LLMOrchestrator

A powerful framework for orchestrating and enhancing Large Language Model (LLM) outputs through multi-model orchestration, chain-of-thought reasoning, and diverse perspectives.

Overview

LLMOrchestrator is designed to enhance LLM outputs by:

• Orchestrating multiple LLMs in various configurations
• Implementing chain-of-thought reasoning with multiple perspectives
• Leveraging different models for their specific strengths
• Introducing controlled randomness and diversity in outputs
• Providing robust verification and validation mechanisms

Features

• Multi-Model Orchestration: Chain different LLMs in various orders
• Chain-of-Thought Reasoning: Break down complex problems into steps
• Diverse Perspectives: Combine outputs from different models
• Adaptive Learning: Optimize prompts based on performance
• Parallel Processing: Handle multiple requests efficiently
• Caching: Improve response times for repeated queries
• Monitoring: Track performance and quality metrics
• Custom Verification: Implement domain-specific validation

Installation

pip install llm-orchestrator

Quick Start

from LLMOrchestrator.models import OpenAIModel, LocalModel
from LLMOrchestrator.controller import Controller, PromptTemplate

# Initialize models
generator = OpenAIModel(
    model_name="gpt-3.5-turbo",
    api_key="your-api-key"
)
verifier = LocalModel(
    model_name="facebook/opt-125m",
    device="cpu"
)

# Create a prompt template
template = PromptTemplate(
    "Please provide a detailed analysis of: {prompt}"
)

# Initialize controller
controller = Controller(
    generator=generator,
    verifier=verifier,
    max_iterations=3,
    max_verifications=2,
    parallel_processing=True,
    cache_enabled=True,
    adaptive_learning=True,
    monitoring_enabled=True,
    prompt_template=template
)

# Execute with a prompt
result = controller.execute(
    prompt="Analyze the impact of artificial intelligence on healthcare.",
    stop_early=False
)

# Get performance metrics
metrics = controller.get_validation_metrics()
print(f"Quality Score: {metrics.quality_score}")
print(f"Confidence: {metrics.confidence_score}")
print(f"Processing Time: {metrics.processing_time}s")

Advanced Usage

Chain-of-Thought Reasoning

# Configure for complex reasoning tasks
controller = Controller(
    generator=generator,
    verifier=verifier,
    max_iterations=5,  # Allow more iterations for complex reasoning
    prompt_template=PromptTemplate(
        "Let's solve this step by step:\n1. First, let's analyze...\n2. Then, we can consider...\n3. Finally, we can conclude...\n\nProblem: {prompt}"
    )
)

result = controller.execute(
    prompt="Explain the relationship between quantum computing and cryptography",
    stop_early=False
)

Parallel Processing with Multiple Models

# Process multiple prompts with different models
prompts = [
    "Generate a technical specification for a new API",
    "Create a user interface design document",
    "Write a security assessment report"
]

results = controller.execute_parallel(
    prompts=prompts,
    max_workers=2,
    stop_early=False
)

# Get performance report
report = controller.get_performance_report()
print(f"Total processing time: {report['total_time']}s")
print(f"Average quality score: {report['avg_quality_score']}")

Custom Controller Implementation

from LLMOrchestrator.controller import CustomController

def custom_processing(output: str) -> str:
    # Add custom processing logic
    return output.upper()

controller = CustomController(
    custom_func=custom_processing,
    generator=generator,
    verifier=verifier,
    parallel_processing=True
)

result = controller.execute(
    prompt="Generate a response",
    n=3  # Generate 3 variations
)

Use Cases

LLMOrchestrator is particularly effective for:

• Complex Reasoning Tasks: Break down multi-step problems
• Content Generation: Combine different models for writing and editing
• Technical Documentation: Generate and validate technical content
• Research Analysis: Synthesize information from multiple perspectives
• Quality Assurance: Implement multiple verification layers
• Performance Optimization: Cache and adapt to improve response times
• Prompt Engineering: Optimize prompts through performance tracking

Configuration

Create a .env file with your API keys:

OPENAI_API_KEY=your-openai-key

Configure advanced settings in orchestration_config.json:

{
    "max_iterations": 3,
    "max_verifications": 2,
    "parallel_processing": true,
    "cache_enabled": true,
    "adaptive_learning": true,
    "monitoring_enabled": true
}

Testing

Run the test suite:

pytest tests/ -v

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this software in your research, please cite:

@software{llmorchestrator2024,
  author = {Srinivasan, Trisanth and Patapati, Santosh},
  title = {LLMOrchestrator: A Multi-Model LLM Orchestration Framework for Reducing Bias and Iterative Reasoning},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/builtbypyro/LLMOrchestrator}
}

Documentation

Full documentation is available in the docs directory.