This might entail learning new skills, pushing one's boundaries, confronting personal obstacles, and evolving as a person.Įthical and Moral Behavior: Some believe that the goal of life is to act ethically and morally. Self-realization and Personal Growth: The pursuit of self-realization and personal development is another common goal in life. This might entail pursuing a profession that benefits others, engaging in volunteer or charitable activities, generating art or literature, or inventing. Meaningful Contribution: Some believe that the purpose of life is to make a meaningful contribution to the world. This might entail locating pursuits that provide joy, establishing significant connections, caring for one's physical and mental health, and pursuing personal goals and interests. Happiness and Well-being: Many individuals believe that the goal of life is to attain personal happiness and well-being. While there is no universally recognized response, many ideas have been put forth, and the response is frequently dependent on individual ideas, beliefs, and life experiences. The query of life's purpose has perplexed people across centuries, cultures, and continents. To display formatted Markdown text, use the to_markdown function: to_markdown(response.text) In simple cases, the response.text accessor is all you need. Response = model.generate_content("What is the meaning of life?")ĬPU times: user 110 ms, sys: 12.3 ms, total: 123 ms In the simplest case, you can pass a prompt string to the GenerativeModel.generate_content method: %%time The available models only support text and images as input, and text as output. The generate_content method can handle a wide variety of use cases, including multi-turn chat and multimodal input, depending on what the underlying model supports. Generate text from text inputsįor text-only prompts, use the gemini-pro model: model = genai.GenerativeModel('gemini-pro') The genai package also supports the PaLM family of models, but only the Gemini models support the generic, multimodal capabilities of the generateContent method. The rate limit for Gemini-Pro models is 60 requests per minute (RPM). We offer options for requesting rate limit increases. 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: This quickstart demonstrates how to use the Python SDK for the Gemini API, which gives you access to Google's Gemini large language models.
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