VERSION 1.0 | ENTERPRISE ARCHITECTURE | DISTRIBUTED SYSTEMS

Retrieval-Augmented Generation

High-Performance Vector Search & LLM Integration Platform

Throughput
1.2K req/s
Latency (p99)
245ms
Vector Dimension
768D
Replication
3x
SLA
99.99%

FRONTEND LAYER

  • Framework: Next.js 15.x
  • Runtime: Node.js 20 LTS
  • Protocol: HTTPS/REST
  • Port: 3000

GATEWAY LAYER

  • Type: API Gateway
  • Rate Limit: 1000 req/min
  • Auth: JWT/OAuth 2.0
  • Port: 8080 (gRPC)

APPLICATION LAYER

  • Framework: Spring Boot 3.2
  • Language: Java 21 LTS
  • Threads: 50-200 (adaptive)
  • Port: 8081 (REST/Kafka)

DATA LAYER

  • Vector DB: Pinecone
  • SQL DB: PostgreSQL 16
  • Cache: Redis 7
  • Storage: AWS S3

DATA FLOW PATTERN

Documents → Chunking → Embeddings → Vector Indexing → Semantic Search

SECURITY POSTURE

mTLS | AES-256 | JWT | Rate Limiting | RBAC

DEPLOYMENT MODEL

Kubernetes | Docker | Multi-Region | Auto-Scaling

SYSTEM ARCHITECTURE DIAGRAM

End-to-end pipeline with event-driven processing and asynchronous workflows

React Frontend

Port 3000 | HTTPS REST

API Gateway

Port 8080 | gRPC/REST

Spring Boot API

Port 8081 | REST/Kafka

Processing Pipeline

4 Services (Hover to expand)

Storage & DB

S3 + PostgreSQL

CI/CD Pipeline

Deployment & Monitor

Technical Specifications

Performance

  • • p99 latency: <500ms
  • • Throughput: 1000 req/s
  • • Cache: Redis (1GB)

Security

  • • mTLS: Service-to-service
  • • Encryption: AES-256
  • • Rate limit: Token bucket

Reliability

  • • Replication: 3x
  • • SLA: 99.9% uptime
  • • Circuit breaker: Enabled

Component Interconnections & Protocols

Frontend ↔ API Gateway

Protocol: HTTPS REST | Port: 3000→8080 | Rate: 1000 req/min | Auth: JWT Bearer Token

API Gateway ↔ Authentication Service

Protocol: Internal gRPC | Port: 8080→8090 | Check: JWT validation, RBAC verification | Cache: 5min TTL

API Gateway ↔ Spring Boot Backend

Protocol: gRPC + REST | Port: 8080→8081 | Load Balancer: Round-robin | Timeout: 30s

Backend ↔ Document Processor

Protocol: Kafka Queue | Topic: document-processing | Partition: 10 | Batch: 32 chunks/sec | Storage: S3 + PostgreSQL metadata

Backend ↔ Embedding Service

Protocol: HTTP REST | Port: 8081→GoogleAPI | Endpoint: /embeddings | Batch: 100 chunks | Dimension: 768D | Retry: 3x exponential

Backend ↔ Vector Database

Protocol: gRPC + REST | Port: 8081→Pinecone | Operations: Upsert, Query, Delete | Similarity: Cosine | TopK: 5 | Namespace: user-id based

Backend ↔ LLM Service

Protocol: HTTP REST | Port: 8081→GoogleAPI | Streaming: Server-sent events | Model: gemini-2.5-flash | Timeout: 60s | Max tokens: 1024

Document Processor ↔ Embedding Service

Protocol: Internal Service Bus | Event: chunk-ready | Payload: text + metadata | Order: Guaranteed (single partition)

Embedding Service ↔ Vector DB

Protocol: gRPC streaming | Batch upsert: 100 vectors/request | Index: Vector + metadata + namespace | Consistency: Eventual (3 replicas)

All Services ↔ Storage Layer

S3: Document storage + backup | PostgreSQL: Metadata + audit logs | Connection: Connection pooling (50 connections) | Replication: 3x cross-region

All Services ↔ CI/CD Pipeline

Monitoring: Prometheus + Grafana | Logs: ELK Stack | Traces: Jaeger | Alerts: PagerDuty | Deployment: GitOps (ArgoCD)

Complete Data Flow Sequences

📄 Document Upload Flow

  1. User uploads via Frontend (3000)
  2. Gateway validates JWT (8080→8090)
  3. Backend receives (8081) + queues to Kafka
  4. Processor extracts text + chunks (512 tokens)
  5. Metadata → PostgreSQL, File → S3
  6. Emit chunk-ready event

🔍 Search & Query Flow

  1. User enters query in Frontend
  2. Backend converts to 768D vector
  3. Calls Pinecone (cosine similarity)
  4. Retrieves top-5 chunks + metadata
  5. Passes to LLM with query
  6. Streams response + source citations

Connection Legend

  • HTTP/REST - Frontend to Gateway
  • Route - Gateway to Backend
  • Upload/Query - Backend to Services
  • Search - Backend to Vector DB
  • Prompt - Backend to LLM
  • Auth Check - Gateway to Auth

Data Flow Directions

  • • User Request: Frontend → Gateway → Backend
  • • Document Upload: Backend → Document Processor → Storage
  • • Vectorization: Processor → Embedding → Vector DB
  • • Query Processing: User Query → Backend → Vector DB (Search)
  • • Response Generation: Backend + Results → LLM → Answer to User
  • • Monitoring: All Services → CI/CD Pipeline

ARCHITECTURAL PRINCIPLES & DESIGN PATTERNS

→ MICROSERVICES ARCHITECTURE: Decoupled, independently deployable services with domain-driven design
→ EVENT-DRIVEN PROCESSING: Kafka message broker for asynchronous document processing pipeline
→ HORIZONTAL SCALABILITY: Kubernetes auto-scaling with resource limits and health checks
→ ZERO-DOWNTIME DEPLOYMENT: Blue-green deployments with rolling updates and circuit breakers
→ CACHING STRATEGY: Multi-level caching (Redis) for embeddings and query results
→ OBSERVABILITY: Distributed tracing, structured logging, and metrics collection

REST API SPECIFICATIONS

POST /api/v1/documents/upload

Request: multipart/form-data

Max Size: 100MB

Response: {documentId, status, chunks}

Auth: Bearer JWT

POST /api/v1/query

Request: {query, topK}

Response Time: p99 < 500ms

Streaming: SSE (Server-Sent Events)

Rate Limit: 1000 req/min

GET /api/v1/status

Response: Service health & metrics

Interval: 30s polling

Circuit Breaker: Enabled

Timeout: 5 seconds

Webhook: POST /webhook/embedding-complete

Trigger: Async embedding generation

Payload: {documentId, vectorCount}

Retry: Exponential backoff 3x

Signature: HMAC-SHA256

DATA FLOW PIPELINE

→Document Upload: Backend → Document Processor → Storage
→Vectorization: Processor → Embedding Service → Vector DB
→Query Processing: User Query → Backend → Vector DB (Search)
→Response: Backend + Results → LLM → Answer to User

PERFORMANCE BENCHMARKS & SLA TARGETS

LATENCY PROFILE

p50: 145ms

p95: 380ms

p99: 500ms

Max (SLO): 600ms

THROUGHPUT & CAPACITY

Peak TPS: 1.2K/sec

Concurrent Users: 10K

Vector Dim: 768D

Indexed Vectors: 50M+

AVAILABILITY & RELIABILITY

SLA: 99.99%

Replication: 3x across AZs

Backup: Daily + RPO 1h

RTO: 15 minutes

COMPLIANCE & SECURITY FRAMEWORK

ENCRYPTION

• Data in Transit: TLS 1.3, HTTPS, mTLS

• Data at Rest: AES-256-GCM (AWS KMS)

• Key Management: AWS Secrets Manager

AUTHENTICATION & AUTHORIZATION

• OAuth 2.0 / OpenID Connect

• JWT with RS256 signature verification

• Role-Based Access Control (RBAC)

MONITORING & LOGGING

• Prometheus metrics collection

• ELK Stack (Elasticsearch, Logstash, Kibana)

• Distributed tracing (Jaeger)

COMPLIANCE STANDARDS

• SOC 2 Type II compliant

• GDPR data residency requirements

• HIPAA audit logging enabled

ARCHITECTURE VERSION 1.0 | LAST UPDATED: DECEMBER 2025 | DISTRIBUTED UNDER ENTERPRISE LICENSE