Getting Started with Gemini Pro on Google AI Studio

Sam Witteveen
13 Dec 202311:04

TLDRThe video introduces Gemini Pro's availability and guides viewers on getting started with it on Google AI studio. It explains the process of obtaining an API key, using the platform to test models like Gemini Pro for text and Gemini Pro Vision, and setting up safety settings. The video also demonstrates coding with Gemini Pro in Google Colab, including model setup, generation, streaming, and advanced configuration. It showcases the use of text and vision models with examples, highlighting the ability to generate detailed responses and interact with images for information retrieval and visual question answering.

Takeaways

  • ๐Ÿš€ Gemini Pro is now available for public use and can be accessed through Google AI studio.
  • ๐Ÿ”‘ To get started, users need to accept terms and conditions and obtain an API key from Google AI studio.
  • ๐Ÿ’ป There are two options for generating an API key: creating a new project or using an existing Google Cloud project.
  • ๐Ÿ“ Google AI studio allows users to test out various models like Gemini Pro for text and Gemini Pro Vision.
  • โœ๏ธ Gemini Pro can generate content based on prompts, such as writing an email to announce its availability.
  • ๐Ÿ”„ The platform supports different types of prompts including freeform, structured, and chat prompts.
  • ๐Ÿ”ง Safety settings can be adjusted within the AI studio to block certain types of content like harassment, hate speech, etc.
  • ๐Ÿ“Š Users can edit the safety settings in code for more control over the AI's output.
  • ๐ŸŒ Gemini Pro Vision can process images and provide information or answer questions based on visual content.
  • ๐Ÿ“š The video also covers how to set up models, generate content, and use streaming features in the code.
  • ๐Ÿ”— A Colab notebook with examples will be provided in the video description for users to experiment with.

Q & A

  • What is Gemini Pro and how can one get started with it?

    -Gemini Pro is a tool available for use with Google AI studio. To get started, one needs to access Google AI studio, accept terms and conditions, and obtain an API key either by creating a new project or generating it within an existing Google Cloud project.

  • How can you test the different models in Google AI studio?

    -You can test the various models like Gemini Pro for text and Gemini Pro Vision directly in Google AI studio by using the interface to input prompts and see the model's responses.

  • What are the types of prompts available in Google AI studio?

    -In Google AI studio, you can use freeform prompts, structured prompts, and chat prompts to interact with the AI models.

  • How can you adjust the safety settings of the AI models in Google AI studio?

    -The safety settings can be adjusted by selecting the categories of content you want the model to block, such as harassment, hate speech, sexually explicit content, or dangerous content, and setting the strength of these blocks.

  • What is the process for setting up a model in Google Colab?

    -To set up a model in Google Colab, you first need to input your API key, then instantiate the model with the desired settings such as generation configuration and safety settings, and finally, you can generate content or start a chat with the model.

  • How does the model handle streaming responses?

    -For streaming responses, you pass in `stream=True` when generating content. The model will then return chunks of text as it is generated, which can be collected and displayed in real-time.

  • What is the role of 'temperature', 'top P', and 'top K' in the model configuration?

    -These parameters control the randomness and diversity of the generated content. 'Temperature' adjusts the randomness of the output, 'top P' is for nucleus sampling which controls the cumulative probability, and 'top K' is for sampling from the top K most likely tokens.

  • How can you structure a chat with the Gemini Pro chat model?

    -To structure a chat, you first start a chat session with `model.startchat()`, then you can send messages using `chat.sendmessage()`, and the model will respond accordingly, maintaining the chat history for context.

  • What kind of information can Gemini Pro Vision provide about images?

    -Gemini Pro Vision can provide general information about an image, answer questions about the content of the image, and even identify and compare different elements in multiple images when provided together.

  • How can you combine text and images for conditional outputs in Gemini Pro Vision?

    -You can combine text and images by passing both as inputs to the model. The model will then generate outputs that are conditioned on both the text and the images, providing more specific and relevant information based on the combined input.

  • What are the next steps for exploring Gemini Pro further?

    -Further exploration of Gemini Pro can be done by looking at future videos that will cover its use with LangChain and function calling capabilities. Users can also experiment with the models themselves using their own API keys in Google Colab.

Outlines

00:00

๐Ÿš€ Introduction to Gemini Pro and Google AI Studio

This paragraph introduces Gemini Pro, a tool now available for public use. The speaker explains how to get started with Gemini Pro on Google AI Studio, mentioning the process of going through the code in a Colab environment. The focus is on exploring the capabilities of the two current models: Gemini Pro for text and Gemini Pro Vision. The speaker also guides the audience on how to navigate Google AI Studio, accept terms and conditions, and obtain an API key, which is essential for using the platform and its models. The video demonstrates testing out the models and highlights the various prompt types supported by the platform, such as freeform, structured, and chat prompts. The speaker emphasizes the adjustable safety settings, which allow users to control the content output based on categories like harassment, hate speech, and explicit material.

05:02

๐Ÿ“ Exploring Features and Customizing Settings

In this paragraph, the speaker delves into the features of Gemini Pro, discussing the options for streaming responses and customizing the model's settings. The speaker explains how to set up generation configurations, including parameters like temperature, top P, top K, and maximum output tokens. A detailed explanation is provided on how to adjust safety settings by specifying categories and their blocking levels, such as harassment, hate speech, sexually explicit content, and dangerous content. The speaker demonstrates how these settings can be applied in code and how they relate to the prompt feedback feature. The paragraph also covers the process of instantiating the model with the configured settings and how to use the chat model by starting a chat and sending messages.

10:03

๐ŸŒŒ Applying Gemini Pro to Vision and Text

The speaker transitions to discussing the application of Gemini Pro in vision and text tasks. A demonstration is provided on how to use the vision model with an image of Saturn from NASA, showcasing the model's ability to provide general information about the planet when only an image is passed. The speaker then shows how the output can be conditioned on both text and an image by asking for the planet's name and related movies. The video also explores the model's capability to handle multiple images, as seen in a comparison between images of Earth and Saturn. The speaker concludes the practical demonstration and provides information on future videos, encouraging viewers to engage with the content, obtain their API key, and experiment with the platform themselves.

Mindmap

Keywords

๐Ÿ’กGemini Pro

Gemini Pro is an advanced AI model discussed in the video, designed for text generation and understanding. It is part of the Google AI studio and can be used for various tasks such as writing emails, generating content, and engaging in conversation. The video demonstrates how to access and utilize Gemini Pro through Google Colab, highlighting its capabilities and integration with the platform.

๐Ÿ’กGoogle AI Studio

Google AI Studio, formerly known as Maker Suite, is a platform where users can interact with and test AI models like Gemini Pro. It offers a range of features, including freeform, structured, and chat prompts, allowing users to engage with AI in different ways. The video emphasizes the importance of obtaining an API key from Google AI Studio to access and use the Gemini Pro models.

๐Ÿ’กAPI Key

An API key is a unique code that allows developers to access specific services or resources from a software application. In the context of the video, obtaining an API key from Google AI Studio is a crucial step to enable the use of Gemini Pro and Gemini Pro Vision models within Google Colab. The API key serves as an authentication credential, linking the user's project to the AI models.

๐Ÿ’กColab

Colab, or Google Colaboratory, is a cloud-based platform that allows users to write and execute Python code in a collaborative environment. It is integrated with Google Drive and provides free access to Jupyter Notebooks, which are web-based interactive computational environments. In the video, the user demonstrates how to utilize Colab to interact with the Gemini Pro models through code.

๐Ÿ’กText Generation

Text generation is the process by which AI models create and produce human-like text based on given inputs or prompts. In the video, Gemini Pro is showcased as a text generation model capable of completing writing tasks, answering questions, and engaging in conversation. The model's text generation capabilities are demonstrated through various examples, such as writing an email and generating content about the solar system.

๐Ÿ’กSafety Settings

Safety settings are configurations within AI models that help to moderate and control the type of content generated, preventing the production of harmful or inappropriate material. In the context of the video, the user can adjust safety settings in Google AI Studio and also through code in Colab to block categories like harassment, hate speech, sexually explicit content, and dangerous content. These settings ensure that the AI's responses adhere to certain standards and guidelines.

๐Ÿ’กStreaming

Streaming in the context of AI models refers to the process of receiving output in smaller, continuous chunks rather than waiting for the complete response. This allows for real-time interaction and the ability to process and act upon the information as it is being generated. The video shows how to configure Gemini Pro for streaming, enabling the model to provide responses in parts as the AI thinks of them.

๐Ÿ’กGemini Pro Vision

Gemini Pro Vision is an AI model specialized in processing and understanding visual data, such as images. It can analyze and provide information about images, answer questions related to visual content, and even identify differences between multiple images. The video demonstrates how Gemini Pro Vision can be used to analyze images of planets and provide relevant information based on the visual input.

๐Ÿ’กVisual Question Answering

Visual Question Answering (VQA) is a subfield of AI that focuses on understanding and responding to questions about visual content. In the video, Gemini Pro Vision is used for VQA, where the model is given an image and a related question, and it generates an appropriate response based on the visual information. This capability allows for interactive engagement with visual data, providing insights and answers to queries about the content of images.

๐Ÿ’กCode Integration

Code integration refers to the process of incorporating AI models into software applications or workflows through programming. In the video, the user shows how to integrate Gemini Pro and Gemini Pro Vision models into a Google Colab notebook using Python code. This integration allows for the execution of complex tasks, such as text generation, analysis of visual content, and real-time interaction with the AI models.

Highlights

Gemini Pro is now available for public use.

The video will guide users on how to get started with Gemini Pro on Google AI studio.

A walkthrough of the code in a Colab environment is provided.

Two current models are publicly available: Gemini Pro for text and Gemini Pro Vision.

Google AI studio, previously known as Maker Suite, offers various prompt types including freeform, structured, and chat prompts.

Safety settings can be adjusted within the Google AI studio to block certain types of content.

The API key is essential for using the models in Google Colab.

Gemini Pro can generate content based on prompts, such as describing the largest planet.

Streaming capabilities allow for the model to provide responses in chunks.

Advanced settings for the model can be configured, including temperature, top P, top K, and maximum output tokens.

The model's safety settings can be programmed in code to control the level of content filtering.

Gemini Pro-Vision can process images and provide information based on visual input.

The model can perform visual question answering, combining text and image inputs for responses.

Multiple images can be compared using Gemini Pro-Vision to highlight differences.

The video includes a demonstration of writing an email to announce the availability of Gemini Pro.

The chat model allows for a conversational interaction with the model, storing chat history.

The video provides instructions on setting up secrets and API keys for Google AI studio use.

Future videos will explore using Gemini Pro with LangChain and function calling capabilities.

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