Metadata-Version: 2.4
Name: opendistillery
Version: 2.0.2
Summary: Advanced Compound AI Systems for Enterprise Workflow Transformation
Project-URL: Homepage, https://github.com/nikjois/OpenDistillery
Project-URL: Documentation, https://docs.opendistillery.ai
Project-URL: Repository, https://github.com/nikjois/OpenDistillery.git
Project-URL: Issues, https://github.com/nikjois/OpenDistillery/issues
Project-URL: Changelog, https://github.com/nikjois/OpenDistillery/blob/main/CHANGELOG.md
Author-email: Nik Jois <nikjois@llamasearch.ai>
Maintainer-email: Nik Jois <nikjois@llamasearch.ai>
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           Copyright 2024 OpenDistillery Team
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License. 
License-File: LICENSE
Keywords: ai,anthropic,artificial-intelligence,automation,compound-ai,enterprise,grok,llm,machine-learning,multi-agent,openai,orchestration,workflow
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: System :: Distributed Computing
Requires-Python: >=3.9
Requires-Dist: aiofiles>=23.2.1
Requires-Dist: alembic>=1.13.0
Requires-Dist: anthropic>=0.7.8
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Requires-Dist: structlog>=23.2.0
Requires-Dist: typer>=0.9.0
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Description-Content-Type: text/markdown

<div align="center">
  <img src="OpenDistillery.png" alt="OpenDistillery Logo" width="400"/>
  
  # OpenDistillery
  
  ## Advanced Compound AI Systems for Enterprise Workflow Transformation
</div>

<div align="center">

[![PyPI version](https://badge.fury.io/py/opendistillery.svg)](https://badge.fury.io/py/opendistillery)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation Status](https://readthedocs.org/projects/opendistillery/badge/?version=latest)](https://opendistillery.readthedocs.io/en/latest/?badge=latest)

</div>

OpenDistillery is a production-ready, enterprise-grade compound AI system designed for Fortune 500 companies and large-scale deployments. It provides seamless integration with the latest AI models from multiple providers, advanced multi-agent orchestration, and sophisticated reasoning capabilities.

## Key Features

### Latest AI Model Support (2025)

**OpenAI Models:**
- **GPT-4.1** (April 2025) - 1M token context, advanced reasoning
- **GPT-o3/o3-mini** (January 2025) - Enhanced reasoning with deliberative alignment
- **GPT-4o** - Multimodal omni model with real-time capabilities
- **o-series** - Specialized reasoning models (o1, o3, o4-mini)

**Anthropic Claude Models:**
- **Claude 4 Opus** (May 2025) - Most capable model with superior reasoning
- **Claude 4 Sonnet** (May 2025) - High-performance enterprise model
- **Claude 3.7 Sonnet** (February 2025) - Extended thinking and hybrid reasoning
- **Claude 3.5** series - Sonnet v2, Haiku with enhanced capabilities

**xAI Grok Models:**
- **Grok 3** (February 2025) - Real-time information with Think and Big Brain modes
- **Grok 2 Beta** - Unfiltered responses with X platform integration
- **Grok 1.5V** - Vision-enabled model

### Enterprise Capabilities

**Multi-Agent Orchestration:**
- Advanced coordination between specialized AI agents
- Compound reasoning with multiple models
- Intelligent task routing and load balancing
- Real-time collaboration and decision-making

**Production-Grade Infrastructure:**
- Horizontal auto-scaling with Docker and Kubernetes
- Enterprise authentication (JWT, MFA, RBAC)
- Comprehensive monitoring with Prometheus and Grafana
- Circuit breakers and fault tolerance
- Structured logging with correlation tracking

**Security & Compliance:**
- SOC2, ISO27001, GDPR, HIPAA compliance ready
- Multi-factor authentication with TOTP
- Role-based access control with granular permissions
- API key management with expiration and rate limiting
- Audit logging with encryption at rest and in transit

## Quick Start

### Installation

```bash
pip install opendistillery
```

### Basic Usage

```python
import asyncio
from opendistillery import get_completion, get_reasoning_completion

# Simple completion with latest models
async def main():
    # Use GPT-4.1 with 1M token context
    response = await get_completion(
        "Analyze the quarterly financial performance trends",
        model="gpt-4.1",
        temperature=0.1
    )
    print(response)
    
    # Advanced reasoning with o3
    reasoning_response = await get_reasoning_completion(
        "Solve this complex mathematical proof step by step",
        model="o3"
    )
    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:
        
        # Use Claude 4 Opus for complex reasoning
        claude_response = await api.chat_completion(
            messages=[{"role": "user", "content": "Analyze this business strategy"}],
            model=AnthropicModel.CLAUDE_4_OPUS.value,
            extended_thinking=True
        )
        
        # Use Grok 3 for real-time information
        grok_response = await api.chat_completion(
            messages=[{"role": "user", "content": "What's trending on X today?"}],
            model=XAIModel.GROK_3.value,
            mode="think",
            real_time_info=True
        )
        
        # Use GPT-4.1 for large document analysis
        gpt_response = await api.chat_completion(
            messages=[{"role": "user", "content": "Summarize this 1000-page report"}],
            model=OpenAIModel.GPT_4_1.value,
            max_tokens=32000
        )

asyncio.run(multi_provider_example())
```

### Multimodal Capabilities

```python
from opendistillery import get_multimodal_completion

async def vision_example():
    # Analyze images with GPT-4o or Claude 4
    result = await get_multimodal_completion(
        prompt="Analyze the financial charts in these images and provide insights",
        images=["data:image/jpeg;base64,/9j/4AAQ...", "https://example.com/chart.png"],
        model="gpt-4o"
    )
    print(result)

asyncio.run(vision_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
- Intelligent routing and failover capabilities
- Rate limiting and cost optimization
- Automatic model selection based on task requirements

**Compound AI System:**
- Multi-agent coordination and orchestration
- Advanced reasoning chain implementations
- Task decomposition and parallel processing
- Result synthesis and validation

**Enterprise Features:**
- Production-ready FastAPI server
- PostgreSQL database with 9 enterprise tables
- Redis caching and session management
- Celery background task processing
- Comprehensive monitoring and alerting

## Advanced Features

### Latest Model Capabilities

**Reasoning Models (2025):**
```python
# OpenAI o3 with advanced reasoning
result = await get_reasoning_completion(
    "Prove that P vs NP is undecidable",
    model="o3",
    reasoning_effort="high"
)

# Claude 3.7 with extended thinking
result = await api.chat_completion(
    messages=[{"role": "user", "content": "Design a quantum algorithm"}],
    model="claude-3-7-sonnet-20250219",
    extended_thinking=True
)
```

**Real-Time Information:**
```python
# Grok 3 with real-time X data
result = await api.chat_completion(
    messages=[{"role": "user", "content": "What's the latest news on AI?"}],
    model="grok-3",
    mode="big_brain",
    real_time_info=True
)
```

**Large Context Processing:**
```python
# GPT-4.1 with 1M token context
result = await api.chat_completion(
    messages=[{"role": "user", "content": "Analyze this entire codebase"}],
    model="gpt-4.1",
    max_tokens=32000
)
```

### Multi-Agent Orchestration

```python
from opendistillery import CompoundAISystem

# Create a sophisticated multi-agent workflow
system = CompoundAISystem()

# Define specialized agents
research_agent = system.create_agent(
    name="research_specialist",
    model="claude-4-opus",
    role="Research and analysis expert",
    tools=["web_search", "document_analysis"]
)

coding_agent = system.create_agent(
    name="code_specialist", 
    model="gpt-4.1",
    role="Software development expert",
    tools=["code_execution", "testing", "debugging"]
)

synthesis_agent = system.create_agent(
    name="synthesis_specialist",
    model="o3",
    role="Strategic thinking and synthesis",
    tools=["reasoning", "decision_making"]
)

# Execute complex workflow
result = await system.execute_workflow(
    task="Build a complete trading algorithm with research, implementation, and testing",
    agents=[research_agent, coding_agent, synthesis_agent],
    coordination_strategy="hierarchical"
)
```

## Production Deployment

### Docker Deployment

```bash
# Quick start with Docker Compose
git clone https://github.com/nikjois/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

# Monitor deployment
kubectl get pods -n opendistillery
kubectl logs -f deployment/opendistillery-api -n opendistillery
```

### 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()

# Define model preferences
hub.set_preference_strategy({
    "reasoning_tasks": ["o3", "claude-4-opus", "claude-3-7-sonnet"],
    "creative_tasks": ["gpt-4.1", "claude-4-sonnet"],
    "real_time_tasks": ["grok-3", "grok-2-beta"],
    "multimodal_tasks": ["gpt-4o", "claude-4-opus", "grok-3"],
    "code_tasks": ["gpt-4.1", "claude-4-sonnet", "o3"]
})

# Automatic model selection
result = await hub.complete_task(
    task="Analyze this 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-4-opus", "gpt-4.1"],
    "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-4-opus", "role": "primary_analyst"},
      {"name": "gpt-4.1", "role": "data_processor"},
      {"name": "o3", "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-4.1/metrics
```

### Prometheus Metrics

```python
# Custom metrics in your application
from opendistillery.monitoring import metrics

# Track model usage
metrics.model_requests.labels(model="claude-4-opus", task_type="analysis").inc()

# Track response times
with metrics.request_duration.labels(model="gpt-4.1").time():
    result = await api.chat_completion(...)

# Track token usage
metrics.tokens_used.labels(model="o3", type="input").inc(prompt_tokens)
metrics.tokens_used.labels(model="o3", type="output").inc(completion_tokens)
```

### Grafana Dashboards

Pre-built dashboards include:
- Model performance and usage statistics
- Token consumption and cost tracking
- Response time percentiles
- Error rates and failure analysis
- 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

## Use Cases

### Enterprise Applications

**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

### Code Examples

**Financial Analysis System:**
```python
from opendistillery import CompoundAISystem

# Create specialized financial AI system
financial_system = CompoundAISystem()
financial_system.configure({
    "domain": "finance",
    "models": {
        "primary": "claude-4-opus",
        "numerical": "gpt-4.1", 
        "reasoning": "o3"
    },
    "tools": ["financial_data_api", "calculation_engine", "charting"]
})

# Analyze quarterly performance
result = await financial_system.analyze_quarterly_performance(
    company="AAPL",
    quarter="Q4-2024",
    include_forecasting=True,
    comparison_companies=["MSFT", "GOOGL"]
)
```

**Research Assistant:**
```python
# Multi-modal research system
research_system = CompoundAISystem()
research_system.add_agent("researcher", model="claude-4-opus", tools=["web_search"])
research_system.add_agent("analyst", model="grok-3", tools=["real_time_data"])
research_system.add_agent("synthesizer", model="o3", tools=["reasoning"])

# Comprehensive research workflow
research_result = await research_system.research_topic(
    topic="Quantum Computing Market Trends 2025",
    depth="comprehensive",
    sources=["academic", "industry", "news"],
    output_format="executive_summary"
)
```

## 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/nikjois/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/nikjois/OpenDistillery/discussions)
- **Stack Overflow**: Tag questions with `opendistillery`

### Enterprise Support
- **Email**: support@opendistillery.ai
- **Enterprise Licensing**: enterprise@opendistillery.ai
- **Professional Services**: consulting@opendistillery.ai

## Roadmap

### 2025 Q2 Planned Features
- **Multi-Modal Agents**: Enhanced vision and audio processing
- **Advanced Reasoning**: Integration with emerging reasoning models
- **Enterprise Integrations**: Salesforce, SAP, Microsoft 365 connectors
- **Performance Optimization**: Model caching and intelligent routing
- **Security Enhancements**: Advanced threat detection and response

### 2025 Q3-Q4
- **Custom Model Training**: Fine-tuning capabilities
- **Edge Deployment**: Lightweight edge computing support
- **Advanced Analytics**: Comprehensive usage and performance analytics
- **Workflow Automation**: Visual workflow designer
- **Global Deployment**: Multi-region support with data residency

## 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.

---

**OpenDistillery** - Advancing Enterprise AI with Cutting-Edge Technology

**Author**: Nik Jois (nikjois@llamasearch.ai)  
**Website**: [https://opendistillery.ai](https://opendistillery.ai)  
**GitHub**: [https://github.com/nikjois/OpenDistillery](https://github.com/nikjois/OpenDistillery)

Copyright © 2024-2025 OpenDistillery. All rights reserved.