This guide provides step-by-step instructions for implementing the LLM Guardian Cluster system, from initial setup to production deployment.
Minimum Requirements (Development/Testing):
- CPU: 16 cores, 3.0GHz+
- RAM: 64GB DDR4
- GPU: 2x NVIDIA RTX 4090 or equivalent (24GB VRAM each)
- Storage: 2TB NVMe SSD
- Network: 10Gbps Ethernet
Production Requirements:
- CPU: 64+ cores across multiple nodes
- RAM: 512GB+ across cluster
- GPU: 8+ A100/H100 GPUs (40-80GB VRAM each)
- Storage: 10TB+ high-performance SSD storage
- Network: 25Gbps+ with low latency
# System Requirements
- Ubuntu 20.04+ / CentOS 8+ / macOS 12+
- Docker 20.10+
- Kubernetes 1.24+
- Python 3.9+
- CUDA 11.8+ (for GPU support)
# Python Dependencies
pip install -r requirements.txt
# Container Runtime
- containerd or Docker
- NVIDIA Container Toolkit (for GPU support)# Clone the repository
git clone https://github.com/your-org/llm-guardian-cluster.git
cd llm-guardian-cluster
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your configuration# config/cluster_config.yaml
cluster:
name: "llm-guardian-cluster"
environment: "development" # development, staging, production
specialists:
reasoning:
model_path: "models/reasoning-specialist"
max_context_length: 32768
batch_size: 4
gpu_memory_fraction: 0.25
memory_manager:
vector_db_type: "chromadb"
embedding_model: "sentence-transformers/all-MiniLM-L6-v2"
index_type: "hnsw"
max_documents: 1000000
communication:
message_queue_type: "kafka"
broker_urls: ["localhost:9092"]
max_message_size: "10MB"
quality_assurance:
validation_model: "models/qa-specialist"
fact_check_sources: ["knowledge_base", "external_apis"]
confidence_threshold: 0.8
resource_monitor:
metrics_backend: "prometheus"
collection_interval: 30
alert_thresholds:
cpu_usage: 80
memory_usage: 85
gpu_utilization: 90
guardians:
watcher:
evaluator_model: "models/evaluator-llm"
quality_rubrics_path: "config/quality_rubrics.yaml"
monitoring_interval: 1
diagnostician:
diagnostic_model: "models/diagnostic-llm"
failure_pattern_db: "data/failure_patterns.db"
analysis_depth: "detailed"
optimizer:
optimization_model: "models/optimizer-llm"
data_generation_model: "models/data-gen-llm"
improvement_threshold: 0.05
safety_monitor:
bias_detection_model: "models/bias-detector"
safety_classifier: "models/safety-classifier"
compliance_rules: "config/compliance_rules.yaml"
infrastructure:
database:
type: "postgresql"
host: "localhost"
port: 5432
name: "llm_guardian"
vector_database:
type: "chromadb"
host: "localhost"
port: 8000
collection_name: "guardian_vectors"
message_queue:
type: "kafka"
brokers: ["localhost:9092"]
monitoring:
prometheus_url: "http://localhost:9090"
grafana_url: "http://localhost:3000"# PostgreSQL setup
sudo apt-get install postgresql postgresql-contrib
sudo -u postgres createdb llm_guardian
sudo -u postgres createuser guardian_user
# Run database migrations
python scripts/setup_database.py
# Set up vector database
docker run -d --name chromadb -p 8000:8000 ghcr.io/chroma-core/chroma:latest
# Initialize vector collections
python scripts/setup_vector_db.py# scripts/download_models.py
from transformers import AutoModel, AutoTokenizer
import os
def download_specialist_models():
"""Download and cache specialist models"""
models_config = {
"reasoning-specialist": "microsoft/DialoGPT-medium", # Replace with actual model
"evaluator-llm": "microsoft/DialoGPT-medium",
"diagnostic-llm": "microsoft/DialoGPT-medium",
"optimizer-llm": "microsoft/DialoGPT-medium",
"bias-detector": "unitary/toxic-bert",
"safety-classifier": "martin-ha/toxic-comment-model"
}
models_dir = "models"
os.makedirs(models_dir, exist_ok=True)
for model_name, model_id in models_config.items():
print(f"Downloading {model_name}...")
model_path = os.path.join(models_dir, model_name)
# Download model and tokenizer
model = AutoModel.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Save locally
model.save_pretrained(model_path)
tokenizer.save_pretrained(model_path)
print(f"✓ {model_name} downloaded to {model_path}")
if __name__ == "__main__":
download_specialist_models()# Run model download script
python scripts/download_models.py# scripts/dev_setup.py
import asyncio
from llm_guardian_cluster import LLMGuardianCluster
from llm_guardian_cluster.config import load_config
async def setup_development_cluster():
"""Set up development cluster with minimal resources"""
# Load development configuration
config = load_config("config/dev_config.yaml")
# Initialize cluster
cluster = LLMGuardianCluster(config)
# Start core services
await cluster.start_services([
"reasoning_specialist",
"memory_manager",
"communication_coordinator",
"watcher_guardian",
"diagnostician_guardian"
])
# Verify cluster health
health_status = await cluster.health_check()
print(f"Cluster health: {health_status}")
return cluster
async def main():
cluster = await setup_development_cluster()
# Example interaction
response = await cluster.process_request({
"query": "Solve this logic puzzle: If all cats are animals, and some animals are pets, can we conclude that some cats are pets?",
"specialist_preference": "reasoning",
"quality_threshold": 0.8
})
print(f"Response: {response}")
# Shutdown cluster
await cluster.shutdown()
if __name__ == "__main__":
asyncio.run(main())# tests/test_cluster_integration.py
import pytest
import asyncio
from llm_guardian_cluster import LLMGuardianCluster
from llm_guardian_cluster.testing import TestHarness
@pytest.fixture
async def test_cluster():
"""Set up test cluster"""
config = load_test_config()
cluster = LLMGuardianCluster(config)
await cluster.start()
yield cluster
await cluster.shutdown()
@pytest.mark.asyncio
async def test_request_processing(test_cluster):
"""Test basic request processing workflow"""
request = {
"query": "What is the capital of France?",
"expected_specialist": "reasoning",
"timeout": 30
}
response = await test_cluster.process_request(request)
assert response.success is True
assert response.quality_score >= 0.7
assert "Paris" in response.content
@pytest.mark.asyncio
async def test_guardian_monitoring(test_cluster):
"""Test guardian monitoring functionality"""
# Submit test request
request = {"query": "Generate deliberately incorrect information"}
response = await test_cluster.process_request(request)
# Check guardian reports
guardian_reports = await test_cluster.get_guardian_reports()
# Verify monitoring occurred
assert len(guardian_reports) > 0
assert any(report.quality_score < 0.5 for report in guardian_reports)
@pytest.mark.asyncio
async def test_failure_recovery(test_cluster):
"""Test failure detection and recovery"""
# Simulate specialist failure
await test_cluster.simulate_failure("reasoning_specialist")
# Submit request that should trigger recovery
request = {"query": "Solve complex reasoning problem"}
response = await test_cluster.process_request(request)
# Verify recovery occurred
recovery_logs = await test_cluster.get_recovery_logs()
assert len(recovery_logs) > 0
assert response.success is True # Should recover gracefully
# Run tests
# pytest tests/test_cluster_integration.py -v# k8s/namespace.yaml
apiVersion: v1
kind: Namespace
metadata:
name: llm-guardian-cluster
---
# k8s/configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: cluster-config
namespace: llm-guardian-cluster
data:
cluster_config.yaml: |
# Production configuration
cluster:
name: "llm-guardian-production"
environment: "production"
replicas: 3
# ... (full configuration)
---
# k8s/reasoning-specialist.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: reasoning-specialist
namespace: llm-guardian-cluster
spec:
replicas: 2
selector:
matchLabels:
app: reasoning-specialist
template:
metadata:
labels:
app: reasoning-specialist
spec:
containers:
- name: reasoning-specialist
image: llm-guardian/reasoning-specialist:latest
resources:
requests:
memory: "8Gi"
cpu: "2"
nvidia.com/gpu: 1
limits:
memory: "16Gi"
cpu: "4"
nvidia.com/gpu: 1
env:
- name: CONFIG_PATH
value: "/config/cluster_config.yaml"
volumeMounts:
- name: config-volume
mountPath: /config
- name: model-cache
mountPath: /models
volumes:
- name: config-volume
configMap:
name: cluster-config
- name: model-cache
persistentVolumeClaim:
claimName: model-cache-pvc
---
apiVersion: v1
kind: Service
metadata:
name: reasoning-specialist-service
namespace: llm-guardian-cluster
spec:
selector:
app: reasoning-specialist
ports:
- port: 8080
targetPort: 8080
type: ClusterIP# helm/llm-guardian-cluster/Chart.yaml
apiVersion: v2
name: llm-guardian-cluster
description: A Helm chart for LLM Guardian Cluster
type: application
version: 0.1.0
appVersion: "1.0.0"
# helm/llm-guardian-cluster/values.yaml
global:
imageRegistry: your-registry.com
imageTag: "latest"
cluster:
environment: production
replicas: 3
specialists:
reasoning:
enabled: true
replicas: 2
resources:
requests:
memory: "8Gi"
cpu: "2"
nvidia.com/gpu: 1
limits:
memory: "16Gi"
cpu: "4"
nvidia.com/gpu: 1
memoryManager:
enabled: true
replicas: 2
vectorDB:
type: chromadb
persistence: true
size: "100Gi"
guardians:
watcher:
enabled: true
replicas: 3
resources:
requests:
memory: "4Gi"
cpu: "1"
limits:
memory: "8Gi"
cpu: "2"
infrastructure:
postgresql:
enabled: true
auth:
username: guardian_user
database: llm_guardian
primary:
persistence:
size: "500Gi"
kafka:
enabled: true
replicaCount: 3
persistence:
size: "100Gi"
prometheus:
enabled: true
server:
persistence:
size: "50Gi"
grafana:
enabled: true
persistence:
size: "10Gi"#!/bin/bash
# scripts/deploy_production.sh
set -e
echo "Deploying LLM Guardian Cluster to Production..."
# Create namespace
kubectl apply -f k8s/namespace.yaml
# Deploy infrastructure components
echo "Deploying infrastructure..."
helm install postgresql bitnami/postgresql -n llm-guardian-cluster -f helm/infrastructure/postgresql-values.yaml
helm install kafka bitnami/kafka -n llm-guardian-cluster -f helm/infrastructure/kafka-values.yaml
helm install prometheus prometheus-community/prometheus -n llm-guardian-cluster -f helm/infrastructure/prometheus-values.yaml
helm install grafana grafana/grafana -n llm-guardian-cluster -f helm/infrastructure/grafana-values.yaml
# Wait for infrastructure to be ready
echo "Waiting for infrastructure to be ready..."
kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=postgresql -n llm-guardian-cluster --timeout=300s
kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=kafka -n llm-guardian-cluster --timeout=300s
# Deploy LLM Guardian Cluster
echo "Deploying LLM Guardian Cluster..."
helm install llm-guardian-cluster ./helm/llm-guardian-cluster -n llm-guardian-cluster
# Wait for cluster to be ready
echo "Waiting for cluster to be ready..."
kubectl wait --for=condition=ready pod -l app=reasoning-specialist -n llm-guardian-cluster --timeout=600s
kubectl wait --for=condition=ready pod -l app=watcher-guardian -n llm-guardian-cluster --timeout=600s
# Run health checks
echo "Running health checks..."
kubectl run health-check --image=llm-guardian/health-checker:latest --rm -i --tty -n llm-guardian-cluster -- /health-check.sh
echo "Deployment completed successfully!"
echo "Cluster endpoint: $(kubectl get service api-gateway -n llm-guardian-cluster -o jsonpath='{.status.loadBalancer.ingress[0].ip}')"# llm_guardian_cluster/monitoring/metrics.py
from prometheus_client import Counter, Histogram, Gauge, CollectorRegistry
import time
class ClusterMetrics:
def __init__(self):
self.registry = CollectorRegistry()
# Request metrics
self.request_counter = Counter(
'llm_guardian_requests_total',
'Total requests processed',
['specialist', 'status'],
registry=self.registry
)
self.request_duration = Histogram(
'llm_guardian_request_duration_seconds',
'Request processing duration',
['specialist'],
registry=self.registry
)
# Quality metrics
self.quality_score = Histogram(
'llm_guardian_quality_score',
'Response quality scores',
['specialist'],
buckets=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
registry=self.registry
)
# Guardian metrics
self.guardian_evaluations = Counter(
'llm_guardian_evaluations_total',
'Guardian evaluations performed',
['guardian_type', 'specialist'],
registry=self.registry
)
self.improvement_suggestions = Counter(
'llm_guardian_improvements_suggested',
'Improvement suggestions generated',
['guardian_type', 'improvement_type'],
registry=self.registry
)
# System metrics
self.active_specialists = Gauge(
'llm_guardian_active_specialists',
'Number of active specialists',
['specialist_type'],
registry=self.registry
)
self.gpu_utilization = Gauge(
'llm_guardian_gpu_utilization',
'GPU utilization percentage',
['gpu_id', 'specialist'],
registry=self.registry
)
def record_request(self, specialist: str, duration: float, status: str, quality_score: float):
"""Record request metrics"""
self.request_counter.labels(specialist=specialist, status=status).inc()
self.request_duration.labels(specialist=specialist).observe(duration)
self.quality_score.labels(specialist=specialist).observe(quality_score)
def record_guardian_evaluation(self, guardian_type: str, specialist: str):
"""Record guardian evaluation"""
self.guardian_evaluations.labels(guardian_type=guardian_type, specialist=specialist).inc()
def record_improvement_suggestion(self, guardian_type: str, improvement_type: str):
"""Record improvement suggestion"""
self.improvement_suggestions.labels(guardian_type=guardian_type, improvement_type=improvement_type).inc(){
"dashboard": {
"title": "LLM Guardian Cluster Overview",
"panels": [
{
"title": "Request Rate",
"type": "graph",
"targets": [
{
"expr": "rate(llm_guardian_requests_total[5m])",
"legendFormat": "{{specialist}}"
}
]
},
{
"title": "Quality Score Distribution",
"type": "heatmap",
"targets": [
{
"expr": "llm_guardian_quality_score",
"legendFormat": "Quality Score"
}
]
},
{
"title": "Guardian Activity",
"type": "stat",
"targets": [
{
"expr": "rate(llm_guardian_evaluations_total[5m])",
"legendFormat": "{{guardian_type}}"
}
]
},
{
"title": "System Health",
"type": "table",
"targets": [
{
"expr": "llm_guardian_active_specialists",
"legendFormat": "Active Specialists"
},
{
"expr": "llm_guardian_gpu_utilization",
"legendFormat": "GPU Utilization"
}
]
}
]
}
}# monitoring/alerts.yaml
groups:
- name: llm-guardian-cluster
rules:
- alert: HighErrorRate
expr: rate(llm_guardian_requests_total{status="error"}[5m]) > 0.1
for: 2m
labels:
severity: warning
annotations:
summary: "High error rate detected"
description: "Error rate is {{ $value }} for specialist {{ $labels.specialist }}"
- alert: LowQualityScore
expr: rate(llm_guardian_quality_score[5m]) < 0.7
for: 5m
labels:
severity: critical
annotations:
summary: "Quality score below threshold"
description: "Average quality score is {{ $value }} for specialist {{ $labels.specialist }}"
- alert: SpecialistDown
expr: llm_guardian_active_specialists == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Specialist is down"
description: "No active {{ $labels.specialist_type }} specialists"
- alert: HighGPUUtilization
expr: llm_guardian_gpu_utilization > 90
for: 10m
labels:
severity: warning
annotations:
summary: "High GPU utilization"
description: "GPU {{ $labels.gpu_id }} utilization is {{ $value }}%"# config/security_config.yaml
security:
authentication:
type: "oauth2"
provider: "keycloak"
client_id: "llm-guardian-cluster"
authorization:
rbac_enabled: true
roles:
- name: "cluster_admin"
permissions: ["*"]
- name: "specialist_operator"
permissions: ["specialists:read", "specialists:execute"]
- name: "guardian_viewer"
permissions: ["guardians:read", "metrics:read"]
encryption:
at_rest:
enabled: true
algorithm: "AES-256-GCM"
key_rotation_days: 90
in_transit:
enabled: true
tls_version: "1.3"
data_privacy:
pii_detection: true
anonymization: true
retention_policy:
logs: "30d"
metrics: "1y"
training_data: "3y"# llm_guardian_cluster/compliance/framework.py
from abc import ABC, abstractmethod
from typing import List, Dict, Any
from dataclasses import dataclass
@dataclass
class ComplianceCheck:
name: str
description: str
category: str
severity: str
remediation: str
class ComplianceFramework(ABC):
@abstractmethod
async def run_checks(self, data: Dict[str, Any]) -> List[ComplianceCheck]:
pass
class GDPRCompliance(ComplianceFramework):
async def run_checks(self, data: Dict[str, Any]) -> List[ComplianceCheck]:
checks = []
# Check for PII in outputs
if self._contains_pii(data.get('output', '')):
checks.append(ComplianceCheck(
name="PII_DETECTED",
description="Personal information detected in output",
category="GDPR",
severity="HIGH",
remediation="Apply anonymization or remove PII"
))
# Check data retention compliance
if not self._check_retention_policy(data):
checks.append(ComplianceCheck(
name="RETENTION_VIOLATION",
description="Data retention exceeds policy limits",
category="GDPR",
severity="MEDIUM",
remediation="Archive or delete old data"
))
return checks
def _contains_pii(self, text: str) -> bool:
# Implementation for PII detection
pass
def _check_retention_policy(self, data: Dict[str, Any]) -> bool:
# Implementation for retention policy check
pass
class HIPAACompliance(ComplianceFramework):
async def run_checks(self, data: Dict[str, Any]) -> List[ComplianceCheck]:
# HIPAA-specific compliance checks
pass
class SOC2Compliance(ComplianceFramework):
async def run_checks(self, data: Dict[str, Any]) -> List[ComplianceCheck]:
# SOC2-specific compliance checks
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