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"""
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.config_provider import ConfigProvider
from utils.logger import log_info, log_error, log_warning, log_debug
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_info(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 == "spark_cloud":
return LLMFactory._create_spark_provider(llm_config, api_key, provider_def)
elif provider_name in ["gpt-4o", "gpt-4o-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 utils.encryption_utils import decrypt
return decrypt(configured_key)
return configured_key
# Then try environment variables
env_mappings = {
"spark": "SPARK_TOKEN",
"gpt-4o": "OPENAI_API_KEY",
"gpt-4o-mini": "OPENAI_API_KEY"
}
env_var = env_mappings.get(provider_name)
if env_var:
key = os.environ.get(env_var)
if key:
log_info(f"📌 Using API key from environment: {env_var}")
return key
raise ValueError(f"No API key found for provider: {provider_name}") |