Groq Events & Async Patterns Skill

Groq Events & Async Patterns

Overview

Build event-driven architectures around Groq's inference API. Groq does not provide native webhooks, but its sub-second latency enables unique patterns: real-time SSE streaming, batch processing with callbacks, queue-based pipelines, and event processors that use Groq as an LLM classification/extraction engine.

Prerequisites

groq-sdk installed, GROQ_API_KEY set
Queue system for batch patterns (BullMQ, Redis, SQS)
Understanding of Server-Sent Events (SSE) for streaming

Instructions

Step 1: SSE Streaming Endpoint

import Groq from "groq-sdk";
import express from "express";

const groq = new Groq();
const app = express();
app.use(express.json());

app.post("/api/chat/stream", async (req, res) => {
  const { messages, model = "llama-3.3-70b-versatile" } = req.body;

  res.writeHead(200, {
    "Content-Type": "text/event-stream",
    "Cache-Control": "no-cache",
    Connection: "keep-alive",
    "X-Accel-Buffering": "no",  // Disable nginx buffering
  });

  try {
    const stream = await groq.chat.completions.create({
      model,
      messages,
      stream: true,
      max_tokens: 2048,
    });

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

    res.write(`data: ${JSON.stringify({ type: "done" })}\n\n`);
  } catch (err: any) {
    res.write(`data: ${JSON.stringify({ type: "error", message: err.message })}\n\n`);
  }

  res.end();
});

Step 2: Batch Processing with BullMQ

import { Queue, Worker } from "bullmq";
import Groq from "groq-sdk";
import { randomUUID } from "crypto";

const groq = new Groq();
const groqQueue = new Queue("groq-batch", { connection: { host: "localhost" } });

// Enqueue a batch of prompts
async function submitBatch(
  prompts: string[],
  callbackUrl: string,
  model = "llama-3.1-8b-instant"
): Promise<string> {
  const batchId = randomUUID();

  for (const [index, prompt] of prompts.entries()) {
    await groqQueue.add("inference", {
      batchId,
      index,
      prompt,
      model,
      callbackUrl,
      total: prompts.length,
    });
  }

  return batchId;
}

// Worker processes queue items
const worker = new Worker("groq-batch", async (job) => {
  const { prompt, model, callbackUrl, batchId, index, total } = job.data;

  const completion = await groq.chat.completions.create({
    model,
    messages: [{ role: "user", content: prompt }],
    temperature: 0,
  });

  const result = {
    batchId,
    index,
    total,
    content: completion.choices[0].message.content,
    model: completion.model,
    usage: completion.usage,
  };

  // Fire callback on completion
  if (callbackUrl) {
    await fetch(callbackUrl, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({
        event: "groq.batch.item_completed",
        data: result,
      }),
    });
  }

  return result;
}, {
  connection: { host: "localhost" },
  concurrency: 5,
  limiter: { max: 25, duration: 60_000 },  // 25 RPM to stay under limits
});

Step 3: Webhook Event Processor

// Use Groq as an LLM engine to process incoming webhook events
async function processWebhookEvent(event: any) {
  // Classify event type and extract key data using fast 8B model
  const classification = await groq.chat.completions.create({
    model: "llama-3.1-8b-instant",
    messages: [
      {
        role: "system",
        content: `Classify this webhook event and extract key fields.
Respond with JSON: {"type": string, "priority": "high"|"medium"|"low", "summary": string, "action": string}`,
      },
      { role: "user", content: JSON.stringify(event) },
    ],
    response_format: { type: "json_object" },
    temperature: 0,
    max_tokens: 200,
  });

  return JSON.parse(classification.choices[0].message.content!);
}

// Express webhook receiver
app.post("/webhook", async (req, res) => {
  const event = req.body;

  // Acknowledge immediately (don't block the sender)
  res.status(202).json({ received: true });

  // Process asynchronously with Groq
  const analysis = await processWebhookEvent(event);

  if (analysis.priority === "high") {
    await notifySlack(`High priority event: ${analysis.summary}`);
  }

  await logEvent({ raw: event, analysis });
});

Step 4: Scheduled Health Monitor

// Periodic Groq API health check with latency tracking
async function monitorGroqHealth() {
  const models = ["llama-3.1-8b-instant", "llama-3.3-70b-versatile"];
  const results: Record<string, any> = {};

  for (const model of models) {
    const start = performance.now();
    try {
      const completion = await groq.chat.completions.create({
        model,
        messages: [{ role: "user", content: "OK" }],
        max_tokens: 1,
      });
      results[model] = {
        status: "ok",
        latencyMs: Math.round(performance.now() - start),
        tokensPerSec: completion.usage!.completion_tokens / ((completion.usage as any).completion_time || 1),
      };
    } catch (err: any) {
      results[model] = {
        status: "error",
        latencyMs: Math.round(performance.now() - start),
        error: `${err.status}: ${err.message}`,
      };
    }
  }

  return results;
}

// Run every 5 minutes
setInterval(() => monitorGroqHealth().then(console.log), 5 * 60_000);

Step 5: Python Async Batch Processing

import asyncio
from groq import AsyncGroq

client = AsyncGroq()

async def process_batch(prompts: list[str], model: str = "llama-3.1-8b-instant"):
    """Process prompts concurrently with rate limit awareness."""
    semaphore = asyncio.Semaphore(5)  # Max 5 concurrent requests

    async def process_one(prompt: str):
        async with semaphore:
            return await client.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                max_tokens=256,
            )

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

    return [
        r.choices[0].message.content if not isinstance(r, Exception) else str(r)
        for r in results
    ]

Event Pattern Summary

| Pattern | Groq Model | Latency | Use Case | |---------|-----------|---------|----------| | SSE streaming | llama-3.3-70b-versatile | ~200ms TTFT | Real-time chat | | Batch queue | llama-3.1-8b-instant | ~80ms TTFT | Document processing | | Webhook processor | llama-3.1-8b-instant | ~80ms TTFT | Event classification | | Health monitor | llama-3.1-8b-instant | ~80ms TTFT | Uptime tracking |

Error Handling

| Issue | Cause | Solution | |-------|-------|----------| | SSE disconnect | Client timeout or network | Implement reconnection with last-event-id | | Batch item fails | Rate limit or model error | Queue retry with exponential backoff | | Webhook timeout | Processing takes too long | Acknowledge immediately (202), process async | | Health check 429 | Monitoring consuming quota | Reduce check frequency, use smallest model |

Resources

Next Steps

For performance optimization, see groq-performance-tuning.

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