mistral-webhooks-events

name	mistral-webhooks-events
description	Implement event handling patterns for Mistral AI integrations. Use when building async workflows, implementing queues, or handling long-running Mistral AI operations. Trigger with phrases like "mistral events", "mistral async", "mistral queue", "mistral background jobs", "mistral webhook".
allowed-tools	Read, Write, Edit, Bash(curl:*)
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Mistral AI Events & Async Patterns

Overview

Implement async patterns and event handling for Mistral AI integrations. Note: Mistral AI does not have native webhooks, so this skill covers async patterns and event-driven architectures.

Prerequisites

• Mistral AI SDK installed
• Queue system (Redis, SQS, etc.) for async processing
• Event emitter or pub/sub for notifications
• Background job processor (BullMQ, etc.)

Instructions

Step 1: Event-Driven Chat Architecture

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

// Custom event types
interface MistralEvents {
  'chat:start': { requestId: string; model: string; timestamp: Date };
  'chat:chunk': { requestId: string; content: string; index: number };
  'chat:complete': { requestId: string; fullResponse: string; usage: any };
  'chat:error': { requestId: string; error: Error };
}

class MistralEventEmitter extends EventEmitter {
  private client: Mistral;

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

   () {
    .(, { requestId, model, :  () });

     {
       stream =  ...({ model, messages });

       fullResponse = ;
       index = ;

        ( event  stream) {
         content = event.?.?.[]?.?.;
         (content) {
          fullResponse += content;
          .(, { requestId, content, : index++ });
        }
      }

       usage = { : fullResponse. /  }; 
      .(, { requestId, fullResponse, usage });

       fullResponse;
    }  (error) {
      .(, { requestId, : error   });
       error;
    }
  }
}


 mistral =  ();

mistral.(,  {
  .();
});

mistral.(,  {
  process..(content);
});

mistral.(,  {
  .();
});

mistral.(,  {
  .(, error.);
});

 mistral.(, [{ : , :  }]);

Step 2: Background Job Processing with BullMQ

import { Queue, Worker, Job } from 'bullmq';
import Mistral from '@mistralai/mistralai';
import Redis from 'ioredis';

const connection = new Redis(process.env.REDIS_URL);

// Define job types
interface ChatJob {
  id: string;
  messages: Array<{ role: string; content: string }>;
  model: string;
  callback?: string; // Webhook URL to call on completion
}

// Create queue
const chatQueue = new Queue<ChatJob>('mistral-chat', { connection });

// Create worker
const chatWorker = new Worker<ChatJob>(
  'mistral-chat',
  async (job: Job<ChatJob>) => {
    const client = new ({ : process..! });

     response =  client..({
      : job..,
      : job..,
    });

     result = {
      : job.,
      : job..,
      : response.?.[]?.?.,
      : response.,
      :  ().(),
    };

    
     (job..) {
       (job.., {
        : ,
        : { :  },
        : .(result),
      });
    }

     result;
  },
  {
    connection,
    : ,
    : {
      : ,
      : , 
    },
  }
);


chatWorker.(,  {
  .(, result);
});

chatWorker.(,  {
  .(, err.);
});


   () {
   body =  request.();

   job =  chatQueue.(, {
    : crypto.(),
    : body.,
    : body. || ,
    : body.,
  }, {
    : ,
    : { : , :  },
  });

   .({
    : job.,
    : ,
    : ,
  });
}

Step 3: Webhook Notification System

import crypto from 'crypto';

interface WebhookPayload {
  event: string;
  timestamp: string;
  data: any;
}

class WebhookNotifier {
  private secret: string;

  constructor(secret: string) {
    this.secret = secret;
  }

  private sign(payload: string): string {
    return crypto
      .createHmac('sha256', this.secret)
      .update(payload)
      .digest('hex');
  }

  async notify(url: string, event: string, data: any): Promise<boolean> {
    const payload: WebhookPayload = {
      event,
      timestamp: new Date().toISOString(),
      data,
    };

    const body = .(payload);
     signature = .(body);

     {
       response =  (url, {
        : ,
        : {
          : ,
          : signature,
          : payload.,
        },
        body,
      });

       response.;
    }  (error) {
      .(, error);
       ;
    }
  }
}


 notifier =  (process..!);


 notifier.(
  ,
  ,
  {
    : ,
    : response.?.[]?.?.,
    : response.,
  }
);

Step 4: Server-Sent Events (SSE) for Real-Time Updates

// api/chat/stream/route.ts
import Mistral from '@mistralai/mistralai';

export async function POST(request: Request) {
  const { messages } = await request.json();

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

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

  const encoder = new TextEncoder();

  const readable = new ReadableStream({
    async start(controller) {
      // Send initial event
      controller.enqueue(encoder.encode(`event: start\ndata: {"status":"started"}\n\n`));

      try {
        let tokenCount = 0;
        for await (const event of stream) {
          const content = event.data?.choices?.[]?.?.;
           (content) {
            tokenCount++;
            controller.(
              encoder.()
            );
          }
        }

        
        controller.(
          encoder.()
        );
      }  (: ) {
        controller.(
          encoder.()
        );
      }

      controller.();
    },
  });

    (readable, {
    : {
      : ,
      : ,
      : ,
    },
  });
}

Step 5: Client-Side SSE Consumption

// Client-side JavaScript
function streamChat(messages: any[]): EventSource {
  const eventSource = new EventSource('/api/chat/stream', {
    // Note: EventSource doesn't support POST directly
    // Use fetch with ReadableStream instead, or use a library
  });

  eventSource.addEventListener('start', (e) => {
    console.log('Stream started');
  });

  eventSource.addEventListener('chunk', (e) => {
    const { content } = JSON.parse(e.data);
    process.stdout.write(content);
  });

  eventSource.addEventListener('complete', (e) => {
    const { totalTokens } = JSON.parse(e.data);
    console.log(`\nComplete. Tokens: ${totalTokens}`);
    eventSource.close();
  });

  eventSource.addEventListener('error', (e) => {
    console.(, e);
    eventSource.();
  });

   eventSource;
}

Output

• Event-driven Mistral AI integration
• Background job processing
• Webhook notification system
• Real-time streaming with SSE

Error Handling

Issue	Cause	Solution
Job stuck in queue	Worker crashed	Implement job timeout and retry
Webhook failed	Network/auth issue	Retry with exponential backoff
SSE disconnected	Client timeout	Implement reconnection logic
Event backpressure	Too many events	Implement buffering

Examples

Python Async Pattern

import asyncio
from mistralai import Mistral

async def process_batch(prompts: list[str]):
    client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))

    async def process_one(prompt: str):
        response = await client.chat.complete_async(
            model="mistral-small-latest",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.choices[0].message.content

    results = await asyncio.gather(*[process_one(p) for p in prompts])
    return results

Resources

• BullMQ Documentation
• Server-Sent Events MDN
• Mistral AI Streaming

Next Steps

For performance optimization, see mistral-performance-tuning.