mistral-core-workflow-b

# 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 ```typescript 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 ```typescript async function batchEmbed( texts: string[], batchSize = 64, ): Promise<number[][]> { const allEmbeddings: number[][] = []; for (let i = 0; i < texts.length; i += batchSize) { const batch = texts.slice(i, i + batchSize); const response = await client.embeddings.create({ model: 'mistral-embed', inputs: batch, }); allEmbeddings.push(...response.data.map(d => d.embedding)); } return allEmbeddings; } // Embed 1000 documents in b...