Google Gen AI Python SDK: A Full Information

Generative AI fashions are altering how we create content material, whether or not it’s textual content, photographs, video, or code. With Google’s Gen AI Python SDK, now you can entry and work together with Google’s generative AI fashions in your Python purposes extra simply, along with utilizing the Gemini Developer API and Vertex AI APIs. Which means builders can extra readily create purposes, together with chatbots, content material turbines, or inventive instruments. On this article, we are going to cowl every thing it’s essential to know to get began utilizing the Google Gen AI Python SDK.

Additionally learn: Construct an LLM Mannequin utilizing Google Gemini API

What’s the Google Gen AI Python SDK?

The Google Gen AI Python SDK is a consumer library for builders to make use of Google’s generative AI talents simply utilizing Python. It supplies:

• Assist for Gemini Developer API (Google’s superior textual content and multimodal generative fashions)
• Integration with Vertex AI APIs for enterprise-scale AI workloads
• Assist for producing textual content, photographs, movies, embeddings, chat conversations, and extra
• Instruments for file administration, caching, and async help
• Superior perform calling and schema enforcement options

This SDK additionally abstracts a lot of the complexity round API calls and lets you deal with constructing AI-powered purposes.

Set up

Putting in the SDK is straightforward. Run:

pip set up google-genai

The above command will set up the Google Gen AI Python SDK bundle utilizing pip. This command downloads every thing you want for the Python surroundings to start out up the Google generative AI providers, together with the sources and all dependencies.

Imports and Consumer Setup

Upon getting put in the SDK, create a Python file and import the SDK:

from google import genai from google.genai import sorts

The SDK has two modules – genai and kinds. The genai module creates a consumer used for API interplay, whereas the categories module has knowledge constructions and courses that function helpers used to construct requests and configure request parameters.

You’ll create an occasion of the consumer for every interplay with the Google generative AI fashions. You’ll instantiate the consumer with completely different strategies relying on the API you might be utilizing.

For the Gemini Developer API, you may instantiate the consumer by passing alongside your API key:

consumer = genai.Consumer(api_key='YOUR_GEMINI_API_KEY')

You instantiate the consumer you may work together with the Gemini Developer API by passing in your API key. This consumer will maintain the entry token and request administration.

Optionally available: Utilizing Google Cloud Vertex AI

consumer = genai.Consumer(    vertexai=True,    mission="your-project-id",    location='us-central1' )

If you’re going to use Google Cloud Vertex AI, you’ll initialise the consumer otherwise by specifying the mission ID and the placement.

Be aware: Utilizing Vertex AI is elective. You may create your mission ID right here.

If you don’t use Vertex AI, you may merely use the API key technique above.

API Model and Configuration

By default, the SDK makes use of beta endpoints to entry beta options. Nonetheless, if you wish to use secure APIs, you may specify the API model utilizing the http_options argument:

from google.genai import sorts consumer = genai.Consumer(    vertexai=True,    mission="your-project-id",    location='us-central1',    http_options=sorts.HttpOptions(api_version='v1') )

It’s as much as you the way you wish to proceed to stability stability with cutting-edge options.

Utilizing Surroundings Variables (Optionally available)

As a substitute of immediately passing keys, we should always first set surroundings variables:

Gemini Developer API:

export GEMINI_API_KEY='your-api-key'

Vertex AI:

export GOOGLE_GENAI_USE_VERTEXAI=true export GOOGLE_CLOUD_PROJECT='your-project-id' export GOOGLE_CLOUD_LOCATION='us-central1'

Then, initialize the consumer merely with:

consumer = genai.Consumer()

Google Gen AI Python SDK Use Circumstances

Listed here are the assorted methods you may put Google Gen AI Python SDK’s capabilities to make use of as soon as arrange.

Content material Technology

The first perform of the SDK is to generate AI content material. You present prompts in numerous kinds, resembling easy strings, structured content material, or advanced multimodal inputs.

Primary Textual content Technology

response = consumer.fashions.generate_content(    mannequin="gemini-2.0-flash-001",    contents="Why Does the solar rises from east" ) print(response.textual content)

Output

This sends a immediate to the mannequin and returns the generated reply.

Structured Content material Inputs

You may insert structured content material throughout numerous roles, like person or mannequin for chatbot, conversational, or multi-turn contexts.

from google.genai import sorts content material = sorts.Content material(    position="person",    components=[types.Part.from_text(text="Tell me a fun fact about work.")] ) response = consumer.fashions.generate_content(mannequin="gemini-2.0-flash-001", contents=content material) print(response.textual content)

Output

The SDK internally interprets many alternative enter sorts to a structured knowledge format for the mannequin.

File Add and Utilization

The Gemini Builders API lets you add recordsdata for the mannequin to course of. That is nice for summarization, or content material extraction:

file = consumer.recordsdata.add(file="/content material/sample_file.txt") response = consumer.fashions.generate_content(    mannequin="gemini-2.0-flash-001",    contents=[file, 'Please summarize this file.'] ) print(response.textual content)

Output

This is a perfect strategy for including AI performance to document-based duties.

Operate Calling

A novel functionality is the power to move Python features as “instruments” for the mannequin to invoke mechanically whereas producing the completion.

def get_current_weather(location: str) -> str:    return 'sunny' response = consumer.fashions.generate_content(    mannequin="gemini-2.0-flash-001",    contents="What's the climate like in Ranchi?",    config=sorts.GenerateContentConfig(instruments=[get_current_weather]) ) print(response.textual content)

Output

This permits dynamic, real-time knowledge integration inside AI responses.

Superior Configuration

You could have the power to customise technology with parameters resembling temperature, max_output_tokens, and security settings to handle randomness, size, and filter dangerous content material.

config = sorts.GenerateContentConfig(    temperature=0.3,    max_output_tokens=100,    safety_settings=[types.SafetySetting(category='HARM_CATEGORY_HATE_SPEECH', threshold='BLOCK_ONLY_HIGH')] ) response = consumer.fashions.generate_content(    mannequin="gemini-2.0-flash-001",    contents=""'Provide some encouraging phrases for somebody beginning a brand new journey.''',    config=config ) print(response.textual content)

Output

This will present granularity over content material high quality and security.

Multimedia Assist: Photos and Movies

The SDK lets you generate and edit photographs and generate movies (in preview).

• Generate photographs utilizing textual content prompts.
• Upscale or regulate photographs generated.
• Generate movies from textual content or photographs.

Instance of Picture Technology:

response = consumer.fashions.generate_images(    mannequin="imagen-3.0-generate-002",    immediate="A tranquil seaside with crystal-clear water and colourful seashells on the shore.",    config=sorts.GenerateImagesConfig(number_of_images=1) ) response.generated_images[0].picture.present()

Output

Instance of Video Technology:

import time operation = consumer.fashions.generate_videos(    mannequin="veo-2.0-generate-001",    immediate="A cat DJ spinning vinyl information at a futuristic nightclub with holographic beats.",    config=sorts.GenerateVideosConfig(number_of_videos=1, duration_seconds=5) ) whereas not operation.performed:    time.sleep(20)    operation = consumer.operations.get(operation) video = operation.response.generated_videos[0].video video.present()

Output:

This enables for inventive, multimodal AI apps.

Chat and Conversations

You can begin chat periods that protect context all through your messages:

chat = consumer.chats.create(mannequin="gemini-2.0-flash-001") response = chat.send_message('Inform me a narrative') print(response.textual content)

response = chat.send_message('Summarize that story in a single sentence') print(response.textual content)

That is helpful for creating conversational AI that remembers earlier dialogue.

Asynchronous Assist

All principal API strategies have async features for higher integration into async Python apps:

response = await consumer.aio.fashions.generate_content(    mannequin="gemini-2.0-flash-001",    contents="Inform a Horror story in 200 phrases." ) print(response.textual content)

Token Counting

Token counting tracks what number of tokens (items of textual content) are in your enter. This helps you keep inside mannequin limits and make cost-effective choices.

token_count = consumer.fashions.count_tokens(    mannequin="gemini-2.0-flash-001",    contents="Why does the sky have a blue hue as a substitute of different colours?" ) print(token_count)

Embeddings

Embeddings flip your textual content into numeric vectors that symbolize its which means, which can be utilized for search, clustering, and AI analysis.

embedding = consumer.fashions.embed_content(    mannequin="text-embedding-004",    contents="Why does the sky have a blue hue as a substitute of different colours?" ) print(embedding)

Utilizing the SDK, you may simply rely tokens and make embeddings to enhance and improve your AI purposes.

Conclusion

The Google Gen AI Python SDK is a strong, versatile software that permits builders to entry Google’s high generative AI fashions of their Python tasks. From textual content technology, chat, and chatbot, to picture/video technology, perform calling, and far more it supplies a sturdy characteristic set with easy interfaces. With a simple bundle set up, easy consumer configuration course of, and help for async programming and multimedia, the SDK makes constructing purposes that leverage AI considerably simpler. Whether or not you’re a newbie or seasoned developer, utilizing the SDK is comparatively painless however highly effective in terms of incorporating generative AI into your workflows.

Continuously Requested Questions