AI Specialization)

AI/ML Engineer

Data Science & Gen AI Enthusiast

📫 Connect with me on LinkedIn

🌐 GitHub: github.com/muqadasejaz