npm install koreshield openai

import { Koreshield } from "koreshield";
import OpenAI from "openai";

const koreshield = new Koreshield({
  apiKey: process.env.KORESHIELD_API_KEY
});

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

export async function secureChat(userMessage: string) {
  const scan = await koreshield.scan({
    content: userMessage,
    userId: "user-123"
  });

  if (scan.threat_detected) {
    throw new Error(`Threat detected: ${scan.threat_type}`);
  }

  const response = await openai.chat.completions.create({
    model: "gpt-5-mini",
    messages: [{ role: "user", content: userMessage }]
  });

  return response.choices[0].message.content;
}

const response = await fetch("http://localhost:8000/v1/chat/completions", {
  method: "POST",
  headers: { "content-type": "application/json" },
  body: JSON.stringify({
    model: "gpt-5-mini",
    messages: [{ role: "user", content: "Summarize the report." }]
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);

const stream = await openai.chat.completions.create({
  model: "gpt-5-mini",
  messages: [{ role: "user", content: "Write a release note." }],
  stream: true
});

for await (const chunk of stream) {
  const delta = chunk.choices[0]?.delta?.content || "";
  process.stdout.write(delta);
}

const tools: OpenAI.Chat.ChatCompletionTool[] = [
  {
    type: "function",
    function: {
      name: "search_database",
      description: "Search the database",
      parameters: {
        type: "object",
        properties: {
          query: { type: "string" }
        },
        required: ["query"]
      }
    }
  }
];

const response = await openai.chat.completions.create({
  model: "gpt-5-mini",
  messages: [{ role: "user", content: "Search for user 123" }],
  tools
});

const response = await openai.embeddings.create({
  model: "text-embedding-3-small",
  input: ["Document text", "Another chunk"]
});

const vectors = response.data.map(d => d.embedding);

await openai.beta.threads.messages.create("thread_id", {
  role: "user",
  content: "Summarize the incident"
});

const run = await openai.beta.threads.runs.create("thread_id", {
  assistant_id: "asst_xxxxx"
});

let status = await openai.beta.threads.runs.retrieve("thread_id", run.id);
while (status.status !== "completed") {
  await new Promise(resolve => setTimeout(resolve, 1000));
  status = await openai.beta.threads.runs.retrieve("thread_id", run.id);
}

{
  "model": "gpt-5-mini",
  "messages": [
    {"role": "system", "content": "You are a security analyst."},
    {"role": "user", "content": "Summarize the incident."},
    {"role": "assistant", "content": "Summary..."},
    {"role": "user", "content": "List next steps."}
  ]
}