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mistral-webhooks-events 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".
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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 <jeremy@intentsolutions.io>
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' ;
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 .MISTRAL_API_KEY ! });
}
async chat (requestId : string , messages : any [], model = 'mistral-small-latest' ) {
this .emit ('chat:start' , { requestId, model, timestamp : new Date () });
try {
const stream = await this .client .chat .stream ({ model, messages });
let fullResponse = '' ;
let index = 0 ;
for await (const event of stream) {
const content = event.data ?.choices ?.[0 ]?.delta ?.content ;
if (content) {
fullResponse += content;
this .emit ('chat:chunk' , { requestId, content, index : index++ });
}
}
const usage = { totalTokens : fullResponse.length / 4 };
this .emit ('chat:complete' , { requestId, fullResponse, usage });
return fullResponse;
} catch (error) {
this .emit ('chat:error' , { requestId, error : error as Error });
throw error;
}
}
}
const mistral = new MistralEventEmitter ();
mistral.on ('chat:start' , ({ requestId, model } ) => {
console .log (`[${requestId} ] Starting chat with ${model} ` );
});
mistral.on ('chat:chunk' , ({ requestId, content } ) => {
process.stdout .write (content);
});
mistral.on ('chat:complete' , ({ requestId, usage } ) => {
console .log (`\n[${requestId} ] Complete. Tokens: ${usage.totalTokens} ` );
});
mistral.on ('chat:error' , ({ requestId, error } ) => {
console .error (`[${requestId} ] Error:` , error.message );
});
await mistral.chat ('req-123' , [{ role : 'user' , content : 'Hello!' }]);
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 );
interface ChatJob {
id : string ;
messages : Array <{ role : string ; content : string }>;
model : string ;
callback ?: string ;
}
const chatQueue = new Queue <ChatJob >('mistral-chat' , { connection });
const chatWorker = new Worker <ChatJob >(
'mistral-chat' ,
async (job : Job <ChatJob >) => {
const client = new Mistral ({ apiKey : process.env .MISTRAL_API_KEY ! });
const response = await client.chat .complete ({
model : job.data .model ,
messages : job.data .messages ,
});
const result = {
jobId : job.id ,
requestId : job.data .id ,
content : response.choices ?.[0 ]?.message ?.content ,
usage : response.usage ,
completedAt : new Date ().toISOString (),
};
if (job.data .callback ) {
await fetch (job.data .callback , {
method : 'POST' ,
headers : { 'Content-Type' : 'application/json' },
body : JSON .stringify (result),
});
}
return result;
},
{
connection,
concurrency : 5 ,
limiter : {
max : 10 ,
duration : 1000 ,
},
}
);
chatWorker.on ('completed' , (job, result ) => {
console .log (`Job ${job.id} completed:` , result);
});
chatWorker.on ('failed' , (job, err ) => {
console .error (`Job ${job?.id} failed:` , err.message );
});
export async function POST (request : Request ) {
const body = await request.json ();
const job = await chatQueue.add ('chat' , {
id : crypto.randomUUID (),
messages : body.messages ,
model : body.model || 'mistral-small-latest' ,
callback : body.callback ,
}, {
attempts : 3 ,
backoff : { type : 'exponential' , delay : 1000 },
});
return Response .json ({
jobId : job.id ,
status : 'queued' ,
statusUrl : `/api/jobs/${job.id} ` ,
});
}
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 = JSON .stringify (payload);
const signature = this .sign (body);
try {
const response = await fetch (url, {
method : 'POST' ,
headers : {
'Content-Type' : 'application/json' ,
'X-Webhook-Signature' : signature,
'X-Webhook-Timestamp' : payload.timestamp ,
},
body,
});
return response.ok ;
} catch (error) {
console .error ('Webhook failed:' , error);
return false ;
}
}
}
const notifier = new WebhookNotifier (process.env .WEBHOOK_SECRET !);
await notifier.notify (
'https://yourapp.com/webhooks/mistral' ,
'chat.completed' ,
{
requestId : 'req-123' ,
content : response.choices ?.[0 ]?.message ?.content ,
usage : response.usage ,
}
);
Step 4: Server-Sent Events (SSE) for Real-Time Updates
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 ) {
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 ?.[0 ]?.delta ?.content ;
if (content) {
tokenCount++;
controller.enqueue (
encoder.encode (`event: chunk\ndata: ${JSON .stringify({ content, tokenCount })} \n\n` )
);
}
}
controller.enqueue (
encoder.encode (`event: complete\ndata: ${JSON .stringify({ totalTokens: tokenCount })} \n\n` )
);
} catch (error : any ) {
controller.enqueue (
encoder.encode (`event: error\ndata: ${JSON .stringify({ error: error.message })} \n\n` )
);
}
controller.close ();
},
});
return new Response (readable, {
headers : {
'Content-Type' : 'text/event-stream' ,
'Cache-Control' : 'no-cache' ,
'Connection' : 'keep-alive' ,
},
});
}
Step 5: Client-Side SSE Consumption
function streamChat (messages : any [] ): EventSource {
const eventSource = new EventSource ('/api/chat/stream' , {
});
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 .error ('Stream error:' , e);
eventSource.close ();
});
return 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
Next Steps For performance optimization, see mistral-performance-tuning.