Architecture

Table of contents

1. Overview
2. Core Components
   1. Server Core
   2. Batch Orchestrator
   3. Cache System
   4. Monitor Tool
   5. Database Layer
3. Data Flow
4. Component Interaction
5. Design Decisions
   1. Batch Processing
   2. Caching Strategy
   3. Monitoring

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Overview

Batch-GPT follows a modular architecture with clear separation of concerns:

server/
├── db/         # Database interactions
├── handlers/   # HTTP request handlers
├── logger/     # Custom logging
├── models/     # Data models
└── services/   # Business logic
    ├── batch/  # Batch processing
    ├── cache/  # Response caching
    ├── client/ # OpenAI client
    ├── config/ # Configuration
    └── utils/  # Common utilities

Core Components

Server Core

• Request routing and validation
• HTTP handlers
• Response formatting

Batch Orchestrator

• Request collection
• Batch timing management
• Response distribution

Cache System

• Request hashing
• Response storage
• Cache invalidation

Monitor Tool

• Status visualization
• Batch tracking
• Progress reporting

Database Layer

• MongoDB integration
• Persistence management
• Status tracking

Data Flow

1. Request Reception
   • Request validation
   • Cache checking
   • Hash generation
2. Batch Processing
   • Request collection
   • Batch formation
   • OpenAI submission
3. Response Handling
   • Response collection
   • Cache updating
   • Client delivery
4. Monitoring
   • Status tracking
   • Progress updates
   • Error logging

Component Interaction

graph TD
    A[Client] --> B[Server Core]
    B --> C[Cache System]
    B --> D[Batch Orchestrator]
    D --> E[OpenAI API]
    D --> F[MongoDB]
    C --> F
    G[Monitor Tool] --> F

Design Decisions

Batch Processing

• Configurable batch windows
• Hash-based deduplication
• Async/sync flexibility

Caching Strategy

• MongoDB-based persistence
• Hash-based indexing
• Cross-session availability

Monitoring

• Real-time updates
• Interactive interface
• Status aggregation ```