Obsidian Elite RAG MCP Server

""" DeepSeek provider implementation. """ import os from typing import List import logging from openai import OpenAI from dotenv import load_dotenv # Load environment variables load_dotenv() # Configure logging logger = logging.getLogger(__name__) # Initialize DeepSeek client with OpenAI-compatible interface client = OpenAI( api_key=os.environ.get("DEEPSEEK_API_KEY"), base_url="https://api.deepseek.com" ) def prompt(text: str, model: str) -> str: """ Send a prompt to DeepSeek and get a response. Args: text: The prompt text model: The model name Returns: Response string from the model """ try: logger.info(f"Sending prompt to DeepSeek model: {model}") # Create chat completion response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": text}], stream=False, ) # Extract response content return response.choices[0].message.content except Exception as e: logger.error(f"Error sending prompt to DeepSeek: {e}") raise ValueError(f"Failed to get response from DeepSeek: {str(e)}") def list_models() -> List[str]: """ List available DeepSeek models. Returns: List of model names """ try: logger.info("Listing DeepSeek models") response = client.models.list() # Extract model IDs models = [model.id for model in response.data] return models except Exception as e: logger.error(f"Error listing DeepSeek models: {e}") # Return some known models if API fails logger.info("Returning hardcoded list of known DeepSeek models") return [ "deepseek-coder", "deepseek-chat", "deepseek-reasoner", "deepseek-coder-v2", "deepseek-reasoner-lite" ]