The Plexe Python library uses Large Language Models (LLMs) as the foundation for its AI agent system. These models power the planning, code generation, analysis, and refinement capabilities that enable Plexe to build ML models from natural language.

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Supported Provider Types

Plexe supports various LLM providers through integration with common APIs, giving you flexibility in choosing which models to use for different aspects of the model building process.

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Standard Providers

These are the primary LLM providers supported by Plexe:
  • OpenAI: Models like GPT-4o, GPT-4o-mini
  • Anthropic: Claude models (Haiku, Sonnet, Opus)
  • Google: Gemini models
  • Cohere: Command models
  • Local Models: Through providers like Ollama for self-hosting

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Provider Formats

When specifying a provider, use the format "vendor/model_name": 
# OpenAI example
model.build(datasets=datasets, provider="openai/gpt-4o-mini")

# Anthropic example
model.build(datasets=datasets, provider="anthropic/claude-3-sonnet-20240229")

# Ollama (local) example
model.build(datasets=datasets, provider="ollama/llama3")

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Provider Configuration

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Default Provider

If you don’t specify a provider, Plexe uses a default provider optimized for model building tasks.

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Custom Provider Configuration

For more advanced use cases, Plexe allows setting different LLM providers for different agent roles through the ProviderConfig class. This lets you optimize for performance in critical stages like code writing while using faster/cheaper models for more routine tasks. 
# Import ProviderConfig from internal module
from plexe.internal.common.provider import ProviderConfig

# Create a custom provider configuration
provider_config = ProviderConfig(
    default_provider="openai/gpt-4o-mini",
    research_provider="openai/gpt-4o",        # For complex planning
    engineer_provider="openai/gpt-4o",        # For code generation
    orchestrator_provider="anthropic/claude-3-sonnet-20240229",
    ops_provider="anthropic/claude-3-sonnet-20240229",
    tool_provider="openai/gpt-4o-mini"        # For internal tools/helpers
)

# Use the custom configuration
model.build(datasets=datasets, provider=provider_config)

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Environment Variables for API Keys

Plexe uses environment variables to securely handle API keys. Set the appropriate environment variable for your chosen provider: 
# OpenAI
export OPENAI_API_KEY="your_openai_api_key"

# Anthropic
export ANTHROPIC_API_KEY="your_anthropic_api_key"

# Google Gemini
export GEMINI_API_KEY="your_gemini_api_key"

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Provider Selection Strategies

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Cost Optimization

If you’re primarily concerned with minimizing costs:
  • Use more economical models like "openai/gpt-4o-mini" or similar for most roles
  • Reserve more powerful models only for the engineer role which handles code generation
  • Example: provider_config = ProviderConfig(default_provider="openai/gpt-4o-mini", engineer_provider="openai/gpt-4o")

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Performance Optimization

If you prioritize quality and performance:
  • Use the most capable models for research and engineering roles
  • Example: provider_config = ProviderConfig(default_provider="anthropic/claude-3-sonnet-20240229", research_provider="anthropic/claude-3-opus-20240229", engineer_provider="anthropic/claude-3-opus-20240229")

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Private Deployment

For organizations with data privacy requirements:
  • Configure with locally-hosted models through providers like Ollama
  • Example: provider_config = ProviderConfig(default_provider="ollama/llama3")

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Internal Provider Handling

When you call model.build() with a provider configuration:
  1. Initialization: Plexe validates the provider configuration
  2. Role Assignment: Appropriate models are assigned to each agent based on the configuration
  3. API Integration: Plexe handles the API calls to the various providers
  4. Fallbacks: If a specific role provider fails, Plexe can fall back to the default provider
By understanding how providers work in Plexe, you can optimize your model building process for your specific requirements, whether prioritizing cost efficiency, performance quality, or specialized capabilities.