mistralai/cookbook resource

Health v2 · maintenance only

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Adoption not part of the score

2317 stars · 535 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebook examples and guides for using Mistral AI models via the Mistral API, contributed by Mistral and the community. It covers chat, embeddings, RAG, function calling, prompting, evaluation, and data generation workflows.

Use cases

• learn how to call the Mistral API for chat and embeddings
• build a RAG pipeline from scratch with Mistral models
• implement function calling and text-to-SQL with Mistral
• write effective prompts for classification and summarization
• evaluate Mistral models on my own tasks
• generate synthetic training data with LLMs
• build a search engine using embeddings and function calling

When to choose

• you are building applications on top of Mistral AI models or APIs
• you want practical, runnable notebook examples for RAG, function calling, or prompting
• you are onboarding a team to the Mistral ecosystem

When to avoid

• you need production-ready application code rather than educational notebooks
• you use only non-Mistral LLM providers
• you want a library or SDK to install rather than example code

Facets

learning-resource · maturity active

llm-inference rag prompt-engineering machine-learning data-science large-language-models artificial-intelligence tutorials python cross-platform jupyter-notebooks mistral-api cookbook examples function-calling embeddings fine-tuning retrieval-augmented-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mistralai/cookbook")