ac-tool/backend/core/config.py

"""
Configuration management for Enhanced Brief Processing System
Loads environment variables and provides configuration validation
"""

import os
from typing import List, Dict, Any, Optional
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

class Config:
    """Centralized configuration management"""
    
    # API Keys — support both docker-compose names and legacy names
    OPENAI_API_KEY: str = os.getenv('OPENAI_API_KEY', '')
    ANTHROPIC_API_KEY: str = os.getenv('ANTHROPIC_API_KEY', '')
    # GEMINI_API_KEY is the docker-compose / .env name; GOOGLE_API_KEY is the legacy name
    GOOGLE_API_KEY: str = os.getenv('GEMINI_API_KEY') or os.getenv('GOOGLE_API_KEY', '')
    # LLAMA_CLOUD_API_KEY is the docker-compose / official SDK name
    LLAMACLOUD_API_KEY: str = os.getenv('LLAMA_CLOUD_API_KEY') or os.getenv('LLAMACLOUD_API_KEY', '')
    
    # OpenAI Configuration
    OPENAI_MODEL: str = os.getenv('OPENAI_MODEL', 'gpt-5.1')
    OPENAI_REASONING_EFFORT: str = os.getenv('OPENAI_REASONING_EFFORT', 'medium')
    OPENAI_TIMEOUT: int = int(os.getenv('OPENAI_TIMEOUT', '3600'))
    OPENAI_MAX_RETRIES: int = int(os.getenv('OPENAI_MAX_RETRIES', '2'))
    
    # Google Configuration
    GOOGLE_MODEL: str = os.getenv('GOOGLE_MODEL', 'gemini-3.1-pro-preview')
    GOOGLE_TEMPERATURE: float = float(os.getenv('GOOGLE_TEMPERATURE', '0.1'))
    GOOGLE_MAX_OUTPUT_TOKENS: int = int(os.getenv('GOOGLE_MAX_OUTPUT_TOKENS', '8192'))
    GOOGLE_THINKING_BUDGET: int = int(os.getenv('GOOGLE_THINKING_BUDGET', '12000'))
    GOOGLE_TIMEOUT: int = int(os.getenv('GOOGLE_TIMEOUT', '300'))
    
    # Anthropic Configuration
    ANTHROPIC_MODEL_OPUS: str = os.getenv('ANTHROPIC_MODEL_OPUS', 'claude-opus-4-5-20251101')
    ANTHROPIC_MODEL_SONNET: str = os.getenv('ANTHROPIC_MODEL_SONNET', 'claude-sonnet-4-5-20250929')
    ANTHROPIC_TEMPERATURE: float = float(os.getenv('ANTHROPIC_TEMPERATURE', '0.1'))
    ANTHROPIC_MAX_TOKENS: int = int(os.getenv('ANTHROPIC_MAX_TOKENS', '32000'))
    ANTHROPIC_THINKING_BUDGET: int = int(os.getenv('ANTHROPIC_THINKING_BUDGET', '12000'))
    ANTHROPIC_TIMEOUT: int = int(os.getenv('ANTHROPIC_TIMEOUT', '300'))
    
    # Processing Configuration
    DEFAULT_PRIMARY_MODELS: str = os.getenv('DEFAULT_PRIMARY_MODELS', 'openai-gpt51,anthropic-sonnet45,google-gemini31')
    DEFAULT_CONSOLIDATION_MODEL: str = os.getenv('DEFAULT_CONSOLIDATION_MODEL', 'openai-gpt51')
    MINIMUM_SUCCESS_THRESHOLD: int = int(os.getenv('MINIMUM_SUCCESS_THRESHOLD', '1'))
    ENABLE_COST_ESTIMATION: bool = os.getenv('ENABLE_COST_ESTIMATION', 'true').lower() == 'true'
    MAX_PROCESSING_COST_USD: float = float(os.getenv('MAX_PROCESSING_COST_USD', '10.00'))
    
    # Model Pricing (per 1M tokens)
    PRICING = {
        'openai-gpt51': {
            'input': 1.25,
            'cached_input': 0.625,
            'output': 10.00
        },
        'anthropic-opus45': {
            'input': 5.00,
            'output': 25.00
        },
        'anthropic-sonnet45': {
            'input': 3.00,
            'output': 15.00
        },
        'google-gemini31': {
            'input': 1.25,
            'output': 5.00
        }
    }
    
    # Model mappings for CLI compatibility
    MODEL_MAPPINGS = {
        'openai-gpt51': ('openai', OPENAI_MODEL),
        'anthropic-opus45': ('anthropic', ANTHROPIC_MODEL_OPUS),
        'anthropic-sonnet45': ('anthropic', ANTHROPIC_MODEL_SONNET),
        'google-gemini31': ('google', GOOGLE_MODEL)
    }
    
    @classmethod
    def validate_api_keys(cls) -> Dict[str, bool]:
        """Validate that required API keys are set"""
        return {
            'openai': bool(cls.OPENAI_API_KEY and cls.OPENAI_API_KEY != 'your-openai-api-key-here'),
            'anthropic': bool(cls.ANTHROPIC_API_KEY and cls.ANTHROPIC_API_KEY != 'your-anthropic-api-key-here'),
            'google': bool(cls.GOOGLE_API_KEY and cls.GOOGLE_API_KEY != 'your-google-api-key-here'),
            'llamacloud': bool(cls.LLAMACLOUD_API_KEY and cls.LLAMACLOUD_API_KEY != 'your-llamacloud-api-key-here')
        }
    
    @classmethod
    def get_provider_config(cls, provider: str) -> Dict[str, Any]:
        """Get configuration for a specific provider"""
        if provider == 'openai':
            return {
                'api_key': cls.OPENAI_API_KEY,
                'model': cls.OPENAI_MODEL,
                'reasoning_effort': cls.OPENAI_REASONING_EFFORT,
                'timeout': cls.OPENAI_TIMEOUT,
                'max_retries': cls.OPENAI_MAX_RETRIES
            }
        elif provider == 'google':
            return {
                'api_key': cls.GOOGLE_API_KEY,
                'model': cls.GOOGLE_MODEL,
                'temperature': cls.GOOGLE_TEMPERATURE,
                'max_output_tokens': cls.GOOGLE_MAX_OUTPUT_TOKENS,
                'thinking_budget': cls.GOOGLE_THINKING_BUDGET,
                'timeout': cls.GOOGLE_TIMEOUT
            }
        elif provider == 'anthropic':
            return {
                'api_key': cls.ANTHROPIC_API_KEY,
                'model_opus': cls.ANTHROPIC_MODEL_OPUS,
                'model_sonnet': cls.ANTHROPIC_MODEL_SONNET,
                'temperature': cls.ANTHROPIC_TEMPERATURE,
                'max_tokens': cls.ANTHROPIC_MAX_TOKENS,
                'thinking_budget': cls.ANTHROPIC_THINKING_BUDGET,
                'timeout': cls.ANTHROPIC_TIMEOUT
            }
        else:
            raise ValueError(f"Unknown provider: {provider}")
    
    @classmethod
    def get_default_primary_models(cls) -> List[str]:
        """Get default list of primary analysis models"""
        return cls.DEFAULT_PRIMARY_MODELS.split(',')
    
    @classmethod
    def get_model_info(cls, model_key: str) -> tuple:
        """Get provider and model name for a model key"""
        if model_key not in cls.MODEL_MAPPINGS:
            raise ValueError(f"Unknown model key: {model_key}. Available: {list(cls.MODEL_MAPPINGS.keys())}")
        return cls.MODEL_MAPPINGS[model_key]
    
    @classmethod
    def estimate_cost(cls, model_key: str, input_tokens: int, output_tokens: int, cached_tokens: int = 0) -> float:
        """Estimate processing cost for a model"""
        if model_key not in cls.PRICING:
            return 0.0
        
        pricing = cls.PRICING[model_key]
        input_cost = (input_tokens / 1_000_000) * pricing['input']
        output_cost = (output_tokens / 1_000_000) * pricing['output']
        cached_cost = (cached_tokens / 1_000_000) * pricing.get('cached_input', pricing['input'])
        
        return input_cost + output_cost + cached_cost

# Global config instance
config = Config()