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Gaji Platform: Architecture Guide

Last Updated: 2025-11-14
Status: Production Ready
Version: 1.0

πŸ“‹ Overview

Gaji (가지, Korean for "branch") is a "What If?" storytelling platform where users explore alternative timelines in classic literature through AI-powered conversations with characters adapted to hypothetical scenarios.

Core Innovation: Git-style forking applied to book discussions

  • Scenario Forking: Unlimited depth meta-scenarios
  • Conversation Forking: ROOT-only (depth 1) with 6-message context
  • AI Adaptation: Characters know complete story for consistent responses

πŸ—οΈ System Architecture

Pattern B: API Gateway βœ…

Frontend (Vue.js :443)
    ↓ HTTPS /api/*
Spring Boot :8080 (API Gateway + Business Logic)
    ↓ Internal WebClient
FastAPI :8000 (AI Service - Internal Only)
    ↓
VectorDB + Gemini API

Why Pattern B?

Factor Weight Score Key Benefit
Security 30% 10/10 FastAPI not exposed, API keys protected
Simplicity 25% 10/10 1 API client, centralized auth/CORS
Performance 20% 8/10 +50ms overhead (1% on 5s AI tasks)
Cost 15% 9/10 -$700/year (SSL/domains)
Operations 10% 9/10 Centralized logging
Total 100% 9.25/10 Winner

🎯 Architecture Decisions

ADR-001: MSA Backend

Decision: Spring Boot (PostgreSQL) + FastAPI (VectorDB)

  • Spring Boot: User management, CRUD operations, business logic
  • FastAPI: AI/ML, RAG, VectorDB, Gemini integration
  • Rationale: Python dominates AI ecosystem, Java excels at enterprise logic

ADR-002: Hybrid Database

Decision: PostgreSQL (metadata) + VectorDB (content/embeddings)

Data Distribution:

  • PostgreSQL: 13 tables (users, novels, scenarios, conversations, messages)
  • VectorDB: 5 collections (passages, characters, locations, events, themes)

Performance: 10x faster semantic search vs pgvector on 768-dim embeddings

ADR-003: API Gateway Pattern

Decision: Frontend β†’ Spring Boot Only β†’ FastAPI (Internal)

Implementation:

// Spring Boot: AIProxyController
@PostMapping("/api/ai/search/passages")
public Mono<ResponseEntity<PassageSearchResponse>> searchPassages(
    @RequestBody PassageSearchRequest request
) {
    return fastApiClient.post()
        .uri("/api/ai/search/passages")
        .bodyValue(request)
        .retrieve()
        .toEntity(PassageSearchResponse.class);
}

Impact:

  • πŸ” Security: -50% attack surface
  • πŸ’° Cost: -$700/year
  • 🎯 Simplicity: 2 API clients β†’ 1
  • ⚑ Performance: +50ms (+1% on AI tasks)

ADR-004: Conversation Forking

Decision: Copy min(6, total) messages on fork

Rationale:

  • Gemini 2.5 Flash: ~2000 token context recommended
  • 6 messages β‰ˆ 600 tokens
  • Users remember 2-3 recent turns

Storage: Reuse messages via conversation_message_links join table

ADR-005: Multirepo Structure

Decision: Separate repositories for each service (Multirepo)

Structure:

gaji-core-backend/         # Repository 1: Spring Boot
β”œβ”€β”€ src/main/java/
β”œβ”€β”€ src/main/resources/
β”œβ”€β”€ build.gradle
└── Dockerfile

gaji-ai-backend/           # Repository 2: FastAPI
β”œβ”€β”€ app/
β”œβ”€β”€ requirements.txt
└── Dockerfile

gaji-frontend/             # Repository 3: Vue.js (Current: gajiFE)
β”œβ”€β”€ src/
β”œβ”€β”€ package.json
β”œβ”€β”€ docs/                  # Project documentation
β”‚   β”œβ”€β”€ epics/            # Epic-level documentation
β”‚   β”œβ”€β”€ stories/          # Story-level implementation details
β”‚   β”œβ”€β”€ PRD.md            # Product Requirements Document
β”‚   β”œβ”€β”€ ARCHITECTURE.md   # This file
β”‚   └── ...
└── Dockerfile

gaji-api-contracts/        # Repository 4: OpenAPI specs (shared)
└── openapi.yaml

Benefits:

  • Independent deployment cycles
  • Clear ownership boundaries
  • Easier CI/CD pipelines per service
  • Better suited for team growth (3+ developers)
  • Documentation co-located with frontend code for easier access

Trade-offs:

  • Type sharing via npm/Maven packages from api-contracts repo
  • Cross-service changes require multiple PRs
  • No monorepo build caching

Documentation Strategy:

  • Epic files (docs/epics/) provide high-level feature descriptions and business value
  • Story files (docs/stories/) contain detailed acceptance criteria and implementation guides
  • See docs/EPIC_STORY_ALIGNMENT_SUMMARY.md for cross-reference mapping

ADR-006: SSE Streaming

Decision: Server-Sent Events for AI message streaming

Performance:

  • Before: 15 polls/conversation (450 requests)
  • After: 1 SSE connection
  • Improvement: 93% fewer requests

Implementation:

// Frontend
const eventSource = new EventSource(`/api/ai/stream/${id}`);
eventSource.onmessage = (event) => appendToken(event.data);
// Spring Boot Proxy
@GetMapping(value = "/ai/stream/{id}", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<ServerSentEvent<String>> streamMessage(@PathVariable UUID id) {
    return fastApiClient.get()
        .uri("/api/ai/stream/" + id)
        .retrieve()
        .bodyToFlux(String.class)
        .map(token -> ServerSentEvent.<String>builder().data(token).build());
}

πŸ“Š Technology Stack

Backend

Component Technology Port Purpose
API Gateway Spring Boot 3.x 8080 Single entry point
AI Service FastAPI 0.110+ 8000 RAG, VectorDB, Gemini
Task Queue Celery + Redis 6379 Async AI operations

Data Layer

Component Technology Access
Metadata DB PostgreSQL 15.x Spring Boot only
Content DB ChromaDB/Pinecone FastAPI only

Frontend

  • Framework: Vue 3 + TypeScript
  • UI Library: PrimeVue
  • Styling: Panda CSS
  • State: Pinia
  • Router: Vue Router

AI/ML

  • LLM: Gemini 2.5 Flash
  • Embeddings: Gemini Embedding API (768-dim)
  • RAG: Custom FastAPI service

πŸ”„ Data Flow Patterns

1. Novel Ingestion

Gutenberg File β†’ FastAPI Parse β†’ Chunk Text
β†’ Gemini Embeddings β†’ VectorDB Storage
β†’ Gemini LLM Analysis (characters/locations/events)
β†’ Spring Boot Metadata Update

2. Scenario Creation

User Request β†’ Spring Boot
β†’ FastAPI VectorDB Search (similar passages)
β†’ Spring Boot Save (PostgreSQL with passage_ids)

3. Conversation Generation

Frontend β†’ Spring Boot
β†’ FastAPI Async (Celery)
β†’ VectorDB Query (character + passages)
β†’ Gemini 2.5 Flash
β†’ Spring Boot Save Messages
β†’ SSE Stream to Frontend

πŸš€ Performance Optimizations

1. Async WebClient

Impact: 40% response time reduction (520ms β†’ 310ms)

2. Circuit Breaker (Resilience4j)

@CircuitBreaker(name = "fastapi", fallbackMethod = "fallbackResponse")
public Mono<Response> callFastAPI() { ... }

Impact: 99.9% availability

3. Redis Caching

@Cacheable(value = "passages", key = "#novelId + ':' + #query")
public List<Passage> searchPassages(UUID novelId, String query) { ... }

Impact: 60% DB load reduction, 70% faster repeated queries

4. Connection Pooling (HikariCP)

Impact: 5x concurrency (200 β†’ 1000 users)

πŸ“ˆ Cost Analysis

Infrastructure (Annual)

Item Cost
SSL + Domain (1 domain) $215
Load Balancer (1 instance) $120
Total $335
Savings vs Pattern A -$335

AI/ML (per 1000 conversations)

Operation Cost
Gemini 2.5 Flash Text $15
Gemini Embedding $5
VectorDB (ChromaDB self-hosted) $0
VectorDB (Pinecone cloud) $70/month

πŸ” Security Measures

  1. API Gateway Protection

    • FastAPI port 8000 internal only
    • Gemini API keys in Spring Boot only
    • Single CORS origin

  2. Authentication

    • JWT tokens (Spring Security)
    • Role-based access control
    • Redis session management

  3. Rate Limiting

    • 10 requests/minute/user (Resilience4j)

  4. Input Validation

    • @Valid annotations (Spring)
    • Pydantic models (FastAPI)

πŸ› οΈ Implementation Roadmap

Phase Epic Hours Focus
1 Epic 0 54h Infrastructure, Novel Ingestion, LLM Setup
2 Epic 1-2 80h Scenarios, AI Adaptation
3 Epic 3-4 72h Discovery, Conversation System
4 Epic 5 24h Tree Visualization
5 Epic 6 60h Auth, Social Features
Total 0-6 290h ~12 weeks

πŸ“Š Success Metrics

Technical KPIs

Metric Target
API Response Time (P95) < 500ms
AI First Token < 1000ms
Error Rate < 0.1%
Test Coverage > 80%

Business KPIs

Metric MVP Beta
Daily Active Users 10 100
Scenarios Created 50 500
Conversations 100 1000

πŸ“š Related Documentation

Core

Implementation

Specifications

🎯 Next Steps

Week 1: Pattern B Migration

  1. Implement AIProxyController (16h)
  2. Update Frontend API client (8h)
  3. Infrastructure updates (4h)
  4. Testing (12h)

Week 2: Epic 0 Foundation

  1. Spring Boot + FastAPI setup
  2. PostgreSQL + VectorDB setup
  3. Docker configuration
  4. Novel ingestion pipeline
  5. LLM character extraction

Month 1: Core Features

  • Epic 1: Scenario Foundation
  • Epic 2: AI Character Adaptation

Status: Ready for Implementation πŸš€
Next Review: After Pattern B migration