mistral-core-workflow-b

name	mistral-core-workflow-b
description	Execute Mistral AI embeddings, function calling, and RAG pipelines. Use when implementing semantic search, RAG applications, tool-augmented LLM interactions, or code embeddings. Trigger with phrases like "mistral embeddings", "mistral function calling", "mistral tools", "mistral RAG", "mistral semantic search".
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","mistral","llm","embeddings","workflow"]
compatibility	Designed for Claude Code

Mistral AI Core Workflow B: Embeddings & Function Calling

Overview

Secondary workflows for Mistral AI: text/code embeddings with mistral-embed (1024 dimensions), function calling (tool use) with any chat model, and RAG pipeline combining both. Mistral supports auto, any, and none tool choice modes.

Prerequisites

• Completed mistral-install-auth setup
• MISTRAL_API_KEY environment variable set
• Familiarity with mistral-core-workflow-a

Instructions

Step 1: Generate Text Embeddings

import { Mistral } from '@mistralai/mistralai';

const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });

// Single text embedding
const response = await client.embeddings.create({
  model: 'mistral-embed',
  inputs: ['Machine learning is fascinating.'],
});

const vector = response.data[0].embedding;
console.log(`Dimensions: ${vector.length}`); // 1024
console.log(`Tokens used: ${response.usage.totalTokens}`);

Step 2: Batch Embeddings with Rate Awareness

async function (): <[][]> {
   : [][] = [];

   ( i = ; i < texts.; i += batchSize) {
     batch = texts.(i, i + batchSize);
     response =  client..({
      : ,
      : batch,
    });
    allEmbeddings.(...response..( d.));
  }

   allEmbeddings;
}


 docs = [, , ];
 embeddings =  (docs);