# services/llm/grok_service.py """ Grok service implementation """ from typing import Dict, List, Optional import json from .base import LLMService from config.api_keys import APIKeyManager class GrokService(LLMService): def __init__(self, model: str = "grok-1", temperature: float = 0.3): api_key = APIKeyManager.get_grok_key() if not api_key: raise ValueError("Grok API key not found. Please set up your API keys.") self.api_key = api_key self.model = model self.temperature = temperature def generate_text(self, prompt: str) -> str: """ Generate text using the Grok API TODO: Update this method when Grok API is available """ try: # Placeholder for Grok API implementation # Update this when the API is released raise NotImplementedError("Grok API is not implemented yet") except Exception as e: print(f"Error in Grok API call: {e}") return None def get_similarity_scores(self, texts_pairs: Dict[str, List[str]]) -> List[float]: """ Calculate similarity scores using the Grok API TODO: Update this method when Grok API is available """ try: system_prompt = ( "Evaluate the semantic similarity between the following table of pairs of texts " "in json format on a scale from 0 to 1. Return the similarity scores for every " "row in JSON format as a list of numbers, without any additional text or formatting." ) request_payload = json.dumps(texts_pairs) # Placeholder for Grok API implementation # Update this when the API is released raise NotImplementedError("Grok API is not implemented yet") except Exception as e: print(f"Error in Grok similarity calculation: {e}") return None # Update config/api_keys.py to include Grok @classmethod def get_grok_key(cls) -> Optional[str]: """Get Grok API key from environment or stored configuration""" return ( os.getenv('GROK_API_KEY') or cls._get_stored_key('grok') )