Generative UI Series - Building a Generative UI App

Introduction

• Continuation of Generative UI series by Brace
• Focus on building a new generative UI app
• App to be deployed on LRA Cloud
• Backend in Langra Python
• Frontend in Next.js with TypeScript

Key Differences

• Entire page used as canvas for generating and placing UI components
• More dynamic than previous chatbot-focused videos
• Magic filter input for user inputs in natural language
• Backend performs query analysis using LLM
• Backend extracts filters, chart types, and renders data
• Dynamic updates in real-time as each node completes

Magic Filter Input

• Takes user input in natural language
• Query analysis extracts filters, chart type, visualization
• Example Inputs:
  • "Orders by status" -> Pie chart
  • "Orders after 2023-01-01" -> Line chart
• Results rendered in real-time

LRA Deploy Overview

• Revisions and deployments tracked in the LRA Deploy dashboard
• Monitors runs and nodes
• Graphs show API calls and prompt templates

Backend Implementation

• Deployed on Langra Cloud, with frontend in Next.js
• Node operations: Generate filters, Generate chart type, Generate display format, Filter data
• State Management with agentState
• Events streamed to client for real-time UI updates

Node 1: Generate Filters

• Uses LLM to analyze user input and select appropriate filters
• Filters include product names, statuses, discount percentages, etc.
• Updates state with selected filters

Node 2: Generate Chart Type

• Determines best chart type: bar, line, or pie, based on context
• Uses LLM to select chart type and updates state

Node 3: Generate Data Display Format

• Selects best display format based on chart type
• Updates state with selected display format

Node 4: Filter Data

• Applies user-selected filters to the dataset
• Final filtered data is sent back to the client

Deployment and API Integration

• Deployment involves connecting GitHub repo and configuring environment variables
• API key and URL used to connect frontend and backend
• Uses streamRunnableUI function for dynamic updates
• Example of handling API integration and UI updates with event handlers

Practical Implementation

• Examples of querying different datasets and visualizations
• Display types such as weekly order volume and quarterly order distribution
• Langra Cloud Playground for development and testing

Conclusion

• Overview of backend and frontend integration
• Benefits of using Langra Cloud for generative UI apps
• Encouragement to try building generative UI apps on LRA Cloud