cookbook

Mistral Cookbook Introduction

The Mistral Cookbook is a comprehensive repository of examples and notebooks designed to showcase the power and versatility of the Mistral framework. This collection is an evergreen project, continuously enriched by contributions from the Mistral community, partners, and enthusiasts eager to share their innovative uses of Mistral models and APIs.

Contributing to Mistral Cookbook

The cookbook is open to contributions, and community members are encouraged to submit their examples. Here is a brief rundown of the submission process:

• File Format: Submissions can be in Markdown (.md) or Jupyter Notebook (.ipynb) format. Ensuring samples run on platforms like Google Colab is appreciated.
• Authorship: Contributors should list their name, GitHub handle, and any affiliations clearly at the beginning of their submission.
• Reproducibility: Contributors must tag package versions in their code to guarantee reproducibility by others.
• Images and Descriptions: When using images, each should be under 500KB. Detailing each example with descriptions aids in understanding its category and intended purpose.
• Tone and Copyright: Contributors should maintain a neutral tone, avoiding excessive marketing language and respecting copyright laws.

Content Guidelines

The Mistral Cookbook strives to present original, well-structured, and valuable content that benefits the community. Contributors are encouraged to present fresh perspectives that address community needs.

Main Notebook Categories

The cookbook is organized into various categories, each serving a specific use case within the Mistral framework:

• Chat and Embeddings: For example, the quickstart.ipynb notebook provides a basic introduction to chat features and embeddings using the Mistral AI API.
• Prompting and Fine-Tuning: With notebooks like prompting_capabilities.ipynb and mistral_finetune_api.ipynb, users can learn to write effective prompts for various tasks and fine-tune models.
• RAG (Retrieval-Augmented Generation): This category, exemplified by notebooks such as basic_RAG.ipynb, explores how Mistral can be used for RAG processes.
• Function Calling: function_calling.ipynb illustrates how to utilize the Mistral API for calling functions within workflows.
• Evaluation and Data Generation: Users can explore methods of model evaluation and synthetic data generation with notebooks like evaluation.ipynb and synthetic_data_gen_and_finetune.ipynb.

Third-Party Tools

The Mistral Cookbook doesn't stop at its native capabilities; it also incorporates third-party tools that extend its functionality:

• Langchain & LlamaIndex: These partners provide enhanced capabilities for RAG and agentic models, offering innovative solutions in the AI landscape.
• Arize Phoenix & Azure AI Search: These integrations offer expanded capabilities in data tracing, evaluation, and advanced search functions using RAG and embeddings.
• UI and Tool Integrations: Various notebooks explore how Mistral can integrate with user interfaces like Gradio, Haystack, and Chainlit for enriched user experiences.

Conclusion

The Mistral Cookbook serves as both a learning resource and a creative sandbox for developers working with Mistral models. By fostering collaboration and sharing community-driven innovations, it continually evolves as a testament to the versatility and capacity of the Mistral framework. Whether you're looking to implement a new feature into an existing model or explore the possibilities offered by Mistral, the Cookbook offers a myriad of starting points and inspirations.