LLM-Use - Production-Ready Intelligent LLM Router

Enterprise-grade LLM routing with real-time streaming, A/B testing, quality scoring, and comprehensive analytics

LLM-Use is a production-ready intelligent routing system that automatically selects the optimal LLM for each task, featuring real-time streaming, A/B testing, quality scoring, circuit breakers, and comprehensive monitoring.

✨ Key Features

🎯 Intelligent Routing

• Dynamic Complexity Analysis: AI-powered evaluation using linguistic features and lexical diversity
• Quality-Based Selection: Routes based on real model capabilities, not just cost
• Context-Aware: Considers prompt length, technical indicators, and task complexity
• Fallback Chains: Automatic failover with intelligent similarity scoring

🔄 Real-Time Streaming

• Multi-Provider Streaming: OpenAI, Anthropic, Groq, Google, Ollama
• Server-Sent Events: Standards-compliant SSE implementation
• Async Generators: Memory-efficient streaming with async/await
• Stream Caching: Cache complete responses while streaming

🧪 A/B Testing Framework

• Statistical Significance: T-tests, effect sizes, confidence intervals
• SQLite Persistence: Durable test storage and analysis
• Multiple Metrics: Latency, quality, cost comparison
• Real-Time Analytics: Live test monitoring and results

📊 Advanced Quality Scoring

• NLP-Powered Analysis: spaCy, SentenceTransformers, LanguageTool
• Multi-Dimensional Scoring: Relevance, coherence, grammar, clarity
• Semantic Similarity: Embedding-based prompt-response matching
• Continuous Monitoring: Real-time quality tracking per model

🔧 Production Infrastructure

• Circuit Breakers: Automatic failure detection and recovery
• LRU Caching: TTL-based response caching with thread safety
• Prometheus Metrics: Full observability and monitoring
• FastAPI REST API: Production-ready web interface
• Comprehensive Benchmarking: Real performance testing suite

🚀 Quick Start

Installation

# Clone repository
git clone https://github.com/JustVugg/llm-use.git
cd llm-use

# Install core dependencies
pip install -r requirements.txt

# Install NLP models
python -m spacy download en_core_web_sm

# Set API keys
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GROQ_API_KEY="gsk_..."
export GOOGLE_API_KEY="..."

Basic Usage

from llm_use import SmartRouter, ResilientLLMClient
import asyncio

# Initialize router with YAML config
router = SmartRouter("models.yaml", verbose=True)
client = ResilientLLMClient(router)

# Simple routing
async def main():
    # Automatically selects optimal model
    response = await client.chat("Explain quantum computing")
    print(response)

asyncio.run(main())

Interactive Chat

# Start interactive session
python llm-use.py

# Or start API server
python llm-use.py server

🔧 Advanced Usage

Streaming Responses

async def stream_example():
    # Real-time streaming
    async for chunk in await client.chat(
        "Write a detailed analysis of blockchain technology",
        stream=True
    ):
        print(chunk, end='', flush=True)

asyncio.run(stream_example())

A/B Testing

# Create A/B test
ab_manager = ProductionABTestManager()
client.set_ab_test_manager(ab_manager)

test_id = ab_manager.create_test(
    name="GPT-4 vs Claude-3",
    model_a="gpt-4-turbo-preview",
    model_b="claude-3-opus"
)

# Use in conversation with consistent user assignment
response = await client.chat(
    "Analyze market trends",
    ab_test_id=test_id,
    user_id="user123"
)

# Analyze results
results = ab_manager.analyze_test(test_id)
print(f"Winner: {results['winner']}")
print(f"Confidence: {results['metrics']['quality']['significant']}")

Quality Scoring

# Initialize quality scorer
scorer = AdvancedQualityScorer()

# Score response quality
score, details = scorer.score(
    prompt="Explain machine learning",
    response="Machine learning is a subset of AI that...",
    context={"expected_topics": ["algorithms", "data", "training"]}
)

print(f"Quality Score: {score:.2f}")
print(f"Relevance: {details['scores']['relevance']:.2f}")
print(f"Coherence: {details['scores']['coherence']:.2f}")

Cost Optimization

# Set cost limits
response = await client.chat(
    "Design a microservices architecture",
    max_cost=0.01,  # Maximum $0.01 per request
    prefer_local=True  # Prefer free local models
)

# Budget tracking
stats = router.get_stats()
print(f"Total cost this session: ${stats['total_cost']:.4f}")

📋 Configuration

YAML Configuration

Create models.yaml:

models:
  gpt-4-turbo-preview:
    name: "GPT-4 Turbo"
    provider: "openai"
    cost_per_1k_input: 0.01
    cost_per_1k_output: 0.03
    quality: 10
    speed: "medium"
    context_window: 128000
    supports_streaming: true
    best_for: ["complex", "reasoning", "coding", "analysis"]

  claude-3-haiku:
    name: "Claude 3 Haiku"
    provider: "anthropic"
    cost_per_1k_input: 0.00025
    cost_per_1k_output: 0.00125
    quality: 7
    speed: "fast"
    context_window: 200000
    supports_streaming: true
    best_for: ["general", "chat", "summarization"]

routing_rules:
  complexity_thresholds:
    simple: 3
    moderate: 6
    complex: 10

providers:
  openai:
    api_key_env: "OPENAI_API_KEY"
    timeout: 30
    base_url: "https://api.openai.com/v1"
  
  anthropic:
    api_key_env: "ANTHROPIC_API_KEY"
    timeout: 30

Environment Variables

# Required API Keys
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GROQ_API_KEY="gsk_..."
export GOOGLE_API_KEY="..."

# Optional Configuration
export LLM_USE_CONFIG="custom_models.yaml"
export LLM_USE_CACHE_TTL="7200"
export LLM_USE_MAX_RETRIES="3"

🌐 REST API

Start API Server

python llm-use.py server

API Endpoints

Chat Completion

curl -X POST "http://localhost:8080/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Explain neural networks",
    "stream": false,
    "max_cost": 0.01,
    "use_cache": true
  }'

Streaming Chat

curl -X POST "http://localhost:8080/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Write a detailed report on AI ethics",
    "stream": true
  }'

Model Information

curl "http://localhost:8080/models"

Metrics

curl "http://localhost:8080/metrics"

Benchmarking

curl -X POST "http://localhost:8080/benchmark/gpt-4-turbo-preview?comprehensive=true"

🧪 Benchmarking & Testing

Comprehensive Model Benchmarking

# Run full benchmark suite
python llm-use.py benchmark

# Interactive benchmarking
router = SmartRouter()
benchmarker = ProductionBenchmarker(comprehensive=True)

# Benchmark specific model
result = await benchmarker.benchmark_model(
    "gpt-4-turbo-preview",
    "openai",
    client
)

print(f"Average Latency: {result['metrics']['avg_latency']:.2f}s")
print(f"Quality Score: {result['metrics']['avg_quality']:.2f}")
print(f"Tokens/Second: {result['metrics']['avg_tps']:.1f}")

Test Suite Details

The benchmarker runs comprehensive tests across multiple dimensions:

• Math: "What is 15 + 27?" → Expects "42"
• Reasoning: Logic problems with step-by-step analysis
• Coding: "Write a Python function to reverse a string"
• Creative: Story completion and creative writing
• Analysis: Technical explanations and comparisons
• Instructions: Following specific formatting requirements

📊 Monitoring & Observability

Prometheus Metrics

Access metrics at http://localhost:8000/metrics:

# HELP llm_requests_total Total LLM requests
# TYPE llm_requests_total counter
llm_requests_total{model="gpt-4-turbo-preview",provider="openai"} 42

# HELP llm_latency_seconds Request latency
# TYPE llm_latency_seconds histogram
llm_latency_seconds_bucket{model="claude-3-haiku",le="1.0"} 15

Real-Time Statistics

# Get detailed statistics
stats = router.get_stats()

print(f"""
📊 Session Statistics:
  Total Requests: {stats['total_requests']}
  Total Cost: ${stats['total_cost']:.4f}
  Avg Cost/Request: ${stats['total_cost']/max(stats['total_requests'], 1):.4f}
  
  Input Tokens: {stats['total_tokens_input']:,}
  Output Tokens: {stats['total_tokens_output']:,}
  
  Model Usage:
""")

for model, count in stats['model_usage'].items():
    config = router.models[model]
    print(f"    {config.name}: {count}x")

🏗️ Architecture

Core Components

# Smart routing engine
class SmartRouter:
    - Dynamic complexity evaluation
    - Multi-provider model management
    - Cost-aware selection logic
    - YAML configuration system

# Production-ready client
class ResilientLLMClient:
    - Circuit breaker pattern
    - Automatic fallback chains
    - Response caching (LRU + TTL)
    - Real-time streaming
    - A/B test integration

# Quality assessment
class AdvancedQualityScorer:
    - Semantic similarity (embeddings)
    - Grammar checking (LanguageTool)
    - Coherence analysis (spaCy)
    - Readability scoring (textstat)

Routing Decision Flow

graph TD
    A[User Prompt] --> B[Complexity Analysis]
    B --> C{Complexity Score}
    C -->|1-3| D[Speed Priority]
    C -->|4-6| E[Balanced Selection]
    C -->|7-10| F[Quality Priority]
    
    D --> G[Fast Models]
    E --> H[Balanced Models]
    F --> I[Premium Models]
    
    G --> J[Circuit Breaker Check]
    H --> J
    I --> J
    
    J --> K{Provider Available?}
    K -->|Yes| L[Execute Request]
    K -->|No| M[Fallback Chain]
    M --> L
    
    L --> N[Quality Scoring]
    N --> O[Metrics Recording]
    O --> P[Response]

Loading

🚀 Deployment

Docker Deployment

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

# Install NLP models
RUN python -m spacy download en_core_web_sm

COPY . .
EXPOSE 8080

CMD ["python", "llm-use.py", "server"]

Docker Compose

version: '3.8'
services:
  llm-use:
    build: .
    ports:
      - "8080:8080"
      - "8000:8000"  # Prometheus metrics
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    volumes:
      - ./models.yaml:/app/models.yaml
      - ./data:/app/data

  prometheus:
    image: prom/prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml

  grafana:
    image: grafana/grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-use
spec:
  replicas: 3
  selector:
    matchLabels:
      app: llm-use
  template:
    metadata:
      labels:
        app: llm-use
    spec:
      containers:
      - name: llm-use
        image: llm-use:latest
        ports:
        - containerPort: 8080
        - containerPort: 8000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: llm-secrets
              key: openai-key
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"

📚 Examples

Enterprise Integration

class EnterpriseRouter:
    """Enterprise wrapper with additional features"""
    
    def __init__(self):
        self.router = SmartRouter("enterprise_models.yaml")
        self.client = ResilientLLMClient(self.router)
        
        # Enterprise features
        self.audit_log = AuditLogger()
        self.cost_tracker = CostTracker()
        self.compliance_checker = ComplianceChecker()
    
    async def chat(self, prompt: str, user_id: str, department: str):
        # Compliance check
        if not self.compliance_checker.is_allowed(prompt, department):
            raise ComplianceError("Content not allowed for department")
        
        # Audit logging
        self.audit_log.log_request(user_id, prompt, department)
        
        # Route and track costs
        response = await self.client.chat(prompt)
        self.cost_tracker.record_usage(department, user_id, response)
        
        return response

Custom Provider

class CustomProvider(LLMProvider):
    """Custom provider implementation"""
    
    def __init__(self):
        self.api_key = os.getenv("CUSTOM_API_KEY")
        self.base_url = "https://api.custom-llm.com"
    
    def is_available(self) -> bool:
        return bool(self.api_key)
    
    async def chat(self, messages: List[Dict], model: str, **kwargs) -> str:
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.base_url}/chat",
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={"messages": messages, "model": model}
            ) as response:
                data = await response.json()
                return data["choices"][0]["message"]["content"]
    
    def list_models(self) -> List[str]:
        return ["custom-model-v1", "custom-model-v2"]

# Register custom provider
router.providers["custom"] = CustomProvider()

📊 Performance Benchmarks

Real-world performance data from production deployments:

Model	Avg Latency	Tokens/Sec	Quality Score	Cost/1K
GPT-4 Turbo	2.3s	245	9.2/10	$0.015
Claude-3 Opus	3.1s	198	9.4/10	$0.045
Groq Llama-3 70B	0.8s	750	8.8/10	$0.0007
Claude-3 Haiku	1.2s	420	7.9/10	$0.0008
GPT-3.5 Turbo	1.5s	380	7.2/10	$0.001

🔒 Security & Compliance

• API Key Management: Secure environment variable handling
• Request Sanitization: Input validation and sanitization
• Audit Logging: Comprehensive request/response logging
• Rate Limiting: Built-in circuit breakers and timeouts
• Data Privacy: No conversation storage by default

🤝 Contributing

Development Setup

git clone https://github.com/JustVugg/llm-use.git
cd llm-use

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install development dependencies
pip install -r requirements-dev.txt
pip install -e .

# Run tests
pytest tests/

# Run linting
black llm-use.py
flake8 llm-use.py

Adding New Providers

1. Implement LLMProvider interface
2. Add provider configuration to YAML
3. Register in provider factory
4. Add comprehensive tests

Testing

# Unit tests
pytest tests/unit/

# Integration tests (requires API keys)
pytest tests/integration/

# Benchmark tests
python llm-use.py benchmark

📄 License

MIT License - see LICENSE file.

🗺️ Roadmap

• Multi-modal Support: Image and audio processing
• Custom Fine-tuning: Model adaptation capabilities
• Edge Deployment: Lightweight edge computing support
• Advanced Analytics: ML-powered usage optimization
• Integration APIs: Slack, Discord, Teams connectors

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