personalized learning path generator student AI

# Instance #426: personalized learning path generator student AI
# Sovereign Python Client for Pak-LLM & coder.pak-llm.com
import os
import requests

PAK_LLM_API_KEY = os.getenv("PAK_LLM_API_KEY", "pk_live_sovereign_token")
ENDPOINT = "https://pak-llm.com/api/v1/chat/completions"

payload = {
    "model": "pak-llm-v3-ummah",
    "messages": [
        {"role": "system", "content": "Sovereign AI Mode: Enforce OWASP/NIST/ISO/SBP compliance and Nastaliq BPE optimization."},
        {"role": "user", "content": "Execute workflow for: personalized learning path generator student AI"}
    ],
    "metadata": {
        "matrixInstanceId": 426,
        "slug": "personalized-learning-path-generator-student-ai",
        "dataResidency": "PK-KHI-SOVEREIGN"
    }
}

response = requests.post(
    ENDPOINT,
    headers={"Authorization": f"Bearer {PAK_LLM_API_KEY}", "Content-Type": "application/json"},
    json=payload,
    timeout=30,
)
print(response.json())

# FastAPI Sovereign Microservice for: personalized learning path generator student AI (Instance #426)
from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel
import httpx

app = FastAPI(title="Pak-LLM Sovereign Gateway - Instance #426")

class SovereignQuery(BaseModel):
    prompt: str = "personalized learning path generator student AI"
    model: str = "pak-llm-v2-azaadi"
    zero_retention: bool = True

@app.post("/v1/sovereign/personalized-learning-path-generator-student-ai")
async def run_sovereign_pipeline(body: SovereignQuery, x_api_key: str = Header(...)):
    if not x_api_key.startswith("pk_"):
        raise HTTPException(status_code=403, detail="Invalid Sovereign API Key")
    async with httpx.AsyncClient(timeout=30.0) as client:
        res = await client.post(
            "https://pak-llm.com/api/v1/chat/completions",
            headers={"Authorization": f"Bearer {x_api_key}"},
            json={"model": body.model, "messages": [{"role": "user", "content": body.prompt}]},
        )
        return {"instanceId": 426, "keyword": "personalized learning path generator student AI", "result": res.json()}

// Next.js Server Integration for Instance #426: personalized learning path generator student AI
export async function invokePakLlmInstance426(userPrompt?: string) {
  const res = await fetch("https://pak-llm.com/api/v1/chat/completions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${process.env.PAK_LLM_API_KEY}`,
    },
    body: JSON.stringify({
      model: "pak-llm-v3-ummah",
      messages: [
        { role: "system", content: "Target Topic: personalized learning path generator student AI (Instance #426)" },
        { role: "user", content: userPrompt ?? "personalized learning path generator student AI" },
      ],
    }),
    cache: "no-store",
  });
  return res.json();
}

<?php
// PHP / Laravel Sovereign Connector for Instance #426: personalized learning path generator student AI
$ch = curl_init("https://pak-llm.com/api/v1/chat/completions");
$payload = json_encode([
    "model" => "pak-llm-v2-azaadi",
    "messages" => [
        ["role" => "system", "content" => "Pak-LLM Enterprise Connector (#426)"],
        ["role" => "user", "content" => "personalized learning path generator student AI"]
    ],
    "residency" => "PK"
]);

curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_POST => true,
    CURLOPT_HTTPHEADER => [
        "Content-Type: application/json",
        "Authorization: Bearer " . getenv("PAK_LLM_API_KEY")
    ],
    CURLOPT_POSTFIELDS => $payload,
    CURLOPT_TIMEOUT => 30,
]);

$result = curl_exec($ch);
curl_close($ch);
echo $result;