BYO RAG System

Build Your Own Retrieval Augmented Generation System

🎯 Project Overview

BYO RAG System is a comprehensive AI-powered knowledge platform that shows you how to build your own enterprise-grade RAG solution from the ground up. This complete implementation demonstrates the intersection of modern software architecture and artificial intelligence through a fully-realized microservices ecosystem that enables intelligent document processing and conversational AI capabilities.

What is RAG?

Retrieval Augmented Generation (RAG) combines the power of large language models with your organization's private knowledge base, enabling AI-powered question answering over your documents while maintaining complete data privacy and control.

Key Value Propositions

🏢 Multi-tenant Architecture: Complete data isolation for multiple organizations
🔒 Enterprise Security: JWT-based authentication with role-based access control
⚡ High Performance: Sub-200ms query responses with vector similarity search
📄 Document Intelligence: Extract insights from PDF, DOCX, TXT, MD, and HTML files
🔍 Hybrid Search: Combines semantic understanding with keyword precision
🚀 Production Ready: Containerized microservices with monitoring and observability
🧪 Testing Infrastructure: 99% test coverage - 594/600 functional tests passing across all services

✅ Current Status: Complete BYO RAG system with all 6 microservices implemented and tested. Docker deployment ready. View detailed status

⚡ Quick Links

First-Time Setup

Clone & Install - Get the project running locally
Prerequisites - What you need before starting
Verify Installation - Check everything works

Common Tasks

Running Tests - Execute unit and integration tests
View Swagger UI - Interactive API documentation
Check Service Health - Health check endpoints
Rebuild a Service - Using make commands
View Logs - Real-time log monitoring

Troubleshooting

Service Won't Start - Port conflicts and startup issues
Database Connection Issues - PostgreSQL connection problems
Test Failures - Debugging failing tests
Known Issues - Current technical debt and bugs

Deployment

Docker Compose Setup - Local container deployment
GCP Cloud Deployment - Production GKE deployment
Kubernetes Guide - K8s manifests and configuration

📚 Table of Contents

🎯 Project Overview
⚡ Quick Links
🏗️ Architecture & Design
🌟 Key Features
🚀 Quick Start Guide
🎓 What's Next?
📊 Development Status
🛠️ Developer Reference
🔒 Security
🔧 Troubleshooting
📈 Performance & Monitoring
🧪 Testing
🎯 Roadmap
📚 Documentation
🤝 Contributing
☁️ GCP Cloud Deployment
📄 License

🏗️ Architecture & Design

This system implements a microservices architecture with complete multi-tenant isolation, demonstrating enterprise-grade patterns and modern cloud-native design principles.

Core Architecture Principles

Domain-Driven Design: Each microservice owns its domain and data
Event-Driven Processing: Asynchronous operations via Apache Kafka (future implementation)
Optimized Persistence: Consolidated database architecture with single PostgreSQL and Redis instances
Security-First: JWT authentication with tenant-scoped data access
Observability: Comprehensive monitoring and distributed tracing

Microservices Overview

🔐 Auth Service (Port 8081)    → JWT authentication & tenant management
📄 Document Service (Port 8082) → File processing & text extraction
🔍 Embedding Service (Port 8083) → Vector generation & similarity search
🤖 RAG Core Service (Port 8084)  → LLM integration & query processing
⚙️  Admin Service (Port 8085)    → Administrative operations & analytics

🏗️ Architecture & Design


> **Note**: The API Gateway has been bypassed in favor of direct service access. See [ADR-001: Bypass API Gateway](docs/development/ADR-001-BYPASS-API-GATEWAY.md) for rationale.

### Refined Data Architecture

**Database Architecture** (Updated Nov 2025):
- **Single PostgreSQL Database**: Uses environment-based naming
  - Development: `byo_rag_local`
  - GCP Dev: `byo_rag_dev`
  - Pattern: `byo_rag_{env}`
  - Used by: Auth Service, Document Service, Admin Service
  - Simplified deployment and maintenance
  
- **Single Redis Database**: DB 0 with key prefixes
  - Pattern: `byo_rag_{env}:{service}:{key}`
  - Used by: Embedding Service (vector storage), Core Service (caching)
  - Complete tenant isolation via key prefixes
  
- **Services Using Synchronous REST**:
  - All inter-service communication currently via REST APIs
  - Kafka integration planned for future async operations

**Key Benefits**:
- ✅ Simplified connection management
- ✅ Lower infrastructure costs (~$206/year savings)
- ✅ Smaller Docker images (~100-160MB reduction per service)
- ✅ Maintained complete multi-tenant isolation
- ✅ 99% test pass rate (594/600 functional tests)

## 🌟 Key Features

### 🔐 Enterprise Security & Multi-Tenancy
- **Complete Data Isolation**: Each tenant's data is fully segregated
- **JWT-Based Authentication**: Secure, stateless authentication
- **Role-Based Access Control**: ADMIN, USER, and READER permissions
- **Audit Logging**: Complete traceability of all operations

### 📄 Intelligent Document Processing
- **Multi-Format Support**: PDF, DOCX, TXT, Markdown, HTML
- **Smart Text Extraction**: Apache Tika-powered content analysis
- **Configurable Chunking**: Optimized for different document types
- **Asynchronous Processing**: Non-blocking operations via Kafka events

### 🤖 Advanced RAG Pipeline  
- **Multiple Embedding Models**: OpenAI, local models, custom implementations
- **Vector Similarity Search**: Redis-powered with tenant isolation
- **Hybrid Search Strategy**: Semantic + keyword search combination
- **LLM Integration**: Support for OpenAI GPT models and local Ollama
- **Streaming Responses**: Real-time answer generation
- **Enterprise Error Handling**: ✅ **Complete** - Comprehensive retry mechanisms, circuit breakers, dead letter queues, and monitoring

### 📊 Administration & Analytics
- **Multi-Tenant Management**: ✅ **Complete** - Full tenant lifecycle operations
- **User Administration**: ✅ **Complete** - Database-backed user management with roles
- **Usage Analytics**: ✅ **Complete** - Comprehensive reporting and monitoring
- **Health Monitoring**: ✅ **Complete** - Deep service health checks and diagnostics
- **Docker Deployment**: ✅ **Complete** - All 6 services operational with health monitoring

## 🚀 Quick Start Guide

### Prerequisites
- **Java 21+** (OpenJDK recommended)
- **Maven 3.8+**
- **Docker & Docker Compose**
- **Git** for version control

### 1️⃣ Setup Your Environment
```bash
# Clone the repository
git clone https://github.com/texican/RAG.git
cd RAG

# Install development tools (git hooks, etc.)
./scripts/setup/install-dev-tools.sh

# Build all services
make build-all

# Start all services
make start

# Verify all services are running
make status

⚠️ IMPORTANT: Do not use manual docker build or docker restart commands. Always use make rebuild SERVICE=name. See CONTRIBUTING.md for details.

2️⃣ Build and Run Services

Option 1: Docker Compose (Recommended)

# Start all services
docker-compose up -d

# Check system health
./scripts/utils/health-check.sh

# View all service status
make status

Option 2: Individual Maven Services

# Build all modules
mvn clean install

# Run each service in a separate terminal
cd rag-auth-service && mvn spring-boot:run        # Port 8081 - Authentication
cd rag-document-service && mvn spring-boot:run    # Port 8082 - Document Processing
cd rag-embedding-service && mvn spring-boot:run   # Port 8083 - Vector Operations
cd rag-core-service && mvn spring-boot:run        # Port 8084 - RAG Pipeline
cd rag-admin-service && mvn spring-boot:run       # Port 8085 - Admin Operations

💡 Development Quick Commands

The project includes a Makefile for common development tasks:

# Rebuild a single service (rebuilds JAR + Docker image + restarts container)
make rebuild SERVICE=rag-auth

# Rebuild with no cache (for stubborn issues)
make rebuild-nc SERVICE=rag-auth

# View logs in real-time
make logs SERVICE=rag-auth

# Show all services status
make status

# Start/stop all services
make start
make stop

# Run tests
make test SERVICE=rag-auth

See make help for all available commands or consult docs/development/DOCKER_DEVELOPMENT.md for detailed Docker workflow guidance.

3️⃣ Verify Installation

Service Status:

Service	Health Check URL	Port	Status
Auth Service	http://localhost:8081/actuator/health	8081	✅ Healthy
Document Service	http://localhost:8082/actuator/health	8082	✅ Healthy
Embedding Service	http://localhost:8083/actuator/health	8083	✅ Healthy
Core Service	http://localhost:8084/actuator/health	8084	✅ Healthy
Admin Service	http://localhost:8085/admin/api/actuator/health	8085	✅ Running

Infrastructure Services:

Service	URL	Status	Notes
PostgreSQL	localhost:5432	✅ Healthy	Single DB: `byo_rag_{env}` pattern
Redis Stack	localhost:6379	✅ Healthy	Single DB 0 with key prefixes
Apache Kafka	localhost:9092	🔄 Future Work	Event streaming planned (see Roadmap)
Ollama LLM	localhost:11434	🔄 Optional	Local LLM inference
Grafana	http://localhost:3000 (admin/admin)	✅ Working	Monitoring dashboards
Prometheus	http://localhost:9090	✅ Working	Metrics collection

4️⃣ Explore the APIs

Interactive API Documentation (Recommended):

# Start with public access (no credentials needed)
open http://localhost:8082/swagger-ui.html  # Document Service

# Authenticated APIs (username: user, see guide for passwords)
open http://localhost:8084/swagger-ui.html  # Core Service
open http://localhost:8083/swagger-ui.html  # Embedding Service
open http://localhost:8085/admin/api/swagger-ui.html  # Admin Service

🔑 Access Credentials: See docs/deployment/SWAGGER_UI_ACCESS_GUIDE.md for complete login credentials and troubleshooting

Test Using curl (Alternative):

# 1. Check service health
curl http://localhost:8081/actuator/health  # Auth Service
curl http://localhost:8082/actuator/health  # Document Service

# 2. Create admin user (first time only)
./scripts/db/create-admin-user.sh

# 3. Login via Auth Service (direct)
curl -X POST http://localhost:8081/auth/login \
  -H "Content-Type: application/json" \
  -d '{
    "email": "[email protected]",
    "password": "admin123"
  }'

# 4. Use the returned JWT token for authenticated requests
TOKEN="your-jwt-token-here"
curl -X GET http://localhost:8085/admin/api/tenants \
  -H "Authorization: Bearer $TOKEN"

Direct Service Testing:

# Run comprehensive system test
./scripts/tests/test-system.sh

# Check service status
./scripts/utils/service-status.sh

🎓 What's Next?

Now that your BYO RAG system is running, here's how to start using it:

1. Upload Your First Document

# Login and get JWT token
TOKEN=$(curl -s -X POST http://localhost:8081/auth/login \
  -H "Content-Type: application/json" \
  -d '{"email":"[email protected]","password":"admin123"}' | jq -r '.token')

# Upload a document
curl -X POST http://localhost:8082/documents/upload \
  -H "Authorization: Bearer $TOKEN" \
  -F "[email protected]" \
  -F "metadata={\"title\":\"My First Document\"}"

2. Query the RAG System

# Ask a question about your documents
curl -X POST http://localhost:8084/rag/query \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query":"What is this document about?","maxResults":5}'

3. Configure LLM Models

OpenAI: Set OPENAI_API_KEY in your environment or application.yml
Ollama: Install locally and configure endpoint in rag-core-service/application.yml
See ollama-chat/README.md for local LLM setup

4. Explore Advanced Features

Multi-tenancy: Create additional tenants via Admin Service API
User Management: Add users with different roles (ADMIN, USER, READER)
Monitoring: Access Grafana dashboards at http://localhost:3000
Analytics: View usage statistics via Admin Service endpoints

5. Deploy to Production

GCP: Follow GCP Cloud Deployment guide
Other Clouds: Adapt Kubernetes manifests in k8s/ directory
Security: Review Security section for production hardening

📚 Next Steps Resources:

API Documentation - Complete API reference
Development Guide - Development workflows
Testing Guide - How to write tests
Contributing - Guidelines for contributing code

📊 Development Status

Current Status: Production-Ready RAG System with GCP Deployment ✅

All 6 microservices operational with comprehensive testing, API documentation, Docker deployment, and GCP cloud deployment ready.

Services Overview

Service	Status	Tests	API Docs	Docker	GCP
rag-shared	✅ Complete	✅ 90/90	N/A	✅ Library	N/A
rag-auth-service	✅ Complete	⚠️ 111/114 (97%)	✅ Swagger UI	✅ Production	✅ Ready
rag-document-service	✅ Complete	✅ 77/77 (100%)	✅ Swagger UI	✅ Production	✅ Ready
rag-embedding-service	✅ Complete	⚠️ 209/214 (98%)	✅ Swagger UI	✅ Production	✅ Ready
rag-admin-service	✅ Complete	✅ 77/77 (100%)	✅ Swagger UI	✅ Production	✅ Ready
rag-core-service	✅ Complete	✅ 108/108 (100%)	✅ Swagger UI	✅ Production	✅ Ready

Test Summary: 594/600 functional tests passing (99% overall pass rate)

⚠️ Auth Service: 3 security configuration tests failing
⚠️ Embedding Service: 5 Ollama configuration tests failing
See BACKLOG.md for TECH-DEBT-006 and TECH-DEBT-007 details

System Capabilities

✅ Multi-tenant Architecture: Complete data isolation with JWT authentication
✅ Document Processing: PDF, DOCX, TXT, MD, HTML with intelligent chunking
✅ Vector Operations: Redis-powered similarity search with enterprise error handling
✅ RAG Pipeline: LLM integration with streaming responses
✅ Admin Operations: Tenant management, user administration, analytics
✅ Testing: 594/600 functional tests passing (99% pass rate)
✅ Documentation: Interactive Swagger UI for all endpoints
✅ Local Deployment: Docker Compose with health monitoring
✅ Cloud Deployment: GCP with GKE, Cloud SQL, Memorystore, Artifact Registry

🛠️ Developer Reference

Architecture Diagram

graph TB
    Client[Client Applications<br/>Direct Access]
    Auth[Auth Service<br/>Port 8081]
    Doc[Document Service<br/>Port 8082]
    Embed[Embedding Service<br/>Port 8083]
    Core[RAG Core Service<br/>Port 8084]
    Admin[Admin Service<br/>Port 8085]

    PG[(PostgreSQL<br/>byo_rag_local<br/>Port 5432)]
    Redis[(Redis DB 0<br/>Key Prefixes<br/>Port 6379)]
    Kafka[(Apache Kafka<br/>Port 9092<br/>Future)]
    Ollama[(Ollama LLM<br/>Port 11434)]

    Client --> Auth
    Client --> Doc
    Client --> Embed
    Client --> Core
    Client --> Admin

    Auth --> PG
    Doc --> PG
    Admin --> PG

    Embed --> Redis
    Core --> Redis

    Doc -.-> Kafka
    Embed -.-> Kafka
    Core -.-> Kafka

    Core --> Ollama
    Embed --> Ollama

    classDef working fill:#4CAF50,stroke:#2E7D32,stroke-width:2px,color:#FFFFFF;
    classDef planned fill:#FFA726,stroke:#F57C00,stroke-width:2px,stroke-dasharray: 5 5,color:#FFFFFF;
    class Client,Auth,Doc,Embed,Core,Admin,PG,Redis,Ollama working;
    class Kafka planned;

Refined Architecture Notes:

Consolidated Database Architecture: Single PostgreSQL and Redis instances for optimal performance

Service Isolation: DB 0 with service-specific key prefixes (byo_rag_{env}:{service}:*)

Kafka Integration: Future work - see Roadmap for planned async event processing

Cost Savings: ~$206/year from infrastructure optimization

Image Size: ~100-160MB reduction per service after removing unused dependencies

Microservices Architecture

Multi-tenant isolation: Complete data separation by tenant (PostgreSQL row-level, Redis key prefixes)
Event-driven processing: Async operations planned via Kafka (current: synchronous REST)
Optimized persistence: Consolidated database architecture
Horizontal scaling: Stateless services with shared infrastructure

Tech Stack Reference

📋 Core Framework & Runtime

Component	Version	Purpose
Java	21 (LTS)	Primary programming language
Spring Boot	3.2.8	Application framework
Spring AI	1.0.0-M1	AI/ML integration
Spring Cloud	2023.0.2	Microservices framework
Maven	3.8+	Build and dependency management

🗄️ Data & Storage (Refined Architecture)

Component	Version	Purpose
PostgreSQL	42.7.3	Single shared database (`byo_rag_{env}` pattern)
Redis Stack	5.0.2	Single DB 0 with key prefixes for multi-tenant isolation
Apache Kafka	3.7.0	Event streaming and messaging (planned - see Roadmap)

Architecture Notes:

PostgreSQL handles: Auth, Document, Admin service data
Redis handles: Embedding vectors, Core service caching
Key prefix pattern: byo_rag_{env}:{service}:{key}
Cost optimization: ~$206/year savings from infrastructure consolidation
Vector Extensions: pgvector support planned for future advanced vector operations
Kafka: Infrastructure planned for future async event processing
See KAFKA_ERROR_HANDLING.md for planned implementation design

🤖 AI/ML Libraries

Component	Version	Purpose
LangChain4j	0.33.0	LLM integration framework
Apache Tika	2.9.2	Document processing and text extraction
OpenAI API	Latest	GPT models and embeddings
Ollama	Latest	Local LLM inference

🧪 Testing & Quality

Component	Version	Purpose
JUnit	5.10.2	Unit testing framework
Testcontainers	1.19.8	Integration testing
Mockito	5.14.2	Mocking framework
WireMock	3.8.0	API mocking

Developer Workflows

Running Tests

# Run all unit tests
mvn test

# Run integration tests (requires Docker)
mvn verify -P integration-tests

# Run tests for a specific service
cd rag-auth-service && mvn test

# Skip tests during development
mvn clean install -DskipTests

Development Mode

# Hot reload enabled by default in Spring Boot DevTools
# Make changes to Java files and they'll auto-reload

# For database schema changes, use Spring Boot's DDL auto-update
# application-dev.yml: spring.jpa.hibernate.ddl-auto=update

Debugging

# Enable debug logging for a service
export LOGGING_LEVEL_COM_ENTERPRISE_RAG=DEBUG

# Debug with remote JVM debugging
mvn spring-boot:run -Dspring-boot.run.jvmArguments="-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=5005"

Working with Docker Services

# View logs for all infrastructure services
docker-compose logs -f

# Restart a specific service
docker-compose restart postgres

# Access PostgreSQL directly
docker exec -it enterprise-rag-postgres psql -U rag_user -d rag_enterprise

# Access Redis CLI
docker exec -it enterprise-rag-redis redis-cli

# View Kafka topics
docker exec -it enterprise-rag-kafka kafka-topics --bootstrap-server localhost:9092 --list

� Security

Authentication & Authorization

JWT-Based Authentication: Stateless token-based security
Token Expiry: Configurable token lifetime (default: 24 hours)
Refresh Tokens: Automatic token renewal for long-lived sessions
Role-Based Access Control (RBAC):
- ADMIN: Full system access, tenant management, user administration
- USER: Document upload, RAG queries, view own data
- READER: Read-only access to documents and query results

Multi-Tenant Security

Complete Data Isolation:
- PostgreSQL: Row-level security with tenant_id filtering
- Redis: Key prefix isolation (byo_rag_{env}:{service}:{tenant_id}:*)
- Each tenant's data is logically and physically separated
Tenant Context: All API requests validated against JWT tenant claims
Cross-Tenant Protection: Automatic enforcement prevents data leakage

API Security

Endpoint Protection: All APIs secured except health checks
Health Endpoints: /actuator/health/** publicly accessible for K8s probes
Input Validation: Request validation with Spring annotations
SQL Injection Prevention: JPA/Hibernate with parameterized queries
CORS Configuration: Configurable allowed origins for frontend integration

Secret Management

Development: Environment variables and application.yml (never commit secrets)
Production (GCP): Google Secret Manager integration
- Database credentials
- Redis passwords
- JWT signing keys
- API keys (OpenAI, etc.)
Kubernetes: Secrets mounted as environment variables

Security Best Practices

# Generate secure JWT secret (development)
openssl rand -base64 32

# Rotate secrets regularly (production)
./scripts/gcp/04-migrate-secrets.sh --rotate

# Audit security configuration
./scripts/security/audit-config.sh

Known Security Considerations

⚠️ Default credentials in development mode - change before production
⚠️ HTTPS required for production deployments
⚠️ Rate limiting not yet implemented (planned)
⚠️ API key rotation not automated (manual process)

See SECURITY_AUDIT_REPORT.md for comprehensive security analysis.

🔧 Troubleshooting

Quick Troubleshooting Index

Issue	Section	Quick Fix
Service won't start	Service Won't Start	Check port conflicts with `netstat -tulpn \| grep :8081`
Database connection fails	Database Issues	Verify `docker-compose ps` shows postgres running
Tests failing	Test Failures	Run with `-Dspring.profiles.active=test`
Docker build issues	Contributing	Always use `make rebuild SERVICE=name`
Port already in use	Service Won't Start	Kill process: `kill $(lsof -t -i:8081)`
Authentication fails	Security	Verify JWT token not expired, check credentials

�🔧 Troubleshooting

🔧 Service Won't Start

# Check if port is already in use
netstat -tulpn | grep :8081

# View application logs
cd rag-auth-service && mvn spring-boot:run

# Check Docker services are running
docker-compose ps

🗄️ Database Connection Issues

# Test PostgreSQL connection
docker exec -it enterprise-rag-postgres psql -U rag_user -d byo_rag_local

# Reset database (development only)
docker-compose down -v && docker-compose up -d

# Check database logs
docker-compose logs postgres

Note: Database name follows pattern byo_rag_{env} where {env} is local, dev, staging, or prod.

🧪 Tests Failing

# Run tests with verbose output
mvn test -Dtest=YourTestClass -Dspring.profiles.active=test

# Integration tests require Docker
docker-compose up -d
mvn verify -P integration-tests

# Check test container logs
docker-compose logs testcontainers

📈 Performance & Monitoring

Target Metrics

Response Time: <200ms (excluding LLM processing)
Throughput: 1000+ concurrent users
Availability: 99.9% uptime target

Monitoring Endpoints

Health Checks: /actuator/health on each service
Metrics: /actuator/prometheus for Prometheus scraping
Info: /actuator/info for build and version details

Local Monitoring Setup

# Prometheus: http://localhost:9090
# Grafana: http://localhost:3000 (admin/admin)
# Kafka UI: http://localhost:9021 (if Confluent Control Center enabled)

🧪 Testing

Test Coverage Overview

Overall: 594/600 functional tests passing (99% pass rate)

Service	Tests Passing	Coverage	Status
rag-shared	90/90 (100%)	Unit tests	✅ All passing
rag-auth-service	111/114 (97%)	Unit + Integration	⚠️ 3 security config tests
rag-document-service	77/77 (100%)	Unit + Integration	✅ All passing
rag-embedding-service	209/214 (98%)	Unit + Integration	⚠️ 5 Ollama config tests
rag-admin-service	77/77 (100%)	Unit + Integration	✅ All passing
rag-core-service	108/108 (100%)	Unit + Integration	✅ All passing

Known Test Issues

TECH-DEBT-006: Auth service security configuration tests (3 failures)
- Spring Security blocking actuator/auth endpoints
- Pre-existing issue, not affecting functionality
- See BACKLOG.md for details
TECH-DEBT-007: Embedding service Ollama client tests (5 failures)
- Profile-based bean configuration in test context
- Functional tests all pass (181/181)
- Service works correctly at runtime
- See BACKLOG.md for details

Running Tests

# Run all tests for all services
mvn test

# Run tests for specific service
cd rag-auth-service && mvn test

# Run excluding infrastructure tests
mvn test -Dtest='!InfrastructureValidationTest,!SecurityConfigurationTest'

# Run with coverage report
mvn test jacoco:report

# Run integration tests (requires Docker)
mvn verify -P integration-tests

Test Categories

Unit Tests: Fast, no external dependencies, mock all services
Integration Tests: Test with real PostgreSQL/Redis via Testcontainers
Infrastructure Tests: Validate Bean configuration and startup
E2E Tests: Full pipeline testing (planned - see E2E_TEST_BLOCKER_ANALYSIS.md)

Writing Tests

See TESTING_BEST_PRACTICES.md for:

Test naming conventions
Mock vs. Integration test guidelines
Spring Boot test annotations
Common testing patterns

Continuous Testing

# Watch mode for development
mvn test -Dtest=YourTest -DfailIfNoTests=false --watch

# Pre-commit hook runs tests automatically
# Installed via: ./scripts/setup/install-dev-tools.sh

🎯 Roadmap

🚀 All Core Services Complete! GCP Deployment Infrastructure Ready!

1. High Priority: GCP Cloud Deployment ✅ COMPLETE

✅ GCP Project Setup: Project byo-rag-dev configured
✅ Secret Management: Google Secret Manager with credentials
✅ Container Registry: Artifact Registry with all service images
✅ Cloud SQL: PostgreSQL 15 database
✅ Cloud Memorystore: Redis Standard HA tier
✅ GKE Cluster: Kubernetes Engine cluster (currently stopped)
✅ Kubernetes Manifests: Complete K8s deployment configs with Kustomize
✅ Persistent Storage: GCS buckets and PVCs configured
✅ Deployment Automation: Scripts for service deployment and validation

2. Medium Priority: System Integration & Testing

✅ Docker orchestration: All services running in containers
✅ Authentication testing: Complete auth service unit testing
✅ Embedding testing: Complete embedding service advanced testing
🔄 End-to-end testing: Complete RAG pipeline validation
🔄 Load testing: Performance testing under concurrent load
✅ API documentation: Comprehensive Swagger UI for all services

3. Lower Priority: Production Features

🔄 Ingress & Load Balancer: External access configuration
🔄 CI/CD pipeline: Automated testing and deployment
🔄 Kafka Integration: Enterprise-grade async event processing
- Comprehensive error handling with DLQ
- Circuit breakers and retry mechanisms
- Event-driven document processing pipeline
- See KAFKA_ERROR_HANDLING.md for design
🔄 Vector Extensions: pgvector support for advanced vector operations
🔄 Security hardening: Advanced security features and audit logging
🔄 Performance optimization: Database indexing and query optimization
🔄 Monitoring & Alerting: Cloud Monitoring dashboards and alerts
🔄 Multi-model support: Additional embedding and LLM model integrations

📚 Documentation

The project documentation is organized into the following categories:

🚀 Deployment & Infrastructure

docs/deployment/DEPLOYMENT.md - Quick deployment guide and setup instructions
docs/deployment/DOCKER.md - Complete Docker setup and management guide
k8s/README.md - Kubernetes deployment guide for GCP GKE
scripts/gcp/ - Complete GCP deployment automation scripts

🛠️ Development & Testing

CLAUDE.md - Detailed project status and technical context
docs/development/METHODOLOGY.md - Development methodology and completed stories management process
docs/development/TESTING_BEST_PRACTICES.md - Comprehensive testing guidelines and standards
docs/development/DOCKER_BEST_PRACTICES.md - Docker image optimization, Spring Boot configuration, and security best practices
docs/development/DOCKER_DEVELOPMENT.md - Docker development workflow and troubleshooting
docs/development/KAFKA_ERROR_HANDLING.md - Comprehensive Kafka error handling implementation guide

📋 Project Management

BACKLOG.md - Active task backlog with priorities and technical debt tracking
docs/project-management/PROJECT_BACKLOG.md - Active task backlog (pending stories only)
docs/project-management/COMPLETED_STORIES.md - Completed stories archive with business impact summaries

📱 Applications & Tools

ollama-chat/README.md - Enhanced Ollama chat frontend with Docker integration ✅ COMPLETED
Service Health Checks - /actuator/health endpoints on all services
Monitoring Dashboards - Grafana at http://localhost:3000
Comprehensive Javadoc - Enterprise-grade API documentation (92.4% coverage)

☁️ GCP Cloud Deployment

The BYO RAG System is fully configured for deployment on Google Cloud Platform with enterprise-grade infrastructure.

Infrastructure Overview

Project: byo-rag-dev (Development Environment)

Component	Service	Status	Configuration
Compute	Google Kubernetes Engine	✅ Ready	`rag-gke-dev` cluster, us-central1
Database	Cloud SQL PostgreSQL 15	✅ Running	`rag-postgres`, single DB: `byo_rag_dev`
Cache	Cloud Memorystore Redis	✅ Running	`rag-redis` Standard HA, DB 0 with key prefixes
Storage	Cloud Storage	✅ Ready	Document and backup buckets
Registry	Artifact Registry	✅ Active	`rag-system` repository
Secrets	Secret Manager	✅ Configured	Database, Redis, JWT credentials
Messaging	Cloud Pub/Sub	🔄 Planned	Future async messaging system

Architecture Benefits:

PostgreSQL database design optimized for multi-tenancy
Redis instance configured for optimal memory usage
Key prefix pattern enables complete tenant isolation
Reduced network traffic and connection overhead

Container Images

All service images are published to Google Artifact Registry:

Registry URL:

us-central1-docker.pkg.dev/byo-rag-dev/rag-system

Available Images:

rag-auth-service:0.8.0
rag-document-service:0.8.0
rag-embedding-service:0.8.0
rag-core-service:0.8.0
rag-admin-service:0.8.0

Pull Example:

docker pull us-central1-docker.pkg.dev/byo-rag-dev/rag-system/rag-core-service:0.8.0

Tags: 0.8.0, latest, <git-sha>, 0.8.0-<git-sha>

Deployment Scripts

Complete automation for GCP deployment:

# Setup scripts (scripts/gcp/)
./00-setup-project.sh          # Initialize GCP project
./01-setup-network.sh          # Configure VPC and networking
./02-setup-service-accounts.sh # Create GCP service accounts
./04-migrate-secrets.sh        # Migrate to Secret Manager
./07-build-and-push-images.sh  # Build and push to Artifact Registry
./08-setup-cloud-sql.sh        # Provision Cloud SQL
./10-setup-memorystore.sh      # Provision Cloud Memorystore
./12-setup-gke-cluster.sh      # Create GKE cluster
./13-sync-secrets-to-k8s.sh    # Sync secrets to Kubernetes
./14-setup-storage.sh          # Configure Cloud Storage
./16-setup-ingress.sh          # Configure ingress and load balancing
./17-deploy-services.sh        # Deploy services to GKE
./18-init-database.sh          # Initialize database schema
./19-validate-deployment.sh    # Validate deployment health

Kubernetes Deployment

Kubernetes manifests with Kustomize overlays for dev/prod:

# Deploy to development
kubectl apply -k k8s/overlays/dev

# Deploy to production
kubectl apply -k k8s/overlays/prod

See k8s/README.md for detailed Kubernetes deployment documentation.

Infrastructure Costs (Estimated)

Development Environment:

GKE Cluster (n1-standard-2, 3 nodes): ~$150/month
Cloud SQL (db-custom-2-7680): ~$120/month
Cloud Memorystore Redis (Standard, 5GB): ~$80/month
Artifact Registry: ~$5/month (storage only)
Cloud Storage: ~$5/month (minimal usage)

Total: ~$360/month for development environment

Note: Costs can be reduced by:

Stopping GKE cluster when not in use
Using smaller Cloud SQL instances
Switching to Basic tier Redis for development

GCP Console Links

Project Dashboard: https://console.cloud.google.com/home/dashboard?project=byo-rag-dev
Artifact Registry: https://console.cloud.google.com/artifacts/docker/byo-rag-dev/us-central1/rag-system
GKE Clusters: https://console.cloud.google.com/kubernetes/list?project=byo-rag-dev
Cloud SQL: https://console.cloud.google.com/sql/instances?project=byo-rag-dev
Secret Manager: https://console.cloud.google.com/security/secret-manager?project=byo-rag-dev

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔥 Ready to contribute? Check out our Contributing Guidelines and start building the future of enterprise RAG systems!


### Infrastructure Costs (Estimated)

**Development Environment:**
- GKE Cluster (n1-standard-2, 3 nodes): ~$150/month
- Cloud SQL (db-custom-2-7680): ~$120/month
- Cloud Memorystore Redis (Standard, 5GB): ~$80/month
- Artifact Registry: ~$5/month (storage only)
- Cloud Storage: ~$5/month (minimal usage)

**Total:** ~$360/month for development environment

**Note:** Costs can be reduced by:
- Stopping GKE cluster when not in use
- Using smaller Cloud SQL instances
- Switching to Basic tier Redis for development

### GCP Console Links

- **Project Dashboard:** https://console.cloud.google.com/home/dashboard?project=byo-rag-dev
- **Artifact Registry:** https://console.cloud.google.com/artifacts/docker/byo-rag-dev/us-central1/rag-system
- **GKE Clusters:** https://console.cloud.google.com/kubernetes/list?project=byo-rag-dev
- **Cloud SQL:** https://console.cloud.google.com/sql/instances?project=byo-rag-dev
- **Secret Manager:** https://console.cloud.google.com/security/secret-manager?project=byo-rag-dev

Name		Name	Last commit message	Last commit date
Latest commit History 163 Commits
.claude/commands		.claude/commands
.githooks		.githooks
.github/workflows		.github/workflows
.specify		.specify
archive		archive
docker		docker
docs-site		docs-site
docs		docs
k8s		k8s
ollama-chat		ollama-chat
postman		postman
rag-admin-service		rag-admin-service
rag-auth-service		rag-auth-service
rag-core-service		rag-core-service
rag-document-service		rag-document-service
rag-embedding-service		rag-embedding-service
rag-integration-tests		rag-integration-tests
rag-shared		rag-shared
scripts		scripts
specs		specs
.dockerignore		.dockerignore
.env.template		.env.template
.gitignore		.gitignore
.quality-checklist		.quality-checklist
BACKLOG.md		BACKLOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
E2E-TEST-RESULTS.md		E2E-TEST-RESULTS.md
FRONTEND_INTEGRATION_GUIDE.md		FRONTEND_INTEGRATION_GUIDE.md
Makefile		Makefile
PROJECT-SUMMARY.md		PROJECT-SUMMARY.md
QUALITY_STANDARDS.md		QUALITY_STANDARDS.md
README.md		README.md
SECURITY_AUDIT_REPORT.md		SECURITY_AUDIT_REPORT.md
cloudbuild-full.yaml		cloudbuild-full.yaml
cloudbuild-single.yaml		cloudbuild-single.yaml
cloudbuild.yaml		cloudbuild.yaml
docker-compose.yml		docker-compose.yml
pom.xml		pom.xml
run-comprehensive-e2e-tests.sh		run-comprehensive-e2e-tests.sh

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BYO RAG System

🎯 Project Overview

What is RAG?

Key Value Propositions

⚡ Quick Links

📚 Table of Contents

🏗️ Architecture & Design

Core Architecture Principles

Microservices Overview

🏗️ Architecture & Design

2️⃣ Build and Run Services

💡 Development Quick Commands

3️⃣ Verify Installation

4️⃣ Explore the APIs

🎓 What's Next?

1. Upload Your First Document

2. Query the RAG System

3. Configure LLM Models

4. Explore Advanced Features

5. Deploy to Production

📊 Development Status

Current Status: Production-Ready RAG System with GCP Deployment ✅

Services Overview

System Capabilities

🛠️ Developer Reference

Architecture Diagram

Microservices Architecture

Tech Stack Reference

Developer Workflows

Running Tests

Development Mode

Debugging

Working with Docker Services

� Security

Authentication & Authorization

Multi-Tenant Security

API Security

Secret Management

Security Best Practices

Known Security Considerations

🔧 Troubleshooting

Quick Troubleshooting Index

�🔧 Troubleshooting

📈 Performance & Monitoring

Target Metrics

Monitoring Endpoints

Local Monitoring Setup

🧪 Testing

Test Coverage Overview

Known Test Issues

Running Tests

Test Categories

Writing Tests

Continuous Testing

🎯 Roadmap

1. High Priority: GCP Cloud Deployment ✅ COMPLETE

2. Medium Priority: System Integration & Testing

3. Lower Priority: Production Features

📚 Documentation

🚀 Deployment & Infrastructure

🛠️ Development & Testing

📋 Project Management

📱 Applications & Tools

☁️ GCP Cloud Deployment

Infrastructure Overview

Container Images

Deployment Scripts

Kubernetes Deployment

Infrastructure Costs (Estimated)

GCP Console Links

📄 License

About

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Resources

Contributing

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