# Groq Event Patterns — Full Implementation

Deep implementation walkthroughs for the batch, webhook-processor, health-monitor,
and Python async patterns. The SSE streaming endpoint (Step 1) lives in `SKILL.md`
because it is the minimal entry point; everything here is the queue-and-callback
depth you drill into once the streaming path works.

All examples assume `groq-sdk` (Node) or `groq` (Python) is installed and
`GROQ_API_KEY` is exported in the environment — the SDK reads it automatically, so
no key is ever hard-coded.

## Step 2: Batch Processing with BullMQ

Enqueue prompts, process them with a rate-limited worker, and fire a callback on
each completed item. The `limiter` keeps the worker under Groq's requests-per-minute
ceiling; `concurrency` bounds parallelism inside that window.

```typescript
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 the LLM engine that classifies and extracts fields from inbound webhook
events. Acknowledge the sender immediately with `202` so a slow classification never
blocks their retry logic, then process asynchronously.

```typescript
// 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

Periodically ping each model with a one-token request, tracking latency and
tokens/sec. Use the smallest model and the lowest cadence that still gives you a
useful signal so the monitor does not consume meaningful quota.

```typescript
// 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

The Python equivalent of Step 2 without the queue: bound concurrency with a
semaphore and let `asyncio.gather` fan out. `return_exceptions=True` keeps one failed
prompt from cancelling the whole batch.

```python
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
    ]
```