gemini-api-dev

name	gemini-api-dev
description	Guides the usage of the Google Gemini API with the Gen AI SDK. Covers common tasks like client initialization, multimodal usage (images, video, audio, files), chat, function calling, structured output, and safety settings. Supports Python, JS/TS, Go, Java, and C#.
compatibility	Requires Gemini API key or Google Cloud credentials.

Gemini API Development

Access Google's latest generative models (Gemini 3.1 Pro, Gemini 3 Flash, etc.) through the unified Google Gen AI SDK.

Core Directives

• Unified SDK: ALWAYS use the Gen AI SDK library (google-genai for Python, @google/genai for JS/TS, google.golang.org/genai for Go, com.google.genai:google-genai for Java, Google.GenAI for C#).
• Legacy SDKs: DO NOT use the legacy library google-generativeai.
• Model Selection: Prefer gemini-3.1-pro-preview for complex tasks and gemini-3-flash-preview for speed and efficiency.

Setup

Python

pip install google-genai

JavaScript/TypeScript

npm install @google/genai

Go

go get google.golang.org/genai

Java

• groupId: com.google.genai, artifactId: google-genai
• Latest version can be found here: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions (let's call it LAST_VERSION)
• Install in build.gradle:

implementation("com.google.genai:google-genai:${LAST_VERSION}")

C#/.NET

dotnet add package Google.GenAI

Initialization

Initialize the client using an API Key.

from google import genai

client = genai.Client(api_key='YOUR_API_KEY')

Common Tasks

Text Generation

response = client.models.generate_content(
    model='gemini-2.0-flash', 
    contents='Tell me a story about a brave knight.'
)
print(response.text)

Multimodal (Images, Video, Audio)

Gemini can process multiple modalities.

Image:

from PIL import Image
img = Image.open('image.jpg')
response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents=['Describe this image:', img]
)

Video/Audio/PDF: Upload files first using the File API before processing.

file = client.files.upload(path='video.mp4')
response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents=['Summarize this video:', file]
)

Chat sessions

Maintain state across multiple turns.

chat = client.chats.create(model='gemini-2.0-flash')
response = chat.send_message('Hi, I am Bob.')
response = chat.send_message('What is my name?')

Function Calling

Allow the model to interact with external tools or APIs.

def get_weather(location: str):
    return f"The weather in {location} is sunny."

client = genai.Client(api_key='YOUR_API_KEY')
response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents='What is the weather in London?',
    config={'tools': [get_weather]}
)

Structured Output (JSON)

Force the model to return data in a specific JSON schema.

from pydantic import BaseModel

class Recipe(BaseModel):
    name: str
    ingredients: list[str]

response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents='Give me a recipe for cookies',
    config={
        'response_mime_type': 'application/json',
        'response_schema': Recipe
    }
)
recipe = response.parsed

API spec & Documentation (source of truth)

When implementing or debugging API integration, refer to the official Gemini API documentation:

• Main Documentation Site: https://ai.google.dev/gemini-api/docs
• API Reference: https://ai.google.dev/api

[!TIP] Use the Developer Knowledge MCP Server: If the search_documents or get_document tools are available, use them to find and retrieve official documentation for Gemini API directly within the context. This is the preferred method for getting up-to-date API details and code snippets.