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ReedAI Assistant is built with a modern frontend architecture focused on performance, scalability, and developer experience. This document explains the key components of the system.

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High-Level Overview

ReedAI Assistant follows a client-centric architecture with AI processing via API services. The main components are:

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Core Components

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Frontend Architecture

ReedAI Assistant is built with React and follows a component-based architecture:
  1. React Components: Modular UI components built with React
  2. State Management: Zustand for state management
  3. Routing: React Router for client-side routing
  4. Styling: Tailwind CSS for utility-first styling

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AI Integration Layer

The AI integration layer connects the frontend with large language models:
All AI requests are streamed to provide real-time feedback to users.
  1. Model Abstraction: Unified interface for different AI models
  2. Stream Processing: Real-time streaming of AI responses
  3. Error Handling: Robust error handling for AI requests
  4. Rate Limiting: Client-side rate limiting and tracking

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Data Flow

The typical data flow in ReedAI Assistant follows these steps:
  1. User input is captured through the UI
  2. Input is processed and formatted as a prompt
  3. The prompt is sent to the LLM API
  4. Streaming responses are received and rendered
  5. Conversation history is updated and persisted

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Technical Stack

ReedAI Assistant uses the following technologies:
CategoryTechnologies
Frontend FrameworkReact
Build ToolVite
State ManagementZustand
StylingTailwind CSS
UI ComponentsRadix UI, Shadcn UI
FormsReact Hook Form, Zod
AI IntegrationAnthropic SDK, OpenAI SDK
RoutingReact Router
HTTP ClientAxios
TestingJest

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Performance Optimizations

ReedAI Assistant implements several performance optimizations:
  1. Component Memoization: React.memo for expensive components
  2. Lazy Loading: Dynamic imports for code-splitting
  3. Request Batching: Batching of AI requests when appropriate
  4. Cache Management: Caching of common responses
  5. State Normalization: Optimized state structure
AI processing can be resource-intensive. Monitor token usage and implement rate limiting to control costs.

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Deployment Architecture

For production deployments, we recommend a JAMstack approach:

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Security Considerations

The architecture includes several security features:
  1. API Key Management: Secure handling of API keys
  2. Input Validation: Validation of all user inputs
  3. Content Filtering: Optional filtering of AI responses
  4. Rate Limiting: Prevention of API abuse
  5. Authentication: Optional user authentication