The Mistral Cookbook features examples contributed by our community and partners. If you have cool examples showcasing Mistral models, feel free to share them by submitting a PR to this repo.
- File Format: Please submit your example in the .md or .ipynb format.
- Runnable on Colab: If you're sharing a notebook example, try to make sure it's runnable on Google Colab.
- Authorship: Kindly include your name and affiliation at the begining of the file.
- Descriptions: Please include your notebook along with its category and descriptions in the table below.
- Tone: Kindly maintain a neural tone and minimize any excessive marketing materials.
- Reproducibility: To ensure others can reproduce your work, kindly tag package versions in your code.
- Image size: If you have images, please make sure each image's size is below 500KB.
- Copyright: Always respect copyright and intellectual property laws.
Disclaimer: Examples contributed by the community and partners do not represent Mistral's views and opinions.
- Originality: Is your content original and offering a fresh perspective?
- Clear: Is your content well-structured and clearly written?
- Value: Is your content valuable to the community? Does the community need it?
Notebook | Category | Description |
---|---|---|
langgraph_crag_mistral.ipynb | RAG | Corrective RAG using self-reflection with LangGraph and Mistral with option to run locally (with Ollama). |
prompting_capabilities.ipynb | prompting | Write prompts for classification, summarization, personalization, and evaluation |
basic_RAG.ipynb | RAG | RAG from scratch with Mistral AI API |
embeddings.ipynb | embeddings | Use Mistral embeddings API for classification and clustering |
llamaindex_agentic_rag.ipynb | RAG, agent | Use Mistral AI with LlamaIndex and ReAct agent |
haystack_chat_with_docs.ipynb | RAG, embeddings | Use Mistral AI with Haystack indexing and RAG pipelines |
Basic Prompting techniques | How to use Prompt engineering techniques for effective prompts and desired responses. Read more at this blog. | |
NLP | Learn techniques for text classification and sentiment analysis, honing your ability to guide language models accurately. The exploration extends to text categorization, text transformation, and translation, where you'll witness language models reshaping and interpreting content. Read more at this blog. |