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langchain-chains

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.

📂 Files Included

  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.

🛠️ Requirements

Python 3.9+

LangChain

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

📖 Purpose

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.

​ Credits

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

👤 Author

Muqadas Ejaz

BS Computer Science (AI Specialization)

AI/ML Engineer

Data Science & Gen AI Enthusiast

📫 Connect with me on LinkedIn

🌐 GitHub: github.com/muqadasejaz