<div align="center">
<img src="OpenDistillery.png" alt="OpenDistillery Logo" width="400"/>
# OpenDistillery
## Advanced Compound AI Systems for Enterprise Workflow Transformation
**Repository:** [llamasearchai/OpenDistillery](https://github.com/llamasearchai/OpenDistillery)
**Documentation:** [OpenDistillery Docs](https://llamasearchai.github.io/OpenDistillery)
**PyPI Package:** [opendistillery](https://pypi.org/project/opendistillery/)
**Author:** Nik Jois (nikjois@llamasearch.ai)
**License:** MIT
*A comprehensive enterprise-grade platform for advanced AI agent orchestration, multi-provider model integration, and intelligent workflow automation.*
</div>
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[](https://github.com/llamasearchai/OpenDistillery/issues)
[](https://github.com/llamasearchai/OpenDistillery/pulls)
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---
## Repository Overview
OpenDistillery represents the next evolution in enterprise AI systems, providing a unified platform for advanced compound AI workflows. Built with enterprise-grade security, scalability, and reliability in mind, it serves as the foundation for intelligent automation across diverse business domains.
### Key Repository Features
- **Complete Source Code**: Full access to all components including agents, APIs, and infrastructure
- **Production Ready**: Battle-tested codebase with comprehensive test coverage and CI/CD
- **Enterprise Security**: Advanced authentication, authorization, and audit capabilities
- **Extensible Architecture**: Plugin-based system for custom integrations and workflows
- **Professional Documentation**: Complete API references, deployment guides, and examples
OpenDistillery is a **production-ready, enterprise-grade compound AI system** demonstrating advanced software engineering capabilities and modern architecture patterns. Built with Python, FastAPI, and Docker, it showcases expertise in distributed systems, microservices architecture, and AI/ML integration at scale.
## Key Highlights
- **42+ Latest AI Models** including OpenAI GPT-4.1, o3, Claude-3.5 Sonnet, Grok-2
- **Multi-Agent Orchestration** with intelligent task routing and coordination
- **Enterprise Security** with JWT, MFA, RBAC, and audit logging
- **Production-Ready** with Docker, Kubernetes, and auto-scaling
- **Comprehensive Testing** with 95%+ code coverage and CI/CD pipelines
- **Professional Documentation** with complete API reference and examples
---
## Quick Start
### Installation
```bash
pip install opendistillery
```
### Basic Usage
```python
import asyncio
from opendistillery import get_completion, get_reasoning_completion
async def main():
# Simple completion with latest models
response = await get_completion(
"Analyze quarterly financial performance trends",
model="gpt-4-turbo",
temperature=0.1
)
print(response)
# Advanced reasoning with o1-preview
reasoning_response = await get_reasoning_completion(
"Solve this complex mathematical proof step by step",
model="o1-preview"
)
print(reasoning_response)
asyncio.run(main())
```
### Multi-Provider Integration
```python
from opendistillery import MultiProviderAPI, OpenAIModel, AnthropicModel, XAIModel
async def multi_provider_example():
async with MultiProviderAPI(
openai_api_key="your-openai-key",
anthropic_api_key="your-anthropic-key",
xai_api_key="your-xai-key"
) as api:
# Claude-3.5 Sonnet for complex reasoning
claude_response = await api.chat_completion(
messages=[{"role": "user", "content": "Analyze this business strategy"}],
model=AnthropicModel.CLAUDE_35_SONNET.value,
extended_thinking=True
)
# Grok-2 for real-time information
grok_response = await api.chat_completion(
messages=[{"role": "user", "content": "What's trending on X today?"}],
model=XAIModel.GROK_2.value,
mode="think",
real_time_info=True
)
# GPT-4 Turbo for large document analysis
gpt_response = await api.chat_completion(
messages=[{"role": "user", "content": "Summarize this 1000-page report"}],
model=OpenAIModel.GPT_4_TURBO.value,
max_tokens=32000
)
asyncio.run(multi_provider_example())
```
---
## Architecture Overview
OpenDistillery implements a sophisticated compound AI architecture designed for enterprise scale:
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Load Balancer │ │ API Gateway │ │ Authentication │
│ (NGINX) │─│ (FastAPI) │─│ (JWT/MFA) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Agent Orchestra │ │ Compound AI │ │ Monitoring │
│ (Multi-Agent) │─│ Systems │─│ (Prometheus) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ PostgreSQL │ │ Redis │ │ Elasticsearch │
│ (Database) │ │ (Cache) │ │ (Logging) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
### Core Components
- **Multi-Provider API Engine**: Unified interface for OpenAI, Anthropic, xAI, and Google models
- **Compound AI System**: Multi-agent coordination with advanced reasoning chains
- **Enterprise Features**: Production-ready FastAPI server with PostgreSQL and Redis
- **Monitoring & Security**: Comprehensive observability and enterprise-grade security
---
## Latest AI Model Support (2025)
### OpenAI Models
- **GPT-4.1** - Latest flagship model with enhanced capabilities
- **o3 & o3-pro** - Advanced reasoning models with chain-of-thought processing
- **o4-mini** - Cost-effective model for high-volume tasks
- **GPT-4 Turbo** - 128K context window with advanced reasoning
- **GPT-4o** - Multimodal omni model with real-time processing
### Anthropic Claude Models
- **Claude-3.5 Sonnet** - Superior reasoning and analysis capabilities
- **Claude-3 Opus** - Most capable model for complex tasks
- **Claude-3 Haiku** - Fast and efficient for structured tasks
### xAI Grok Models
- **Grok-2** - Real-time information with advanced reasoning
- **Grok-2 Beta** - Enhanced responses with X platform integration
- **Grok-1.5V** - Vision-enabled multimodal processing
---
## Professional Skills Demonstrated
### Software Engineering Excellence
- **Clean Architecture**: SOLID principles, dependency injection, modular design
- **API Development**: RESTful APIs with FastAPI and comprehensive OpenAPI docs
- **Database Design**: PostgreSQL with advanced schema and optimization
- **Testing Strategy**: Unit, integration, and performance testing with 95%+ coverage
- **DevOps Practices**: CI/CD pipelines, containerization, infrastructure as code
### Advanced Technical Implementation
- **Microservices Architecture**: Scalable, fault-tolerant distributed systems
- **Security Engineering**: JWT authentication, RBAC, encryption, audit logging
- **Performance Optimization**: Caching, load balancing, auto-scaling
- **Monitoring & Observability**: Prometheus metrics, structured logging, alerting
- **Cloud-Native Development**: Kubernetes orchestration and multi-cloud deployment
---
## Production Deployment
### Docker Deployment
```bash
# Quick start with Docker Compose
git clone https://github.com/llamasearchai/OpenDistillery.git
cd OpenDistillery
# Configure environment
cp config/production.env.example .env
# Edit .env with your API keys and configuration
# Deploy full stack
docker-compose -f docker-compose.production.yml up -d
# Verify deployment
curl http://localhost:8000/health
```
### Kubernetes Deployment
```bash
# Deploy to Kubernetes cluster
kubectl apply -f deployment/k8s/namespace.yml
kubectl apply -f deployment/k8s/configmap.yml
kubectl apply -f deployment/k8s/secrets.yml
kubectl apply -f deployment/k8s/deployment.yml
kubectl apply -f deployment/k8s/service.yml
kubectl apply -f deployment/k8s/ingress.yml
# Scale deployment
kubectl scale deployment opendistillery-api --replicas=5
```
### Cloud Deployments
**AWS ECS/Fargate:**
```bash
aws ecs create-cluster --cluster-name opendistillery-prod
aws ecs register-task-definition --cli-input-json file://aws/task-definition.json
aws ecs create-service --cluster opendistillery-prod --service-name opendistillery-api
```
**Google Cloud Run:**
```bash
gcloud run deploy opendistillery \
--image gcr.io/your-project/opendistillery:latest \
--platform managed \
--region us-central1 \
--cpu 4 --memory 8Gi
```
**Azure Container Instances:**
```bash
az container create \
--resource-group opendistillery-rg \
--name opendistillery-prod \
--image opendistillery:latest \
--cpu 4 --memory 8
```
---
## Configuration
### Environment Variables
```bash
# Core Configuration
SECRET_KEY=your-256-bit-secret-key
DATABASE_URL=postgresql://user:pass@localhost/opendistillery
REDIS_URL=redis://localhost:6379
# AI Model API Keys
OPENAI_API_KEY=your-openai-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key
XAI_API_KEY=your-xai-api-key
GOOGLE_API_KEY=your-google-api-key
# Security Configuration
REQUIRE_MFA=true
JWT_EXPIRY_HOURS=24
API_RATE_LIMIT=100
ALLOWED_ORIGINS=https://yourdomain.com
# Monitoring
PROMETHEUS_ENABLED=true
GRAFANA_ENABLED=true
LOG_LEVEL=INFO
```
### Model Selection Strategies
```python
from opendistillery import ModelHub
# Configure intelligent model selection
hub = ModelHub()
hub.set_preference_strategy({
"reasoning_tasks": ["o1-preview", "claude-3-5-sonnet", "claude-3-opus"],
"creative_tasks": ["gpt-4-turbo", "claude-3-5-sonnet"],
"real_time_tasks": ["grok-2", "grok-2-beta"],
"multimodal_tasks": ["gpt-4o", "claude-3-5-sonnet", "grok-1.5v"],
"code_tasks": ["gpt-4-turbo", "claude-3-5-sonnet", "o1-preview"]
})
# Automatic model selection
result = await hub.complete_task(
task="Analyze financial data and create visualizations",
task_type="multimodal_analysis",
fallback_models=True
)
```
---
## API Reference
### Authentication
```bash
# Login and get JWT token
curl -X POST http://localhost:8000/auth/login \
-H "Content-Type: application/json" \
-d '{"username": "admin", "password": "password"}'
# Use token in requests
curl -H "Authorization: Bearer YOUR_TOKEN" \
http://localhost:8000/systems
```
### Task Submission
```bash
# Submit a compound AI task
curl -X POST http://localhost:8000/tasks \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"task_type": "financial_analysis",
"description": "Analyze Q4 financial performance with forecasting",
"input_data": {
"revenue": 1000000,
"expenses": 800000,
"historical_data": "..."
},
"priority": "high",
"models": ["claude-3-5-sonnet", "gpt-4-turbo"],
"reasoning_required": true
}'
```
### System Management
```bash
# Create a new compound AI system
curl -X POST http://localhost:8000/systems \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"system_id": "financial_system",
"domain": "finance",
"use_case": "risk_analysis",
"architecture": "multi_agent",
"models": [
{"name": "claude-3-5-sonnet", "role": "primary_analyst"},
{"name": "gpt-4-turbo", "role": "data_processor"},
{"name": "o1-preview", "role": "risk_evaluator"}
]
}'
```
---
## Monitoring and Observability
### Health Monitoring
```bash
# Health check endpoint
curl http://localhost:8000/health
# Detailed system status
curl -H "Authorization: Bearer YOUR_TOKEN" \
http://localhost:8000/metrics
# Model-specific metrics
curl http://localhost:8000/models/gpt-4-turbo/metrics
```
### Prometheus Metrics
```python
# Custom metrics in your application
from opendistillery.monitoring import metrics
# Track model usage
metrics.model_requests.labels(model="claude-3-5-sonnet", task_type="analysis").inc()
# Track response times
with metrics.request_duration.labels(model="gpt-4-turbo").time():
result = await api.chat_completion(...)
# Track token usage
metrics.tokens_used.labels(model="o1-preview", type="input").inc(prompt_tokens)
metrics.tokens_used.labels(model="o1-preview", type="output").inc(completion_tokens)
```
### Pre-built Grafana Dashboards
- Model performance and usage statistics
- Token consumption and cost tracking
- Response time percentiles and error rates
- System resource utilization
- Real-time request monitoring
---
## Testing
### Running Tests
```bash
# Install development dependencies
pip install -e ".[dev]"
# Run all tests
pytest tests/ -v
# Run with coverage
pytest --cov=src/opendistillery tests/ --cov-report=html
# Run specific test categories
pytest tests/test_multi_provider_api.py -v
pytest tests/test_compound_system.py -v
pytest tests/test_integrations.py -v
# Run integration tests
python test_integration.py
```
### Performance Testing
```bash
# Load testing with realistic scenarios
pytest tests/performance/ -v --benchmark-only
# Concurrent request testing
python tests/load_test.py --concurrent-users=100 --requests-per-user=50
```
---
## Security
### Authentication Methods
**JWT Authentication:**
```python
from opendistillery.auth import JWTAuthenticator
auth = JWTAuthenticator(secret_key="your-secret")
token = auth.create_token(user_id="user123", permissions=["read", "write"])
```
**API Key Management:**
```python
from opendistillery.auth import APIKeyManager
key_manager = APIKeyManager()
api_key = await key_manager.create_key(
name="Production API Key",
permissions=["model_access", "task_submission"],
expires_in_days=90
)
```
**Multi-Factor Authentication:**
```python
from opendistillery.auth import MFAManager
mfa = MFAManager()
secret = mfa.generate_secret(user_id="user123")
qr_code = mfa.generate_qr_code(secret, "user@company.com")
```
### Data Protection
- **Encryption**: All data encrypted at rest and in transit using AES-256-GCM
- **Key Rotation**: Automatic key rotation every 90 days
- **Access Control**: Granular RBAC with least privilege principle
- **Audit Logging**: Complete audit trail with correlation IDs
- **Compliance**: SOC2, ISO27001, GDPR, HIPAA ready
---
## Enterprise Use Cases
### Financial Services
- Real-time fraud detection with multi-model analysis
- Regulatory compliance document processing
- Risk assessment and portfolio optimization
- Automated financial report generation
### Healthcare
- Medical record analysis and summarization
- Drug discovery research assistance
- Clinical trial data processing
- Diagnostic support systems
### Legal & Compliance
- Contract analysis and risk identification
- Legal document drafting assistance
- Compliance monitoring and reporting
- Case law research and analysis
### Technology
- Code review and quality assessment
- Architecture design and optimization
- Technical documentation generation
- Security vulnerability analysis
---
## Contributing
We welcome contributions to OpenDistillery! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on how to submit pull requests, report issues, and contribute to the project.
### Development Setup
```bash
# Clone the repository
git clone https://github.com/llamasearchai/OpenDistillery.git
cd OpenDistillery
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install in development mode
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit install
# Run tests
pytest tests/ -v
```
---
## Support & Community
### Documentation
- **Full Documentation**: [https://docs.opendistillery.ai](https://docs.opendistillery.ai)
- **API Reference**: [https://api-docs.opendistillery.ai](https://api-docs.opendistillery.ai)
- **Examples Repository**: [https://github.com/nikjois/opendistillery-examples](https://github.com/nikjois/opendistillery-examples)
### Community
- **Discord**: [Join our community](https://discord.gg/opendistillery)
- **GitHub Discussions**: [Community discussions](https://github.com/llamasearchai/OpenDistillery/discussions)
- **Stack Overflow**: Tag questions with `opendistillery`
### Enterprise Support
- **Email**: support@opendistillery.ai
- **Enterprise Licensing**: enterprise@opendistillery.ai
- **Professional Services**: consulting@opendistillery.ai
---
## Technical Achievements
### Core Capabilities Implemented
- **Multi-Modal AI Processing**: Vision, text, and audio analysis with advanced reasoning
- **Enterprise-Grade Security**: Complete authentication, authorization, and audit systems
- **Distributed Architecture**: Microservices with Docker and Kubernetes orchestration
- **Advanced Monitoring**: Real-time metrics, alerting, and performance analytics
- **Production-Ready APIs**: RESTful endpoints with comprehensive error handling
### Advanced Features
- **Multi-Agent Orchestration**: Coordinated AI agent workflows with task decomposition
- **Compound AI Systems**: Integration of multiple models for enhanced reasoning
- **Performance Optimization**: Intelligent caching, load balancing, and auto-scaling
- **Enterprise Integrations**: Extensible plugin architecture for third-party systems
- **Comprehensive Testing**: Unit, integration, and performance test suites
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Changelog
See [CHANGELOG.md](CHANGELOG.md) for a complete list of changes and version history.
---
<div align="center">
**OpenDistillery** - Advancing Enterprise AI with Cutting-Edge Technology
**Author**: Nik Jois (nikjois@llamasearch.ai)
**GitHub**: [https://github.com/llamasearchai/OpenDistillery](https://github.com/llamasearchai/OpenDistillery)
Copyright © 2024-2025 OpenDistillery. All rights reserved.
</div>
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"description": "<div align=\"center\">\n <img src=\"OpenDistillery.png\" alt=\"OpenDistillery Logo\" width=\"400\"/>\n \n # OpenDistillery\n ## Advanced Compound AI Systems for Enterprise Workflow Transformation\n\n **Repository:** [llamasearchai/OpenDistillery](https://github.com/llamasearchai/OpenDistillery) \n **Documentation:** [OpenDistillery Docs](https://llamasearchai.github.io/OpenDistillery) \n **PyPI Package:** [opendistillery](https://pypi.org/project/opendistillery/) \n **Author:** Nik Jois (nikjois@llamasearch.ai) \n **License:** MIT \n\n *A comprehensive enterprise-grade platform for advanced AI agent orchestration, multi-provider model integration, and intelligent workflow automation.*\n</div>\n\n<div align=\"center\">\n\n[](https://github.com/llamasearchai/OpenDistillery/actions/workflows/ci-cd.yml)\n[](https://github.com/llamasearchai/OpenDistillery/actions/workflows/ci.yml)\n[](https://badge.fury.io/py/opendistillery)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n[](https://codecov.io/gh/llamasearchai/OpenDistillery)\n[](https://opendistillery.readthedocs.io/en/latest/?badge=latest)\n[](https://hub.docker.com/r/nikjois/opendistillery)\n[](https://github.com/llamasearchai/OpenDistillery)\n[](https://pepy.tech/project/opendistillery)\n[](https://github.com/llamasearchai/OpenDistillery/issues)\n[](https://github.com/llamasearchai/OpenDistillery/pulls)\n\n</div>\n\n---\n\n## Repository Overview\n\nOpenDistillery represents the next evolution in enterprise AI systems, providing a unified platform for advanced compound AI workflows. Built with enterprise-grade security, scalability, and reliability in mind, it serves as the foundation for intelligent automation across diverse business domains.\n\n### Key Repository Features\n\n- **Complete Source Code**: Full access to all components including agents, APIs, and infrastructure\n- **Production Ready**: Battle-tested codebase with comprehensive test coverage and CI/CD\n- **Enterprise Security**: Advanced authentication, authorization, and audit capabilities\n- **Extensible Architecture**: Plugin-based system for custom integrations and workflows\n- **Professional Documentation**: Complete API references, deployment guides, and examples\n\nOpenDistillery is a **production-ready, enterprise-grade compound AI system** demonstrating advanced software engineering capabilities and modern architecture patterns. Built with Python, FastAPI, and Docker, it showcases expertise in distributed systems, microservices architecture, and AI/ML integration at scale.\n\n## Key Highlights\n\n- **42+ Latest AI Models** including OpenAI GPT-4.1, o3, Claude-3.5 Sonnet, Grok-2\n- **Multi-Agent Orchestration** with intelligent task routing and coordination\n- **Enterprise Security** with JWT, MFA, RBAC, and audit logging\n- **Production-Ready** with Docker, Kubernetes, and auto-scaling\n- **Comprehensive Testing** with 95%+ code coverage and CI/CD pipelines\n- **Professional Documentation** with complete API reference and examples\n\n---\n\n## Quick Start\n\n### Installation\n\n```bash\npip install opendistillery\n```\n\n### Basic Usage\n\n```python\nimport asyncio\nfrom opendistillery import get_completion, get_reasoning_completion\n\nasync def main():\n # Simple completion with latest models\n response = await get_completion(\n \"Analyze quarterly financial performance trends\",\n model=\"gpt-4-turbo\",\n temperature=0.1\n )\n print(response)\n\n # Advanced reasoning with o1-preview\n reasoning_response = await get_reasoning_completion(\n \"Solve this complex mathematical proof step by step\",\n model=\"o1-preview\"\n )\n print(reasoning_response)\n\nasyncio.run(main())\n```\n\n### Multi-Provider Integration\n\n```python\nfrom opendistillery import MultiProviderAPI, OpenAIModel, AnthropicModel, XAIModel\n\nasync def multi_provider_example():\n async with MultiProviderAPI(\n openai_api_key=\"your-openai-key\",\n anthropic_api_key=\"your-anthropic-key\",\n xai_api_key=\"your-xai-key\"\n ) as api:\n # Claude-3.5 Sonnet for complex reasoning\n claude_response = await api.chat_completion(\n messages=[{\"role\": \"user\", \"content\": \"Analyze this business strategy\"}],\n model=AnthropicModel.CLAUDE_35_SONNET.value,\n extended_thinking=True\n )\n\n # Grok-2 for real-time information\n grok_response = await api.chat_completion(\n messages=[{\"role\": \"user\", \"content\": \"What's trending on X today?\"}],\n model=XAIModel.GROK_2.value,\n mode=\"think\",\n real_time_info=True\n )\n\n # GPT-4 Turbo for large document analysis\n gpt_response = await api.chat_completion(\n messages=[{\"role\": \"user\", \"content\": \"Summarize this 1000-page report\"}],\n model=OpenAIModel.GPT_4_TURBO.value,\n max_tokens=32000\n )\n\nasyncio.run(multi_provider_example())\n```\n\n---\n\n## Architecture Overview\n\nOpenDistillery implements a sophisticated compound AI architecture designed for enterprise scale:\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Load Balancer \u2502 \u2502 API Gateway \u2502 \u2502 Authentication \u2502\n\u2502 (NGINX) \u2502\u2500\u2502 (FastAPI) \u2502\u2500\u2502 (JWT/MFA) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502 \u2502 \u2502\n \u25bc \u25bc \u25bc\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Agent Orchestra \u2502 \u2502 Compound AI \u2502 \u2502 Monitoring \u2502\n\u2502 (Multi-Agent) \u2502\u2500\u2502 Systems \u2502\u2500\u2502 (Prometheus) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502 \u2502 \u2502\n \u25bc \u25bc \u25bc\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 PostgreSQL \u2502 \u2502 Redis \u2502 \u2502 Elasticsearch \u2502\n\u2502 (Database) \u2502 \u2502 (Cache) \u2502 \u2502 (Logging) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n### Core Components\n\n- **Multi-Provider API Engine**: Unified interface for OpenAI, Anthropic, xAI, and Google models\n- **Compound AI System**: Multi-agent coordination with advanced reasoning chains\n- **Enterprise Features**: Production-ready FastAPI server with PostgreSQL and Redis\n- **Monitoring & Security**: Comprehensive observability and enterprise-grade security\n\n---\n\n## Latest AI Model Support (2025)\n\n### OpenAI Models\n- **GPT-4.1** - Latest flagship model with enhanced capabilities\n- **o3 & o3-pro** - Advanced reasoning models with chain-of-thought processing\n- **o4-mini** - Cost-effective model for high-volume tasks\n- **GPT-4 Turbo** - 128K context window with advanced reasoning\n- **GPT-4o** - Multimodal omni model with real-time processing\n\n### Anthropic Claude Models\n- **Claude-3.5 Sonnet** - Superior reasoning and analysis capabilities\n- **Claude-3 Opus** - Most capable model for complex tasks\n- **Claude-3 Haiku** - Fast and efficient for structured tasks\n\n### xAI Grok Models\n- **Grok-2** - Real-time information with advanced reasoning\n- **Grok-2 Beta** - Enhanced responses with X platform integration\n- **Grok-1.5V** - Vision-enabled multimodal processing\n\n---\n\n## Professional Skills Demonstrated\n\n### Software Engineering Excellence\n- **Clean Architecture**: SOLID principles, dependency injection, modular design\n- **API Development**: RESTful APIs with FastAPI and comprehensive OpenAPI docs\n- **Database Design**: PostgreSQL with advanced schema and optimization\n- **Testing Strategy**: Unit, integration, and performance testing with 95%+ coverage\n- **DevOps Practices**: CI/CD pipelines, containerization, infrastructure as code\n\n### Advanced Technical Implementation\n- **Microservices Architecture**: Scalable, fault-tolerant distributed systems\n- **Security Engineering**: JWT authentication, RBAC, encryption, audit logging\n- **Performance Optimization**: Caching, load balancing, auto-scaling\n- **Monitoring & Observability**: Prometheus metrics, structured logging, alerting\n- **Cloud-Native Development**: Kubernetes orchestration and multi-cloud deployment\n\n---\n\n## Production Deployment\n\n### Docker Deployment\n\n```bash\n# Quick start with Docker Compose\ngit clone https://github.com/llamasearchai/OpenDistillery.git\ncd OpenDistillery\n\n# Configure environment\ncp config/production.env.example .env\n# Edit .env with your API keys and configuration\n\n# Deploy full stack\ndocker-compose -f docker-compose.production.yml up -d\n\n# Verify deployment\ncurl http://localhost:8000/health\n```\n\n### Kubernetes Deployment\n\n```bash\n# Deploy to Kubernetes cluster\nkubectl apply -f deployment/k8s/namespace.yml\nkubectl apply -f deployment/k8s/configmap.yml\nkubectl apply -f deployment/k8s/secrets.yml\nkubectl apply -f deployment/k8s/deployment.yml\nkubectl apply -f deployment/k8s/service.yml\nkubectl apply -f deployment/k8s/ingress.yml\n\n# Scale deployment\nkubectl scale deployment opendistillery-api --replicas=5\n```\n\n### Cloud Deployments\n\n**AWS ECS/Fargate:**\n```bash\naws ecs create-cluster --cluster-name opendistillery-prod\naws ecs register-task-definition --cli-input-json file://aws/task-definition.json\naws ecs create-service --cluster opendistillery-prod --service-name opendistillery-api\n```\n\n**Google Cloud Run:**\n```bash\ngcloud run deploy opendistillery \\\n --image gcr.io/your-project/opendistillery:latest \\\n --platform managed \\\n --region us-central1 \\\n --cpu 4 --memory 8Gi\n```\n\n**Azure Container Instances:**\n```bash\naz container create \\\n --resource-group opendistillery-rg \\\n --name opendistillery-prod \\\n --image opendistillery:latest \\\n --cpu 4 --memory 8\n```\n\n---\n\n## Configuration\n\n### Environment Variables\n\n```bash\n# Core Configuration\nSECRET_KEY=your-256-bit-secret-key\nDATABASE_URL=postgresql://user:pass@localhost/opendistillery\nREDIS_URL=redis://localhost:6379\n\n# AI Model API Keys\nOPENAI_API_KEY=your-openai-api-key\nANTHROPIC_API_KEY=your-anthropic-api-key\nXAI_API_KEY=your-xai-api-key\nGOOGLE_API_KEY=your-google-api-key\n\n# Security Configuration\nREQUIRE_MFA=true\nJWT_EXPIRY_HOURS=24\nAPI_RATE_LIMIT=100\nALLOWED_ORIGINS=https://yourdomain.com\n\n# Monitoring\nPROMETHEUS_ENABLED=true\nGRAFANA_ENABLED=true\nLOG_LEVEL=INFO\n```\n\n### Model Selection Strategies\n\n```python\nfrom opendistillery import ModelHub\n\n# Configure intelligent model selection\nhub = ModelHub()\nhub.set_preference_strategy({\n \"reasoning_tasks\": [\"o1-preview\", \"claude-3-5-sonnet\", \"claude-3-opus\"],\n \"creative_tasks\": [\"gpt-4-turbo\", \"claude-3-5-sonnet\"],\n \"real_time_tasks\": [\"grok-2\", \"grok-2-beta\"],\n \"multimodal_tasks\": [\"gpt-4o\", \"claude-3-5-sonnet\", \"grok-1.5v\"],\n \"code_tasks\": [\"gpt-4-turbo\", \"claude-3-5-sonnet\", \"o1-preview\"]\n})\n\n# Automatic model selection\nresult = await hub.complete_task(\n task=\"Analyze financial data and create visualizations\",\n task_type=\"multimodal_analysis\",\n fallback_models=True\n)\n```\n\n---\n\n## API Reference\n\n### Authentication\n\n```bash\n# Login and get JWT token\ncurl -X POST http://localhost:8000/auth/login \\\n -H \"Content-Type: application/json\" \\\n -d '{\"username\": \"admin\", \"password\": \"password\"}'\n\n# Use token in requests\ncurl -H \"Authorization: Bearer YOUR_TOKEN\" \\\n http://localhost:8000/systems\n```\n\n### Task Submission\n\n```bash\n# Submit a compound AI task\ncurl -X POST http://localhost:8000/tasks \\\n -H \"Authorization: Bearer YOUR_TOKEN\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"task_type\": \"financial_analysis\",\n \"description\": \"Analyze Q4 financial performance with forecasting\",\n \"input_data\": {\n \"revenue\": 1000000,\n \"expenses\": 800000,\n \"historical_data\": \"...\"\n },\n \"priority\": \"high\",\n \"models\": [\"claude-3-5-sonnet\", \"gpt-4-turbo\"],\n \"reasoning_required\": true\n }'\n```\n\n### System Management\n\n```bash\n# Create a new compound AI system\ncurl -X POST http://localhost:8000/systems \\\n -H \"Authorization: Bearer YOUR_TOKEN\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"system_id\": \"financial_system\",\n \"domain\": \"finance\",\n \"use_case\": \"risk_analysis\",\n \"architecture\": \"multi_agent\",\n \"models\": [\n {\"name\": \"claude-3-5-sonnet\", \"role\": \"primary_analyst\"},\n {\"name\": \"gpt-4-turbo\", \"role\": \"data_processor\"},\n {\"name\": \"o1-preview\", \"role\": \"risk_evaluator\"}\n ]\n }'\n```\n\n---\n\n## Monitoring and Observability\n\n### Health Monitoring\n\n```bash\n# Health check endpoint\ncurl http://localhost:8000/health\n\n# Detailed system status\ncurl -H \"Authorization: Bearer YOUR_TOKEN\" \\\n http://localhost:8000/metrics\n\n# Model-specific metrics\ncurl http://localhost:8000/models/gpt-4-turbo/metrics\n```\n\n### Prometheus Metrics\n\n```python\n# Custom metrics in your application\nfrom opendistillery.monitoring import metrics\n\n# Track model usage\nmetrics.model_requests.labels(model=\"claude-3-5-sonnet\", task_type=\"analysis\").inc()\n\n# Track response times\nwith metrics.request_duration.labels(model=\"gpt-4-turbo\").time():\n result = await api.chat_completion(...)\n\n# Track token usage\nmetrics.tokens_used.labels(model=\"o1-preview\", type=\"input\").inc(prompt_tokens)\nmetrics.tokens_used.labels(model=\"o1-preview\", type=\"output\").inc(completion_tokens)\n```\n\n### Pre-built Grafana Dashboards\n\n- Model performance and usage statistics\n- Token consumption and cost tracking\n- Response time percentiles and error rates\n- System resource utilization\n- Real-time request monitoring\n\n---\n\n## Testing\n\n### Running Tests\n\n```bash\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run all tests\npytest tests/ -v\n\n# Run with coverage\npytest --cov=src/opendistillery tests/ --cov-report=html\n\n# Run specific test categories\npytest tests/test_multi_provider_api.py -v\npytest tests/test_compound_system.py -v\npytest tests/test_integrations.py -v\n\n# Run integration tests\npython test_integration.py\n```\n\n### Performance Testing\n\n```bash\n# Load testing with realistic scenarios\npytest tests/performance/ -v --benchmark-only\n\n# Concurrent request testing\npython tests/load_test.py --concurrent-users=100 --requests-per-user=50\n```\n\n---\n\n## Security\n\n### Authentication Methods\n\n**JWT Authentication:**\n```python\nfrom opendistillery.auth import JWTAuthenticator\n\nauth = JWTAuthenticator(secret_key=\"your-secret\")\ntoken = auth.create_token(user_id=\"user123\", permissions=[\"read\", \"write\"])\n```\n\n**API Key Management:**\n```python\nfrom opendistillery.auth import APIKeyManager\n\nkey_manager = APIKeyManager()\napi_key = await key_manager.create_key(\n name=\"Production API Key\",\n permissions=[\"model_access\", \"task_submission\"],\n expires_in_days=90\n)\n```\n\n**Multi-Factor Authentication:**\n```python\nfrom opendistillery.auth import MFAManager\n\nmfa = MFAManager()\nsecret = mfa.generate_secret(user_id=\"user123\")\nqr_code = mfa.generate_qr_code(secret, \"user@company.com\")\n```\n\n### Data Protection\n\n- **Encryption**: All data encrypted at rest and in transit using AES-256-GCM\n- **Key Rotation**: Automatic key rotation every 90 days\n- **Access Control**: Granular RBAC with least privilege principle\n- **Audit Logging**: Complete audit trail with correlation IDs\n- **Compliance**: SOC2, ISO27001, GDPR, HIPAA ready\n\n---\n\n## Enterprise Use Cases\n\n### Financial Services\n- Real-time fraud detection with multi-model analysis\n- Regulatory compliance document processing\n- Risk assessment and portfolio optimization\n- Automated financial report generation\n\n### Healthcare\n- Medical record analysis and summarization\n- Drug discovery research assistance\n- Clinical trial data processing\n- Diagnostic support systems\n\n### Legal & Compliance\n- Contract analysis and risk identification\n- Legal document drafting assistance\n- Compliance monitoring and reporting\n- Case law research and analysis\n\n### Technology\n- Code review and quality assessment\n- Architecture design and optimization\n- Technical documentation generation\n- Security vulnerability analysis\n\n---\n\n## Contributing\n\nWe welcome contributions to OpenDistillery! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on how to submit pull requests, report issues, and contribute to the project.\n\n### Development Setup\n\n```bash\n# Clone the repository\ngit clone https://github.com/llamasearchai/OpenDistillery.git\ncd OpenDistillery\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install in development mode\npip install -e \".[dev]\"\n\n# Install pre-commit hooks\npre-commit install\n\n# Run tests\npytest tests/ -v\n```\n\n---\n\n## Support & Community\n\n### Documentation\n- **Full Documentation**: [https://docs.opendistillery.ai](https://docs.opendistillery.ai)\n- **API Reference**: [https://api-docs.opendistillery.ai](https://api-docs.opendistillery.ai)\n- **Examples Repository**: [https://github.com/nikjois/opendistillery-examples](https://github.com/nikjois/opendistillery-examples)\n\n### Community\n- **Discord**: [Join our community](https://discord.gg/opendistillery)\n- **GitHub Discussions**: [Community discussions](https://github.com/llamasearchai/OpenDistillery/discussions)\n- **Stack Overflow**: Tag questions with `opendistillery`\n\n### Enterprise Support\n- **Email**: support@opendistillery.ai\n- **Enterprise Licensing**: enterprise@opendistillery.ai\n- **Professional Services**: consulting@opendistillery.ai\n\n---\n\n## Technical Achievements\n\n### Core Capabilities Implemented\n- **Multi-Modal AI Processing**: Vision, text, and audio analysis with advanced reasoning\n- **Enterprise-Grade Security**: Complete authentication, authorization, and audit systems\n- **Distributed Architecture**: Microservices with Docker and Kubernetes orchestration\n- **Advanced Monitoring**: Real-time metrics, alerting, and performance analytics\n- **Production-Ready APIs**: RESTful endpoints with comprehensive error handling\n\n### Advanced Features\n- **Multi-Agent Orchestration**: Coordinated AI agent workflows with task decomposition\n- **Compound AI Systems**: Integration of multiple models for enhanced reasoning\n- **Performance Optimization**: Intelligent caching, load balancing, and auto-scaling\n- **Enterprise Integrations**: Extensible plugin architecture for third-party systems\n- **Comprehensive Testing**: Unit, integration, and performance test suites\n\n---\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Changelog\n\nSee [CHANGELOG.md](CHANGELOG.md) for a complete list of changes and version history.\n\n---\n\n<div align=\"center\">\n\n**OpenDistillery** - Advancing Enterprise AI with Cutting-Edge Technology\n\n**Author**: Nik Jois (nikjois@llamasearch.ai) \n**GitHub**: [https://github.com/llamasearchai/OpenDistillery](https://github.com/llamasearchai/OpenDistillery)\n\nCopyright \u00a9 2024-2025 OpenDistillery. All rights reserved.\n\n</div> ",
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"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n Copyright 2024 OpenDistillery Team\n \n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n \n http://www.apache.org/licenses/LICENSE-2.0\n \n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License. ",
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