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

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 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 batches of 64
const docs = ['doc1...', 'doc2...', /* ... */];
const embeddings = await batchEmbed(docs);

Step 3: Semantic Search with Cosine Similarity

function cosineSimilarity(a: number[], b: number[]): number {
  let dot = 0, normA = 0, normB = 0;
  for (let i = 0; i < a.length; i++) {
    dot += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }
  return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}

class SemanticSearch {
  private documents: Array<{ text: string; embedding: number[] }> = [];
  private client: Mistral;

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

  async index(texts: string[]): Promise<void> {
    const response = await this.client.embeddings.create({
      model: 'mistral-embed',
      inputs: texts,
    });
    this.documents = texts.map((text, i) => ({
      text,
      embedding: response.data[i].embedding,
    }));
  }

  async search(query: string, topK = 5): Promise<Array<{ text: string; score: number }>> {
    const qEmbed = await this.client.embeddings.create({
      model: 'mistral-embed',
      inputs: [query],
    });
    const qVec = qEmbed.data[0].embedding;

    return this.documents
      .map(doc => ({ text: doc.text, score: cosineSimilarity(qVec, doc.embedding) }))
      .sort((a, b) => b.score - a.score)
      .slice(0, topK);
  }
}

Step 4: Function Calling (Tool Use)

// 1. Define tools with JSON Schema
const tools = [
  {
    type: 'function' as const,
    function: {
      name: 'get_weather',
      description: 'Get current weather for a city',
      parameters: {
        type: 'object',
        properties: {
          city: { type: 'string', description: 'City name (e.g., "Paris")' },
          units: { type: 'string', enum: ['celsius', 'fahrenheit'], default: 'celsius' },
        },
        required: ['city'],
      },
    },
  },
  {
    type: 'function' as const,
    function: {
      name: 'search_database',
      description: 'Search product database by query',
      parameters: {
        type: 'object',
        properties: {
          query: { type: 'string' },
          limit: { type: 'integer', default: 10 },
        },
        required: ['query'],
      },
    },
  },
];

// 2. Send request with tools
const response = await client.chat.complete({
  model: 'mistral-large-latest', // Large recommended for complex tool use
  messages: [{ role: 'user', content: "What's the weather in Paris?" }],
  tools,
  toolChoice: 'auto', // 'auto' | 'any' | 'none'
});

Step 5: Tool Execution Loop

// Tool registry maps function names to implementations
const toolRegistry: Record<string, (args: any) => Promise<any>> = {
  get_weather: async ({ city, units }) => ({ city, temp: 22, units: units ?? 'celsius' }),
  search_database: async ({ query, limit }) => ({ results: [], total: 0 }),
};

async function chatWithTools(userMessage: string): Promise<string> {
  const messages: any[] = [{ role: 'user', content: userMessage }];

  while (true) {
    const response = await client.chat.complete({
      model: 'mistral-large-latest',
      messages,
      tools,
      toolChoice: 'auto',
    });

    const choice = response.choices?.[0];
    if (!choice) throw new Error('No response from model');

    // If model wants to call tools
    if (choice.message.toolCalls?.length) {
      messages.push(choice.message); // Add assistant message with tool_calls

      for (const call of choice.message.toolCalls) {
        const fn = toolRegistry[call.function.name];
        if (!fn) throw new Error(`Unknown tool: ${call.function.name}`);

        const args = JSON.parse(call.function.arguments);
        const result = await fn(args);

        messages.push({
          role: 'tool',
          name: call.function.name,
          content: JSON.stringify(result),
          toolCallId: call.id,
        });
      }
      continue; // Let model process tool results
    }

    // Model returned final text response
    return choice.message.content ?? '';
  }
}

Step 6: RAG Pipeline (Retrieval-Augmented Generation)

async function ragChat(
  query: string,
  searcher: SemanticSearch,
  topK = 3,
): Promise<{ answer: string; sources: string[] }> {
  // 1. Retrieve relevant documents
  const results = await searcher.search(query, topK);
  const context = results.map((r, i) => `[${i + 1}] ${r.text}`).join('\n\n');

  // 2. Generate answer grounded in context
  const response = await client.chat.complete({
    model: 'mistral-small-latest',
    messages: [
      {
        role: 'system',
        content: `Answer based ONLY on the provided context. Cite sources as [1], [2], etc. If the context doesn't contain the answer, say "I don't have enough information."`,
      },
      {
        role: 'user',
        content: `Context:\n${context}\n\nQuestion: ${query}`,
      },
    ],
    temperature: 0.1,
  });

  return {
    answer: response.choices?.[0]?.message?.content ?? '',
    sources: results.map(r => r.text),
  };
}

Output

• Text embeddings with mistral-embed (1024 dimensions)
• Semantic search with cosine similarity ranking
• Function calling with tool execution loop
• RAG pipeline combining retrieval and generation

Error Handling

Issue	Cause	Resolution
Empty embeddings	Invalid input text	Validate non-empty strings before API call
Tool not found	Unknown function name	Check tool registry matches tool definitions
Infinite tool loop	Model keeps calling tools	Add max iteration count (e.g., 10)
RAG hallucination	Insufficient context	Add more documents, increase topK
400 Bad Request	Missing toolCallId	Each tool result must include the matching toolCallId

Resources

• Embeddings API
• Function Calling
• RAG Guide
• Code Embeddings

Next Steps

For SDK patterns, see mistral-sdk-patterns. For agents, see mistral-webhooks-events.