Runnable in LangChain

This repository explores different types of Runnables in LangChain, a framework for building AI applications. It demonstrates how to structure chains using RunnableSequence, RunnableParallel, RunnableBranch, RunnableLambda, and RunnablePassthrough to create dynamic workflows with LLMs.

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📁 Repository Structure

File	Description
runnable_branch.py	Demonstrates conditional execution using RunnableBranch, generating a report and summarizing it based on length.
runnable_lambda.py	Implements RunnableLambda to compute word count in parallel with joke generation.
runnable_sequence.py	Chains multiple steps sequentially using RunnableSequence to generate and explain jokes.
runnable_parallel.py	Uses RunnableParallel to create separate social media posts (Tweet & LinkedIn) concurrently.
runnable_passthrough.py	Showcases RunnablePassthrough, ensuring some inputs remain unchanged while processing others.

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🚀 Quick Start

1. Clone this repo

   git clone https://github.com/HaseebUlHassan437/runnable-in-langchain.git
   cd runnable-in-langchain

2. Create & activate a virtual environment

   python3 -m venv venv
   source venv/bin/activate   # macOS/Linux
   venv\Scripts\activate      # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Add your API key to a .env file

   GOOGLE_API_KEY=your_gemini_api_key_here
   

5. Run any example script

   python runnable_branch.py
   python runnable_lambda.py
   python runnable_sequence.py

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🔧 Tech Stack

• LangChain — AI workflow orchestration
• Google Gemini (PaLM) via langchain-google-genai
• Python dotenv — Environment management
• Pydantic — Data validation (if needed)

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📄 License

MIT © 2025

📫 Contact

Questions or feedback? Email haseebulhassan1172003@gmail.com.