🛠️ Runnables with LangChain & Groq

This project demonstrates how to use LangChain's Runnable interfaces with Groq LLMs for building modular, composable, and flexible AI pipelines.
Each runnable script explores different patterns like sequence, parallel, branching, passthrough, and lambda operations.

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📂 Project Structure

runnables/
├── branch_runnable.py         # Branching based on input conditions
├── lambda_runnable.py         # Using lambda runnable for custom logic
├── parelle_runnable.py        # Parallel execution of multiple tasks
├── passthrough_runnable.py    # Passthrough and explanation chain
├── sequence_runnable.py       # Sequential chaining of prompts
├── requirements.txt           # Python dependencies

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🚀 Getting Started

Follow these steps to set up and run the project on your local machine.

1. Clone the Repository

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

2. Create a Virtual Environment (Optional)

python -m venv venv
source venv/bin/activate     # On Windows: venv\Scripts\activate

3. Install the Required Packages

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the project root with the following content:

GROQ_API_KEY=your_groq_api_key_here

(Replace your_groq_api_key_here with your actual Groq API Key.)

5. Run the Scripts

You can execute any runnable script individually:

python branch_runnable.py
python lambda_runnable.py
python parelle_runnable.py
python passthrough_runnable.py
python sequence_runnable.py

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🛠️ Explanation of Each Runnable

File	Description
branch_runnable.py	Uses RunnableBranch to dynamically choose the next step based on the word length of input text.
lambda_runnable.py	Integrates a custom Python function with RunnableLambda inside the AI pipeline.
parelle_runnable.py	Demonstrates parallel execution: generates a tweet and a LinkedIn post simultaneously.
passthrough_runnable.py	Generates a joke, then passes it through to generate an explanation using a second model call.
sequence_runnable.py	Chains prompts and LLM responses sequentially to first generate a joke and then explain it.

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🛆 Requirements

• Python 3.8 or above
• LangChain
• Groq
• python-dotenv

All dependencies are listed in requirements.txt.

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📜 License

This project is licensed under the MIT License.

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❤️ Acknowledgments

• LangChain
• Groq LLMs

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✨ Quick Demo

Each script prints its final output directly to the console after execution.