This repository serves as a community-driven collection of examples and guides for utilizing Mistral AI models. It targets developers and researchers looking to implement various AI tasks, from basic API interaction to advanced RAG and fine-tuning, providing practical code snippets and demonstrations.

How It Works

The cookbook offers a curated set of Jupyter notebooks and Markdown files, each demonstrating specific functionalities of Mistral's models. Examples cover core capabilities like chat and embeddings, advanced patterns such as Retrieval-Augmented Generation (RAG) and function calling, and specialized use cases like fine-tuning, moderation, and multimodal processing with Pixtral. Many examples integrate with popular third-party tools and frameworks like Langchain, LlamaIndex, and Gradio, showcasing interoperability.

Quick Start & Requirements

• Examples are primarily provided as .ipynb (Jupyter Notebook) files.
• Runnable on Google Colab is a stated goal for notebook submissions.
• Specific API keys or environment configurations may be required for certain examples, as detailed within individual notebooks.
• Links to official quick-start guides or demos are not explicitly provided within this README, but individual notebooks may link to external resources.

Highlighted Details

• Comprehensive coverage of Mistral AI API features including chat, embeddings, function calling, and fine-tuning.
• Extensive examples of RAG implementation with various frameworks (Langchain, LlamaIndex, Haystack, Pinecone, Azure AI Search, Neo4j).
• Demonstrations of multimodal capabilities using the Pixtral model for image processing and OCR.
• Integration examples with numerous third-party tools for UI, tracing, evaluation, and agent development.

Maintenance & Community

• The repository encourages community contributions via Pull Requests.
• Submission guidelines emphasize originality, clarity, value, and reproducibility.
• No specific community channels (Discord/Slack) or roadmap links are provided in the README.

Licensing & Compatibility

• The repository itself is not explicitly licensed in the README.
• Individual examples may have dependencies on libraries with various licenses.
• Compatibility for commercial use would depend on the licenses of the underlying Mistral models and any third-party tools used in specific examples.

Limitations & Caveats

The README does not specify licensing for the repository's content or code examples. Some examples may require specific API keys or configurations not detailed here, and the "runnable on Colab" claim is a submission guideline, not a guarantee for all notebooks.