Getting Started with Mistral ✨

This repository contains the Jupyter notebooks used in the Getting Started with Mistral short course, created by DeepLearning.AI in collaboration with Mistral AI.

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📚 Course Content

The notebooks here follow the modules in the course:

1. Introduction & Overview – Explore Mistral model families
2. Prompting – Learn prompting strategies for text, reasoning, and coding tasks
3. Model Selection – Choose between Small, Medium, Large, and Mixtral models
4. Function Calling – Connect models with external tools/APIs
5. JSON Mode – Generate structured responses for downstream use
6. RAG from Scratch – Build a retrieval pipeline with embeddings + vector search
7. Chatbot – Create an interactive chatbot around Mistral models

Each notebook is self-contained and corresponds to a lesson in the course.

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/sdivyanshu90/Getting-Started-with-Mistral.git
cd Getting-Started-with-Mistral

2. Create environment

We recommend Python 3.9+ and a virtual environment.

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

3. Set up API key

You’ll need a Mistral API key for running most notebooks. Set it as an environment variable:

export MISTRAL_API_KEY="your_api_key_here"

(Windows PowerShell)

$env:MISTRAL_API_KEY="your_api_key_here"

4. Launch Jupyter

jupyter notebook

Open the notebook of your choice and start exploring 🚀

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🛠 Requirements

• Python 3.9 or above
• Jupyter Notebook or JupyterLab

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📖 References

• Course page – DeepLearning.AI
• Mistral AI documentation
• DeepLearning.AI Short Courses

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📜 License

This repo shares example notebooks for educational purposes, adapted from the official DeepLearning.AI x Mistral course. Please check the course terms of use before redistributing.

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Happy learning, and enjoy building with Mistral models! 🚀