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"""
⚙️ Configuration Management for CourseCrafter AI
Centralized configuration system with environment variable support and validation.
"""
import os
import json
from typing import Dict, Any, Optional, List
from dataclasses import dataclass, field
from pathlib import Path
from dotenv import load_dotenv
from ..types import LLMProvider
@dataclass
class LLMProviderConfig:
"""Configuration for a specific LLM provider"""
api_key: str
model: str
temperature: float = 0.7
max_tokens: Optional[int] = None
timeout: int = 60
base_url: Optional[str] = None
class Config:
"""
Centralized configuration management for Course Creator AI
Handles environment variables, API keys, and URL configurations.
"""
def __init__(self):
# Load environment variables
load_dotenv()
# Initialize configuration
self._config = self._load_default_config()
self._validate_config()
def _load_default_config(self) -> Dict[str, Any]:
"""Load default configuration with environment variable overrides"""
# Get default model from env or fallback
default_model = os.getenv("DEFAULT_MODEL", "gpt-4.1-nano")
# Get default LLM provider from env or fallback to first available
default_llm_provider = os.getenv("DEFAULT_LLM_PROVIDER", "openai")
return {
# LLM Provider Configurations
"llm_providers": {
"openai": {
"api_key": os.getenv("OPENAI_API_KEY", ""),
"model": os.getenv("OPENAI_MODEL", default_model),
"temperature": float(os.getenv("OPENAI_TEMPERATURE", "0.7")),
"max_tokens": int(os.getenv("OPENAI_MAX_TOKENS", "20000")) if os.getenv("OPENAI_MAX_TOKENS") else None,
"timeout": int(os.getenv("OPENAI_TIMEOUT", "60"))
},
"anthropic": {
"api_key": os.getenv("ANTHROPIC_API_KEY", ""),
"model": os.getenv("ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022"),
"temperature": float(os.getenv("ANTHROPIC_TEMPERATURE", "0.7")),
"max_tokens": int(os.getenv("ANTHROPIC_MAX_TOKENS", "20000")) if os.getenv("ANTHROPIC_MAX_TOKENS") else None,
"timeout": int(os.getenv("ANTHROPIC_TIMEOUT", "60"))
},
"google": {
"api_key": os.getenv("GOOGLE_API_KEY", ""),
"model": os.getenv("GOOGLE_MODEL", "gemini-2.0-flash"),
"temperature": float(os.getenv("GOOGLE_TEMPERATURE", "0.7")),
"max_tokens": int(os.getenv("GOOGLE_MAX_TOKENS", "20000")) if os.getenv("GOOGLE_MAX_TOKENS") else None,
"timeout": int(os.getenv("GOOGLE_TIMEOUT", "60"))
},
"openai_compatible": {
"api_key": os.getenv("OPENAI_COMPATIBLE_API_KEY", "dummy"),
"base_url": os.getenv("OPENAI_COMPATIBLE_BASE_URL", ""),
"model": os.getenv("OPENAI_COMPATIBLE_MODEL", ""),
"temperature": float(os.getenv("OPENAI_COMPATIBLE_TEMPERATURE", "0.7")),
"max_tokens": int(os.getenv("OPENAI_COMPATIBLE_MAX_TOKENS", "20000")) if os.getenv("OPENAI_COMPATIBLE_MAX_TOKENS") else None,
"timeout": int(os.getenv("OPENAI_COMPATIBLE_TIMEOUT", "60"))
}
},
# Course Generation Settings
"course_generation": {
"default_difficulty": "beginner",
"default_lesson_count": 5,
"max_lesson_duration": 30,
"include_images": True,
"include_flashcards": True,
"include_quizzes": True,
"research_depth": "comprehensive"
},
# Image Generation Settings
"image_generation": {
"pollinations_api_token": os.getenv("POLLINATIONS_API_TOKEN", ""),
"pollinations_api_reference": os.getenv("POLLINATIONS_API_REFERENCE", ""),
"default_width": 1280,
"default_height": 720,
"default_model": "gptimage",
"enhance_prompts": True,
"no_logo": True
},
# Export Settings
"export": {
"default_formats": ["pdf", "markdown"],
"output_directory": os.getenv("COURSECRAFTER_OUTPUT_DIR", "./output"),
"max_file_size": 50 * 1024 * 1024, # 50MB
"compression": True
},
# UI Settings
"ui": {
"theme": "soft",
"show_progress": True,
"auto_scroll": True,
"max_concurrent_generations": 3
},
# System Settings
"system": {
"default_llm_provider": default_llm_provider,
"max_turns": 25,
"timeout": 300, # 5 minutes
"retry_attempts": 3,
"log_level": os.getenv("LOG_LEVEL", "INFO"),
"debug_mode": os.getenv("DEBUG", "false").lower() == "true"
}
}
def _validate_config(self):
"""Validate configuration and warn about missing required settings"""
warnings = []
# Check LLM provider API keys
for provider, config in self._config["llm_providers"].items():
if provider == "openai_compatible":
# For openai_compatible, check for base_url instead of api_key
if not config.get("base_url"):
warnings.append(f"Missing base_url for {provider}")
else:
# For other providers, check for api_key
if not config["api_key"]:
warnings.append(f"Missing API key for {provider}")
# Check if at least one LLM provider is configured
has_provider = False
for provider, config in self._config["llm_providers"].items():
if provider == "openai_compatible":
if config.get("base_url"):
has_provider = True
break
else:
if config["api_key"]:
has_provider = True
break
if not has_provider:
# Only warn instead of raising error - allows app to start for UI configuration
print("⚠️ Warning: No LLM providers configured. Please configure at least one provider in the UI.")
def get_llm_config(self, provider: LLMProvider) -> LLMProviderConfig:
"""Get configuration for a specific LLM provider"""
# Reload config to pick up any environment variable changes
self._config = self._load_default_config()
if provider not in self._config["llm_providers"]:
raise ValueError(f"Unknown LLM provider: {provider}")
config = self._config["llm_providers"][provider]
return LLMProviderConfig(**config)
def get_available_llm_providers(self) -> List[LLMProvider]:
"""Get list of available LLM providers with API keys"""
# Reload config to pick up any environment variable changes
self._config = self._load_default_config()
available = []
for provider, config in self._config["llm_providers"].items():
if provider == "openai_compatible":
# For openai_compatible, require base_url instead of api_key
if config.get("base_url"):
available.append(provider)
else:
# For other providers, require api_key
if config["api_key"]:
available.append(provider)
return available
def get_default_llm_provider(self) -> LLMProvider:
"""Get the default LLM provider, falling back to first available if not configured"""
# Reload config to pick up any environment variable changes
self._config = self._load_default_config()
default_provider = self._config["system"]["default_llm_provider"]
available_providers = self.get_available_llm_providers()
# If the default provider is available, use it
if default_provider in available_providers:
return default_provider
# Otherwise, use the first available provider
if available_providers:
print(f"⚠️ Default provider '{default_provider}' not configured, using '{available_providers[0]}'")
return available_providers[0]
# If no providers are available, return a fallback instead of raising an error
print("⚠️ Warning: No LLM providers are configured. Returning 'google' as fallback.")
return "google" # Return a fallback provider that can be configured later
def get_image_generation_config(self) -> Dict[str, Any]:
"""Get image generation configuration"""
return self._config["image_generation"]
def get(self, key: str, default: Any = None) -> Any:
"""Get a configuration value using dot notation"""
keys = key.split(".")
value = self._config
try:
for k in keys:
value = value[k]
return value
except (KeyError, TypeError):
return default
def set(self, key: str, value: Any):
"""Set a configuration value using dot notation"""
keys = key.split(".")
config = self._config
for k in keys[:-1]:
if k not in config:
config[k] = {}
config = config[k]
config[keys[-1]] = value
def update_llm_provider(self, provider: LLMProvider, **kwargs):
"""Update LLM provider configuration"""
if provider not in self._config["llm_providers"]:
raise ValueError(f"Unknown LLM provider: {provider}")
self._config["llm_providers"][provider].update(kwargs)
def to_dict(self) -> Dict[str, Any]:
"""Convert configuration to dictionary"""
return self._config.copy()
def save_to_file(self, filepath: str):
"""Save configuration to JSON file"""
with open(filepath, 'w') as f:
json.dump(self._config, f, indent=2)
@classmethod
def load_from_file(cls, filepath: str) -> 'Config':
"""Load configuration from JSON file"""
instance = cls()
if os.path.exists(filepath):
with open(filepath, 'r') as f:
file_config = json.load(f)
instance._config.update(file_config)
instance._validate_config()
return instance
# Global configuration instance
config = Config() |