Helix AI Gateway – Production Implementation

Open-Source LLM Proxy that Saves Money, Reduces Latency, and Stops PII Leaks

Built on top of LiteLLM with enterprise-grade features for AI/ML workloads.

🚀 Quick Start

Prerequisites

• Docker and Docker Compose
• Redis Stack (with RediSearch)
• PostgreSQL 15+
• Python 3.11+ (for development)

One-Line Setup

# Clone and start Helix
git clone https://github.com/berriai/litellm.git
cd litellm
git checkout -b helix-gateway
cp .env.example .env
# Add your API keys to .env
docker-compose -f docker-compose.helix.yml up -d

That's it! Your Helix AI Gateway is now running:

• AI Gateway Proxy: http://localhost:4000 (OpenAI-compatible API)
• Dashboard: http://localhost:8501 (Real-time monitoring)
• Grafana: http://localhost:3000 (Advanced analytics)
• Redis Commander: http://localhost:8081 (Redis GUI)

🎯 One-Code-Change Integration

Replace your OpenAI client base URL:

# Before
import openai
openai.api_key = "sk-your-key"
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

# After - ONE LINE CHANGE
import openai
openai.api_key = "any"  # Helix handles auth
openai.api_base = "http://localhost:4000/v1"  # ← Helix
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Everything else stays the same – but now you get:

• 30-60% cost savings from intelligent caching
• 10x faster responses on cached queries
• Enterprise-grade PII protection
• Real-time spend tracking and budget controls

📊 Features Overview

🚀 Intelligent Caching

• Exact caching: Perfect match detection for repeated queries
• Semantic caching: Vector similarity search with configurable thresholds
• Hybrid approach: Combines exact and semantic for optimal performance
• Smart eviction: LRU + TTL + memory management
• Cache analytics: Real-time hit rates, savings metrics, performance tracking

🔒 PII Protection & Redaction

• Microsoft Presidio integration: Enterprise-grade PII detection
• 100+ entity types: Custom recognizers, ML-based detection
• Real-time redaction: Automatic masking before reaching LLM providers
• Audit logging: Complete compliance trail for detected incidents
• Configurable policies: Per-user, per-team, per-organization rules

💰 Cost Optimization

• Real-time spend tracking: Per-request cost calculation
• Budget management: Daily/weekly/monthly limits with alerts
• Model swapping: Intelligent routing to cost-effective alternatives
• Cache savings calculation: Automatic savings attribution
• Spend alerts: Configurable thresholds and notifications

📈 Real-time Monitoring

• Streamlit dashboard: Live metrics, charts, and analytics
• Grafana integration: Advanced visualizations and alerts
• Prometheus metrics: Full observability stack
• User leaderboards: Top spenders, usage patterns, insights
• Performance metrics: Latency, throughput, error rates

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Client App    │───▶│   Helix Gateway  │───▶│  LLM Provider   │
│                 │    │                  │    │ (OpenAI/Anthropic│
│ OpenAI SDK      │    │ • Semantic Cache │    │  /Groq/etc.)   │
│ • 1-line change │    │ • PII Redaction │    │                 │
│ • Transparent    │    │ • Cost Tracking  │    │                 │
└─────────────────┘    │ • Optimization   │    └─────────────────┘
                       └──────────────────┘
                              │
                       ┌──────┴──────┐
                       │             │
                ┌──────▼─────┐ ┌─────▼─────┐
                │   Redis     │ │PostgreSQL  │
                │ • Vector DB │ │ • Users    │
                │ • Cache     │ │ • Config   │
                │ • Metrics   │ │ • Logs     │
                └────────────┘ └────────────┘

📋 Configuration

Environment Variables

# Core Configuration
HELIX_ENABLED=true
REDIS_URL=redis://localhost:6379
DATABASE_URL=postgresql://helix:password@localhost:5432/helix

# API Keys (add yours)
OPENAI_API_KEY=sk-your-openai-key
ANTHROPIC_API_KEY=sk-your-anthropic-key
GROQ_API_KEY=gsk_your-groq-key
GOOGLE_API_KEY=your-google-key

# Monitoring
LANGFUSE_PUBLIC_KEY=your-langfuse-public
LANGFUSE_SECRET_KEY=your-langfuse-secret
LANGFUSE_HOST=https://cloud.langfuse.com

# Security
MASTER_KEY=your-master-key
GRAFANA_PASSWORD=your-grafana-password
POSTGRES_PASSWORD=your-postgres-password

Configuration File

See config/helix.example.yaml for comprehensive configuration options:

helix_settings:
  semantic_cache:
    enabled: true
    similarity_threshold: 0.88
    embedding_model: "all-MiniLM-L6-v2"
    ttl: 2592000  # 30 days

  pii_protection:
    enabled: true
    strict_mode: true
    entities: ["CREDIT_CARD", "EMAIL_ADDRESS", "PHONE_NUMBER"]
    action: "redact"

  cost_optimization:
    enabled: true
    model_swapping: true
    budget_alerts: true

  dashboard:
    enabled: true
    port: 8501
    refresh_interval: 5

🚢 Production Deployment

Docker Compose (Recommended)

# Production deployment with all components
docker-compose -f docker-compose.helix.yml up -d

# Check services status
docker-compose -f docker-compose.helix.yml ps

# View logs
docker-compose -f docker-compose.helix.yml logs -f

Kubernetes

See k8s/ directory for Kubernetes manifests:

# Deploy to Kubernetes
kubectl apply -f k8s/

# Check deployment
kubectl get pods -l app=helix
kubectl port-forward service/helix-proxy 4000:4000

Manual Installation

# Install dependencies
pip install -e ".[proxy]"
pip install sentence-transformers presidio-analyzer presidio-anonymizer redis streamlit

# Initialize Redis
redis-cli -u redis://localhost:6379 < redis/helix_init.redis

# Start proxy
uvicorn litellm.proxy.proxy_server:app --host 0.0.0.0 --port 4000

# Start dashboard
streamlit run helix/dashboard/dashboard.py --server.port=8501

📊 Dashboard & Monitoring

Streamlit Dashboard (http://localhost:8501)

• Overview: Real-time metrics, savings, performance
• Cost Analysis: Spend trends, budget tracking, model costs
• Cache Performance: Hit rates, latency, memory usage
• PII Incidents: Detection logs, redaction statistics
• User Leaderboard: Top spenders, usage patterns
• System Health: Service status, resource usage

Grafana (http://localhost:3000)

• Advanced visualizations: Custom dashboards, drill-downs
• Alerting: Configurable thresholds, notifications
• Historical analysis: Long-term trends, capacity planning
• Integration: Prometheus, custom metrics

Default credentials: admin / your-grafana-password

Redis Commander (http://localhost:8081)

• Redis GUI: Browse cache entries, inspect vectors
• Performance monitoring: Memory usage, connection stats
• Manual operations: Cache management, key inspection

🔧 Development

Project Structure

litellm/
├── proxy/
│   ├── helix_hooks.py           # Main Helix integration
│   └── example_config_yaml/
│       └── helix_config.yaml    # Production config
helix/
├── dashboard/
│   ├── dashboard.py              # Streamlit dashboard
│   ├── Dockerfile
│   └── requirements.txt
├── core/
│   └── config.py                # Configuration management
├── migrations/
│   └── helix_schema_extensions.prisma  # Database schema
├── redis/
│   ├── redis.conf               # Redis optimization
│   └── helix_init.redis       # Initialization script
├── database/
│   └── init.sql                # PostgreSQL schema
├── monitoring/
│   ├── prometheus.yml          # Prometheus config
│   └── helix_rules.yml        # Alerting rules
└── nginx/
    └── nginx.conf              # Reverse proxy config

Adding Custom Features

1. Custom PII Recognizers:

# In helix_config.yaml
pii_protection:
  custom_recognizers:
    - name: "internal_token"
      pattern: "token_[a-f0-9]{32}"
      entity_type: "API_KEY"
      confidence_level: 0.95

2. Cost Optimization Rules:

# In helix_config.yaml
cost_optimization:
  rules:
    - rule_name: "simple_queries"
      conditions:
        max_tokens: 1000
      action:
        model: "fast-model"
        reason: "Cost optimization for simple queries"

3. Custom Metrics:

# In helix_hooks.py
async def custom_metric_hook(data, response, user_api_key_dict):
    # Add your custom tracking logic
    await track_custom_metrics(data, response)

Testing

# Run tests
make test

# Run specific test
pytest tests/test_helix/ -v

# Load testing
locust -f tests/performance/locustfile.py

🔍 Monitoring & Debugging

Health Checks

# Proxy health
curl http://localhost:4000/health

# Redis health
redis-cli ping

# Dashboard health
curl http://localhost:8501/_stcore/health

Metrics Endpoints

# Prometheus metrics
curl http://localhost:4000/metrics

# Custom Helix metrics
curl http://localhost:4000/helix/metrics

Debug Mode

# Enable debug logging
HELIX_DEBUG=true docker-compose -f docker-compose.helix.yml up

# View detailed logs
docker-compose -f docker-compose.helix.yml logs litellm -f

📚 API Reference

Proxy Endpoints

• POST /v1/chat/completions: OpenAI-compatible completion endpoint
• POST /v1/embeddings: Embedding generation
• GET /health: Health check
• GET /metrics: Prometheus metrics
• GET /helix/stats: Helix-specific statistics

Cache Management

# Manual cache invalidation
redis-cli DEL helix:exact:<hash>

# Clear semantic cache
redis-cli FT.DROPINDEX helix:semantic:index DD

# View cache stats
redis-cli HGETALL helix:cache:metrics

Configuration Management

# Reload configuration
curl -X POST http://localhost:4000/helix/reload-config

# View current config
curl http://localhost:4000/helix/config

# Validate configuration
curl http://localhost:4000/helix/config/validate

🚨 Troubleshooting

Common Issues

1. Cache misses:

   • Check Redis connection: redis-cli ping
   • Verify vector index: redis-cli FT.INFO helix:semantic:index
   • Check similarity threshold in config

2. PII not detected:

   • Verify Presidio configuration
   • Check entity list in config
   • Enable debug logging for PII processing

3. High latency:

   • Monitor Redis memory usage
   • Check cache hit rates
   • Verify embedding model performance

4. Dashboard not loading:

   • Check Redis connection in dashboard
   • Verify Streamlit configuration
   • Check for missing dependencies

Performance Tuning

# Redis optimization
maxmemory: 2gb
maxmemory-policy: allkeys-lru

# Vector search optimization
# In redis.conf
M 32                    # Better recall
ef_construction 400      # Better graph
ef_runtime 100          # Better search

# Cache settings
similarity_threshold: 0.88    # Balance precision/recall
cache_ttl: 2592000            # 30 days

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

Development Setup

# Clone repository
git clone https://github.com/berriai/litellm.git
cd litellm

# Install development dependencies
make install-dev
make install-proxy-dev

# Start development stack
docker-compose -f docker-compose.helix.yml up -d

# Run tests
make test

📄 License

Apache 2.0 License - see LICENSE file for details.

🙏 Acknowledgments

• LiteLLM: Foundation for the proxy functionality
• Microsoft Presidio: Enterprise-grade PII detection
• Sentence Transformers: Semantic similarity search
• Redis Stack: High-performance caching and vector search
• Streamlit: Real-time dashboard framework

📞 Support

• Documentation: Helix Wiki
• Issues: GitHub Issues
• Discord: LiteLLM Discord
• Email: support@berri.ai

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Built with ❤️ by the LiteLLM team