name: gemini-api-guides description: | Comprehensive reference for Google's Gemini API. Use when building applications with: (1) Gemini models (Gemini 3 Pro, 2.5 Flash/Pro/Flash-Lite) for text and multimodal generation, (2) Image generation (Imagen, Nano Banana), video (Veo 3.1), music (Lyria), (3) Function calling, structured outputs, and agentic workflows, (4) Built-in tools: Google Search, Maps, Code Execution, URL Context, Computer Use, File Search, (5) Live API for real-time voice/video streaming, (6) Long context (1M+ tokens), embeddings, document/audio/video understanding, (7) Batch API, context caching, safety settings. Triggers: "gemini api", "google ai", "genai sdk", "gemini model", "veo", "imagen", "nano banana", "lyria", "live api", "vertex ai"

Gemini API Skill

Build AI applications with Google's Gemini models and tools.

Quick Start

Installation

# Python
pip install google-genai

# JavaScript/Node.js
npm install @google/genai

# Go
go get google.golang.org/genai

Environment Setup

export GEMINI_API_KEY="your-api-key"

Basic Usage

Python:

from google import genai

client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Your prompt here"
)
print(response.text)

JavaScript:

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

const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
    model: "gemini-2.5-flash",
    contents: "Your prompt here"
});
console.log(response.text);

REST:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{"contents": [{"parts": [{"text": "Your prompt here"}]}]}'

Model Selection

Reference Documentation Index

Getting Started

Models & Pricing

Core Capabilities

Advanced Features

Tools

Live API (Real-time Streaming)

Guides

Troubleshooting & Migration

Large Files - Search Patterns

For large reference files (>30K), use grep to find specific sections:

image-generation-gemini.md (174K):

grep -n "## " references/image-generation-gemini.md  # List sections
grep -n "edit" references/image-generation-gemini.md  # Find editing info
grep -n "style" references/image-generation-gemini.md  # Find style transfer

veo.md (69K):

grep -n "## " references/veo.md  # List sections
grep -n "audio" references/veo.md  # Find audio generation info

models-overview.md (67K):

grep -n "gemini-3" references/models-overview.md
grep -n "context" references/models-overview.md

function-calling.md (54K):

grep -n "## " references/function-calling.md
grep -n "parallel" references/function-calling.md  # Parallel function calls

Common Patterns

Multimodal Input (Image + Text)

from google import genai
from google.genai import types

client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents=[
        types.Part.from_image(image_path),
        types.Part.from_text("Describe this image")
    ]
)

Function Calling

tools = [
    types.Tool(function_declarations=[{
        "name": "get_weather",
        "description": "Get weather for a location",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"]
        }
    }])
]

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What's the weather in Paris?",
    config=types.GenerateContentConfig(tools=tools)
)

Google Search Grounding

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What are the latest AI developments?",
    config=types.GenerateContentConfig(
        tools=[types.Tool(google_search=types.GoogleSearch())]
    )
)

Thinking Mode

response = client.models.generate_content(
    model="gemini-2.5-pro",
    contents="Solve this complex problem...",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget_tokens=10000)
    )
)

Streaming

for chunk in client.models.generate_content_stream(
    model="gemini-2.5-flash",
    contents="Write a story"
):
    print(chunk.text, end="")

Key Concepts

Tool Execution Flow

Built-in tools (Google Search, Code Execution): Executed by Google

1. Send prompt with tool config → Model executes tool → Response with grounded results

Custom tools (Function Calling): You execute

1. Send prompt with function declarations → Model returns function call JSON
2. You execute function, send result back → Model generates final response

Thought Signatures (Important)

• If using official SDKs with chat feature: Thought signatures are handled automatically. No action needed.
• If manually managing conversation history: Read thought-signatures.md for Gemini 3 Pro function calling requirements.

API Endpoints

Base URL: https://generativelanguage.googleapis.com