I built a fullstack PaLM AI app in just 2 minutes
TLDRIn this video, the creator demonstrates the ease of building a full-stack application using Google's PaLM 2 large language model for text summarization. The app allows users to input content from various news sources and receive a concise summary in real-time. The process is made incredibly simple thanks to Firebase, which enables server-side rendering and real-time UI updates. The app also utilizes Firestore for scalable data storage and integrates with the PaLM API via a cloud function. The tutorial showcases the installation of Firebase SDK, setting up a Firebase project with Firestore, and deploying the app with Firebase Hosting. Additionally, the video touches on other Firebase features such as Python runtime for cloud functions, server-side filtering with new queries, and Terraform support for project configuration as code. The presenter also hints at a future video on creating a more complex video chat app using Firebase and the Vonage API.
Takeaways
- 🚀 **Quick Deployment**: The app was built and deployed in just 2 minutes using Firebase's powerful tools.
- 📄 **Text Summarization**: The app leverages Google's PaLM 2 model to provide text summarization.
- 🌐 **Real-time Updates**: The user interface updates in real time as content is summarized.
- 🔄 **Scalable Database**: Results are stored in a scalable Cloud Firestore database.
- 🛠️ **Firebase Extensions**: The app utilizes Firebase Extensions, including one for text summarization with the PaLM API.
- 📱 **Server-Side Rendering**: The app supports server-side rendering with frameworks like Next.js and Nuxt.
- 🧩 **Integration with Svelte**: Svelte is used for the app's frontend, with Svelte Fire for Firebase integration.
- 🔗 **Real-time Data Subscription**: The UI subscribes to database changes for live updates.
- 📝 **User Interaction**: A form is provided for users to submit text for summarization.
- 🤖 **AI-Powered Summary**: The PaLM API is called to generate summaries whenever the text field is updated.
- 🌟 **New Firebase Features**: Mentioned are new features like Python runtime for Cloud Functions and experimental support for Flask and Django apps.
- 📈 **Improved Query Performance**: Firestore's new on-demand queries allow for server-side filtering, enhancing performance and reducing document reads.
Q & A
What is the main feature of the full stack app described in the transcript?
-The main feature of the app is a text summarizer that uses Google's PaLM 2 large language model to provide quick summaries of content pasted from websites like CNN or Fox News.
How does the app update the user interface?
-The user interface updates in real time as the text is summarized and the result is stored in a Cloud database.
What is the role of Firebase in building this app?
-Firebase is used for deploying the app to the web, providing a scalable Cloud database (Firestore), and enabling server-side rendering through Firebase Hosting.
How long did it take to build the app?
-The app was built in just two minutes, thanks to the ease of use and automation provided by Firebase and Spelled Kit.
What is the purpose of installing the Firebase SDK and Svelte Fire?
-The Firebase SDK is installed to integrate Firebase services with the app, and Svelte Fire is used to integrate Firebase with the Svelte framework for reactive data handling.
How does the app handle real-time updates to the database?
-The app uses a dollar sign in front of the document store from Spellfire to subscribe to the data in the database, which allows the UI to update in real time.
What is the function of the 'handle submit' in the form?
-The 'handle submit' function takes the text from the form and calls the setDoc function with a document reference to update the text field in the database, triggering the cloud function to call the PaLM API and update the summary.
What is the significance of the Firebase Extensions Hub?
-The Firebase Extensions Hub allows users to easily add functionalities to their Firebase projects, such as the 'summarize text' extension which deploys a cloud function for text summarization.
How does the app leverage the PaLM API for summarization?
-The app uses a cloud function that is triggered by updates to a document in the text documents collection. This function calls the PaLM API to generate a summary and updates the summary field in real time.
What are the new features announced by Firebase?
-New features announced by Firebase include a Python runtime for cloud functions, improved queries for Firestore, and experimental support for deploying Flask and Django apps. Additionally, there is now Terraform support for representing Firebase project configurations as code.
What is the benefit of using the new 'ore queries' in Firestore?
-Ore queries allow for filtering documents server-side, which can make complex queries faster and reduce the total number of document reads needed, leading to improved performance and cost efficiency.
How can one become proficient in using Firebase?
-One can become proficient in using Firebase by becoming a pro member at Fireship IO, which offers in-depth tutorials and resources for mastering Firebase.
Outlines
🚀 Building a Real-Time Server-Rendered App with Firebase and Google's Palm 2
The video introduces a full stack app that uses Google's Palm 2 language model to summarize text. The app allows users to input lengthy content from news sites and receive concise summaries. It utilizes Firebase for real-time UI updates and stores results in a scalable Cloud database. The presenter shares their experience of quickly building the app with Firebase and Spelled Kit, and guides viewers through the process of setting up a Firebase project, enabling Firestore, and integrating Firebase with the app. The video also demonstrates how to use Firebase Extensions to install a text summarization function using the Palm API, which automatically summarizes documents in real time. Finally, the presenter shows how to deploy the app using Firebase Hosting and mentions other Firebase features and extensions.
Mindmap
Keywords
💡Fullstack
💡PaLM (Palm 2 Large Language Model)
💡Firebase
💡SvelteKit
💡Server-Side Rendering (SSR)
💡Firestore
💡Cloud Functions
💡Real-Time Updates
💡AI Extensions
💡Terraform
💡Python Runtime
💡Ore Queries
Highlights
Built a real-time server rendered full stack app in just 2 minutes using Google's PaLM 2 large language model.
The app is a text summarizer that allows users to paste in content from websites and get a quick summary.
The user interface updates in real-time and stores results in a scalable Cloud database.
Firebase's magic made the app building process incredibly fast and efficient.
Spell Kit and Firebase SDK were used for the project's creation and Firebase integration.
Firestore was enabled for the Firebase project to manage the scalable database.
Project credentials from Firebase were used to initialize the project within a Spelled component.
A Firebase extension using the PaLM API was installed to summarize text.
The app demonstrates real-time updates of document summaries generated by the PaLM API.
Spellfire's doc store was imported to view documents in real-time within the UI.
A form was created to allow users to update documents, triggering a cloud function to use the PaLM API.
The app is a full stack, real-time application utilizing the latest advancements in AI.
Firebase Experiments enabled web Frameworks for deploying the server-rendered app.
Firebase Deploy provisioned a cloud function for server-side rendering, making the app live on the internet.
The presenter also used Firebase with a Vonage API to create a video chat app.
Firebase introduced a Python runtime for cloud functions, allowing the use of Python libraries.
There is experimental support for the deployment of Flask and Django apps on Firebase.
Firebase introduced new queries that allow for server-side document filtering, improving performance and reducing document reads.
Firebase now supports Terraform, allowing project configurations to be represented as code.
For mastering Firebase, the presenter recommends becoming a pro member at Fireship IO.