🎯 Key Takeaways for quick navigation: 00:02 🚀 *Introduction to Google's G Pro Model* - Google has opened access to the G Pro model via API for testing. - Overview of the G Pro model and its pricing structure. - Instructions on obtaining the API key from Google AI Studio. 02:20 🔐 *Setting up API Key in Google Colab* - Demonstrates how to set up the API key in Google Colab using the Secrets tab. - Instructions for installing required libraries in the Colab notebook. - Importance of securing the API key and using environment variables. 03:31 📚 *Overview of Required Libraries* - Importing necessary libraries for working with Google's generative AI. - Explanation of the purpose of each library in the context of the tutorial. - Ensuring the notebook is well-prepared for utilizing G Promodel. 05:25 🤖 *Exploring G Pro and G Pro Vision Models* - Explanation of available models: G Pro and G Pro Vision. - Differentiating use cases for G Pro (text input) and G Pro Vision (text and image input). - Demonstrates querying the available models using the API. 07:06 🎨 *Formatting Text Output with Markdown* - Showing how to format the generated text output using the Markdown library. - Providing a function for enhancing the visual presentation of the model's response. - Improving the readability and structure of the generated text. 08:52 🚫 *Handling Failures and Feedback* - Handling potential failures in API response using `prompt_feedback` method. - Explaining common reasons for API failure, such as unsafe content. - Retrieving and reviewing multiple responses generated by the G Pro model. 10:15 🔄 *Streaming Responses for Real-time Output* - Enabling real-time streaming of the response during the generation process. - Demonstrating how to set up and use the streaming feature with G Pro model. - Improving user experience by providing responses as they are generated. 11:52 📷 *Working with G Pro Vision Model (Text + Image Input)* - Using G Pro Vision model for generating text output based on an input image. - Demonstrating the integration of image input and obtaining textual responses. - Showing how to combine text and image input for more comprehensive queries. 13:34 🧠 *Generating Text Embeddings with G Pro* - Introduction to using G Pro for generating text embeddings (vectors). - Utilizing the `embed_content` method to convert text into floating-point vectors. - Demonstrating the versatility of G Pro beyond text generation. 15:17 🎓 *Conclusion and Summary* - Summarizing the tutorial and its coverage of G Pro model usage. - Expressing gratitude for the audience's attention and potential learning. - Encouraging viewers to explore G Pro model capabilities further. Made with HARPA AI
