LangChain Chains

A practical collection of LangChain chain implementations demonstrating how to orchestrate Large Language Models (LLMs) into structured workflows. This repository explores different chain patterns used to build scalable, maintainable, and production-ready AI applications.

Overview

LangChain Chains enable developers to connect prompts, models, parsers, and custom logic into reusable workflows. This repository demonstrates the core chain architectures that form the foundation of advanced AI systems.

The examples focus on building sequential processing pipelines, parallel execution workflows, and conditional routing mechanisms using LangChain Expression Language (LCEL) and Runnables.

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Topics Covered

Normal Chains

• Basic prompt → model → parser workflow
• Chain composition fundamentals
• Response processing

Sequential Chains

• Multi-step execution pipelines
• Passing outputs between chain components
• Workflow orchestration

Parallel Chains

• Concurrent task execution
• Multiple prompt processing
• Performance optimization patterns

Conditional Chains

• Dynamic routing based on model output
• Branching workflows
• Decision-based execution paths

Runnable Components

• RunnableLambda
• RunnableBranch
• Chain composition with LCEL

Output Parsing

• String Output Parser
• Pydantic Output Parser
• Structured response generation

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Repository Structure

LangChain-Chains/
│
├── normal_chain.py
├── sequential_chain.py
├── parallel_chain.py
├── conditional_chain.py
├── requirements.txt
└── .env

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Technologies Used

• Python
• LangChain
• Hugging Face
• Llama Models
• Pydantic
• LangChain Expression Language (LCEL)

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Key Learning Outcomes

• Building modular AI workflows
• Creating multi-step reasoning pipelines
• Implementing conditional execution logic
• Executing tasks in parallel
• Designing scalable LLM applications
• Working with LangChain Runnables

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Example Use Cases

• Customer Support Automation
• Content Generation Pipelines
• Multi-Step AI Reasoning
• Sentiment-Based Routing
• AI Workflow Automation
• Enterprise LLM Applications

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Installation

Clone the repository:

git clone <repository-url>
cd LangChain-Chains

Create a virtual environment:

python -m venv venv

Activate the environment:

Windows

venv\Scripts\activate

Linux/macOS

source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

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Environment Variables

Create a .env file:

HUGGINGFACEHUB_API_TOKEN=your_api_token

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Ongoing Development

This repository is actively maintained and will continue to expand with:

• LangChain Expression Language (LCEL)
• Router Chains
• Advanced Runnable Patterns
• Chain Monitoring
• Error Handling Strategies
• Production AI Workflows
• RAG Pipelines
• Agentic Workflows

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Author

Bhupendra Shivhare

AI Engineer | Machine Learning Practitioner | Generative AI Developer

Focused on building practical AI solutions and educational content around LangChain, LLMs, RAG, AI Agents, and modern Generative AI systems.