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Google Gemini AI Course for Beginners

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💫 Short Summary

This video is a comprehensive course on Google's AI model Gemini, covering its introduction, the concept of AI, llms, getting an API key, and building AI chatbots. Ana Kubo, a software developer, guides the viewers through the course, which includes practical demonstrations and examples using Node.js to interact with the Gemini API. The video teaches how to use Gemini's generative AI models to create text and image prompts and build multi-turn conversations with the chatbot.The video section covers the implementation of the Gemini model for creating chatbots that can maintain conversation history and context. It also demonstrates the process of building embeddings and provides a code walkthrough for incorporating the model into a chat application. Additionally, it showcases the integration of the model into a React project and explains how to obtain the necessary API key.In this video, the speaker demonstrates the integration of a chat application with a generative AI model using Express for the backend and Google's GPT-3 for the AI capability. The demonstration includes sending chat history and messages to the backend, receiving and processing the data, and displaying the AI's text-based responses in the chat interface. The end result is a fully functional chat application that can engage in multi-turn conversations based on the context of previous messages.

✨ Highlights
📊 Transcript
This is a comprehensive video course on Google's AI model Gemini, which teaches how to use the Gemini API and build an AI chatbot.
00:00
The course is led by Ana Kubo, a software developer and course creator.
The video will cover an introduction to Gemini, the AI model by Google, and its capabilities.
The FAQ explains that Gemini is a series of multimodal generative AI models developed by Google, which can accept text and image prompts and output text responses.
02:28
Gemini can process text prompts such as asking about the day of the week and provide text responses.
It can also handle image prompts, such as a picture of a cat in a hat, and generate text responses describing the image.
The video demonstrates how to get an API key for the Gemini API, emphasizing the importance of keeping the key secure to prevent misuse.
05:51
The API key should not be shared publicly or exposed in client-side code.
Requests using the API key should be rooted through a backend server for security.
The process of obtaining the API key is demonstrated through the Google AI studio.
The speaker explains the tokenization process for Gemini models and recommends counting tokens before sending content to the model.
12:42
A command is provided to demonstrate how to obtain a template for the code to count tokens.
The speaker uses the 'count tokens' method to show the equivalence of 100 tokens to about 60-80 English words.
The video demonstrates how to initialize the generative model using Node.js and npm, and how to create a text prompt and get a story generated based on the prompt.
18:00
A new Node.js project is created and the generative model is initialized.
A text prompt is defined and the result is obtained by sending the prompt to the model.
The demonstration shows how to use the Gemini provision model to input both text and images and generate creative content using the AI model.
25:06
The process involves defining a prompt and providing text and image inputs to the Gemini provision model.
A function is used to convert local file information to a Google generative AI part object.
An async function is created to run the model and obtain the response for the text and image input.
This section demonstrates how to use the Gemini pro model to initialize a chat and send messages, and the two possible roles associated with it: user and model.
31:35
The chatbot will take into account the history of the conversation to understand the context of the next question.
The Gemini pro model is used to initialize the chat and send messages.
Two possible roles, user and model, are associated with the chat, allowing for the provision of prompts and responses.
Code is provided to demonstrate the use of the Gemini pro model for starting a chat and sending messages, including chat history and a new user message.
The video discusses the creation of embeddings using the Gemini model, which involves representing text as a list of floating point numbers in an array.
35:12
Embeddings are used to represent text in a vectorized form, allowing for easier comparison and contrast of text.
The video demonstrates how to create embeddings using the Gemini model's `embedContent` method.
A sample text is provided, and the resulting embedding is displayed as a vector with numerical values.
The video explains that similar text should have similar embeddings, which can be identified through mathematical comparison techniques.
The speaker demonstrates how to create a new project, 'Gemini app', using the MPX create react app command, and obtains the API key from the Gemini documentation.
39:06
The project directory for the 'Gemini app' is created and the necessary files are generated using the MPX create react app command.
The speaker navigates to the Gemini documentation to obtain the API key for the project.
The process of obtaining the API key is demonstrated by signing in to the Google AI Studio and navigating to the appropriate section.
The video emphasizes the importance of keeping the API key safe to prevent unauthorized usage.
The section is a tutorial on creating a chat interface and integrating the Gemini model for text generation. It covers the initialization of the chat, sending user messages, and displaying the model's responses.
43:12
The video demonstrates the process of creating a chat interface by adding HTML elements such as a message input and a send button.
A function is implemented to handle user input, send the message to the model, and display the model's response.
The 'Surprise me' button is designed to generate a random question for the model.
The video showcases the integration of the Gemini model for text generation within the chat interface.
The speaker styles the 'Surprise me' button, the input container, the input field, and the search result display. The button is given a white background with black text and a border radius to make it look like a pill. The input container is styled with a border and a box shadow to give it a more prominent look. The input field is set to take 90% of the width with a light gray text color.
54:24
The 'Surprise me' button is styled with a white background, black text, and a border radius to make it look like a pill.
The input container is styled with a border and a box shadow to give it a more prominent look.
The input field is set to take 90% of the width with a light gray text color.
The search result display is styled with a margin and overflow property to allow scrolling through the results.
Create a function called get response to handle the API response and show an error if no value exists.
01:04:23
The get response function is designed to handle the communication with the API.
If there is no value or question provided, an error message is displayed prompting the user to ask a question.
Set up the back end to receive and process data from the front end, including the chat history and the latest message.
01:06:09
Define the port for the back end to listen on.
Install and import necessary packages such as Express, qus, EnV, and Google generative AI.
Access the secret API key using the EnV package.
Implement the logic to receive and handle data from the front end on the back end.
Install and import necessary packages, such as Express, qus, EnV, and Google generative AI, for the project.
01:07:56
Packages like Express, qus, EnV, and Google generative AI are essential for the project.
The video demonstrates the installation process and the import of these packages.
The secret API key is accessed and used in the project.
Set up the back end to listen on a specific port and handle the post request with the chat history and message data.
01:10:57
The back end is configured to listen on a specified port.
The post request is designed to receive data, including chat history and message, from the front end.
Console logs are used to display the received data for verification.
The data is then sent to the Google AI for further processing.
Define the model and chat history, and send the message to the chat to get the result.
01:13:07
The video shows the process of defining the model and chat history.
The message is extracted from the request body.
The defined model and chat history are used to get the result.
The response text is sent back to the front end for display.
Update the UI to display the chat history and handle the clearing of chat and error messages.
01:15:14
The chat history and messages are displayed in the UI.
Functionality to clear the chat and error messages is implemented.
The system is tested with a "when is Christmas" query, and it successfully handles the follow-up question about the date.
💫 FAQs about This YouTube Video

1. What will I learn in this video course on Google's AI model Gemini?

In this video course, you will learn about AI as developers through the use of Google's AI model Gemini. The course covers a wide range of topics, including an introduction to Gemini, understanding AI, llms (large language models), getting an API key, tokenization, and building AI chatbots.

2. What is Gemini in the context of artificial intelligence?

Gemini is a series of multimodal generative AI models developed by Google. These models can accept text and image prompts and output text responses, allowing for interactive and diverse AI capabilities.

3. How can I build my own AI chatbot using Gemini?

You can build your own AI chatbot using Gemini through the following steps: 1. Understand the fundamentals of AI and the Gemini model. 2. Get an API key to access Gemini's capabilities. 3. Learn about tokenization and the models available with Gemini. 4. Use the Gemini API to interact with the chatbot and create multi-turn conversations.

4. What is the process for obtaining an API key to interact with Gemini?

The process for obtaining an API key to interact with Gemini involves the following steps: 1. Navigate to the designated website and choose to build with Gemini. 2. Sign in and access the API key in Google AI studio. 3. Use the API key securely by not sharing it publicly and ensuring that requests are rooted through a backend server.

5. What are the fundamental concepts and methods involved in using Gemini's AI models?

The fundamental concepts and methods involved in using Gemini's AI models include understanding llms (large language models), creating embeddings, and using the generate content method to prompt with text and/or images and receive text responses.

6. What is the process for getting a response from the API in the chat application?

In the chat application, the process of getting a response from the API involves sending the chat history and the latest message to the backend, then the backend forwards this data to the Google generative AI, and finally, the AI's response is sent back to the front end for display.

7. How does the chat application ensure that an error is displayed if no value exists?

The chat application checks for the existence of a value and displays an error if the user tries to submit a blank message. This ensures that an error is displayed if no value exists, prompting the user to ask a question.

8. What backend framework and additional packages are used in the chat application?

The chat application uses Express as the backend framework and additional packages such as qus, EnV, and Google generative AI to facilitate the handling of requests, management of environment variables, and integration with the AI model.

9. In the chat application, how is the AI model's response formatted and displayed after receiving the text from the backend?

In the chat application, the AI model's response is formatted and displayed by creating an object that includes the user's question, the model's response, and the role of the user or the model in the conversation. This formatted response is then sent to the frontend and displayed in the chat interface.