Model Selection and Configuration

SYDA supports multiple large language model (LLM) providers, allowing you to choose the model that best fits your needs in terms of capabilities, cost, and performance.

API Keys

Before using any LLM provider, you must set the appropriate API keys as environment variables:

# For Anthropic Claude
export ANTHROPIC_API_KEY=your_anthropic_key

# For OpenAI
export OPENAI_API_KEY=your_openai_key

Alternatively, you can create a .env file in your project root:

ANTHROPIC_API_KEY=your_anthropic_key
OPENAI_API_KEY=your_openai_key

Refer to the Quickstart Guide for more details on environment setup.

Basic Configuration

The ModelConfig class is used to specify which LLM provider and model you want to use:

from syda import SyntheticDataGenerator, ModelConfig
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Basic configuration with default parameters
config = ModelConfig(
    provider="anthropic",  # Choose provider: 'anthropic' or 'openai'
    model_name="claude-3-5-haiku-20241022",  # Specify model name
    temperature=0.7,  # Control randomness (0.0-1.0)
    max_tokens=8000   # Maximum number of tokens to generate
)

# Initialize generator with this configuration
generator = SyntheticDataGenerator(model_config=config)

Using Different Model Providers

SYDA currently supports two main LLM providers:

Anthropic Claude Models

Claude is the default model provider for SYDA, offering strong performance for data generation tasks:

# Using Anthropic Claude
config = ModelConfig(
    provider="anthropic",
    model_name="claude-3-5-haiku-20241022",  # Default model
    temperature=0.5,  # Control randomness (0.0-1.0)
    max_tokens=8000   # Maximum number of tokens to generate
)

Available Claude models include:

For the latest information about available Claude models and their capabilities, refer to Anthropic's Claude documentation.

OpenAI Models

SYDA also supports OpenAI's GPT models:

# Using OpenAI GPT
config = ModelConfig(
    provider="openai",
    model_name="gpt-4-turbo",
    temperature=0.7,
    max_tokens=4000
)

For the latest information about available OpenAI models and their capabilities, refer to OpenAI's models documentation.

Model Parameters

You can fine-tune model behavior with these parameters:

Parameter	Description	Range	Default
temperature	Controls randomness in generation	0.0-1.0	None
max_tokens	Maximum tokens to generate	Integer	None
max_completion_tokens	Maximum completion tokens to generate for latest openai models	Integer	None

Advanced: Direct Access to LLM Client

For advanced use cases, you can access the underlying LLM client directly:

from syda import SyntheticDataGenerator, ModelConfig

config = ModelConfig(provider="anthropic", model_name="claude-3-5-haiku-20241022")
generator = SyntheticDataGenerator(model_config=config)

# Access the underlying client
llm_client = generator.llm_client

# Use the client directly (provider-specific)
if config.provider == "anthropic":
    response = llm_client.messages.create(
        model=config.model_name,
        max_tokens=1000,
        messages=[{"role": "user", "content": "Generate a list of book titles about AI"}]
    )

    print(response.content[0].text)

This gives you direct access to provider-specific features while still using SYDA for schema management.

Best Practices

1. Start with Default Models: Begin with claude-3-5-haiku-20241022 (Anthropic) or gpt-4-turbo (OpenAI)
2. Adjust Temperature: Lower for more consistent results, higher for more variety
3. Consider Cost vs. Quality: Higher-end models provide better quality but at higher cost
4. Test Different Models: Compare results from different models for your specific use case
5. Handle API Rate Limits: Implement appropriate backoff strategies for large generations
6. Select Highest Output Token Model for Higher Sample Sizes: For larger sample sizes, use models with higher max_tokens

Examples

Explore Anthropic Claude Example and Openai Gpt Example to see model configuration in action.