Model Validation: Add validation of model names when initializing LLM class config #23
Description
Add model name validation and constants for LLM providers
Description
Currently, when initializing the configuration classes for LLM providers (e.g. AnthropicConfig, OpenAIConfig, GoogleConfig), we pass model names as raw strings:
cfg = kit.summaries.AnthropicConfig(api_key=key, model="claude-3-haiku-20240307")This approach has the following drawbacks (across all providers):
- No validation of model names
- Prone to typos and errors (e.g. "claude-3-haiku" vs. "claude-3-haiku-20240307")
- Hard to discover available models
Proposed Solution
- Create enum or constants for each LLM provider's models
- Add validation in
__post_init__to check if the provided model name is valid - Provide better developer experience with autocomplete and documentation
Example Implementation
from enum import Enum
class AnthropicModels(str, Enum):
CLAUDE_3_OPUS = "claude-3-opus-20240229"
CLAUDE_3_SONNET = "claude-3-sonnet-20240229"
...
class OpenAIModels(str, Enum):
GPT_4O = "gpt-4o"
GPT_4_TURBO = "gpt-4-turbo"
# Add other models as needed
class AnthropicConfig:
"""Configuration for Anthropic API access."""
api_key: Optional[str] = field(default_factory=lambda: os.environ.get("ANTHROPIC_API_KEY"))
model: str = AnthropicModels.CLAUDE_3_OPUS.value
temperature: float = 0.7
max_tokens: int = 1000
def __post_init__(self):
if not self.api_key:
raise ValueError(
"Anthropic API key not found. "
"Set ANTHROPIC_API_KEY environment variable or pass api_key directly."
)
# Validate model name
valid_models = [model.value for model in AnthropicModels]
if self.model not in valid_models:
raise ValueError(
f"Invalid model name for Anthropic: {self.model}. "
f"Please use one of: {', '.join(valid_models)}"
)
# Similar implementations for OpenAIConfig and GoogleConfig classesWith this implementation, users could initialize the config like:
# Using string (with validation)
cfg = kit.summaries.AnthropicConfig(api_key=key, model="claude-3-haiku-20240307")
# Or better, using constants
cfg = kit.summaries.AnthropicConfig(api_key=key, model=AnthropicModels.CLAUDE_3_HAIKU.value)Benefits
- Type safety and validation across all LLM providers
- Better IDE support with autocomplete
- Clear documentation of available models
- Easier maintenance when adding/removing models