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"""
Expert LLM service for handling expert model interactions
"""
import logging
from lpm_kernel.api.services.user_llm_config_service import UserLLMConfigService
from typing import Optional, Dict, Any
from openai import OpenAI
from lpm_kernel.configs.config import Config
logger = logging.getLogger(__name__)
class ExpertLLMService:
"""Service for managing expert LLM client"""
def __init__(self):
self._client = None
self.user_llm_config_service = UserLLMConfigService()
# self.user_llm_config = self.user_llm_config_service.get_available_llm()
# self._config = None
# Load configuration during initialization
# self.config # Trigger configuration loading
# self.configure() # Initialize using configuration from environment variables
# @property
# def config(self) -> Config:
# """Get the configuration"""
# if self._config is None:
# self._config = Config.from_env()
# return self._config
@property
def client(self) -> OpenAI:
"""Get the OpenAI client for expert LLM"""
if self._client is None:
self.user_llm_config = self.user_llm_config_service.get_available_llm()
self._client = OpenAI(
api_key=self.user_llm_config.chat_api_key,
base_url=self.user_llm_config.chat_endpoint,
)
return self._client
def get_model_params(self) -> Dict[str, Any]:
"""
Get model specific parameters for expert LLM
Returns:
Dict containing model specific parameters
"""
return {
"model": self.user_llm_config.chat_model_name,
"response_format": {"type": "text"},
"seed": 42, # Optional: Fixed random seed to get consistent responses
"tools": None, # Optional: If function calling or similar features are needed
"tool_choice": None, # Optional: If function calling or similar features are needed
}
# def configure(self,
# api_key: Optional[str] = None,
# base_url: Optional[str] = None,
# model_name: Optional[str] = None,
# **model_params):
# """
# Configure the expert LLM client and its parameters
#
# Args:
# api_key: Optional API key. If None, uses LLM_API_KEY from config
# base_url: Optional base URL. If None, uses LLM_BASE_URL from config
# model_name: Optional model name to use
# **model_params: Additional model specific parameters
# """
# # Reset existing client
# self._client = None
#
# # Update config if provided
# # if api_key:
# # self.config.set("LLM_API_KEY", api_key)
# # if base_url:
# # self.config.set("LLM_BASE_URL", base_url)
# # if model_name:
# # self.config.set("DATA_GEN_MODEL", model_name)
#
# # Update any additional model parameters
# # for key, value in model_params.items():
# # self.config.set(f"EXPERT_MODEL_{key.upper()}", value)
# #
# # logger.info("Expert LLM service configured with new settings")
# # logger.info(f"Using model: {self.config.get('DATA_GEN_MODEL')}")
# # logger.info(f"Using base URL: {self.config.get('LLM_BASE_URL')}")
# return self.client
# Global instance
expert_llm_service = ExpertLLMService()
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