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Note: For detailed information about the available models, including their capabilities and rate limits, see Gemini models. If 'generateContent' in m.supported_generation_methods: gemini-pro-vision: optimized for text-and-images prompts.gemini-pro: optimized for text-only prompts.Use list_models to see the available Gemini models: GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY') # Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable.

Put the key in the GOOGLE_API_KEY environment variable (the SDK will automatically pick it up from there).Once you have the API key, pass it to the SDK.

In Colab, add the key to the secrets manager under the '🔑' in the left panel. If you don't already have one, create a key with one click in Google AI Studio. Return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))īefore you can use the Gemini API, you must first obtain an API key. Install the dependency using pip: pip install -q -U google-generativeai Import packages The Python SDK for the Gemini API, is contained in the google-generativeai package.

An installation of jupyter to run the notebook.

You can run this quickstart in Google Colab, which runs this notebook directly in the browser and does not require additional environment configuration.Īlternatively, to complete this quickstart locally, ensure that your development environment meets the following requirements:

Use embeddings for large language models.

Use Gemini for multi-turn conversations (chat).

Generate text responses from multimodal inputs (text and images).

Generate text responses from text inputs.

Set up your development environment and API access to use Gemini.

In this quickstart, you will learn how to:

Key for pycharm professional how to#

This quickstart demonstrates how to use the Python SDK for the Gemini API, which gives you access to Google's Gemini large language models.