1. Overview

This system architecture outlines the key components of a cloud-based platform where three distinct AI personalities generate discussions. The platform incorporates natural language processing (NLP) models, a vector database for search, and a web interface for user interaction. The system is designed for scalability, search-based interaction, and high availability using cloud infrastructure.

2. Key Components

1. Frontend:
   • A web interface built using a modern JavaScript framework (React).
   • Search functionality integrated into the UI for querying past discussions using natural language.
   • Responsive design is not required.
2. Backend:
   • API Server: Built using a lightweight framework like FastAPI, handling API requests from the frontend.
   • AI Processing: The system integrates NLP models (such as GPT) for generating AI personalities' responses. These models are hosted on AWS Lambda to ensure scalability and cost-efficiency.
   • Discussion Flow Control: Manages the generation of AI conversations at scheduled intervals and updates the vector database with new data.
3. Vector Store:
   • A vector database such as Milvus is used for storing AI-generated discussion transcripts in a vectorized format.
   • The database supports natural language search, allowing users to query past discussions by semantic similarity.
4. Cloud Infrastructure:
   • Hosted on AWS to ensure high availability, scalability, and cost-effective resource management.
   • AWS Lambda: Serverless processing for AI response generation.
   • AWS S3: Used for static file hosting for the frontend application.
5. CI/CD Pipeline:
   • Automated build, testing, and deployment using GitHub Actions or AWS CodePipeline.
   • Ensures seamless updates to both frontend and backend components, with automated testing to maintain system stability.

3. Data Flow

1. User Interaction:
   • Users access the web interface to input search queries.
2. AI Response Generation:
   • AI models generate responses based on pre-scheduled prompts. Real-time generation of responses is not required.
   • AI-generated responses are stored as transcripts and indexed in the vector database.
3. Search and Retrieval:
   • When users perform a search, the vector database retrieves relevant discussion transcripts based on semantic similarity and presents them on the frontend.

4. Security Design

• Authentication: Implement OAuth 2.0 or JWT for secure user authentication, particularly for administrative access.
• Encrypted Communication: All communications are encrypted using HTTPS.
• Data Privacy: Minimize the storage of sensitive data, and encrypt all stored data, including user inputs and AI discussions.

5. Scalability and Performance Considerations