"""LLM client for Opus Orchestrator. Supports MiniMax and OpenAI providers - both async and sync. """ import os import asyncio from typing import Any, Optional import httpx import requests class LLMClient: """Simple LLM client for making API calls - supports both sync and async.""" def __init__( self, api_key: Optional[str] = None, provider: str = "minimax", model: str = "MiniMax/MiniMax-M2.1", base_url: Optional[str] = None, ): """Initialize LLM client.""" self.api_key = api_key or os.environ.get("MINIMAX_API_KEY") or os.environ.get("OPENAI_API_KEY") self.provider = provider self.model = model # Normalize model name for MiniMax if provider == "minimax": self.minimax_model = model.split("/")[-1] if "/" in model else model # Set base URL based on provider if base_url: self.base_url = base_url elif provider == "minimax": self.base_url = "https://api.minimax.chat/v1" elif provider == "openai": self.base_url = "https://api.openai.com/v1" else: self.base_url = "https://api.openai.com/v1" # Async client self._async_client = httpx.AsyncClient(timeout=120.0) def complete( self, system_prompt: str, user_prompt: str, temperature: float = 0.7, max_tokens: Optional[int] = None, ) -> str: """Make a completion request (SYNC - for LangGraph compatibility).""" headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", } if self.provider == "minimax": return self._complete_minimax_sync( system_prompt, user_prompt, temperature, max_tokens, headers ) elif self.provider == "openai": return self._complete_openai_sync( system_prompt, user_prompt, temperature, max_tokens, headers ) else: raise ValueError(f"Unsupported provider: {self.provider}") async def complete_async( self, system_prompt: str, user_prompt: str, temperature: float = 0.7, max_tokens: Optional[int] = None, ) -> str: """Make a completion request (ASYNC).""" headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", } if self.provider == "minimax": return await self._complete_minimax_async( system_prompt, user_prompt, temperature, max_tokens, headers ) elif self.provider == "openai": return await self._complete_openai_async( system_prompt, user_prompt, temperature, max_tokens, headers ) else: raise ValueError(f"Unsupported provider: {self.provider}") async def _complete_minimax( self, system_prompt: str, user_prompt: str, temperature: float, max_tokens: Optional[int], headers: dict, ) -> str: """Call MiniMax API.""" # MiniMax chat completion format payload = { "model": self.minimax_model, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], "temperature": temperature, } if max_tokens: payload["max_tokens"] = max_tokens response = await self._async_client.post( f"{self.base_url}/text/chatcompletion_v2", headers=headers, json=payload, ) # Debug output if response.status_code != 200: print(f"MiniMax API error: {response.status_code}") print(f"Response: {response.text[:500]}") response.raise_for_status() data = response.json() # Handle different response formats if "choices" in data: return data["choices"][0]["message"]["content"] elif "choices" in data.get("data", {}): return data["data"]["choices"][0]["message"]["content"] else: # Try to find content in response raise Exception(f"Unexpected MiniMax response: {data}") async def _complete_openai( self, system_prompt: str, user_prompt: str, temperature: float, max_tokens: Optional[int], headers: dict, ) -> str: """Call OpenAI API.""" payload = { "model": self.model, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], "temperature": temperature, } if max_tokens: payload["max_tokens"] = max_tokens response = await self._async_client.post( f"{self.base_url}/chat/completions", headers=headers, json=payload, ) response.raise_for_status() data = response.json() return data["choices"][0]["message"]["content"] async def close(self): """Close the HTTP client.""" await self._async_client.aclose() # ========================================================================= # SYNC VERSIONS (for LangGraph compatibility) # ========================================================================= def _complete_minimax_sync( self, system_prompt: str, user_prompt: str, temperature: float, max_tokens: Optional[int], headers: dict, ) -> str: """Call MiniMax API (sync).""" payload = { "model": self.minimax_model, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], "temperature": temperature, } if max_tokens: payload["max_tokens"] = max_tokens response = requests.post( f"{self.base_url}/text/chatcompletion_v2", headers=headers, json=payload, timeout=120, ) if response.status_code != 200: print(f"MiniMax API error: {response.status_code}") print(f"Response: {response.text[:500]}") response.raise_for_status() data = response.json() if "choices" in data: return data["choices"][0]["message"]["content"] elif "choices" in data.get("data", {}): return data["data"]["choices"][0]["message"]["content"] else: raise Exception(f"Unexpected MiniMax response: {data}") def _complete_openai_sync( self, system_prompt: str, user_prompt: str, temperature: float, max_tokens: Optional[int], headers: dict, ) -> str: """Call OpenAI API (sync).""" payload = { "model": self.model, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], "temperature": temperature, } if max_tokens: payload["max_tokens"] = max_tokens response = requests.post( f"{self.base_url}/chat/completions", headers=headers, json=payload, timeout=120, ) response.raise_for_status() data = response.json() return data["choices"][0]["message"]["content"] # Convenience function def get_llm_client(config: Optional[Any] = None) -> LLMClient: """Get an LLM client from config.""" from opus_orchestrator.config import get_config cfg = config or get_config() return LLMClient( api_key=cfg.agent.api_key, provider=cfg.agent.provider, model=cfg.agent.model, )