# services/llm/grok_service.py """ Grok (xAI) service implementation """ import httpx from typing import Dict, List, Optional import json from .base import LLMService from config.api_keys import APIKeyManager from utils.logger import setup_logger class GrokService(LLMService): def __init__( self, model: str = "grok-3-mini-fast", temperature: float = 0.3, max_tokens: int = 16000, ): # Debemos usar el modelo grok-3-mini-fast api_key = APIKeyManager.get_grok_key() if not api_key: raise ValueError( "Grok API key not found. Please set the GROK_API_KEY environment variable." ) self.api_key = api_key self.model = model self.temperature = temperature self.max_tokens = max_tokens self.base_url = "https://api.x.ai/v1" self.client = httpx.Client( headers={ "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", } ) self.logger = setup_logger("grok_xai") def _send_request(self, payload: Dict) -> Optional[Dict]: """Sends a request to the Grok API.""" try: response = self.client.post( f"{self.base_url}/chat/completions", json=payload, timeout=60 ) response.raise_for_status() return response.json() except httpx.HTTPStatusError as e: self.logger.error( f"Error in Grok API call: {e.response.status_code} - {e.response.text}" ) print( f"Error in Grok API call: {e.response.status_code} - {e.response.text}" ) return None except Exception as e: self.logger.error(f"An unexpected error occurred: {e}") print(f"An unexpected error occurred: {e}") return None def generate_text(self, prompt: str) -> str: self.logger.info(f"--- PROMPT ---\n{prompt}") payload = { "model": self.model, "messages": [{"role": "user", "content": prompt}], "temperature": self.temperature, "max_tokens": self.max_tokens, } response_data = self._send_request(payload) if response_data and response_data.get("choices"): response_content = response_data["choices"][0]["message"]["content"] self.logger.info(f"--- RESPONSE ---\n{response_content}") return response_content return "Failed to get a response from Grok." def get_similarity_scores(self, texts_pairs: Dict[str, List[str]]) -> List[float]: system_prompt = ( "You are an expert in semantic analysis. Evaluate the semantic similarity between the pairs of texts provided. " "Return your response ONLY as a JSON object containing a single key 'similarity_scores' with a list of floats from 0.0 to 1.0. " "Do not include any other text, explanation, or markdown formatting. The output must be a valid JSON." ) request_payload = json.dumps(texts_pairs) payload = { "model": self.model, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": request_payload}, ], "temperature": self.temperature, "max_tokens": self.max_tokens, "response_format": {"type": "json_object"}, } response_data = self._send_request(payload) if response_data and response_data.get("choices"): response_content = response_data["choices"][0]["message"]["content"] try: scores_data = json.loads(response_content) if isinstance(scores_data, dict) and "similarity_scores" in scores_data: return scores_data["similarity_scores"] else: raise ValueError("Unexpected JSON format from Grok.") except (json.JSONDecodeError, ValueError) as e: print(f"Error decoding Grok JSON response: {e}") return None return None