Gemini Native SDK-Anbindung

npm install @google/genai
# Erfordert >= 2.0.0; empfohlen: latest

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({
  apiKey: "sk-***", // Ersetzen Sie dies durch Ihren bei AIHubMix generierten API Key
  httpOptions: {
    baseUrl: "https://aihubmix.com/gemini",
  },
});

const interaction = await ai.interactions.create({
  model: "gemini-3.5-flash",
  input: "Erklaere Quantencomputing in einem Satz",
});

console.log(interaction.output_text);
console.log(interaction.usage);
// { total_tokens, total_input_tokens, total_output_tokens, ... }

import fs from "node:fs";

const interaction = await ai.interactions.create({
  model: "gemini-3.1-flash-image",
  input: "A translucent banana-shaped glass lamp on a white desk, soft studio lighting.",
  response_modalities: ["text", "image"],
  response_format: { type: "image", aspect_ratio: "1:1", image_size: "1K" },
});

// Methode 1: output_image-Komfort-Eigenschaft (letztes generiertes Bild)
if (interaction.output_image?.data) {
  fs.writeFileSync("output.png", Buffer.from(interaction.output_image.data, "base64"));
}

// Methode 2: Steps durchlaufen, geeignet fuer mehrstufige gemischte Ausgaben
for (const step of interaction.steps ?? []) {
  for (const block of step.content ?? []) {
    if (block.type === "image" && block.data) {
      fs.writeFileSync("output.png", Buffer.from(block.data, "base64"));
    }
  }
}

const response = await ai.models.embedContent({
  model: "gemini-embedding-2-preview",
  contents: "What is the meaning of life?",
  config: {
    outputDimensionality: 768, // Optional: Ausgabedimension angeben (128-3072), Standard 3072
  },
});

console.log("dimensions:", response.embeddings[0].values.length); // 768

const longDocument = "Langer Text, der wiederholt referenziert wird...".repeat(500);

const cache = await ai.caches.create({
  model: "gemini-3.5-flash",
  config: {
    contents: longDocument,
    ttl: "300s",
  },
});

console.log("CachedContent name:", cache.name);
// Format: cachedContents/xxx

const response = await ai.models.generateContent({
  model: "gemini-3.5-flash",
  contents: "Fassen Sie die Kernaussagen des obigen Dokuments zusammen",
  config: { cachedContent: cache.name },
});

console.log(response.text);
console.log("cached tokens:", response.usageMetadata?.cachedContentTokenCount);

// CachedContent-Metadaten abfragen
const info = await ai.caches.get({ name: cache.name });
console.log("model:", info.model, "expireTime:", info.expireTime);

// Cache loeschen
await ai.caches.delete({ name: cache.name });