google-generativeai & google-genai: a detailed comparison with integration guides

The two libraries, google-generativeai and google-genai, are both related to Google’s generative AI models, but they serve different purposes and have distinct features:

google-generativeai

• Purpose: This is the official Python SDK for the Gemini API, providing access to Google’s Gemini models created by DeepMind.
• Capabilities: It supports multimodal capabilities, allowing you to work seamlessly across text, images, and code.
• Integration: Designed for direct integration with the Gemini API, offering high-level API client tools.
• Features: Provides access to the original Gemini models and can be used with Gemini 2 models, though with a limited feature set.
• Use Cases: Ideal for projects requiring direct access to Google’s multimodal models, such as advanced AI research and development.

google-genai

• Purpose: This is a wrapper around the official Google AI libraries and APIs, simplifying the use of Google’s generative AI models.
• Capabilities: Provides a simplified interface for accessing Google’s generative AI models, including recent additions like the multimodal live API (audio + video streaming), improved tool usage (code execution, function calling, and integrated Google search grounding), and media generation (Imagen).
• Integration: Part of the LangChain libraries, making it easier to integrate with other tools and services.
• Features: Offers a more user-friendly approach to using Google’s generative AI models, with a focus on ease of use and integration.
• Use Cases: Suitable for developers looking for a simplified way to access and use Google’s generative AI models, especially in applications that require quick prototyping and integration.

Key Differences

• Complexity: google-generativeai is more complex and offers direct access to the Gemini API, while google-genai simplifies the process by providing a wrapper around the official libraries.
• Features: google-genai includes additional features like the multimodal live API and improved tool usage, which are not fully supported by google-generativeai.
• Integration: google-generativeai is designed for direct integration with the Gemini API, whereas google-genai is part of the LangChain libraries, making it easier to integrate with other tools and services.

In summary, if you need direct access to Google’s multimodal models and are comfortable with a more complex setup, google-generativeai is the way to go. On the other hand, if you prefer a simplified interface and easier integration with other tools, google-genai is a better choice.

To use these services, you need to get an API key from Google (yes, you can use the same API service keys for both. They have free tiers and pay tiers). Follow these steps:

1. Go to the Google Cloud Console:

• Visit the Google Cloud Console.

1. Create a New Project:

• If you don’t already have a project, click on the project dropdown at the top of the page and select “New Project.”
• Give your project a name and click “Create.”

1. Enable APIs and Services:

• In the left-hand menu, navigate to “APIs & Services” and then “Library.”
• Search for the API you need (e.g., Google Maps, Google Cloud Storage) and click on it.
• Click “Enable” to enable the API for your project.

1. Create Credentials:

• Go to “APIs & Services” > “Credentials” in the left-hand menu.
• Click on “Create Credentials” and select “API Key.”
• Your new API key will be generated and displayed. You can copy it for use in your applications.

1. Restrict Your API Key (Optional but recommended):

• Click on the “Edit” icon next to your API key.
• Under “Key restrictions,” you can specify which websites, IP addresses, or apps can use this key.
• Under “API restrictions,” you can restrict the key to specific APIs to enhance security.

Examples:

Here are some examples of how you might use google-generativeai and google-genai in different scenarios:

google-generativeai

This library is used for direct interaction with the Gemini API, enabling more complex and multimodal tasks.

Example 1: Text Generation

import google.generativeai as gemini

# Initialize the Gemini API client
client = gemini.Client(api_key='YOUR_API_KEY')

# Generate text using the Gemini model
response = client.generate_text(prompt='Once upon a time in a faraway land')
print(response['text'])

Example 2: Image Generation

import google.generativeai as gemini

# Initialize the Gemini API client
client = gemini.Client(api_key='YOUR_API_KEY')

# Generate an image based on a textual description
response = client.generate_image(prompt='A serene lake surrounded by mountains during sunset')
image_data = response['image']

# Save or display the generated image
with open('generated_image.png', 'wb') as f:
    f.write(image_data)

google-genai

This library simplifies access to Google’s generative AI models and is part of the LangChain libraries.

Example 1: Simplified Text Generation

import google_genai as genai

# Initialize the generative AI client
client = genai.Client(api_key='YOUR_API_KEY')

# Generate text using a simplified interface
response = client.generate(prompt='Once upon a time in a faraway land')
print(response['text'])

Example 2: Code Execution with Improved Tool Usage

import google_genai as genai

# Initialize the generative AI client
client = genai.Client(api_key='YOUR_API_KEY')

# Generate code based on a prompt
code_prompt = 'Write a Python function to calculate the factorial of a number'
response = client.generate(prompt=code_prompt)
print(response['text'])

# Execute the generated code (be cautious with executing generated code)
exec(response['text'])

Key Differences in Usage

• Complexity: google-generativeai involves more detailed setup and direct interaction with the Gemini API, making it suitable for advanced users and specific tasks.
• Simplification: google-genai abstracts some of the complexity, making it easier and faster to get started with generative AI tasks, ideal for developers looking for quick prototyping.

These examples demonstrate how you can leverage both libraries for different tasks and use cases.

More examples can be found in their cookbook at https://github.com/google-gemini/cookbook