Technical Design Document

Technical Design Document (TDD)

Technical Overview: The proposed solution is a generative UI system that allows AI agents to build personalized, dynamic dashboards in real-time. The system utilizes the AG UI protocol, Copilot Kit, and Pydantic AI to create a seamless user experience where the agent plays a significant role in determining the layout, structure, and components of the dashboard.
Technology Stack:

Backend:
- Programming Language: Python
- Framework: FastAPI
- AI/ML Library: Pydantic AI
- UI Protocol: AG UI
Frontend:
- Programming Language: JavaScript
- Framework: React.js
- UI Components: Copilot Kit
Database: Depending on the data requirements, a suitable database solution (e.g., MongoDB, PostgreSQL) may be chosen.

System Architecture: The system architecture consists of the following components:
- AI Agent: The AI agent is responsible for generating the personalized dashboard configurations based on the user's preferences and data. It utilizes Pydantic AI to define the data models and generate the JSON configurations for the dashboard.
- Backend Server: The backend server, built using FastAPI, serves as the intermediary between the AI agent and the frontend. It receives the JSON configurations from the AI agent and exposes an API endpoint for the frontend to consume.
- Frontend Application: The frontend application, built using React.js and Copilot Kit, is responsible for rendering the dynamic dashboard based on the JSON configurations received from the backend server via the AG UI protocol.
- Database: The database stores the user preferences, historical data, and any other relevant information required for the AI agent to generate the personalized dashboard.
API Specifications:
- Endpoint: /dashboard
- Method: POST
- Request Body: { "user_id": "string" }
- Response Body: { "dashboard_config": { "components": [...], "layout": {...} } }
The frontend application will make a POST request to the /dashboard endpoint, passing the user's ID in the request body. The backend server will then retrieve the necessary data from the database, pass it to the AI agent, and receive the generated dashboard configuration in JSON format. The backend will then return the dashboard configuration to the frontend for rendering.

Data Models:

User:

class User(BaseModel):
    id: str
    name: str
    email: str
    preferences: Dict[str, Any]

DashboardComponent:

class DashboardComponent(BaseModel):
    type: str
    config: Dict[str, Any]

DashboardLayout:

class DashboardLayout(BaseModel):
    grid_columns: int
    grid_rows: int
    component_placements: List[Tuple[int, int]]

DashboardConfig:

class DashboardConfig(BaseModel):
    components: List[DashboardComponent]
    layout: DashboardLayout

Security Design:
- Authentication: The system will implement a secure authentication mechanism, such as JWT-based authentication, to ensure only authorized users can access the dashboard.
- Authorization: The system will also implement role-based access control to ensure users can only access and modify the data they are authorized to.
- Data Encryption: All sensitive data, such as user preferences and historical data, will be encrypted at rest and in transit using industry-standard encryption algorithms.
Performance Considerations:
- Caching: The system will implement caching mechanisms, such as in-memory caching or a caching layer like Redis, to improve the response times for frequently accessed data.
- Asynchronous Processing: The process of generating the dashboard configurations can be offloaded to a background task using a message queue or an asynchronous task runner like Celery, to ensure the frontend application does not experience any delays.
- Scalability: The system architecture will be designed to be scalable, allowing for easy horizontal scaling of the backend servers and the database as the user base grows.
Testing Strategy:
- Unit Tests: Unit tests will be written for the individual components, such as the AI agent, the backend server, and the frontend application, to ensure the correct behavior of each component.
- Integration Tests: Integration tests will be performed to ensure the various components of the system work together as expected, including the communication between the AI agent, the backend server, and the frontend application.
- End-to-End (E2E) Tests: E2E tests will be implemented to simulate the complete user journey, from generating the dashboard configurations to rendering the dashboard in the frontend application.
- Performance Tests: Performance tests will be conducted to measure the system's response times, throughput, and scalability under various load conditions.

Code Examples:

Backend Server (FastAPI):

from fastapi import FastAPI
from pydantic import BaseModel
from .agent import generate_dashboard_config

app = FastAPI()

class DashboardRequest(BaseModel):
    user_id: str

@app.post("/dashboard")
async def get_dashboard_config(request: DashboardRequest):
    dashboard_config = await generate_dashboard_config(request.user_id)
    return {"dashboard_config": dashboard_config}

AI Agent (Pydantic AI):

from pydantic import BaseModel
from typing import List, Tuple, Dict, Any

class DashboardComponent(BaseModel):
    type: str
    config: Dict[str, Any]

class DashboardLayout(BaseModel):
    grid_columns: int
    grid_rows: int
    component_placements: List[Tuple[int, int]]

class DashboardConfig(BaseModel):
    components: List[DashboardComponent]
    layout: DashboardLayout

async def generate_dashboard_config(user_id: str) -> DashboardConfig:
    # Fetch user preferences and historical data from the database
    user_preferences = await fetch_user_preferences(user_id)
    historical_data = await fetch_historical_data(user_id)

    # Generate the dashboard configuration using the fetched data
    dashboard_components = generate_components(user_preferences, historical_data)
    dashboard_layout = generate_layout(len(dashboard_components))

    return DashboardConfig(
        components=dashboard_components,
        layout=dashboard_layout
    )

Frontend Application (React.js + Copilot Kit):

import React, { useState, useEffect } from 'react';
import { DashboardComponent, DashboardLayout } from './types';
import { fetchDashboardConfig } from './api';
import { CopilotGrid, CopilotWidget } from '@copilot-ui/react';

const Dashboard = () => {
  const [dashboardConfig, setDashboardConfig] = useState<{
    components: DashboardComponent[];
    layout: DashboardLayout;
  } | null>(null);

  useEffect(() => {
    const fetchData = async () => {
      const config = await fetchDashboardConfig('user-123');
      setDashboardConfig(config);
    };
    fetchData();
  }, []);

  if (!dashboardConfig) {
    return <div>Loading...</div>;
  }

  return (
    <CopilotGrid
      columns={dashboardConfig.layout.grid_columns}
      rows={dashboardConfig.layout.grid_rows}
    >
      {dashboardConfig.components.map((component, index) => (
        <CopilotWidget
          key={index}
          x={dashboardConfig.layout.component_placements[index][0]}
          y={dashboardConfig.layout.component_placements[index][1]}
          type={component.type}
          config={component.config}
        />
      ))}
    </CopilotGrid>
  );
};

export default Dashboard;

This Technical Design Document outlines the key aspects of the generative UI system, including the technical overview, technology stack, system architecture, API specifications, data models, security design, performance considerations, and testing strategy. The code examples provided demonstrate the implementation of the backend server, AI agent, and frontend application, showcasing the integration of the various components.