gemini-api-integration

name	gemini-api-integration
type	reference
description	Use when integrating Google Gemini API into projects. Covers model selection, multimodal inputs, streaming, function calling, and production best practices.
paths	["**/*.py","**/*.ts","**/google*","**/gemini*","**/vertex*"]
effort	3
allowed-tools	Read, Glob, Grep, Write, Edit, Bash
user-invocable	true
when_to_use	When integrating Google Gemini API into projects for text generation, multimodal inputs, streaming, or function calling

Gemini API Integration

Overview

This skill guides AI agents through integrating Google Gemini API into applications — from basic text generation to advanced multimodal, function calling, and streaming use cases. It covers the full Gemini SDK lifecycle with production-grade patterns.

When to Use This Skill

• Use when setting up Gemini API for the first time in a Node.js, Python, or browser project
• Use when implementing multimodal inputs (text + image/audio/video)
• Use when adding streaming responses to improve perceived latency
• Use when implementing function calling / tool use with Gemini
• Use when optimizing model selection (Flash vs Pro vs Ultra) for cost and performance
• Use when debugging Gemini API errors, rate limits, or quota issues

Step-by-Step Guide

1. Installation & Setup

Node.js / TypeScript:

npm install @google/generative-ai

Python:

pip install google-generativeai

Set your API key securely:

export GEMINI_API_KEY="your-api-key-here"

2. Basic Text Generation

Node.js:

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

const result = await model.generateContent("Explain async/await in JavaScript");
console.log(result.response.text());

Python:

import google.generativeai as genai
import os

genai.configure(api_key=os.environ["GEMINI_API_KEY"])
model = genai.GenerativeModel("gemini-1.5-flash")

response = model.generate_content("Explain async/await in JavaScript")
print(response.text)

3. Streaming Responses

const result = await model.generateContentStream("Write a detailed blog post about AI");

for await (const chunk of result.stream) {
  process.stdout.write(chunk.text());
}

4. Multimodal Input (Text + Image)

import fs from "fs";

const imageData = fs.readFileSync("screenshot.png");
const imagePart = {
  inlineData: {
    data: imageData.toString("base64"),
    mimeType: "image/png",
  },
};

const result = await model.generateContent(["Describe this image:", imagePart]);
console.log(result.response.text());

5. Function Calling / Tool Use

const tools = [{
  functionDeclarations: [{
    name: "get_weather",
    description: "Get current weather for a city",
    parameters: {
      type: "OBJECT",
      properties: {
        city: { type: "STRING", description: "City name" },
      },
      required: ["city"],
    },
  }],
}];

const model = genAI.getGenerativeModel({ model: "gemini-1.5-pro", tools });
const result = await model.generateContent("What's the weather in Mumbai?");

const call = result.response.functionCalls()?.[0];
if (call) {
  // Execute the actual function
  const weatherData = await getWeather(call.args.city);
  // Send result back to model
}

6. Multi-turn Chat

const chat = model.startChat({
  history: [
    { role: "user", parts: [{ text: "You are a helpful coding assistant." }] },
    { role: "model", parts: [{ text: "Sure! I'm ready to help with code." }] },
  ],
});

const response = await chat.sendMessage("How do I reverse a string in Python?");
console.log(response.response.text());

7. Model Selection Guide

Model	Best For	Speed	Cost
gemini-1.5-flash	High-throughput, cost-sensitive tasks	Fast	Low
gemini-1.5-pro	Complex reasoning, long context	Medium	Medium
gemini-2.0-flash	Latest fast model, multimodal	Very Fast	Low
gemini-2.0-pro	Most capable, advanced tasks	Slow	High

Best Practices

• ✅ Do: Use gemini-1.5-flash for most tasks — it's fast and cost-effective
• ✅ Do: Always stream responses for user-facing chat UIs to reduce perceived latency
• ✅ Do: Store API keys in environment variables, never hard-code them
• ✅ Do: Implement exponential backoff for rate limit (429) errors
• ✅ Do: Use systemInstruction to set persistent model behavior
• ❌ Don't: Use gemini-pro for simple tasks — Flash is cheaper and faster
• ❌ Don't: Send large base64 images inline for files > 20MB — use File API instead
• ❌ Don't: Ignore safety ratings in responses for production apps

Error Handling

try {
  const result = await model.generateContent(prompt);
  return result.response.text();
} catch (error) {
  if (error.status === 429) {
    // Rate limited — wait and retry with exponential backoff
    await new Promise(r => setTimeout(r, 2 ** retryCount * 1000));
  } else if (error.status === 400) {
    // Invalid request — check prompt or parameters
    console.error("Invalid request:", error.message);
  } else {
    throw error;
  }
}

Troubleshooting

Problem: API_KEY_INVALID error Solution: Ensure GEMINI_API_KEY environment variable is set and the key is active in Google AI Studio.

Problem: Response blocked by safety filters Solution: Check result.response.promptFeedback.blockReason and adjust your prompt or safety settings.

Problem: Slow response times Solution: Switch to gemini-1.5-flash and enable streaming. Consider caching repeated prompts.

Problem: RESOURCE_EXHAUSTED (quota exceeded) Solution: Check your quota in Google Cloud Console. Implement request queuing and exponential backoff.