Welcome to the official notebook repository for the course LangChain for LLM Application Development, brought to you by DeepLearning.AI in collaboration with LangChain.
This repo is designed to help you learn by doing — follow along with the course, experiment with the code, and start building your own applications powered by large language models (LLMs). 🚀
By working through these notebooks, you’ll:
- Explore the core building blocks of LangChain.
- Design prompt chains that guide LLM reasoning.
- Use memory to give applications context and persistence.
- Build agents that can decide which tools to use.
- Connect LLMs to external data and APIs.
- Deploy and refine your own LLM-powered apps.
This course is hands-on, so expect to write, run, and experiment with code throughout.
- Clone the repository
git clone https://github.com/sdivyanshu90/LangChain-for-LLM-Application-Development.git
cd LangChain-for-LLM-Application-Development- Set your API key
Most notebooks require access to an LLM provider (such as OpenAI, Gemini, etc). Set your API key as an environment variable, and before committing and pushing the repo, make sure not to push the API key to GitHub:
export OPENAI_API_KEY="your_api_key_here"(Windows PowerShell)
setx OPENAI_API_KEY "your_api_key_here"Start Jupyter Notebook or JupyterLab:
jupyter notebookThen open the notebook that matches the lesson you’re currently taking in the course. Each notebook is structured to help you follow along step by step.
This repository is for learning purposes only. Feel free to explore, modify, and experiment with the notebooks as you go through the course.
Happy learning and coding! 💡✨