# mistralai/cookbook

Repository: https://github.com/mistralai/cookbook
Canonical: https://ross.abutalabs.com/products/mistralai-cookbook
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-08-25T15:42:43+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 69
- inputs: {"age_days": 971, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2317, forks 535 (observed 2026-08-28T04:06:36.840618+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: llm-inference, rag, prompt-engineering, machine-learning, data-science
- domain: large-language-models, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, mistral-api, cookbook, examples, function-calling, embeddings, fine-tuning, retrieval-augmented-generation

## Member repositories
- mistralai/cookbook (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:36.840618+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:38:56.320954+00:00, confidence not recorded.
  - readme: https://github.com/mistralai/cookbook (fetched 2026-08-28T04:06:36.840618+00:00, sha 9c878dd3e29c)
- Data as of 2026-08-30T08:39:29.467469+00:00.