llm_factory.py

"""
LLM Provider Factory for Flare
"""
import os
from typing import Optional
from dotenv import load_dotenv

from llm_interface import LLMInterface
from llm_spark import SparkLLM
from llm_openai import OpenAILLM
from config_provider import ConfigProvider
from utils import log

class LLMFactory:
    @staticmethod
    def create_provider() -> LLMInterface:
        """Create LLM provider based on configuration"""
        cfg = ConfigProvider.get()
        llm_config = cfg.global_config.llm_provider
        
        if not llm_config:
            raise ValueError("No LLM provider configured")
        
        provider_name = llm_config.name
        log(f"🏭 Creating LLM provider: {provider_name}")
        
        # Get provider definition
        provider_def = cfg.global_config.get_provider_config("llm", provider_name)
        if not provider_def:
            raise ValueError(f"Unknown LLM provider: {provider_name}")
        
        # Get API key
        api_key = LLMFactory._get_api_key(provider_name, llm_config.api_key)
        
        # Create provider based on name
        if provider_name == "spark":
            return LLMFactory._create_spark_provider(llm_config, api_key, provider_def)
        elif provider_name in ["gpt4o", "gpt4o-mini"]:
            return LLMFactory._create_gpt_provider(llm_config, api_key, provider_def)
        else:
            raise ValueError(f"Unsupported LLM provider: {provider_name}")
    
    @staticmethod
    def _create_spark_provider(llm_config, api_key, provider_def):
        """Create Spark LLM provider"""
        endpoint = llm_config.endpoint
        if not endpoint:
            raise ValueError("Spark endpoint not configured")
        
        # Determine variant based on environment
        is_cloud = bool(os.environ.get("SPACE_ID"))
        variant = "hfcloud" if is_cloud else "on-premise"
        
        return SparkLLM(
            spark_endpoint=endpoint,
            spark_token=api_key,
            provider_variant=variant,
            settings=llm_config.settings
        )
    
    @staticmethod
    def _create_gpt_provider(llm_config, api_key, provider_def):
        """Create OpenAI GPT provider"""
        return OpenAILLM(
            api_key=api_key,
            model=llm_config.name,
            settings=llm_config.settings
        )
    
    @staticmethod
    def _get_api_key(provider_name: str, configured_key: Optional[str]) -> str:
        """Get API key from config or environment"""
        # First try configured key
        if configured_key:
            # Handle encrypted keys
            if configured_key.startswith("enc:"):
                from encryption_utils import decrypt
                return decrypt(configured_key)
            return configured_key
        
        # Then try environment variables
        env_mappings = {
            "spark": "SPARK_TOKEN",
            "gpt4o": "OPENAI_API_KEY",
            "gpt4o-mini": "OPENAI_API_KEY"
        }
        
        env_var = env_mappings.get(provider_name)
        if env_var:
            key = os.environ.get(env_var)
            if key:
                log(f"📌 Using API key from environment: {env_var}")
                return key
        
        raise ValueError(f"No API key found for provider: {provider_name}")