proffesor-for-testing__agentic-qe/v2/docker

RuVector Docker Setup

This directory contains Docker Compose configurations for running RuVector as part of the Agentic QE Fleet.

Quick Start

1. Start RuVector (Production)

./scripts/ruvector-start.sh start

2. Start RuVector (Development)

./scripts/ruvector-start.sh start development

3. Check Status

./scripts/ruvector-start.sh status

4. View Logs

./scripts/ruvector-start.sh logs

5. Stop RuVector

./scripts/ruvector-start.sh stop

Configuration

Environment Variables

Create a .env file in the project root to customize settings:

# Data Paths
RUVECTOR_DATA_PATH=/custom/path/to/data
RUVECTOR_MODELS_PATH=/custom/path/to/models

# Performance Tuning
RUVECTOR_MAX_MEMORY_GB=8
RUVECTOR_CACHE_SIZE_MB=1024

# Learning Configuration
RUVECTOR_LORA_RANK=16
RUVECTOR_EWC_LAMBDA=0.5

Profiles

Production Profile (Default)

• Optimized for performance
• JSON logging
• Resource limits: 2-4GB RAM, 1-2 CPUs
• Ports: 8080 (REST), 9090 (gRPC)

Development Profile

• Debug logging enabled
• Text format logs for readability
• Profiling endpoint exposed (port 6060)
• More aggressive learning settings
• Separate data volumes

Architecture

Ports

Port	Service	Description
8080	REST API	HTTP/JSON endpoints
9090	gRPC	High-performance RPC
6060	Profiling	Go pprof (dev only)

Volumes

Volume	Purpose	Path
ruvector-data	Vector storage	/data
ruvector-models	LoRA models	/models

Health Checks

The container includes automatic health monitoring:

• Endpoint: http://localhost:8080/health
• Interval: 30 seconds
• Timeout: 10 seconds
• Retries: 3
• Start Period: 40 seconds

Advanced Usage

Manual Docker Compose Commands

# Start with specific profile
docker compose -f docker/docker-compose.ruvector.yml --profile production up -d

# View logs
docker compose -f docker/docker-compose.ruvector.yml logs -f

# Stop and remove volumes
docker compose -f docker/docker-compose.ruvector.yml down -v

Custom Configuration File

Mount a custom config file:

volumes:
  - ./config/ruvector.yml:/app/config.yml:ro

Resource Limits

Adjust in docker-compose.ruvector.yml:

deploy:
  resources:
    limits:
      cpus: '4.0'
      memory: 8G
    reservations:
      cpus: '2.0'
      memory: 4G

Integration with Agentic QE

1. Initialize AQE with RuVector

aqe init --vector-db ruvector --ruvector-url http://localhost:8080

2. Configure Agent Learning

# Enable self-learning for specific agent
aqe learn enable --agent qe-test-generator

# Set RuVector as backend
aqe config set learning.backend ruvector

3. Verify Integration

# Check RuVector connection
curl http://localhost:8080/health

# Test vector operations
curl -X POST http://localhost:8080/api/v1/vectors \
  -H "Content-Type: application/json" \
  -d '{"embedding": [0.1, 0.2, 0.3], "metadata": {"test": true}}'

Monitoring

Container Stats

docker stats agentic-qe-ruvector

Health Status

./scripts/ruvector-start.sh health

Logs Analysis

# Last 100 lines
./scripts/ruvector-start.sh logs 100

# Stream logs
./scripts/ruvector-start.sh logs follow

# Filter for errors
docker compose -f docker/docker-compose.ruvector.yml logs | grep ERROR

Troubleshooting

Container Won't Start

1. Check Docker is running:

   docker info
   

2. Check port availability:

   lsof -i :8080
   lsof -i :9090
   

3. View startup logs:

   ./scripts/ruvector-start.sh logs
   

Health Check Failing

1. Verify endpoint manually:

   curl -v http://localhost:8080/health
   

2. Check container logs:

   ./scripts/ruvector-start.sh logs 50
   

3. Restart container:

   ./scripts/ruvector-start.sh restart
   

Out of Memory

1. Increase memory limit in docker-compose.ruvector.yml
2. Reduce cache size: RUVECTOR_CACHE_SIZE_MB=256
3. Lower max memory: RUVECTOR_MAX_MEMORY_GB=2

Permission Errors

Ensure data directories are writable:

chmod -R 755 ./data/ruvector ./data/ruvector-models

Performance Tuning

HNSW Index Optimization

environment:
  RUVECTOR_HNSW_M: 64              # Higher = more accurate, more memory
  RUVECTOR_HNSW_EF_CONSTRUCTION: 400  # Higher = slower build, better quality
  RUVECTOR_HNSW_EF_SEARCH: 200     # Higher = slower search, more accurate

Learning Rate Tuning

environment:
  RUVECTOR_LORA_RANK: 16           # Higher = more capacity, more memory
  RUVECTOR_LORA_ALPHA: 32          # Scaling factor for LoRA updates
  RUVECTOR_EWC_LAMBDA: 0.5         # Higher = more forgetting prevention

Backup and Recovery

Backup Data

# Stop container
./scripts/ruvector-start.sh stop

# Backup volumes
tar -czf ruvector-backup-$(date +%Y%m%d).tar.gz ./data/ruvector ./data/ruvector-models

# Restart
./scripts/ruvector-start.sh start

Restore Data

# Stop container
./scripts/ruvector-start.sh stop

# Restore from backup
tar -xzf ruvector-backup-20250101.tar.gz

# Restart
./scripts/ruvector-start.sh start

References

• RuVector GitHub
• RuVector Documentation
• Phase 0.5 Implementation Guide

Support

For issues specific to:

• Docker setup: Check this README and container logs
• RuVector service: See RuVector docs
• AQE integration: See Phase 0.5 docs