This repository demonstrates how to design and implement multi-step Large Language Model (LLM) workflows using LangChain. The project focuses on the use of chains as a core abstraction for composing prompts, managing intermediate outputs, and routing tasks dynamically.
The notebook illustrates practical patterns that are commonly used in production-grade LLM systems.
Modern LLM applications often require more than a single prompt. This project explores how to structure complex reasoning and text transformation pipelines using LangChain’s chaining mechanisms.
The notebook covers:
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Prompt templating and LLM configuration
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Sequential reasoning pipelines
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Explicit variable management across steps
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Dynamic routing of user inputs to specialized prompts
- Defines a single prompt-to-LLM execution unit
- Uses prompt templates with input variables
- Controls model behavior through parameters such as temperature
- Passes output from one chain directly into the next
- Useful for linear transformations and multi-step generation
- Supports multiple inputs and named outputs
- Enables modular and reusable pipeline components
- Better suited for production-style workflows
- Classifies inputs and routes them to appropriate downstream chains
- Demonstrates task-aware prompt routing
- Lays the foundation for scalable multi-capability LLM systems
- Clone the repository
git clone https://github.com/seguyy/LangChain.git cd LangChain - Install dependencies
pip install langchain openai jupyter
- Set your OpenAI API key (macOS / Linux)
export OPENAI_API_KEY="your-api-key"
- Launch the notebook
jupyter notebook Chains.ipynb