Mistral Core Workflow A

Mistral AI Core Workflow A: Chat Completions

Overview

Primary money-path workflow for Mistral AI: Chat completions with streaming support.

Prerequisites

• Completed mistral-install-auth setup
• Understanding of Mistral AI models
• Valid API credentials configured

Instructions

Step 1: Basic Chat Completion

TypeScript

import Mistral from '@mistralai/mistralai';

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

async function basicChat(userMessage: string): Promise<string> {
  const response = await client.chat.complete({
    model: 'mistral-small-latest',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: userMessage },
    ],
  });

  return response.choices?.[0]?.message?.content ?? '';
}

// Usage
const answer = await basicChat('What is the capital of France?');
console.log(answer); // Paris is the capital of France...

Step 2: Multi-Turn Conversation

interface Message {
  role: 'system' | 'user' | 'assistant';
  content: string;
}

class MistralConversation {
  private messages: Message[] = [];
  private client: Mistral;
  private model: string;

  constructor(systemPrompt: string, model = 'mistral-small-latest') {
    this.client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });
    this.model = model;
    this.messages.push({ role: 'system', content: systemPrompt });
  }

  async chat(userMessage: string): Promise<string> {
    this.messages.push({ role: 'user', content: userMessage });

    const response = await this.client.chat.complete({
      model: this.model,
      messages: this.messages,
    });

    const assistantMessage = response.choices?.[0]?.message?.content ?? '';
    this.messages.push({ role: 'assistant', content: assistantMessage });

    return assistantMessage;
  }

  getHistory(): Message[] {
    return [...this.messages];
  }

  clearHistory(): void {
    const systemMessage = this.messages[0];
    this.messages = [systemMessage];
  }
}

// Usage
const conv = new MistralConversation('You are a helpful coding assistant.');
const response1 = await conv.chat('How do I create a list in Python?');
const response2 = await conv.chat('How do I add items to it?');

Step 3: Streaming Responses

async function streamingChat(
  userMessage: string,
  onChunk: (chunk: string) => void
): Promise<string> {
  const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });

  const stream = await client.chat.stream({
    model: 'mistral-small-latest',
    messages: [
      { role: 'user', content: userMessage },
    ],
  });

  let fullResponse = '';

  for await (const event of stream) {
    const content = event.data?.choices?.[0]?.delta?.content;
    if (content) {
      fullResponse += content;
      onChunk(content);
    }
  }

  return fullResponse;
}

// Usage
const response = await streamingChat(
  'Write a short poem about coding.',
  (chunk) => process.stdout.write(chunk)
);

Step 4: With Generation Parameters

interface ChatConfig {
  temperature?: number;      // 0-1, default 0.7
  maxTokens?: number;        // Max tokens to generate
  topP?: number;             // Nucleus sampling, 0-1
  randomSeed?: number;       // For reproducibility
  safePrompt?: boolean;      // Enable safety checks
}

async function configuredChat(
  messages: Message[],
  config: ChatConfig = {}
): Promise<{ content: string; usage: any }> {
  const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });

  const response = await client.chat.complete({
    model: 'mistral-large-latest',
    messages,
    temperature: config.temperature ?? 0.7,
    maxTokens: config.maxTokens,
    topP: config.topP,
    randomSeed: config.randomSeed,
    safePrompt: config.safePrompt ?? false,
  });

  return {
    content: response.choices?.[0]?.message?.content ?? '',
    usage: response.usage,
  };
}

// Example: Deterministic output
const result = await configuredChat(
  [{ role: 'user', content: 'Summarize quantum computing in 2 sentences.' }],
  { temperature: 0, randomSeed: 42, maxTokens: 100 }
);

Step 5: Model Selection

type MistralModel =
  | 'mistral-large-latest'   // Most capable, complex reasoning
  | 'mistral-medium-latest'  // Balanced
  | 'mistral-small-latest'   // Fast, cost-effective
  | 'open-mistral-7b'        // Open source
  | 'open-mixtral-8x7b';     // Open source MoE

function selectModel(task: 'complex' | 'balanced' | 'fast'): MistralModel {
  switch (task) {
    case 'complex':
      return 'mistral-large-latest';
    case 'balanced':
      return 'mistral-medium-latest';
    case 'fast':
      return 'mistral-small-latest';
  }
}

Output

• Chat completions with configurable parameters
• Multi-turn conversation management
• Real-time streaming responses
• Model selection based on task

Error Handling

Examples

Express.js Streaming Endpoint

import express from 'express';
import Mistral from '@mistralai/mistralai';

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

app.post('/chat/stream', async (req, res) => {
  res.setHeader('Content-Type', 'text/event-stream');
  res.setHeader('Cache-Control', 'no-cache');
  res.setHeader('Connection', 'keep-alive');

  const stream = await client.chat.stream({
    model: 'mistral-small-latest',
    messages: req.body.messages,
  });

  for await (const event of stream) {
    const content = event.data?.choices?.[0]?.delta?.content;
    if (content) {
      res.write(`data: ${JSON.stringify({ content })}\n\n`);
    }
  }

  res.write('data: [DONE]\n\n');
  res.end();
});

Token Usage Tracking

let totalTokens = 0;

async function trackedChat(messages: Message[]): Promise<string> {
  const response = await client.chat.complete({
    model: 'mistral-small-latest',
    messages,
  });

  if (response.usage) {
    totalTokens += response.usage.totalTokens || 0;
    console.log(`Tokens used: ${response.usage.totalTokens}, Total: ${totalTokens}`);
  }

  return response.choices?.[0]?.message?.content ?? '';
}

Resources

• Mistral AI Chat Completions
• Mistral AI Models
• Mistral AI Streaming

Next Steps

For embeddings and function calling, see mistral-core-workflow-b.