What is the maximum file size supported for uploads?

The Mistral AI Automation skill supports a maximum file size of 512 MB per file for uploads.

Does this skill support OCR processing?

Yes, you can upload files specifically for the 'ocr' purpose to leverage Mistral's document understanding capabilities.

Can I build RAG pipelines with this skill?

Yes, you can use the library management tools to create document libraries and upload documents for use with RAG-enabled Mistral AI agents.

Which file formats are supported for Mistral AI fine-tuning?

For fine-tuning uploads, only .jsonl files are supported. Other formats will be rejected by the Mistral AI API.

How do I authenticate the Mistral AI connection?

Authentication is handled through the Composio MCP integration using your Mistral AI API key when prompted during setup.

Mistral AI Automation

Name: Mistral AI Automation
Author: Tako3832

byTako3832

0•

Data Science & ML

Automates Mistral AI workflows by managing document libraries for RAG, handling file uploads for fine-tuning, and tracking model training jobs.

This skill integrates Mistral AI's robust capabilities directly into Claude Code via the Composio MCP, enabling developers to programmatically manage datasets and AI infrastructure. It streamlines the creation of RAG (Retrieval-Augmented Generation) pipelines through library management, automates the staging of .jsonl files for fine-tuning, and provides real-time tracking for model optimization jobs. By bridging the gap between local code environments and Mistral's cloud services, this skill allows for automated OCR processing, batch job management, and the construction of complex AI-driven data pipelines.

Key Features

01Creation and organization of document libraries for RAG-enabled agents

02Support for Weights & Biases (W&B) integration tracking

03Asynchronous document processing for scalable knowledge base building

04Comprehensive file management for fine-tuning, batch processing, and OCR

050 GitHub stars

06Real-time monitoring and filtering of Mistral AI fine-tuning jobs

Use Cases

01Processing high-volume document batches via Mistral's OCR and batch endpoints

02Building and maintaining dynamic knowledge bases for RAG-based AI applications

03Automating the preparation and upload of training data for custom Mistral model fine-tuning

Key Features

01Creation and organization of document libraries for RAG-enabled agents

02Support for Weights & Biases (W&B) integration tracking

03Asynchronous document processing for scalable knowledge base building

04Comprehensive file management for fine-tuning, batch processing, and OCR

050 GitHub stars

06Real-time monitoring and filtering of Mistral AI fine-tuning jobs

Use Cases

01Processing high-volume document batches via Mistral's OCR and batch endpoints

02Building and maintaining dynamic knowledge bases for RAG-based AI applications

03Automating the preparation and upload of training data for custom Mistral model fine-tuning