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Configuration class for SmartStat service that manages directory paths and API keys for various LLM providers integrated into Vice AI.

Purpose

SmartStatConfig is a configuration container class that initializes and manages file system directories for SmartStat service operations (uploads, scripts, reports, output) and consolidates API credentials for multiple LLM providers (OpenAI, Gemini, Azure OpenAI, Anthropic). It ensures all required directories exist on instantiation and pulls API keys from a global LLM_CONFIG dictionary.

Source Code

class SmartStatConfig:
    """Configuration for SmartStat service integrated into Vice AI"""
    def __init__(self):
        from pathlib import Path
        BASE_DIR = Path(__file__).parent
        self.UPLOAD_FOLDER = BASE_DIR / 'smartstat_uploads'
        self.GENERATED_SCRIPTS_FOLDER = BASE_DIR / 'smartstat_scripts'
        self.REPORTS_FOLDER = BASE_DIR / 'smartstat_reports'
        self.OUTPUT_DIR = BASE_DIR / 'smartstat_output'
        
        # Ensure directories exist
        for directory in [self.UPLOAD_FOLDER, self.GENERATED_SCRIPTS_FOLDER, 
                         self.REPORTS_FOLDER, self.OUTPUT_DIR]:
            directory.mkdir(parents=True, exist_ok=True)
        
        # Use Vice AI's LLM config
        self.OPENAI_API_KEY = LLM_CONFIG['openai_api_key']
        self.GEMINI_API_KEY = LLM_CONFIG['gemini_api_key']
        self.AZURE_OPENAI_ENDPOINT = LLM_CONFIG['azure_openai_endpoint']
        self.AZURE_OPENAI_API_KEY = LLM_CONFIG['azure_openai_api_key']
        self.ANTHROPIC_API_KEY = LLM_CONFIG['anthropic_api_key']

Parameters

Parameter Details

No parameters: The __init__ method takes no parameters. All configuration is derived from the file system location and the global LLM_CONFIG dictionary.

Return Value

Instantiation returns a SmartStatConfig object with initialized directory paths (as pathlib.Path objects) and API key strings. The class has no methods that return values; it serves purely as a configuration container.

Class Interface

Methods

Attributes

Dependencies

• pathlib

Required Imports

from pathlib import Path

Usage Example

# Ensure LLM_CONFIG is defined globally
LLM_CONFIG = {
    'openai_api_key': 'sk-...',
    'gemini_api_key': 'AIza...',
    'azure_openai_endpoint': 'https://...',
    'azure_openai_api_key': 'abc123...',
    'anthropic_api_key': 'sk-ant-...'
}

# Instantiate the config
config = SmartStatConfig()

# Access directory paths
upload_path = config.UPLOAD_FOLDER
scripts_path = config.GENERATED_SCRIPTS_FOLDER

# Access API keys
openai_key = config.OPENAI_API_KEY
gemini_key = config.GEMINI_API_KEY

# Use paths for file operations
file_path = config.UPLOAD_FOLDER / 'data.csv'
with open(file_path, 'w') as f:
    f.write('sample data')

Best Practices

• Ensure LLM_CONFIG dictionary is properly defined in the global scope before instantiating SmartStatConfig
• Instantiate SmartStatConfig once at application startup and reuse the instance throughout the application lifecycle
• Do not modify the directory paths after instantiation as other services may depend on these locations
• Ensure the application has appropriate file system permissions to create directories in the module's parent directory
• Keep API keys secure and never commit LLM_CONFIG values to version control
• The class creates directories automatically on instantiation, so no manual directory setup is needed
• All directory paths are pathlib.Path objects, use them with the / operator for path joining
• The class has no cleanup or teardown methods; directories persist after the object is destroyed

Tags

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